{"parentUuid":null,"isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","type":"progress","data":{"type":"hook_progress","hookEvent":"SessionStart","hookName":"SessionStart:startup","command":"entire hooks claude-code session-start"},"parentToolUseID":"0c16e233-330c-4a85-8f82-ccfa3eaa4d4d","toolUseID":"0c16e233-330c-4a85-8f82-ccfa3eaa4d4d","timestamp":"2026-03-04T15:20:41.444Z","uuid":"21c6dd11-ec98-469d-bae2-5edc74e7bbc2"}
{"type":"file-history-snapshot","messageId":"4d2bc99d-ae2d-427d-a61e-4efa87185e34","snapshot":{"messageId":"4d2bc99d-ae2d-427d-a61e-4efa87185e34","trackedFileBackups":{},"timestamp":"2026-03-04T15:20:50.366Z"},"isSnapshotUpdate":false}
{"parentUuid":"21c6dd11-ec98-469d-bae2-5edc74e7bbc2","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","type":"user","message":{"role":"user","content":"<local-command-caveat>Caveat: The messages below were generated by the user while running local commands. DO NOT respond to these messages or otherwise consider them in your response unless the user explicitly asks you to.</local-command-caveat>"},"isMeta":true,"uuid":"8e2486f5-a057-43a7-9b73-f872f36854ec","timestamp":"2026-03-04T15:20:50.366Z"}
{"parentUuid":"8e2486f5-a057-43a7-9b73-f872f36854ec","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","type":"user","message":{"role":"user","content":"<command-name>/model</command-name>\n            <command-message>model</command-message>\n            <command-args></command-args>"},"uuid":"4d2bc99d-ae2d-427d-a61e-4efa87185e34","timestamp":"2026-03-04T15:20:50.366Z"}
{"parentUuid":"4d2bc99d-ae2d-427d-a61e-4efa87185e34","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","type":"user","message":{"role":"user","content":"<local-command-stdout>Set model to \u001b[1mopus (claude-opus-4-6)\u001b[22m</local-command-stdout>"},"uuid":"fadcb2ed-21f5-4dd5-bed3-74f785732fc7","timestamp":"2026-03-04T15:20:50.366Z"}
{"type":"file-history-snapshot","messageId":"f229a6ee-e515-4508-8a4a-9da03d346b2f","snapshot":{"messageId":"f229a6ee-e515-4508-8a4a-9da03d346b2f","trackedFileBackups":{},"timestamp":"2026-03-04T15:23:39.476Z"},"isSnapshotUpdate":false}
{"parentUuid":"fadcb2ed-21f5-4dd5-bed3-74f785732fc7","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"user","message":{"role":"user","content":"The buildQueries logic on @pkg/metricstore/query.go  is diverged from the buildQueries logic in @internal/metricstoreclient/cc-metric-store-queries.go . As it appears the latter is the cleaner implementation. Analyse the difference, synchronize and evaluate if the code can be consolidated.  "},"uuid":"f229a6ee-e515-4508-8a4a-9da03d346b2f","timestamp":"2026-03-04T15:23:38.818Z","permissionMode":"plan"}
{"parentUuid":"f229a6ee-e515-4508-8a4a-9da03d346b2f","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","message":{"model":"claude-opus-4-6","id":"msg_01T2iXfJxMcDpFuKKEChgtvZ","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"Let me analyze the differences between the two implementations and explore whether they can be consolidated.","signature":"EpQCCkYICxgCKkCzn1ma6CWrs2niOGQqBQLYrq0JaS88mC7ITdqYVkUvTp/6renrcF1BTaViREHauMrgaZm6lvKNIG1toCR2yvlcEgz0IAQd2IpmDjvXwUwaDOhcy+CSIee7aqVRcCIwQQLjf18wOVT18UjocWrGyNfTXDstigimohFDdTdFI/8yD6NR0R8YljtJ5MZP5/H6KnyUlJQ/r0C5DDUYjERMzaDzh+HfdDD5YaaOB4pKHuR2uTSaJv+0H8uZEF1QrQs1N80R4gxFknqGhB/cLO8VbjrRm5WBUwJQzK8Z3S4D0vx9TpKb1qhuxuIBVoPnUDr/n/nHEMbalKaATEWTrBKvRs9MiymmGTMazUsBjp5/GAE="}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":49690,"cache_read_input_tokens":10398,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":49690},"output_tokens":9,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYiGe9ev3vcJYHwVohrne","type":"assistant","uuid":"183c79b1-a18f-4467-adbc-1af2558526f6","timestamp":"2026-03-04T15:23:42.788Z"}
{"parentUuid":"183c79b1-a18f-4467-adbc-1af2558526f6","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","message":{"model":"claude-opus-4-6","id":"msg_01T2iXfJxMcDpFuKKEChgtvZ","type":"message","role":"assistant","content":[{"type":"text","text":"I'll explore both implementations and their surrounding context to understand the full picture."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":49690,"cache_read_input_tokens":10398,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":49690},"output_tokens":9,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYiGe9ev3vcJYHwVohrne","type":"assistant","uuid":"cad932cc-5030-4933-ae99-97a90279135f","timestamp":"2026-03-04T15:23:43.212Z"}
{"parentUuid":"cad932cc-5030-4933-ae99-97a90279135f","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","message":{"model":"claude-opus-4-6","id":"msg_01T2iXfJxMcDpFuKKEChgtvZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JPUhKVS2cG5n57jLbfbvoS","name":"Agent","input":{"description":"Explore metricstore package structure","prompt":"Explore the pkg/metricstore/ package thoroughly. I need to understand:\n1. What types are defined (APIQuery, APIQueryRequest, etc.) - check metricstore.go and any type definition files\n2. How FetchData works\n3. What InternalMetricStore is and how it relates to the metric store interface\n4. The full list of files in this package\n\nFocus on understanding the package's role and its types/interfaces.","subagent_type":"Explore"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":49690,"cache_read_input_tokens":10398,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":49690},"output_tokens":9,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYiGe9ev3vcJYHwVohrne","type":"assistant","uuid":"11e11771-ff9d-464d-948f-664179635a98","timestamp":"2026-03-04T15:23:47.304Z"}
{"parentUuid":"11e11771-ff9d-464d-948f-664179635a98","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"type":"hook_progress","hookEvent":"PreToolUse","hookName":"PreToolUse:Agent","command":"entire hooks claude-code pre-task"},"toolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"e2f6c587-99b9-44a1-8f33-e00b5ae095a7","timestamp":"2026-03-04T15:23:47.306Z"}
{"parentUuid":"e2f6c587-99b9-44a1-8f33-e00b5ae095a7","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"type":"text","text":"Explore the pkg/metricstore/ package thoroughly. I need to understand:\n1. What types are defined (APIQuery, APIQueryRequest, etc.) - check metricstore.go and any type definition files\n2. How FetchData works\n3. What InternalMetricStore is and how it relates to the metric store interface\n4. The full list of files in this package\n\nFocus on understanding the package's role and its types/interfaces."}]},"uuid":"9c600fb5-8920-4d25-86e7-80ed817e2b1e","timestamp":"2026-03-04T15:23:47.599Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"Explore the pkg/metricstore/ package thoroughly. I need to understand:\n1. What types are defined (APIQuery, APIQueryRequest, etc.) - check metricstore.go and any type definition files\n2. How FetchData works\n3. What InternalMetricStore is and how it relates to the metric store interface\n4. The full list of files in this package\n\nFocus on understanding the package's role and its types/interfaces.","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"97aecf84-dd46-4492-b5df-d6f98c7ea4d0","timestamp":"2026-03-04T15:23:47.599Z"}
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{"parentUuid":"b4cf9ce6-d303-4cb9-acca-64c4d801571b","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","message":{"model":"claude-opus-4-6","id":"msg_01T2iXfJxMcDpFuKKEChgtvZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01An2GQTxJmdB2UZE56PWWKf","name":"Agent","input":{"description":"Explore metricstoreclient package","prompt":"Explore the internal/metricstoreclient/ package thoroughly. I need to understand:\n1. All files in this package\n2. The CCMetricStore type and its methods (especially LoadData, LoadStats, LoadScopedStats, LoadNodeData, LoadNodeListData)\n3. How it defines APIQuery and related types\n4. The getTopology and getTopologyByNode helper methods\n5. How this package relates to pkg/metricstore/\n\nFocus on understanding the full structure and how it parallels pkg/metricstore/query.go.","subagent_type":"Explore"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":49690,"cache_read_input_tokens":10398,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":49690},"output_tokens":9,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYiGe9ev3vcJYHwVohrne","type":"assistant","uuid":"87dce091-59a3-440c-aa2e-2f4904c0ff79","timestamp":"2026-03-04T15:23:50.441Z"}
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{"parentUuid":"9afd2ecb-67cb-461b-a202-1b0e30acc23c","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-04T15:23:51.645Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01MuyU5G2cVQ6XdczYpQUV9Q","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017cdEbhcELimVKt6P8imsfB","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/metricstore.go"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":5,"cache_creation_input_tokens":908,"cache_read_input_tokens":14862,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":908},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYiGetPfmedob5iESbHqk","uuid":"16e71ae8-961c-4bdf-9cc9-12f4cb17dc7d"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"c9414907-5b9f-4d77-9d6b-12b766086173","timestamp":"2026-03-04T15:23:51.645Z"}
{"parentUuid":"c9414907-5b9f-4d77-9d6b-12b766086173","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017cdEbhcELimVKt6P8imsfB","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved. This file is part of cc-backend.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// Package metricstore provides an efficient in-memory time-series metric storage system\n     7→// with support for hierarchical data organization, checkpointing, and archiving.\n     8→//\n     9→// The package organizes metrics in a tree structure (cluster → host → component) and\n    10→// provides concurrent read/write access to metric data with configurable aggregation strategies.\n    11→// Background goroutines handle periodic checkpointing (JSON or Avro format), archiving old data,\n    12→// and enforcing retention policies.\n    13→//\n    14→// Key features:\n    15→//   - In-memory metric storage with configurable retention\n    16→//   - Hierarchical data organization (selectors)\n    17→//   - Concurrent checkpoint/archive workers\n    18→//   - Support for sum and average aggregation\n    19→//   - NATS integration for metric ingestion\n    20→package metricstore\n    21→\n    22→import (\n    23→\t\"bytes\"\n    24→\t\"context\"\n    25→\t\"encoding/json\"\n    26→\t\"errors\"\n    27→\t\"fmt\"\n    28→\t\"runtime\"\n    29→\t\"runtime/debug\"\n    30→\t\"slices\"\n    31→\t\"sync\"\n    32→\t\"time\"\n    33→\n    34→\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n    35→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    36→\t\"github.com/ClusterCockpit/cc-lib/v2/resampler\"\n    37→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    38→\t\"github.com/ClusterCockpit/cc-lib/v2/util\"\n    39→)\n    40→\n    41→// GlobalState holds the global state for the metric store with thread-safe access.\n    42→type GlobalState struct {\n    43→\tmu                sync.RWMutex\n    44→\tlastRetentionTime int64\n    45→\tselectorsExcluded bool\n    46→}\n    47→\n    48→var (\n    49→\tsingleton  sync.Once\n    50→\tmsInstance *MemoryStore\n    51→\t// shutdownFunc stores the context cancellation function created in Init\n    52→\t// and is called during Shutdown to cancel all background goroutines\n    53→\tshutdownFunc   context.CancelFunc\n    54→\tshutdownFuncMu sync.Mutex // Protects shutdownFunc from concurrent access\n    55→\t// Create a global instance\n    56→\tstate = &GlobalState{}\n    57→)\n    58→\n    59→// NodeProvider provides information about nodes currently in use by running jobs.\n    60→//\n    61→// This interface allows metricstore to query job information without directly\n    62→// depending on the repository package, breaking the import cycle.\n    63→//\n    64→// Implementations should return nodes that are actively processing jobs started\n    65→// before the given timestamp. These nodes will be excluded from retention-based\n    66→// garbage collection to prevent data loss for jobs that are still running or\n    67→// recently completed.\n    68→type NodeProvider interface {\n    69→\t// GetUsedNodes returns a map of cluster names to sorted lists of unique hostnames\n    70→\t// that are currently in use by jobs that started before the given timestamp.\n    71→\t//\n    72→\t// Parameters:\n    73→\t//   - ts: Unix timestamp threshold - returns nodes with jobs started before this time\n    74→\t//\n    75→\t// Returns:\n    76→\t//   - Map of cluster names to lists of node hostnames that should be excluded from garbage collection\n    77→\t//   - Error if the query fails\n    78→\tGetUsedNodes(ts int64) (map[string][]string, error)\n    79→}\n    80→\n    81→// Metric represents a single metric data point to be written to the store.\n    82→type Metric struct {\n    83→\tName  string\n    84→\tValue schema.Float\n    85→\t// MetricConfig contains frequency and aggregation settings for this metric.\n    86→\t// If Frequency is 0, configuration will be looked up from MemoryStore.Metrics during Write().\n    87→\tMetricConfig MetricConfig\n    88→}\n    89→\n    90→// MemoryStore is the main in-memory time-series metric storage implementation.\n    91→//\n    92→// It organizes metrics in a hierarchical tree structure where each level represents\n    93→// a component of the system hierarchy (e.g., cluster → host → CPU). Each level can\n    94→// store multiple metrics as time-series buffers.\n    95→//\n    96→// The store is initialized as a singleton via InitMetrics() and accessed via GetMemoryStore().\n    97→// All public methods are safe for concurrent use.\n    98→type MemoryStore struct {\n    99→\tMetrics      map[string]MetricConfig\n   100→\troot         Level\n   101→\tnodeProvider NodeProvider\n   102→}\n   103→\n   104→// Init initializes the metric store from configuration and starts background workers.\n   105→//\n   106→// This function must be called exactly once before any other metricstore operations.\n   107→// It performs the following initialization steps:\n   108→//  1. Validates and decodes the metric store configuration\n   109→//  2. Configures worker pool size (defaults to NumCPU/2+1, max 10)\n   110→//  3. Loads metric configurations from all registered clusters\n   111→//  4. Restores checkpoints within the retention window\n   112→//  5. Starts background workers for retention, checkpointing, archiving, and monitoring\n   113→//  6. Optionally subscribes to NATS for real-time metric ingestion\n   114→//\n   115→// Parameters:\n   116→//   - rawConfig: JSON configuration for the metric store (see MetricStoreConfig)\n   117→//   - wg: WaitGroup that will be incremented for each background goroutine started\n   118→//\n   119→// The function will call cclog.Fatal on critical errors during initialization.\n   120→// Use Shutdown() to cleanly stop all background workers started by Init().\n   121→//\n   122→// Note: Signal handling must be implemented by the caller. Call Shutdown() when\n   123→// receiving termination signals to ensure checkpoint data is persisted.\n   124→func Init(rawConfig json.RawMessage, metrics map[string]MetricConfig, wg *sync.WaitGroup) {\n   125→\tstartupTime := time.Now()\n   126→\n   127→\tif rawConfig != nil {\n   128→\t\tconfig.Validate(configSchema, rawConfig)\n   129→\t\tdec := json.NewDecoder(bytes.NewReader(rawConfig))\n   130→\t\tdec.DisallowUnknownFields()\n   131→\t\tif err := dec.Decode(&Keys); err != nil {\n   132→\t\t\tcclog.Abortf(\"[METRICSTORE]> Metric Store Config Init: Could not decode config file '%s'.\\nError: %s\\n\", rawConfig, err.Error())\n   133→\t\t}\n   134→\t}\n   135→\n   136→\t// Set NumWorkers from config or use default\n   137→\tif Keys.NumWorkers <= 0 {\n   138→\t\tKeys.NumWorkers = min(runtime.NumCPU()/2+1, DefaultMaxWorkers)\n   139→\t}\n   140→\tcclog.Debugf(\"[METRICSTORE]> Using %d workers for checkpoint/archive operations\\n\", Keys.NumWorkers)\n   141→\n   142→\t// Pass the config.MetricStoreKeys\n   143→\tInitMetrics(metrics)\n   144→\n   145→\tms := GetMemoryStore()\n   146→\n   147→\td, err := time.ParseDuration(Keys.RetentionInMemory)\n   148→\tif err != nil {\n   149→\t\tcclog.Fatal(err)\n   150→\t}\n   151→\n   152→\trestoreFrom := startupTime.Add(-d)\n   153→\tcclog.Infof(\"[METRICSTORE]> Loading checkpoints newer than %s\\n\", restoreFrom.Format(time.RFC3339))\n   154→\tfiles, err := ms.FromCheckpointFiles(Keys.Checkpoints.RootDir, restoreFrom.Unix())\n   155→\tloadedData := ms.SizeInBytes() / 1024 / 1024 // In MB\n   156→\tif err != nil {\n   157→\t\tcclog.Fatalf(\"[METRICSTORE]> Loading checkpoints failed: %s\\n\", err.Error())\n   158→\t} else {\n   159→\t\tcclog.Infof(\"[METRICSTORE]> Checkpoints loaded (%d files, %d MB, that took %fs)\\n\", files, loadedData, time.Since(startupTime).Seconds())\n   160→\t}\n   161→\n   162→\t// Try to use less memory by forcing a GC run here and then\n   163→\t// lowering the target percentage. The default of 100 means\n   164→\t// that only once the ratio of new allocations execeds the\n   165→\t// previously active heap, a GC is triggered.\n   166→\t// Forcing a GC here will set the \"previously active heap\"\n   167→\t// to a minumum.\n   168→\t// runtime.GC()\n   169→\n   170→\tctx, shutdown := context.WithCancel(context.Background())\n   171→\n   172→\tRetention(wg, ctx)\n   173→\tCheckpointing(wg, ctx)\n   174→\tCleanUp(wg, ctx)\n   175→\tWALStaging(wg, ctx)\n   176→\tMemoryUsageTracker(wg, ctx)\n   177→\n   178→\t// Note: Signal handling has been removed from this function.\n   179→\t// The caller is responsible for handling shutdown signals and calling\n   180→\t// the shutdown() function when appropriate.\n   181→\t// Store the shutdown function for later use by Shutdown()\n   182→\tshutdownFuncMu.Lock()\n   183→\tshutdownFunc = shutdown\n   184→\tshutdownFuncMu.Unlock()\n   185→\n   186→\tif Keys.Subscriptions != nil {\n   187→\t\terr = ReceiveNats(ms, 1, ctx)\n   188→\t\tif err != nil {\n   189→\t\t\tcclog.Fatal(err)\n   190→\t\t}\n   191→\t}\n   192→}\n   193→\n   194→// InitMetrics initializes the singleton MemoryStore instance with the given metric configurations.\n   195→//\n   196→// This function must be called before GetMemoryStore() and can only be called once due to\n   197→// the singleton pattern. It assigns each metric an internal offset for efficient buffer indexing.\n   198→//\n   199→// Parameters:\n   200→//   - metrics: Map of metric names to their configurations (frequency and aggregation strategy)\n   201→//\n   202→// Panics if any metric has Frequency == 0, which indicates an invalid configuration.\n   203→//\n   204→// After this call, the global msInstance is ready for use via GetMemoryStore().\n   205→func InitMetrics(metrics map[string]MetricConfig) {\n   206→\tsingleton.Do(func() {\n   207→\t\toffset := 0\n   208→\t\tfor key, cfg := range metrics {\n   209→\t\t\tif cfg.Frequency == 0 {\n   210→\t\t\t\tpanic(\"[METRICSTORE]> invalid frequency\")\n   211→\t\t\t}\n   212→\n   213→\t\t\tmetrics[key] = MetricConfig{\n   214→\t\t\t\tFrequency:   cfg.Frequency,\n   215→\t\t\t\tAggregation: cfg.Aggregation,\n   216→\t\t\t\toffset:      offset,\n   217→\t\t\t}\n   218→\t\t\toffset += 1\n   219→\t\t}\n   220→\n   221→\t\tmsInstance = &MemoryStore{\n   222→\t\t\troot: Level{\n   223→\t\t\t\tmetrics:  make([]*buffer, len(metrics)),\n   224→\t\t\t\tchildren: make(map[string]*Level),\n   225→\t\t\t},\n   226→\t\t\tMetrics: metrics,\n   227→\t\t}\n   228→\t})\n   229→}\n   230→\n   231→// GetMemoryStore returns the singleton MemoryStore instance.\n   232→//\n   233→// Returns the initialized MemoryStore singleton. Calls cclog.Fatal if InitMetrics() was not called first.\n   234→//\n   235→// This function is safe for concurrent use after initialization.\n   236→func GetMemoryStore() *MemoryStore {\n   237→\tif msInstance == nil {\n   238→\t\tcclog.Warnf(\"[METRICSTORE]> MemoryStore not initialized!\")\n   239→\t}\n   240→\n   241→\treturn msInstance\n   242→}\n   243→\n   244→func (ms *MemoryStore) GetMetricFrequency(metricName string) (int64, error) {\n   245→\tif metric, ok := ms.Metrics[metricName]; ok {\n   246→\t\treturn metric.Frequency, nil\n   247→\t}\n   248→\treturn 0, fmt.Errorf(\"[METRICSTORE]> metric %s not found\", metricName)\n   249→}\n   250→\n   251→// SetNodeProvider sets the NodeProvider implementation for the MemoryStore.\n   252→// This must be called during initialization to provide job state information\n   253→// for selective buffer retention during Free operations.\n   254→// If not set, the Free function will fall back to freeing all buffers.\n   255→func (ms *MemoryStore) SetNodeProvider(provider NodeProvider) {\n   256→\tms.nodeProvider = provider\n   257→}\n   258→\n   259→// Shutdown performs a graceful shutdown of the metric store.\n   260→//\n   261→// This function cancels all background goroutines started by Init() and writes\n   262→// a final checkpoint to disk before returning. It should be called when the\n   263→// application receives a termination signal.\n   264→//\n   265→// The function will:\n   266→//  1. Cancel the context to stop all background workers\n   267→//  2. Close the WAL messages channel if using WAL format\n   268→//  3. Write a final checkpoint to preserve in-memory data\n   269→//  4. Log any errors encountered during shutdown\n   270→//\n   271→// Note: This function blocks until the final checkpoint is written.\n   272→func Shutdown() {\n   273→\tshutdownFuncMu.Lock()\n   274→\tdefer shutdownFuncMu.Unlock()\n   275→\tif shutdownFunc != nil {\n   276→\t\tshutdownFunc()\n   277→\t}\n   278→\n   279→\tif Keys.Checkpoints.FileFormat == \"wal\" {\n   280→\t\tclose(WALMessages)\n   281→\t}\n   282→\n   283→\tcclog.Infof(\"[METRICSTORE]> Writing to '%s'...\\n\", Keys.Checkpoints.RootDir)\n   284→\tvar files int\n   285→\tvar err error\n   286→\n   287→\tms := GetMemoryStore()\n   288→\n   289→\tlastCheckpointMu.Lock()\n   290→\tfrom := lastCheckpoint\n   291→\tlastCheckpointMu.Unlock()\n   292→\n   293→\tif Keys.Checkpoints.FileFormat == \"wal\" {\n   294→\t\tvar hostDirs []string\n   295→\t\tfiles, hostDirs, err = ms.ToCheckpointWAL(Keys.Checkpoints.RootDir, from.Unix(), time.Now().Unix())\n   296→\t\tif err == nil {\n   297→\t\t\tRotateWALFilesAfterShutdown(hostDirs)\n   298→\t\t}\n   299→\t} else {\n   300→\t\tfiles, err = ms.ToCheckpoint(Keys.Checkpoints.RootDir, from.Unix(), time.Now().Unix())\n   301→\t}\n   302→\n   303→\tif err != nil {\n   304→\t\tcclog.Errorf(\"[METRICSTORE]> Writing checkpoint failed: %s\\n\", err.Error())\n   305→\t}\n   306→\tcclog.Infof(\"[METRICSTORE]> Done! (%d files written)\\n\", files)\n   307→}\n   308→\n   309→// Retention starts a background goroutine that periodically frees old metric data.\n   310→//\n   311→// This worker runs at half the retention interval and calls Free() to remove buffers\n   312→// older than the configured retention time. It respects the NodeProvider to preserve\n   313→// data for nodes with active jobs.\n   314→//\n   315→// Parameters:\n   316→//   - wg: WaitGroup to signal completion when context is cancelled\n   317→//   - ctx: Context for cancellation signal\n   318→//\n   319→// The goroutine exits when ctx is cancelled.\n   320→func Retention(wg *sync.WaitGroup, ctx context.Context) {\n   321→\tms := GetMemoryStore()\n   322→\n   323→\twg.Go(func() {\n   324→\t\td, err := time.ParseDuration(Keys.RetentionInMemory)\n   325→\t\tif err != nil {\n   326→\t\t\tcclog.Fatal(err)\n   327→\t\t}\n   328→\t\tif d <= 0 {\n   329→\t\t\treturn\n   330→\t\t}\n   331→\n   332→\t\ttickInterval := d / 2\n   333→\t\tif tickInterval <= 0 {\n   334→\t\t\treturn\n   335→\t\t}\n   336→\t\tticker := time.NewTicker(tickInterval)\n   337→\t\tdefer ticker.Stop()\n   338→\n   339→\t\tfor {\n   340→\t\t\tselect {\n   341→\t\t\tcase <-ctx.Done():\n   342→\t\t\t\treturn\n   343→\t\t\tcase <-ticker.C:\n   344→\t\t\t\tstate.mu.Lock()\n   345→\n   346→\t\t\t\tt := time.Now().Add(-d)\n   347→\n   348→\t\t\t\tstate.lastRetentionTime = t.Unix()\n   349→\n   350→\t\t\t\tcclog.Infof(\"[METRICSTORE]> start freeing buffers (older than %s)...\\n\", t.Format(time.RFC3339))\n   351→\n   352→\t\t\t\tfreed, err := Free(ms, t)\n   353→\t\t\t\tif err != nil {\n   354→\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> freeing up buffers failed: %s\\n\", err.Error())\n   355→\t\t\t\t} else {\n   356→\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> done: %d buffers freed\\n\", freed)\n   357→\t\t\t\t}\n   358→\n   359→\t\t\t\tstate.mu.Unlock()\n   360→\n   361→\t\t\t\t// Clean up the buffer pool\n   362→\t\t\t\tbufferPool.Clean(state.lastRetentionTime)\n   363→\t\t\t}\n   364→\t\t}\n   365→\t})\n   366→}\n   367→\n   368→// MemoryUsageTracker starts a background goroutine that monitors memory usage.\n   369→//\n   370→// This worker checks actual process memory usage (via runtime.MemStats) periodically\n   371→// and force-frees buffers if memory exceeds the configured cap. It uses FreeOSMemory()\n   372→// to return memory to the OS after freeing buffers, avoiding aggressive GC that causes\n   373→// performance issues.\n   374→//\n   375→// The tracker logs both actual memory usage (heap allocated) and metric data size for\n   376→// visibility into memory overhead from Go runtime structures and allocations.\n   377→//\n   378→// Parameters:\n   379→//   - wg: WaitGroup to signal completion when context is cancelled\n   380→//   - ctx: Context for cancellation signal\n   381→//\n   382→// The goroutine exits when ctx is cancelled.\n   383→func MemoryUsageTracker(wg *sync.WaitGroup, ctx context.Context) {\n   384→\tms := GetMemoryStore()\n   385→\n   386→\twg.Go(func() {\n   387→\t\td := DefaultMemoryUsageTrackerInterval\n   388→\n   389→\t\tif d <= 0 {\n   390→\t\t\treturn\n   391→\t\t}\n   392→\n   393→\t\tticker := time.NewTicker(d)\n   394→\t\tdefer ticker.Stop()\n   395→\n   396→\t\tfor {\n   397→\t\t\tselect {\n   398→\t\t\tcase <-ctx.Done():\n   399→\t\t\t\treturn\n   400→\t\t\tcase <-ticker.C:\n   401→\t\t\t\tvar mem runtime.MemStats\n   402→\t\t\t\truntime.ReadMemStats(&mem)\n   403→\t\t\t\tactualMemoryGB := float64(mem.Alloc) / 1e9\n   404→\t\t\t\tmetricDataGB := ms.SizeInGB()\n   405→\t\t\t\tcclog.Infof(\"[METRICSTORE]> memory usage: %.2f GB actual (%.2f GB metric data)\", actualMemoryGB, metricDataGB)\n   406→\n   407→\t\t\t\tfreedExcluded := 0\n   408→\t\t\t\tfreedEmergency := 0\n   409→\t\t\t\tvar err error\n   410→\n   411→\t\t\t\tstate.mu.RLock()\n   412→\t\t\t\tlastRetention := state.lastRetentionTime\n   413→\t\t\t\tselectorsExcluded := state.selectorsExcluded\n   414→\t\t\t\tstate.mu.RUnlock()\n   415→\n   416→\t\t\t\tif lastRetention != 0 && selectorsExcluded {\n   417→\t\t\t\t\tfreedExcluded, err = ms.Free(nil, lastRetention)\n   418→\t\t\t\t\tif err != nil {\n   419→\t\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> error while force-freeing the excluded buffers: %s\", err)\n   420→\t\t\t\t\t}\n   421→\n   422→\t\t\t\t\tif freedExcluded > 0 {\n   423→\t\t\t\t\t\tdebug.FreeOSMemory()\n   424→\t\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> done: %d excluded buffers force-freed\", freedExcluded)\n   425→\t\t\t\t\t}\n   426→\t\t\t\t}\n   427→\n   428→\t\t\t\truntime.ReadMemStats(&mem)\n   429→\t\t\t\tactualMemoryGB = float64(mem.Alloc) / 1e9\n   430→\n   431→\t\t\t\tbufferPool.Clear()\n   432→\t\t\t\tcclog.Infof(\"[METRICSTORE]> Cleaned up bufferPool\\n\")\n   433→\n   434→\t\t\t\tif actualMemoryGB > float64(Keys.MemoryCap) {\n   435→\t\t\t\t\tcclog.Warnf(\"[METRICSTORE]> memory usage %.2f GB exceeds cap %d GB, starting emergency buffer freeing\", actualMemoryGB, Keys.MemoryCap)\n   436→\n   437→\t\t\t\t\tconst maxIterations = 100\n   438→\n   439→\t\t\t\t\tfor i := range maxIterations {\n   440→\t\t\t\t\t\tif actualMemoryGB < float64(Keys.MemoryCap) {\n   441→\t\t\t\t\t\t\tbreak\n   442→\t\t\t\t\t\t}\n   443→\n   444→\t\t\t\t\t\tfreed, err := ms.ForceFree()\n   445→\t\t\t\t\t\tif err != nil {\n   446→\t\t\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> error while force-freeing buffers: %s\", err)\n   447→\t\t\t\t\t\t}\n   448→\t\t\t\t\t\tif freed == 0 {\n   449→\t\t\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> no more buffers to free after %d emergency frees, memory usage %.2f GB still exceeds cap %d GB\", freedEmergency, actualMemoryGB, Keys.MemoryCap)\n   450→\t\t\t\t\t\t\tbreak\n   451→\t\t\t\t\t\t}\n   452→\t\t\t\t\t\tfreedEmergency += freed\n   453→\n   454→\t\t\t\t\t\tif i%10 == 0 && freedEmergency > 0 {\n   455→\t\t\t\t\t\t\truntime.ReadMemStats(&mem)\n   456→\t\t\t\t\t\t\tactualMemoryGB = float64(mem.Alloc) / 1e9\n   457→\t\t\t\t\t\t}\n   458→\t\t\t\t\t}\n   459→\n   460→\t\t\t\t\t// if freedEmergency > 0 {\n   461→\t\t\t\t\t// \tdebug.FreeOSMemory()\n   462→\t\t\t\t\t// }\n   463→\n   464→\t\t\t\t\truntime.ReadMemStats(&mem)\n   465→\t\t\t\t\tactualMemoryGB = float64(mem.Alloc) / 1e9\n   466→\n   467→\t\t\t\t\tif actualMemoryGB >= float64(Keys.MemoryCap) {\n   468→\t\t\t\t\t\tcclog.Errorf(\"[METRICSTORE]> after %d emergency frees, memory usage %.2f GB still at/above cap %d GB\", freedEmergency, actualMemoryGB, Keys.MemoryCap)\n   469→\t\t\t\t\t} else {\n   470→\t\t\t\t\t\tcclog.Infof(\"[METRICSTORE]> emergency freeing complete: %d buffers freed, memory now %.2f GB\", freedEmergency, actualMemoryGB)\n   471→\t\t\t\t\t}\n   472→\t\t\t\t}\n   473→\t\t\t}\n   474→\t\t}\n   475→\t})\n   476→}\n   477→\n   478→// Free removes metric data older than the given time while preserving data for active nodes.\n   479→//\n   480→// This function implements intelligent retention by consulting the NodeProvider (if configured)\n   481→// to determine which nodes are currently in use by running jobs. Data for these nodes is\n   482→// preserved even if older than the retention time.\n   483→//\n   484→// Parameters:\n   485→//   - ms: The MemoryStore instance\n   486→//   - t: Time threshold - buffers with data older than this will be freed\n   487→//\n   488→// Returns:\n   489→//   - Number of buffers freed\n   490→//   - Error if NodeProvider query fails\n   491→//\n   492→// Behavior:\n   493→//   - If no NodeProvider is set: frees all buffers older than t\n   494→//   - If NodeProvider returns empty map: frees all buffers older than t\n   495→//   - Otherwise: preserves buffers for nodes returned by GetUsedNodes(), frees others\n   496→func Free(ms *MemoryStore, t time.Time) (int, error) {\n   497→\t// If no NodeProvider is configured, free all buffers older than t\n   498→\tif ms.nodeProvider == nil {\n   499→\t\treturn ms.Free(nil, t.Unix())\n   500→\t}\n   501→\n   502→\texcludeSelectors, err := ms.nodeProvider.GetUsedNodes(t.Unix())\n   503→\tif err != nil {\n   504→\t\treturn 0, err\n   505→\t}\n   506→\n   507→\tswitch lenMap := len(excludeSelectors); lenMap {\n   508→\n   509→\t// If the length of the map returned by GetUsedNodes() is 0,\n   510→\t// then use default Free method with nil selector\n   511→\tcase 0:\n   512→\t\tstate.selectorsExcluded = false\n   513→\t\treturn ms.Free(nil, t.Unix())\n   514→\n   515→\t// Else formulate selectors, exclude those from the map\n   516→\t// and free the rest of the selectors\n   517→\tdefault:\n   518→\t\tstate.selectorsExcluded = true\n   519→\t\tselectors := GetSelectors(ms, excludeSelectors)\n   520→\t\treturn FreeSelected(ms, selectors, t)\n   521→\t}\n   522→}\n   523→\n   524→// FreeSelected frees buffers for specific selectors while preserving others.\n   525→//\n   526→// This function is used when we want to retain some specific nodes beyond the retention time.\n   527→// It iterates through the provided selectors and frees their associated buffers.\n   528→//\n   529→// Parameters:\n   530→//   - ms: The MemoryStore instance\n   531→//   - selectors: List of selector paths to free (e.g., [[\"cluster1\", \"node1\"], [\"cluster2\", \"node2\"]])\n   532→//   - t: Time threshold for freeing buffers\n   533→//\n   534→// Returns the total number of buffers freed and any error encountered.\n   535→func FreeSelected(ms *MemoryStore, selectors [][]string, t time.Time) (int, error) {\n   536→\tfreed := 0\n   537→\n   538→\tfor _, selector := range selectors {\n   539→\n   540→\t\tfreedBuffers, err := ms.Free(selector, t.Unix())\n   541→\t\tif err != nil {\n   542→\t\t\tcclog.Errorf(\"error while freeing selected buffers: %#v\", err)\n   543→\t\t}\n   544→\t\tfreed += freedBuffers\n   545→\n   546→\t}\n   547→\n   548→\treturn freed, nil\n   549→}\n   550→\n   551→// GetSelectors returns all selectors at depth 2 (cluster/node level) that are NOT in the exclusion map.\n   552→//\n   553→// This function generates a list of selectors whose buffers should be freed by excluding\n   554→// selectors that correspond to nodes currently in use by running jobs.\n   555→//\n   556→// Parameters:\n   557→//   - ms: The MemoryStore instance\n   558→//   - excludeSelectors: Map of cluster names to node hostnames that should NOT be freed\n   559→//\n   560→// Returns a list of selectors ([]string paths) that can be safely freed.\n   561→//\n   562→// Example:\n   563→//\n   564→//\tIf the tree has paths [\"emmy\", \"node001\"] and [\"emmy\", \"node002\"],\n   565→//\tand excludeSelectors contains {\"emmy\": [\"node001\"]},\n   566→//\tthen only [[\"emmy\", \"node002\"]] is returned.\n   567→func GetSelectors(ms *MemoryStore, excludeSelectors map[string][]string) [][]string {\n   568→\tallSelectors := ms.GetPaths(2)\n   569→\n   570→\tfilteredSelectors := make([][]string, 0, len(allSelectors))\n   571→\n   572→\tfor _, path := range allSelectors {\n   573→\t\tif len(path) < 2 {\n   574→\t\t\tcontinue\n   575→\t\t}\n   576→\n   577→\t\tkey := path[0]   // The \"Key\" (Level 1)\n   578→\t\tvalue := path[1] // The \"Value\" (Level 2)\n   579→\n   580→\t\texclude := false\n   581→\n   582→\t\t// Check if the key exists in our exclusion map\n   583→\t\tif excludedValues, exists := excludeSelectors[key]; exists {\n   584→\t\t\t// The key exists, now check if the specific value is in the exclusion list\n   585→\t\t\tif slices.Contains(excludedValues, value) {\n   586→\t\t\t\texclude = true\n   587→\t\t\t}\n   588→\t\t}\n   589→\n   590→\t\tif !exclude {\n   591→\t\t\tfilteredSelectors = append(filteredSelectors, path)\n   592→\t\t}\n   593→\t}\n   594→\n   595→\treturn filteredSelectors\n   596→}\n   597→\n   598→// GetPaths returns a list of lists (paths) to the specified depth.\n   599→func (ms *MemoryStore) GetPaths(targetDepth int) [][]string {\n   600→\tvar results [][]string\n   601→\n   602→\t// Start recursion. Initial path is empty.\n   603→\t// We treat Root as depth 0.\n   604→\tms.root.collectPaths(0, targetDepth, []string{}, &results)\n   605→\n   606→\treturn results\n   607→}\n   608→\n   609→// Write all values in `metrics` to the level specified by `selector` for time `ts`.\n   610→// Look at `findLevelOrCreate` for how selectors work.\n   611→func (m *MemoryStore) Write(selector []string, ts int64, metrics []Metric) error {\n   612→\tvar ok bool\n   613→\tfor i, metric := range metrics {\n   614→\t\tif metric.MetricConfig.Frequency == 0 {\n   615→\t\t\tmetric.MetricConfig, ok = m.Metrics[metric.Name]\n   616→\t\t\tif !ok {\n   617→\t\t\t\tcclog.Debugf(\"[METRICSTORE]> Unknown metric '%s' in Write() - skipping\", metric.Name)\n   618→\t\t\t\tmetric.MetricConfig.Frequency = 0\n   619→\t\t\t}\n   620→\t\t\tmetrics[i] = metric\n   621→\t\t}\n   622→\t}\n   623→\n   624→\treturn m.WriteToLevel(&m.root, selector, ts, metrics)\n   625→}\n   626→\n   627→func (m *MemoryStore) GetLevel(selector []string) *Level {\n   628→\treturn m.root.findLevelOrCreate(selector, len(m.Metrics))\n   629→}\n   630→\n   631→// WriteToLevel assumes that `minfo` in `metrics` is filled in\n   632→func (m *MemoryStore) WriteToLevel(l *Level, selector []string, ts int64, metrics []Metric) error {\n   633→\tl = l.findLevelOrCreate(selector, len(m.Metrics))\n   634→\tl.lock.Lock()\n   635→\tdefer l.lock.Unlock()\n   636→\n   637→\tfor _, metric := range metrics {\n   638→\t\tif metric.MetricConfig.Frequency == 0 {\n   639→\t\t\tcontinue\n   640→\t\t}\n   641→\n   642→\t\tb := l.metrics[metric.MetricConfig.offset]\n   643→\t\tif b == nil {\n   644→\t\t\t// First write to this metric and level\n   645→\t\t\tb = newBuffer(ts, metric.MetricConfig.Frequency)\n   646→\t\t\tl.metrics[metric.MetricConfig.offset] = b\n   647→\t\t}\n   648→\n   649→\t\tnb, err := b.write(ts, metric.Value)\n   650→\t\tif err != nil {\n   651→\t\t\treturn err\n   652→\t\t}\n   653→\n   654→\t\t// Last write created a new buffer...\n   655→\t\tif b != nb {\n   656→\t\t\tl.metrics[metric.MetricConfig.offset] = nb\n   657→\t\t}\n   658→\t}\n   659→\treturn nil\n   660→}\n   661→\n   662→// Read returns all values for metric `metric` from `from` to `to` for the selected level(s).\n   663→// If the level does not hold the metric itself, the data will be aggregated recursively from the children.\n   664→// The second and third return value are the actual from/to for the data. Those can be different from\n   665→// the range asked for if no data was available.\n   666→func (m *MemoryStore) Read(selector util.Selector, metric string, from, to, resolution int64) ([]schema.Float, int64, int64, int64, error) {\n   667→\tif from > to {\n   668→\t\treturn nil, 0, 0, 0, errors.New(\"[METRICSTORE]> invalid time range\")\n   669→\t}\n   670→\n   671→\tminfo, ok := m.Metrics[metric]\n   672→\tif !ok {\n   673→\t\treturn nil, 0, 0, 0, errors.New(\"[METRICSTORE]> unknown metric: \" + metric)\n   674→\t}\n   675→\n   676→\tn, data := 0, make([]schema.Float, (to-from)/minfo.Frequency+1)\n   677→\n   678→\terr := m.root.findBuffers(selector, minfo.offset, func(b *buffer) error {\n   679→\t\tcdata, cfrom, cto, err := b.read(from, to, data)\n   680→\t\tif err != nil {\n   681→\t\t\treturn err\n   682→\t\t}\n   683→\n   684→\t\tif n == 0 {\n   685→\t\t\tfrom, to = cfrom, cto\n   686→\t\t} else if from != cfrom || to != cto || len(data) != len(cdata) {\n   687→\t\t\tmissingfront, missingback := int((from-cfrom)/minfo.Frequency), int((to-cto)/minfo.Frequency)\n   688→\t\t\tif missingfront != 0 {\n   689→\t\t\t\treturn ErrDataDoesNotAlign\n   690→\t\t\t}\n   691→\n   692→\t\t\tnewlen := len(cdata) - missingback\n   693→\t\t\tif newlen < 1 {\n   694→\t\t\t\treturn ErrDataDoesNotAlign\n   695→\t\t\t}\n   696→\t\t\tcdata = cdata[0:newlen]\n   697→\t\t\tif len(cdata) != len(data) {\n   698→\t\t\t\treturn ErrDataDoesNotAlign\n   699→\t\t\t}\n   700→\n   701→\t\t\tfrom, to = cfrom, cto\n   702→\t\t}\n   703→\n   704→\t\tdata = cdata\n   705→\t\tn += 1\n   706→\t\treturn nil\n   707→\t})\n   708→\n   709→\tif err != nil {\n   710→\t\treturn nil, 0, 0, 0, err\n   711→\t} else if n == 0 {\n   712→\t\treturn nil, 0, 0, 0, ErrNoHostOrMetric\n   713→\t} else if n > 1 {\n   714→\t\tif minfo.Aggregation == AvgAggregation {\n   715→\t\t\tnormalize := 1. / schema.Float(n)\n   716→\t\t\tfor i := 0; i < len(data); i++ {\n   717→\t\t\t\tdata[i] *= normalize\n   718→\t\t\t}\n   719→\t\t} else if minfo.Aggregation != SumAggregation {\n   720→\t\t\treturn nil, 0, 0, 0, errors.New(\"[METRICSTORE]> invalid aggregation\")\n   721→\t\t}\n   722→\t}\n   723→\n   724→\tdata, resolution, err = resampler.LargestTriangleThreeBucket(data, minfo.Frequency, resolution)\n   725→\tif err != nil {\n   726→\t\treturn nil, 0, 0, 0, err\n   727→\t}\n   728→\n   729→\treturn data, from, to, resolution, nil\n   730→}\n   731→\n   732→// Free releases all buffers for the selected level and all its children that\n   733→// contain only values older than `t`.\n   734→func (m *MemoryStore) Free(selector []string, t int64) (int, error) {\n   735→\treturn m.GetLevel(selector).free(t)\n   736→}\n   737→\n   738→// ForceFree unconditionally removes the oldest buffer from each metric chain.\n   739→func (m *MemoryStore) ForceFree() (int, error) {\n   740→\treturn m.GetLevel(nil).forceFree()\n   741→}\n   742→\n   743→func (m *MemoryStore) FreeAll() error {\n   744→\tfor k := range m.root.children {\n   745→\t\tdelete(m.root.children, k)\n   746→\t}\n   747→\n   748→\treturn nil\n   749→}\n   750→\n   751→func (m *MemoryStore) SizeInBytes() int64 {\n   752→\treturn m.root.sizeInBytes()\n   753→}\n   754→\n   755→func (m *MemoryStore) SizeInGB() float64 {\n   756→\treturn float64(m.root.sizeInBytes()) / 1e9\n   757→}\n   758→\n   759→// ListChildren , given a selector, returns a list of all children of the level\n   760→// selected.\n   761→func (m *MemoryStore) ListChildren(selector []string) []string {\n   762→\tlvl := &m.root\n   763→\tfor lvl != nil && len(selector) != 0 {\n   764→\t\tlvl.lock.RLock()\n   765→\t\tnext := lvl.children[selector[0]]\n   766→\t\tlvl.lock.RUnlock()\n   767→\t\tlvl = next\n   768→\t\tselector = selector[1:]\n   769→\t}\n   770→\n   771→\tif lvl == nil {\n   772→\t\treturn nil\n   773→\t}\n   774→\n   775→\tlvl.lock.RLock()\n   776→\tdefer lvl.lock.RUnlock()\n   777→\n   778→\tchildren := make([]string, 0, len(lvl.children))\n   779→\tfor child := range lvl.children {\n   780→\t\tchildren = append(children, child)\n   781→\t}\n   782→\n   783→\treturn children\n   784→}\n   785→"}]},"uuid":"d4817ea9-0e0e-425d-84a8-0740829bb7dd","timestamp":"2026-03-04T15:23:51.653Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"9cdbb057-c44f-4748-9964-d1468a639d54","timestamp":"2026-03-04T15:23:51.899Z"}
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{"parentUuid":"1d08f46f-e142-47a0-b6d0-0f8835f86966","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012M2iQy2V9h4Qje74uByPf3","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved. This file is part of cc-backend.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// This file contains the API types and data fetching logic for querying metric data\n     7→// from the in-memory metric store. It provides structures for building complex queries\n     8→// with support for aggregation, scaling, padding, and statistics computation.\n     9→package metricstore\n    10→\n    11→import (\n    12→\t\"errors\"\n    13→\t\"fmt\"\n    14→\t\"math\"\n    15→\n    16→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    17→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    18→\t\"github.com/ClusterCockpit/cc-lib/v2/util\"\n    19→)\n    20→\n    21→var (\n    22→\t// ErrNoHostOrMetric is returned when the metric store does not find the host or the metric\n    23→\tErrNoHostOrMetric error = errors.New(\"[METRICSTORE]> metric or host not found\")\n    24→\t// ErrInvalidTimeRange is returned when a query has 'from' >= 'to'\n    25→\tErrInvalidTimeRange = errors.New(\"[METRICSTORE]> invalid time range: 'from' must be before 'to'\")\n    26→\t// ErrEmptyCluster is returned when a query with ForAllNodes has no cluster specified\n    27→\tErrEmptyCluster = errors.New(\"[METRICSTORE]> cluster name cannot be empty\")\n    28→)\n    29→\n    30→// APIMetricData represents the response data for a single metric query.\n    31→//\n    32→// It contains both the time-series data points and computed statistics (avg, min, max).\n    33→// If an error occurred during data retrieval, the Error field will be set and other\n    34→// fields may be incomplete.\n    35→type APIMetricData struct {\n    36→\tError      *string           `json:\"error,omitempty\"`\n    37→\tData       schema.FloatArray `json:\"data,omitempty\"`\n    38→\tFrom       int64             `json:\"from\"`\n    39→\tTo         int64             `json:\"to\"`\n    40→\tResolution int64             `json:\"resolution\"`\n    41→\tAvg        schema.Float      `json:\"avg\"`\n    42→\tMin        schema.Float      `json:\"min\"`\n    43→\tMax        schema.Float      `json:\"max\"`\n    44→}\n    45→\n    46→// APIQueryRequest represents a batch query request for metric data.\n    47→//\n    48→// It supports two modes of operation:\n    49→//  1. Explicit queries via the Queries field\n    50→//  2. Automatic query generation via ForAllNodes (queries all specified metrics for all nodes in the cluster)\n    51→//\n    52→// The request can be customized with flags to include/exclude statistics, raw data, and padding.\n    53→type APIQueryRequest struct {\n    54→\tCluster     string     `json:\"cluster\"`\n    55→\tQueries     []APIQuery `json:\"queries\"`\n    56→\tForAllNodes []string   `json:\"for-all-nodes\"`\n    57→\tFrom        int64      `json:\"from\"`\n    58→\tTo          int64      `json:\"to\"`\n    59→\tWithStats   bool       `json:\"with-stats\"`\n    60→\tWithData    bool       `json:\"with-data\"`\n    61→\tWithPadding bool       `json:\"with-padding\"`\n    62→}\n    63→\n    64→// APIQueryResponse represents the response to an APIQueryRequest.\n    65→//\n    66→// Results is a 2D array where each outer element corresponds to a query,\n    67→// and each inner element corresponds to a selector within that query\n    68→// (e.g., multiple CPUs or cores).\n    69→type APIQueryResponse struct {\n    70→\tQueries []APIQuery        `json:\"queries,omitempty\"`\n    71→\tResults [][]APIMetricData `json:\"results\"`\n    72→}\n    73→\n    74→// APIQuery represents a single metric query with optional hierarchical selectors.\n    75→//\n    76→// The hierarchical selection works as follows:\n    77→//   - Hostname: The node to query\n    78→//   - Type + TypeIds: First level of hierarchy (e.g., \"cpu\" + [\"0\", \"1\", \"2\"])\n    79→//   - SubType + SubTypeIds: Second level of hierarchy (e.g., \"core\" + [\"0\", \"1\"])\n    80→//\n    81→// If Aggregate is true, data from multiple type/subtype IDs will be aggregated according\n    82→// to the metric's aggregation strategy. Otherwise, separate results are returned for each combination.\n    83→type APIQuery struct {\n    84→\tType        *string      `json:\"type,omitempty\"`\n    85→\tSubType     *string      `json:\"subtype,omitempty\"`\n    86→\tMetric      string       `json:\"metric\"`\n    87→\tHostname    string       `json:\"host\"`\n    88→\tResolution  int64        `json:\"resolution\"`\n    89→\tTypeIds     []string     `json:\"type-ids,omitempty\"`\n    90→\tSubTypeIds  []string     `json:\"subtype-ids,omitempty\"`\n    91→\tScaleFactor schema.Float `json:\"scale-by,omitempty\"`\n    92→\tAggregate   bool         `json:\"aggreg\"`\n    93→}\n    94→\n    95→// AddStats computes and populates the Avg, Min, and Max fields from the Data array.\n    96→//\n    97→// NaN values in the data are ignored during computation. If all values are NaN,\n    98→// the statistics fields will be set to NaN.\n    99→//\n   100→// TODO: Optimize this, just like the stats endpoint!\n   101→func (data *APIMetricData) AddStats() {\n   102→\tn := 0\n   103→\tsum, min, max := 0.0, math.MaxFloat64, -math.MaxFloat64\n   104→\tfor _, x := range data.Data {\n   105→\t\tif x.IsNaN() {\n   106→\t\t\tcontinue\n   107→\t\t}\n   108→\n   109→\t\tn += 1\n   110→\t\tsum += float64(x)\n   111→\t\tmin = math.Min(min, float64(x))\n   112→\t\tmax = math.Max(max, float64(x))\n   113→\t}\n   114→\n   115→\tif n > 0 {\n   116→\t\tavg := sum / float64(n)\n   117→\t\tdata.Avg = schema.Float(avg)\n   118→\t\tdata.Min = schema.Float(min)\n   119→\t\tdata.Max = schema.Float(max)\n   120→\t} else {\n   121→\t\tdata.Avg, data.Min, data.Max = schema.NaN, schema.NaN, schema.NaN\n   122→\t}\n   123→}\n   124→\n   125→// ScaleBy multiplies all data points and statistics by the given factor.\n   126→//\n   127→// This is commonly used for unit conversion (e.g., bytes to gigabytes).\n   128→// Scaling by 0 or 1 is a no-op for performance reasons.\n   129→func (data *APIMetricData) ScaleBy(f schema.Float) {\n   130→\tif f == 0 || f == 1 {\n   131→\t\treturn\n   132→\t}\n   133→\n   134→\tdata.Avg *= f\n   135→\tdata.Min *= f\n   136→\tdata.Max *= f\n   137→\tfor i := 0; i < len(data.Data); i++ {\n   138→\t\tdata.Data[i] *= f\n   139→\t}\n   140→}\n   141→\n   142→// PadDataWithNull pads the beginning of the data array with NaN values if needed.\n   143→//\n   144→// This ensures that the data aligns with the requested 'from' timestamp, even if\n   145→// the metric store doesn't have data for the earliest time points. This is useful\n   146→// for maintaining consistent array indexing across multiple queries.\n   147→//\n   148→// Parameters:\n   149→//   - ms: MemoryStore instance to lookup metric configuration\n   150→//   - from: The requested start timestamp\n   151→//   - to: The requested end timestamp (unused but kept for API consistency)\n   152→//   - metric: The metric name to lookup frequency information\n   153→func (data *APIMetricData) PadDataWithNull(ms *MemoryStore, from, to int64, metric string) {\n   154→\tminfo, ok := ms.Metrics[metric]\n   155→\tif !ok {\n   156→\t\treturn\n   157→\t}\n   158→\n   159→\tif (data.From / minfo.Frequency) > (from / minfo.Frequency) {\n   160→\t\tpadfront := int((data.From / minfo.Frequency) - (from / minfo.Frequency))\n   161→\t\tndata := make([]schema.Float, 0, padfront+len(data.Data))\n   162→\t\tfor range padfront {\n   163→\t\t\tndata = append(ndata, schema.NaN)\n   164→\t\t}\n   165→\t\tfor j := 0; j < len(data.Data); j++ {\n   166→\t\t\tndata = append(ndata, data.Data[j])\n   167→\t\t}\n   168→\t\tdata.Data = ndata\n   169→\t}\n   170→}\n   171→\n   172→// FetchData executes a batch metric query request and returns the results.\n   173→//\n   174→// This is the primary API for retrieving metric data from the memory store. It supports:\n   175→//   - Individual queries via req.Queries\n   176→//   - Batch queries for all nodes via req.ForAllNodes\n   177→//   - Hierarchical selector construction (cluster → host → type → subtype)\n   178→//   - Optional statistics computation (avg, min, max)\n   179→//   - Optional data scaling\n   180→//   - Optional data padding with NaN values\n   181→//\n   182→// The function constructs selectors based on the query parameters and calls MemoryStore.Read()\n   183→// for each selector. If a query specifies Aggregate=false with multiple type/subtype IDs,\n   184→// separate results are returned for each combination.\n   185→//\n   186→// Parameters:\n   187→//   - req: The query request containing queries, time range, and options\n   188→//\n   189→// Returns:\n   190→//   - APIQueryResponse containing results for each query, or error if validation fails\n   191→//\n   192→// Errors:\n   193→//   - ErrInvalidTimeRange if req.From > req.To\n   194→//   - ErrEmptyCluster if req.ForAllNodes is used without specifying a cluster\n   195→//   - Error if MemoryStore is not initialized\n   196→//   - Individual query errors are stored in APIMetricData.Error field\n   197→func FetchData(req APIQueryRequest) (*APIQueryResponse, error) {\n   198→\tif req.From > req.To {\n   199→\t\treturn nil, ErrInvalidTimeRange\n   200→\t}\n   201→\tif req.Cluster == \"\" && req.ForAllNodes != nil {\n   202→\t\treturn nil, ErrEmptyCluster\n   203→\t}\n   204→\n   205→\treq.WithData = true\n   206→\tms := GetMemoryStore()\n   207→\tif ms == nil {\n   208→\t\treturn nil, fmt.Errorf(\"[METRICSTORE]> memorystore not initialized\")\n   209→\t}\n   210→\n   211→\tresponse := APIQueryResponse{\n   212→\t\tResults: make([][]APIMetricData, 0, len(req.Queries)),\n   213→\t}\n   214→\tif req.ForAllNodes != nil {\n   215→\t\tnodes := ms.ListChildren([]string{req.Cluster})\n   216→\t\tfor _, node := range nodes {\n   217→\t\t\tfor _, metric := range req.ForAllNodes {\n   218→\t\t\t\tq := APIQuery{\n   219→\t\t\t\t\tMetric:   metric,\n   220→\t\t\t\t\tHostname: node,\n   221→\t\t\t\t}\n   222→\t\t\t\treq.Queries = append(req.Queries, q)\n   223→\t\t\t\tresponse.Queries = append(response.Queries, q)\n   224→\t\t\t}\n   225→\t\t}\n   226→\t}\n   227→\n   228→\tfor _, query := range req.Queries {\n   229→\t\tsels := make([]util.Selector, 0, 1)\n   230→\t\tif query.Aggregate || query.Type == nil {\n   231→\t\t\tsel := util.Selector{{String: req.Cluster}, {String: query.Hostname}}\n   232→\t\t\tif query.Type != nil {\n   233→\t\t\t\tif len(query.TypeIds) == 1 {\n   234→\t\t\t\t\tsel = append(sel, util.SelectorElement{String: *query.Type + query.TypeIds[0]})\n   235→\t\t\t\t} else {\n   236→\t\t\t\t\tids := make([]string, len(query.TypeIds))\n   237→\t\t\t\t\tfor i, id := range query.TypeIds {\n   238→\t\t\t\t\t\tids[i] = *query.Type + id\n   239→\t\t\t\t\t}\n   240→\t\t\t\t\tsel = append(sel, util.SelectorElement{Group: ids})\n   241→\t\t\t\t}\n   242→\n   243→\t\t\t\tif query.SubType != nil {\n   244→\t\t\t\t\tif len(query.SubTypeIds) == 1 {\n   245→\t\t\t\t\t\tsel = append(sel, util.SelectorElement{String: *query.SubType + query.SubTypeIds[0]})\n   246→\t\t\t\t\t} else {\n   247→\t\t\t\t\t\tids := make([]string, len(query.SubTypeIds))\n   248→\t\t\t\t\t\tfor i, id := range query.SubTypeIds {\n   249→\t\t\t\t\t\t\tids[i] = *query.SubType + id\n   250→\t\t\t\t\t\t}\n   251→\t\t\t\t\t\tsel = append(sel, util.SelectorElement{Group: ids})\n   252→\t\t\t\t\t}\n   253→\t\t\t\t}\n   254→\t\t\t}\n   255→\t\t\tsels = append(sels, sel)\n   256→\t\t} else {\n   257→\t\t\tfor _, typeID := range query.TypeIds {\n   258→\t\t\t\tif query.SubType != nil {\n   259→\t\t\t\t\tfor _, subTypeID := range query.SubTypeIds {\n   260→\t\t\t\t\t\tsels = append(sels, util.Selector{\n   261→\t\t\t\t\t\t\t{String: req.Cluster},\n   262→\t\t\t\t\t\t\t{String: query.Hostname},\n   263→\t\t\t\t\t\t\t{String: *query.Type + typeID},\n   264→\t\t\t\t\t\t\t{String: *query.SubType + subTypeID},\n   265→\t\t\t\t\t\t})\n   266→\t\t\t\t\t}\n   267→\t\t\t\t} else {\n   268→\t\t\t\t\tsels = append(sels, util.Selector{\n   269→\t\t\t\t\t\t{String: req.Cluster},\n   270→\t\t\t\t\t\t{String: query.Hostname},\n   271→\t\t\t\t\t\t{String: *query.Type + typeID},\n   272→\t\t\t\t\t})\n   273→\t\t\t\t}\n   274→\t\t\t}\n   275→\t\t}\n   276→\n   277→\t\tvar err error\n   278→\t\tres := make([]APIMetricData, 0, len(sels))\n   279→\t\tfor _, sel := range sels {\n   280→\t\t\tdata := APIMetricData{}\n   281→\n   282→\t\t\tdata.Data, data.From, data.To, data.Resolution, err = ms.Read(sel, query.Metric, req.From, req.To, query.Resolution)\n   283→\t\t\tif err != nil {\n   284→\t\t\t\t// Skip Error If Just Missing Host or Metric, Continue\n   285→\t\t\t\t// Empty Return For Metric Handled Gracefully By Frontend\n   286→\t\t\t\tif err != ErrNoHostOrMetric {\n   287→\t\t\t\t\tmsg := err.Error()\n   288→\t\t\t\t\tdata.Error = &msg\n   289→\t\t\t\t\tres = append(res, data)\n   290→\t\t\t\t} else {\n   291→\t\t\t\t\tcclog.Warnf(\"failed to fetch '%s' from host '%s' (cluster: %s): %s\", query.Metric, query.Hostname, req.Cluster, err.Error())\n   292→\t\t\t\t}\n   293→\t\t\t\tcontinue\n   294→\t\t\t}\n   295→\n   296→\t\t\tif req.WithStats {\n   297→\t\t\t\tdata.AddStats()\n   298→\t\t\t}\n   299→\t\t\tif query.ScaleFactor != 0 {\n   300→\t\t\t\tdata.ScaleBy(query.ScaleFactor)\n   301→\t\t\t}\n   302→\t\t\tif req.WithPadding {\n   303→\t\t\t\tdata.PadDataWithNull(ms, req.From, req.To, query.Metric)\n   304→\t\t\t}\n   305→\t\t\tif !req.WithData {\n   306→\t\t\t\tdata.Data = nil\n   307→\t\t\t}\n   308→\t\t\tres = append(res, data)\n   309→\t\t}\n   310→\t\tresponse.Results = append(response.Results, res)\n   311→\t}\n   312→\n   313→\treturn &response, nil\n   314→}\n   315→"}]},"uuid":"f73f6676-a1a2-4046-9023-6327001b3f70","timestamp":"2026-03-04T15:23:51.908Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"c916ac03-62d2-47a8-af66-ea8cfdc51d3a","timestamp":"2026-03-04T15:23:52.001Z"}
{"parentUuid":"c916ac03-62d2-47a8-af66-ea8cfdc51d3a","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-04T15:23:52.016Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01MuyU5G2cVQ6XdczYpQUV9Q","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0124p3CnoYZLaGEEA47wjZ6c","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/query.go"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":5,"cache_creation_input_tokens":908,"cache_read_input_tokens":14862,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":908},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYiGetPfmedob5iESbHqk","uuid":"0db88922-873c-4810-9261-e46f608ca48c"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"b97d3d26-7804-4104-a52f-59c9d82a43c8","timestamp":"2026-03-04T15:23:52.016Z"}
{"parentUuid":"b97d3d26-7804-4104-a52f-59c9d82a43c8","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0124p3CnoYZLaGEEA47wjZ6c","type":"tool_result","content":"<persisted-output>\nOutput too large (50.6KB). Full output saved to: /Users/jan/.claude/projects/-Users-jan-prg-ClusterCockpit-cc-backend/eec1fc87-d8f6-42f6-b296-d7a2f79af002/tool-results/REDACTED.txt\n\nPreview (first 2KB):\n     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved. This file is part of cc-backend.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// This file implements high-level query functions for loading job metric data\n     7→// with automatic scope transformation and aggregation.\n     8→//\n     9→// Key Concepts:\n    10→//\n    11→// Metric Scopes: Metrics are collected at different granularities (native scope):\n    12→//   - HWThread: Per hardware thread\n    13→//   - Core: Per CPU core\n    14→//   - Socket: Per CPU socket\n    15→//   - MemoryDomain: Per memory domain (NUMA)\n    16→//   - Accelerator: Per GPU/accelerator\n    17→//   - Node: Per compute node\n    18→//\n    19→// Scope Transformation: The buildQueries functions transform between native scope\n    20→// and requested scope by:\n    21→//   - Aggregating finer-grained data (e.g., HWThread → Core → Socket → Node)\n    22→//   - Rejecting requests for finer granularity than available\n    23→//   - Handling special cases (e.g., Accelerator metrics)\n    24→//\n    25→// Query Building: Constructs APIQuery structures with proper selectors (Type, TypeIds)\n    26→// based on cluster topology and job resources.\n    27→package metricstore\n    28→\n    29→import (\n    30→\t\"context\"\n    31→\t\"fmt\"\n    32→\t\"strconv\"\n    33→\t\"strings\"\n    34→\t\"time\"\n    35→\n    36→\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n    37→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    38→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    39→)\n    40→\n    41→type InternalMetricStore struct{}\n    42→\n    43→var MetricStoreHandle *InternalMetricStore\n    44→\n    45→// HealthCheck delegates to the internal MemoryStore's HealthCheck.\n    46→func (ccms *InternalMetricStore) HealthCheck(cluster string,\n    47→\tnodes []string, metrics []string,\n    48→) (map[string]HealthCheckResult, error) {\n...\n</persisted-output>"}]},"uuid":"b660f3a6-c1bb-41ff-86ae-bda41dc5a28b","timestamp":"2026-03-04T15:23:52.260Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"0bf966a1-6ef5-4e31-82ea-615bbb3959eb","timestamp":"2026-03-04T15:23:52.261Z"}
{"parentUuid":"0bf966a1-6ef5-4e31-82ea-615bbb3959eb","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"type":"text","text":"Explore the internal/metricstoreclient/ package thoroughly. I need to understand:\n1. All files in this package\n2. The CCMetricStore type and its methods (especially LoadData, LoadStats, LoadScopedStats, LoadNodeData, LoadNodeListData)\n3. How it defines APIQuery and related types\n4. The getTopology and getTopologyByNode helper methods\n5. How this package relates to pkg/metricstore/\n\nFocus on understanding the full structure and how it parallels pkg/metricstore/query.go."}]},"uuid":"c6e5d51b-ffd2-437a-aba5-d3a05791c65d","timestamp":"2026-03-04T15:23:50.735Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"Explore the internal/metricstoreclient/ package thoroughly. I need to understand:\n1. All files in this package\n2. The CCMetricStore type and its methods (especially LoadData, LoadStats, LoadScopedStats, LoadNodeData, LoadNodeListData)\n3. How it defines APIQuery and related types\n4. The getTopology and getTopologyByNode helper methods\n5. How this package relates to pkg/metricstore/\n\nFocus on understanding the full structure and how it parallels pkg/metricstore/query.go.","agentId":"ac0e18c809f60baa6"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01An2GQTxJmdB2UZE56PWWKf","uuid":"8a2cf584-bd24-441f-a8fa-0bacbb3f292c","timestamp":"2026-03-04T15:23:50.736Z"}
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{"parentUuid":"e27b90c7-2b89-4d17-b1e5-b3f9b3b2cb37","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-04T15:23:54.210Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01VwTNRLXqvvk6y33WGAwVyE","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BH3FfJPKwgVbnKFvRV867F","name":"Bash","input":{"command":"ls -la /Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":68,"cache_creation_input_tokens":492,"cache_read_input_tokens":14889,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":492},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYiGf71SduqTMXZqQg9JF","uuid":"d33ad7db-8420-4f6e-a467-7c943d1df083"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"ac0e18c809f60baa6"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01An2GQTxJmdB2UZE56PWWKf","uuid":"22da3f50-1a79-4447-8f6b-ea4f4791463d","timestamp":"2026-03-04T15:23:54.210Z"}
{"parentUuid":"22da3f50-1a79-4447-8f6b-ea4f4791463d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BH3FfJPKwgVbnKFvRV867F","type":"tool_result","content":".rw-r--r--@ 16k jan  4 Mar 10:09 cc-metric-store-queries.go\n.rw-r--r--@ 28k jan  4 Mar 10:09 cc-metric-store.go","is_error":false}]},"uuid":"f635ac73-ee8d-40d5-a155-29057389be87","timestamp":"2026-03-04T15:23:54.257Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"ac0e18c809f60baa6"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01An2GQTxJmdB2UZE56PWWKf","uuid":"7840d034-191c-417c-bbd5-f24a7b0c194b","timestamp":"2026-03-04T15:23:54.258Z"}
{"parentUuid":"7840d034-191c-417c-bbd5-f24a7b0c194b","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"type":"text","text":"Find the MetricDataRepository interface or similar interface that both pkg/metricstore and internal/metricstoreclient implement. Search for:\n1. Any shared interface definition (likely in pkg/ or internal/)\n2. How the two metric store implementations are registered/used\n3. Any shared types between the two packages (APIQuery, etc.)\n4. Check if there's a common types file or if each package defines its own\n\nSearch in pkg/metricstore/, internal/metricstoreclient/, and any relevant interface definition files. Also check if there's a metricdata or similar package."}]},"uuid":"909f2618-fcf5-4732-9f26-b0de5a721f71","timestamp":"2026-03-04T15:23:54.272Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"Find the MetricDataRepository interface or similar interface that both pkg/metricstore and internal/metricstoreclient implement. Search for:\n1. Any shared interface definition (likely in pkg/ or internal/)\n2. How the two metric store implementations are registered/used\n3. Any shared types between the two packages (APIQuery, etc.)\n4. Check if there's a common types file or if each package defines its own\n\nSearch in pkg/metricstore/, internal/metricstoreclient/, and any relevant interface definition files. Also check if there's a metricdata or similar package.","agentId":"ae13c6665fcb142fa"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_016dkeKh2MTmVpo34xzdvp12","uuid":"e901f21e-55ee-43ca-94d4-727e17beda59","timestamp":"2026-03-04T15:23:54.272Z"}
{"parentUuid":"e901f21e-55ee-43ca-94d4-727e17beda59","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-04T15:23:54.469Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_014ZEVkwFugQY1kKVpseVFqX","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MPqwZ8isTNT7JjyJ6zg7Dg","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/query.go","offset":40,"limit":200},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":6,"cache_creation_input_tokens":15851,"cache_read_input_tokens":15770,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":15851},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYiGf5QCuGQwhQtwRsz5W","uuid":"07e29792-5561-4c64-9278-c30d3a4b73fe"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"0ae833df-5fe1-4d1e-8306-9a34d6121884","timestamp":"2026-03-04T15:23:54.469Z"}
{"parentUuid":"0ae833df-5fe1-4d1e-8306-9a34d6121884","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MPqwZ8isTNT7JjyJ6zg7Dg","type":"tool_result","content":"    40→\n    41→type InternalMetricStore struct{}\n    42→\n    43→var MetricStoreHandle *InternalMetricStore\n    44→\n    45→// HealthCheck delegates to the internal MemoryStore's HealthCheck.\n    46→func (ccms *InternalMetricStore) HealthCheck(cluster string,\n    47→\tnodes []string, metrics []string,\n    48→) (map[string]HealthCheckResult, error) {\n    49→\treturn GetMemoryStore().HealthCheck(cluster, nodes, metrics)\n    50→}\n    51→\n    52→// TestLoadDataCallback allows tests to override LoadData behavior for testing purposes.\n    53→// When set to a non-nil function, LoadData will call this function instead of the default implementation.\n    54→var TestLoadDataCallback func(job *schema.Job, metrics []string, scopes []schema.MetricScope, ctx context.Context, resolution int) (schema.JobData, error)\n    55→\n    56→// LoadData loads metric data for a specific job with automatic scope transformation.\n    57→//\n    58→// This is the primary function for retrieving job metric data. It handles:\n    59→//   - Building queries with scope transformation via buildQueries\n    60→//   - Fetching data from the metric store\n    61→//   - Organizing results by metric and scope\n    62→//   - Converting NaN statistics to 0 for JSON compatibility\n    63→//   - Partial error handling (returns data for successful queries even if some fail)\n    64→//\n    65→// Parameters:\n    66→//   - job: Job metadata including cluster, resources, and time range\n    67→//   - metrics: List of metric names to load\n    68→//   - scopes: Requested metric scopes (will be transformed to match native scopes)\n    69→//   - ctx: Context for cancellation (currently unused but reserved for future use)\n    70→//   - resolution: Data resolution in seconds (0 for native resolution)\n    71→//\n    72→// Returns:\n    73→//   - JobData: Map of metric → scope → JobMetric with time-series data and statistics\n    74→//   - Error: Returns error if query building or fetching fails, or partial error listing failed hosts\n    75→//\n    76→// Example:\n    77→//\n    78→//\tjobData, err := LoadData(job, []string{\"cpu_load\", \"mem_used\"}, []schema.MetricScope{schema.MetricScopeNode}, ctx, 60)\n    79→func (ccms *InternalMetricStore) LoadData(\n    80→\tjob *schema.Job,\n    81→\tmetrics []string,\n    82→\tscopes []schema.MetricScope,\n    83→\tctx context.Context,\n    84→\tresolution int,\n    85→) (schema.JobData, error) {\n    86→\tif TestLoadDataCallback != nil {\n    87→\t\treturn TestLoadDataCallback(job, metrics, scopes, ctx, resolution)\n    88→\t}\n    89→\n    90→\tqueries, assignedScope, err := buildQueries(job, metrics, scopes, int64(resolution))\n    91→\tif err != nil {\n    92→\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n    93→\t\treturn nil, err\n    94→\t}\n    95→\n    96→\t// Verify assignment is correct - log any inconsistencies for debugging\n    97→\tif len(queries) != len(assignedScope) {\n    98→\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildQueries: %d vs %d\",\n    99→\t\t\tlen(queries), len(assignedScope))\n   100→\t}\n   101→\n   102→\treq := APIQueryRequest{\n   103→\t\tCluster:   job.Cluster,\n   104→\t\tFrom:      job.StartTime,\n   105→\t\tTo:        job.StartTime + int64(job.Duration),\n   106→\t\tQueries:   queries,\n   107→\t\tWithStats: true,\n   108→\t\tWithData:  true,\n   109→\t}\n   110→\n   111→\tresBody, err := FetchData(req)\n   112→\tif err != nil {\n   113→\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n   114→\t\treturn nil, err\n   115→\t}\n   116→\n   117→\tvar errors []string\n   118→\tjobData := make(schema.JobData)\n   119→\n   120→\t// Add safety check for potential index out of range errors\n   121→\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n   122→\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n   123→\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n   124→\t\tif len(resBody.Results) > len(req.Queries) {\n   125→\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n   126→\t\t}\n   127→\t\tif len(assignedScope) > len(req.Queries) {\n   128→\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n   129→\t\t}\n   130→\t}\n   131→\n   132→\tfor i, row := range resBody.Results {\n   133→\t\t// Safety check to prevent index out of range errors\n   134→\t\tif i >= len(req.Queries) || i >= len(assignedScope) {\n   135→\t\t\tcclog.Warnf(\"Index out of range prevented: i=%d, queries=%d, assignedScope=%d\",\n   136→\t\t\t\ti, len(req.Queries), len(assignedScope))\n   137→\t\t\tcontinue\n   138→\t\t}\n   139→\n   140→\t\tquery := req.Queries[i]\n   141→\t\tmetric := query.Metric\n   142→\t\tscope := assignedScope[i]\n   143→\t\tmc := archive.GetMetricConfig(job.Cluster, metric)\n   144→\n   145→\t\tif mc == nil {\n   146→\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, job.Cluster)\n   147→\t\t\tcontinue\n   148→\t\t}\n   149→\n   150→\t\tif _, ok := jobData[metric]; !ok {\n   151→\t\t\tjobData[metric] = make(map[schema.MetricScope]*schema.JobMetric)\n   152→\t\t}\n   153→\n   154→\t\tres := mc.Timestep\n   155→\t\tif len(row) > 0 {\n   156→\t\t\tres = int(row[0].Resolution)\n   157→\t\t}\n   158→\n   159→\t\tjobMetric, ok := jobData[metric][scope]\n   160→\t\tif !ok {\n   161→\t\t\tjobMetric = &schema.JobMetric{\n   162→\t\t\t\tUnit:     mc.Unit,\n   163→\t\t\t\tTimestep: res,\n   164→\t\t\t\tSeries:   make([]schema.Series, 0),\n   165→\t\t\t}\n   166→\t\t\tjobData[metric][scope] = jobMetric\n   167→\t\t}\n   168→\n   169→\t\tfor ndx, res := range row {\n   170→\t\t\tif res.Error != nil {\n   171→\t\t\t\t/* Build list for \"partial errors\", if any */\n   172→\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n   173→\t\t\t\tcontinue\n   174→\t\t\t}\n   175→\n   176→\t\t\tid := (*string)(nil)\n   177→\t\t\tif query.Type != nil {\n   178→\t\t\t\t// Check if ndx is within the bounds of TypeIds slice\n   179→\t\t\t\tif ndx < len(query.TypeIds) {\n   180→\t\t\t\t\tid = new(string)\n   181→\t\t\t\t\t*id = query.TypeIds[ndx]\n   182→\t\t\t\t} else {\n   183→\t\t\t\t\t// Log the error but continue processing\n   184→\t\t\t\t\tcclog.Warnf(\"TypeIds index out of range: %d with length %d for metric %s on host %s\",\n   185→\t\t\t\t\t\tndx, len(query.TypeIds), query.Metric, query.Hostname)\n   186→\t\t\t\t}\n   187→\t\t\t}\n   188→\n   189→\t\t\tsanitizeStats(&res)\n   190→\n   191→\t\t\tjobMetric.Series = append(jobMetric.Series, schema.Series{\n   192→\t\t\t\tHostname: query.Hostname,\n   193→\t\t\t\tID:       id,\n   194→\t\t\t\tStatistics: schema.MetricStatistics{\n   195→\t\t\t\t\tAvg: float64(res.Avg),\n   196→\t\t\t\t\tMin: float64(res.Min),\n   197→\t\t\t\t\tMax: float64(res.Max),\n   198→\t\t\t\t},\n   199→\t\t\t\tData: res.Data,\n   200→\t\t\t})\n   201→\t\t}\n   202→\n   203→\t\t// So that one can later check len(jobData):\n   204→\t\tif len(jobMetric.Series) == 0 {\n   205→\t\t\tdelete(jobData[metric], scope)\n   206→\t\t\tif len(jobData[metric]) == 0 {\n   207→\t\t\t\tdelete(jobData, metric)\n   208→\t\t\t}\n   209→\t\t}\n   210→\t}\n   211→\n   212→\tif len(errors) != 0 {\n   213→\t\t/* Returns list for \"partial errors\" */\n   214→\t\treturn jobData, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n   215→\t}\n   216→\treturn jobData, nil\n   217→}\n   218→\n   219→// Pre-converted scope strings avoid repeated string(MetricScope) allocations during\n   220→// query construction. These are used in APIQuery.Type field throughout buildQueries\n   221→// and buildNodeQueries functions. Converting once at package initialization improves\n   222→// performance for high-volume query building.\n   223→var (\n   224→\thwthreadString     = string(schema.MetricScopeHWThread)\n   225→\tcoreString         = string(schema.MetricScopeCore)\n   226→\tmemoryDomainString = string(schema.MetricScopeMemoryDomain)\n   227→\tsocketString       = string(schema.MetricScopeSocket)\n   228→\tacceleratorString  = string(schema.MetricScopeAccelerator)\n   229→)\n   230→\n   231→// buildQueries constructs APIQuery structures with automatic scope transformation for a job.\n   232→//\n   233→// This function implements the core scope transformation logic, handling all combinations of\n   234→// native metric scopes and requested scopes. It uses the cluster topology to determine which\n   235→// hardware IDs to include in each query.\n   236→//\n   237→// Scope Transformation Rules:\n   238→//   - If native scope >= requested scope: Aggregates data (Aggregate=true in APIQuery)\n   239→//   - If native scope < requested scope: Returns error (cannot increase granularity)"}]},"uuid":"ce4c9cf6-f4ba-4d1e-bb14-7988c0dec60f","timestamp":"2026-03-04T15:23:54.477Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"693a0d6a-f807-47e9-aedb-b77325332e21","timestamp":"2026-03-04T15:23:54.677Z"}
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{"parentUuid":"8edaa68e-7fe9-481d-960a-178f08654eaf","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01S6Ws6NgAUfFThRpt6Piwdb","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved. This file is part of cc-backend.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// Package metricstore provides level.go: Hierarchical tree structure for metric storage.\n     7→//\n     8→// # Level Architecture\n     9→//\n    10→// The Level type forms a tree structure where each node represents a level in the\n    11→// ClusterCockpit hierarchy: cluster → host → socket → core → hwthread, with special\n    12→// nodes for memory domains and accelerators.\n    13→//\n    14→// Structure:\n    15→//\n    16→//\tRoot Level (cluster=\"emmy\")\n    17→//\t├─ Level (host=\"node001\")\n    18→//\t│  ├─ Level (socket=\"0\")\n    19→//\t│  │  ├─ Level (core=\"0\") [stores cpu0 metrics]\n    20→//\t│  │  └─ Level (core=\"1\") [stores cpu1 metrics]\n    21→//\t│  └─ Level (socket=\"1\")\n    22→//\t│     └─ ...\n    23→//\t└─ Level (host=\"node002\")\n    24→//\t   └─ ...\n    25→//\n    26→// Each Level can:\n    27→//   - Hold data (metrics slice of buffer pointers)\n    28→//   - Have child nodes (children map[string]*Level)\n    29→//   - Both simultaneously (inner nodes can store aggregated metrics)\n    30→//\n    31→// # Selector Paths\n    32→//\n    33→// Selectors are hierarchical paths: []string{\"cluster\", \"host\", \"component\"}.\n    34→// Example: []string{\"emmy\", \"node001\", \"cpu0\"} navigates to the cpu0 core level.\n    35→//\n    36→// # Concurrency\n    37→//\n    38→// RWMutex protects children map and metrics slice. Read-heavy workload (metric reads)\n    39→// uses RLock. Writes (new levels, buffer updates) use Lock. Double-checked locking\n    40→// prevents races during level creation.\n    41→package metricstore\n    42→\n    43→import (\n    44→\t\"sync\"\n    45→\t\"time\"\n    46→\t\"unsafe\"\n    47→\n    48→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    49→\t\"github.com/ClusterCockpit/cc-lib/v2/util\"\n    50→)\n    51→\n    52→// Level represents a node in the hierarchical metric storage tree.\n    53→//\n    54→// Can be both a leaf or inner node. Inner nodes hold data in 'metrics' for aggregated\n    55→// values (e.g., socket-level metrics derived from core-level data). Named \"Level\"\n    56→// instead of \"node\" to avoid confusion with cluster nodes (hosts).\n    57→//\n    58→// Fields:\n    59→//   - children: Map of child level names to Level pointers (e.g., \"cpu0\" → Level)\n    60→//   - metrics:  Slice of buffer pointers (one per metric, indexed by MetricConfig.offset)\n    61→//   - lock:     RWMutex for concurrent access (read-heavy, write-rare)\n    62→type Level struct {\n    63→\tchildren map[string]*Level\n    64→\tmetrics  []*buffer\n    65→\tlock     sync.RWMutex\n    66→}\n    67→\n    68→// findLevelOrCreate navigates to or creates the level specified by selector.\n    69→//\n    70→// Recursively descends the tree, creating missing levels as needed. Uses double-checked\n    71→// locking: RLock first (fast path), then Lock if creation needed (slow path), then\n    72→// re-check after acquiring Lock to handle races.\n    73→//\n    74→// Example selector: []string{\"emmy\", \"node001\", \"cpu0\"}\n    75→// Navigates: root → emmy → node001 → cpu0, creating levels as needed.\n    76→//\n    77→// Parameters:\n    78→//   - selector: Hierarchical path (consumed recursively, decreasing depth)\n    79→//   - nMetrics: Number of metric slots to allocate in new levels\n    80→//\n    81→// Returns:\n    82→//   - *Level: The target level (existing or newly created)\n    83→//\n    84→// Note: sync.Map may improve performance for high-concurrency writes, but current\n    85→// approach suffices for read-heavy workload.\n    86→func (l *Level) findLevelOrCreate(selector []string, nMetrics int) *Level {\n    87→\tif len(selector) == 0 {\n    88→\t\treturn l\n    89→\t}\n    90→\n    91→\t// Allow concurrent reads:\n    92→\tl.lock.RLock()\n    93→\tvar child *Level\n    94→\tvar ok bool\n    95→\tif l.children == nil {\n    96→\t\t// Children map needs to be created...\n    97→\t\tl.lock.RUnlock()\n    98→\t} else {\n    99→\t\tchild, ok = l.children[selector[0]]\n   100→\t\tl.lock.RUnlock()"}]},"uuid":"44dc36a8-e35a-4bdc-81c7-5a73c13dd29d","timestamp":"2026-03-04T15:23:54.901Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"684ec62b-e156-40aa-bb04-2e8c0c2b72cc","timestamp":"2026-03-04T15:23:54.985Z"}
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{"parentUuid":"ba98d257-45ae-4b21-abeb-b78f3fade737","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01H3Hahudv2FmUPDHxN4ZcWv","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved. This file is part of cc-backend.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// Package metricstore provides buffer.go: Time-series data buffer implementation.\n     7→//\n     8→// # Buffer Architecture\n     9→//\n    10→// Each metric at each hierarchical level (cluster/host/cpu/etc.) uses a linked-list\n    11→// chain of fixed-size buffers to store time-series data. This design:\n    12→//\n    13→//   - Avoids reallocation/copying when growing (new links added instead)\n    14→//   - Enables efficient pooling (buffers returned to sync.Pool)\n    15→//   - Supports traversal back in time (via prev pointers)\n    16→//   - Maintains temporal ordering (newer data in later buffers)\n    17→//\n    18→// # Buffer Chain Example\n    19→//\n    20→//\t[oldest buffer] <- prev -- [older] <- prev -- [newest buffer (head)]\n    21→//\t  start=1000               start=1512           start=2024\n    22→//\t  data=[v0...v511]         data=[v0...v511]     data=[v0...v42]\n    23→//\n    24→// When the head buffer reaches capacity (BufferCap = 512), a new buffer becomes\n    25→// the new head and the old head is linked via prev.\n    26→//\n    27→// # Pooling Strategy\n    28→//\n    29→// sync.Pool reduces GC pressure for the common case (BufferCap-sized allocations).\n    30→// Non-standard capacity buffers are not pooled (e.g., from checkpoint deserialization).\n    31→//\n    32→// # Time Alignment\n    33→//\n    34→// Timestamps are aligned to measurement frequency intervals:\n    35→//\n    36→//\tindex = (timestamp - buffer.start) / buffer.frequency\n    37→//\tactualTime = buffer.start + (frequency / 2) + (index * frequency)\n    38→//\n    39→// Missing data points are represented as NaN values. The read() function performs\n    40→// linear interpolation where possible.\n    41→package metricstore\n    42→\n    43→import (\n    44→\t\"errors\"\n    45→\t\"sync\"\n    46→\t\"time\"\n    47→\n    48→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    49→)\n    50→\n    51→// BufferCap is the default buffer capacity.\n    52→// buffer.data will only ever grow up to its capacity and a new link\n    53→// in the buffer chain will be created if needed so that no copying\n    54→// of data or reallocation needs to happen on writes.\n    55→const BufferCap int = DefaultBufferCapacity\n    56→\n    57→// BufferPool is the global instance.\n    58→// It is initialized immediately when the package loads.\n    59→var bufferPool = NewPersistentBufferPool()\n    60→\n    61→type PersistentBufferPool struct {\n    62→\tpool []*buffer\n    63→\tmu   sync.Mutex\n    64→}\n    65→\n    66→// NewPersistentBufferPool creates a dynamic pool for buffers.\n    67→func NewPersistentBufferPool() *PersistentBufferPool {\n    68→\treturn &PersistentBufferPool{\n    69→\t\tpool: make([]*buffer, 0),\n    70→\t}\n    71→}\n    72→\n    73→func (p *PersistentBufferPool) Get() *buffer {\n    74→\tp.mu.Lock()\n    75→\tdefer p.mu.Unlock()\n    76→\n    77→\tn := len(p.pool)\n    78→\tif n == 0 {\n    79→\t\t// Pool is empty, allocate a new one\n    80→\t\treturn &buffer{\n    81→\t\t\tdata: make([]schema.Float, 0, BufferCap),\n    82→\t\t}\n    83→\t}\n    84→\n    85→\t// Reuse existing buffer from the pool\n    86→\tb := p.pool[n-1]\n    87→\tp.pool[n-1] = nil // Avoid memory leak\n    88→\tp.pool = p.pool[:n-1]\n    89→\treturn b\n    90→}\n    91→\n    92→// Put returns b to the pool. The caller must set b.lastUsed = time.Now().Unix()\n    93→// before calling Put so that Clean() can evict idle entries correctly.\n    94→func (p *PersistentBufferPool) Put(b *buffer) {\n    95→\t// Reset the buffer before putting it back\n    96→\tb.data = b.data[:0]\n    97→\n    98→\tp.mu.Lock()\n    99→\tdefer p.mu.Unlock()\n   100→"}]},"uuid":"e8c8731a-3dca-4db4-9bdc-945f3fe2cecc","timestamp":"2026-03-04T15:23:55.013Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"fbb02b4c-3787-49e4-a91c-fe678347ed01","timestamp":"2026-03-04T15:23:55.014Z"}
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{"parentUuid":"3a3f4456-2013-459e-90ce-7e8251eaaf37","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TmR7ZVBaRq3CveHQvU2Ffe","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// Package metricstoreclient provides a client for querying the cc-metric-store time series database.\n     7→//\n     8→// The cc-metric-store is a high-performance time series database optimized for HPC metric data.\n     9→// This client handles HTTP communication, query construction, scope transformations, and data retrieval\n    10→// for job and node metrics across different metric scopes (node, socket, core, hwthread, accelerator).\n    11→//\n    12→// # Architecture\n    13→//\n    14→// The package is split into two main components:\n    15→//   - Client Operations (cc-metric-store.go): HTTP client, request handling, data loading methods\n    16→//   - Query Building (cc-metric-store-queries.go): Query construction and scope transformation logic\n    17→//\n    18→// # Basic Usage\n    19→//\n    20→//\tstore := NewCCMetricStore(\"http://localhost:8080\", \"jwt-token\")\n    21→//\n    22→//\t// Load job data\n    23→//\tjobData, err := store.LoadData(job, metrics, scopes, ctx, resolution)\n    24→//\tif err != nil {\n    25→//\t    log.Fatal(err)\n    26→//\t}\n    27→//\n    28→// # Metric Scopes\n    29→//\n    30→// The client supports hierarchical metric scopes that map to HPC hardware topology:\n    31→//   - MetricScopeAccelerator: GPU/accelerator level metrics\n    32→//   - MetricScopeHWThread: Hardware thread (SMT) level metrics\n    33→//   - MetricScopeCore: CPU core level metrics\n    34→//   - MetricScopeSocket: CPU socket level metrics\n    35→//   - MetricScopeMemoryDomain: NUMA domain level metrics\n    36→//   - MetricScopeNode: Full node level metrics\n    37→//\n    38→// The client automatically handles scope transformations, aggregating finer-grained metrics\n    39→// to coarser scopes when needed (e.g., aggregating core metrics to socket level).\n    40→//\n    41→// # Error Handling\n    42→//\n    43→// The client supports partial errors - if some queries fail, it returns both the successful\n    44→// data and an error listing the failed queries. This allows processing partial results\n    45→// when some nodes or metrics are temporarily unavailable.\n    46→//\n    47→// # API Versioning\n    48→//\n    49→// The client uses cc-metric-store API v2, which includes support for:\n    50→//   - Data resampling for bandwidth optimization\n    51→//   - Multi-scope queries in a single request\n    52→//   - Aggregation across hardware topology levels\n    53→package metricstoreclient\n    54→\n    55→import (\n    56→\t\"bufio\"\n    57→\t\"bytes\"\n    58→\t\"context\"\n    59→\t\"encoding/json\"\n    60→\t\"fmt\"\n    61→\t\"net/http\"\n    62→\t\"strings\"\n    63→\t\"time\"\n    64→\n    65→\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n    66→\t\"github.com/ClusterCockpit/cc-backend/pkg/metricstore\"\n    67→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    68→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    69→)\n    70→\n    71→// CCMetricStore is the HTTP client for communicating with cc-metric-store.\n    72→// It manages connection details, authentication, and provides methods for querying metrics.\n    73→type CCMetricStore struct {\n    74→\tclient        http.Client                 // HTTP client with 10-second timeout\n    75→\tjwt           string                      // JWT Bearer token for authentication\n    76→\turl           string                      // Base URL of cc-metric-store instance\n    77→\tqueryEndpoint string                      // Full URL to query API endpoint\n    78→\ttopologyCache map[string]*schema.Topology // cluster -> topology cache\n    79→}\n    80→\n    81→// APIQueryRequest represents a request to the cc-metric-store query API.\n    82→// It supports both explicit queries and \"for-all-nodes\" bulk queries.\n    83→type APIQueryRequest struct {\n    84→\tCluster     string     `json:\"cluster\"`       // Target cluster name\n    85→\tQueries     []APIQuery `json:\"queries\"`       // Explicit list of metric queries\n    86→\tForAllNodes []string   `json:\"for-all-nodes\"` // Metrics to query for all nodes\n    87→\tFrom        int64      `json:\"from\"`          // Start time (Unix timestamp)\n    88→\tTo          int64      `json:\"to\"`            // End time (Unix timestamp)\n    89→\tWithStats   bool       `json:\"with-stats\"`    // Include min/avg/max statistics\n    90→\tWithData    bool       `json:\"with-data\"`     // Include time series data points\n    91→}\n    92→\n    93→// APIQuery specifies a single metric query with optional scope filtering.\n    94→// Type and TypeIds define the hardware scope (core, socket, accelerator, etc.).\n    95→type APIQuery struct {\n    96→\tType       *string  `json:\"type,omitempty\"`        // Scope type (e.g., \"core\", \"socket\")\n    97→\tSubType    *string  `json:\"subtype,omitempty\"`     // Sub-scope type (reserved for future use)\n    98→\tMetric     string   `json:\"metric\"`                // Metric name\n    99→\tHostname   string   `json:\"host\"`                  // Target hostname\n   100→\tResolution int      `json:\"resolution\"`            // Data resolution in seconds (0 = native)\n   101→\tTypeIds    []string `json:\"type-ids,omitempty\"`    // IDs for the scope type (e.g., core IDs)\n   102→\tSubTypeIds []string `json:\"subtype-ids,omitempty\"` // IDs for sub-scope (reserved)\n   103→\tAggregate  bool     `json:\"aggreg\"`                // Aggregate across TypeIds\n   104→}\n   105→\n   106→// APIQueryResponse contains the results from a cc-metric-store query.\n   107→// Results align with the Queries slice by index.\n   108→type APIQueryResponse struct {\n   109→\tQueries []APIQuery        `json:\"queries,omitempty\"` // Echoed queries (for bulk requests)\n   110→\tResults [][]APIMetricData `json:\"results\"`           // Result data, indexed by query\n   111→}\n   112→\n   113→// APIMetricData represents time series data and statistics for a single metric series.\n   114→// Error is set if this particular series failed to load.\n   115→type APIMetricData struct {\n   116→\tError      *string        `json:\"error\"`      // Error message if query failed\n   117→\tData       []schema.Float `json:\"data\"`       // Time series data points\n   118→\tFrom       int64          `json:\"from\"`       // Actual start time of data\n   119→\tTo         int64          `json:\"to\"`         // Actual end time of data\n   120→\tResolution int            `json:\"resolution\"` // Actual resolution of data in seconds\n   121→\tAvg        schema.Float   `json:\"avg\"`        // Average value across time range\n   122→\tMin        schema.Float   `json:\"min\"`        // Minimum value in time range\n   123→\tMax        schema.Float   `json:\"max\"`        // Maximum value in time range\n   124→}\n   125→\n   126→// NewCCMetricStore creates and initializes a new (external) CCMetricStore client.\n   127→// The url parameter should include the protocol and port (e.g., \"http://localhost:8080\").\n   128→// The token parameter is a JWT used for Bearer authentication; pass empty string if auth is disabled.\n   129→func NewCCMetricStore(url string, token string) *CCMetricStore {\n   130→\treturn &CCMetricStore{\n   131→\t\turl:           url,\n   132→\t\tqueryEndpoint: fmt.Sprintf(\"%s/api/query\", url),\n   133→\t\tjwt:           token,\n   134→\t\tclient: http.Client{\n   135→\t\t\tTimeout: 10 * time.Second,\n   136→\t\t},\n   137→\t\ttopologyCache: make(map[string]*schema.Topology),\n   138→\t}\n   139→}\n   140→\n   141→// doRequest executes an HTTP POST request to the cc-metric-store query API.\n   142→// It handles JSON encoding/decoding, authentication, and API versioning.\n   143→// The request body is automatically closed to prevent resource leaks.\n   144→func (ccms *CCMetricStore) doRequest(\n   145→\tctx context.Context,\n   146→\tbody *APIQueryRequest,\n   147→) (*APIQueryResponse, error) {\n   148→\tbuf := &bytes.Buffer{}\n   149→\tif err := json.NewEncoder(buf).Encode(body); err != nil {\n   150→\t\tcclog.Errorf(\"Error while encoding request body: %s\", err.Error())\n   151→\t\treturn nil, err\n   152→\t}\n   153→\n   154→\treq, err := http.NewRequestWithContext(ctx, http.MethodGet, ccms.queryEndpoint, buf)\n   155→\tif err != nil {\n   156→\t\tcclog.Errorf(\"Error while building request body: %s\", err.Error())\n   157→\t\treturn nil, err\n   158→\t}\n   159→\tif ccms.jwt != \"\" {\n   160→\t\treq.Header.Add(\"Authorization\", fmt.Sprintf(\"Bearer %s\", ccms.jwt))\n   161→\t}\n   162→\n   163→\t// versioning the cc-metric-store query API.\n   164→\t// v2 = data with resampling\n   165→\t// v1 = data without resampling\n   166→\tq := req.URL.Query()\n   167→\tq.Add(\"version\", \"v2\")\n   168→\treq.URL.RawQuery = q.Encode()\n   169→\n   170→\tres, err := ccms.client.Do(req)\n   171→\tif err != nil {\n   172→\t\tcclog.Errorf(\"Error while performing request: %s\", err.Error())\n   173→\t\treturn nil, err\n   174→\t}\n   175→\tdefer res.Body.Close()\n   176→\n   177→\tif res.StatusCode != http.StatusOK {\n   178→\t\treturn nil, fmt.Errorf(\"'%s': HTTP Status: %s\", ccms.queryEndpoint, res.Status)\n   179→\t}\n   180→\n   181→\tvar resBody APIQueryResponse\n   182→\tif err := json.NewDecoder(bufio.NewReader(res.Body)).Decode(&resBody); err != nil {\n   183→\t\tcclog.Errorf(\"Error while decoding result body: %s\", err.Error())\n   184→\t\treturn nil, err\n   185→\t}\n   186→\n   187→\treturn &resBody, nil\n   188→}\n   189→\n   190→// getTopology returns the topology for a given cluster and subcluster, caching it if not already present\n   191→func (ccms *CCMetricStore) getTopology(cluster, subCluster string) (*schema.Topology, error) {\n   192→\tcacheKey := fmt.Sprintf(\"%s:%s\", cluster, subCluster)\n   193→\tif topology, ok := ccms.topologyCache[cacheKey]; ok {\n   194→\t\treturn topology, nil\n   195→\t}\n   196→\n   197→\tsubcluster, err := archive.GetSubCluster(cluster, subCluster)\n   198→\tif err != nil {\n   199→\t\treturn nil, err\n   200→\t}\n   201→\n   202→\tccms.topologyCache[cacheKey] = &subcluster.Topology\n   203→\treturn &subcluster.Topology, nil\n   204→}\n   205→\n   206→// getTopologyByNode returns the topology for a given cluster and node, caching it if not already present\n   207→func (ccms *CCMetricStore) getTopologyByNode(cluster, node string) (*schema.Topology, error) {\n   208→\tsubCluster, err := archive.GetSubClusterByNode(cluster, node)\n   209→\tif err != nil {\n   210→\t\treturn nil, err\n   211→\t}\n   212→\n   213→\treturn ccms.getTopology(cluster, subCluster)\n   214→}\n   215→\n   216→// LoadData retrieves time series data and statistics for the specified job and metrics.\n   217→// It queries data for the job's time range and resources, handling scope transformations automatically.\n   218→//\n   219→// Parameters:\n   220→//   - job: Job metadata including cluster, time range, and allocated resources\n   221→//   - metrics: List of metric names to retrieve\n   222→//   - scopes: Requested metric scopes (node, socket, core, etc.)\n   223→//   - ctx: Context for cancellation and timeouts\n   224→//   - resolution: Data resolution in seconds (0 for native resolution)\n   225→//\n   226→// Returns JobData organized as: metric -> scope -> series list.\n   227→// Supports partial errors: returns available data even if some queries fail.\n   228→func (ccms *CCMetricStore) LoadData(\n   229→\tjob *schema.Job,\n   230→\tmetrics []string,\n   231→\tscopes []schema.MetricScope,\n   232→\tctx context.Context,\n   233→\tresolution int,\n   234→) (schema.JobData, error) {\n   235→\tqueries, assignedScope, err := ccms.buildQueries(job, metrics, scopes, resolution)\n   236→\tif err != nil {\n   237→\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n   238→\t\treturn nil, err\n   239→\t}\n   240→\n   241→\t// Verify assignment is correct - log any inconsistencies for debugging\n   242→\tif len(queries) != len(assignedScope) {\n   243→\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildQueries: %d vs %d\",\n   244→\t\t\tlen(queries), len(assignedScope))\n   245→\t}\n   246→\n   247→\treq := APIQueryRequest{\n   248→\t\tCluster:   job.Cluster,\n   249→\t\tFrom:      job.StartTime,\n   250→\t\tTo:        job.StartTime + int64(job.Duration),\n   251→\t\tQueries:   queries,\n   252→\t\tWithStats: true,\n   253→\t\tWithData:  true,\n   254→\t}\n   255→\n   256→\tresBody, err := ccms.doRequest(ctx, &req)\n   257→\tif err != nil {\n   258→\t\tcclog.Errorf(\"Error while performing request for job %d: %s\", job.JobID, err.Error())\n   259→\t\treturn nil, err\n   260→\t}\n   261→\n   262→\tvar errors []string\n   263→\tjobData := make(schema.JobData)\n   264→\n   265→\t// Add safety check for potential index out of range errors\n   266→\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n   267→\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n   268→\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n   269→\t\tif len(resBody.Results) > len(req.Queries) {\n   270→\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n   271→\t\t}\n   272→\t\tif len(assignedScope) > len(req.Queries) {\n   273→\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n   274→\t\t}\n   275→\t}\n   276→\n   277→\tfor i, row := range resBody.Results {\n   278→\t\t// Safety check to prevent index out of range errors\n   279→\t\tif i >= len(req.Queries) || i >= len(assignedScope) {\n   280→\t\t\tcclog.Warnf(\"Index out of range prevented: i=%d, queries=%d, assignedScope=%d\",\n   281→\t\t\t\ti, len(req.Queries), len(assignedScope))\n   282→\t\t\tcontinue\n   283→\t\t}\n   284→\n   285→\t\tquery := req.Queries[i]\n   286→\t\tmetric := query.Metric\n   287→\t\tscope := assignedScope[i]\n   288→\t\tmc := archive.GetMetricConfig(job.Cluster, metric)\n   289→\n   290→\t\tif mc == nil {\n   291→\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, job.Cluster)\n   292→\t\t\tcontinue\n   293→\t\t}\n   294→\n   295→\t\tif _, ok := jobData[metric]; !ok {\n   296→\t\t\tjobData[metric] = make(map[schema.MetricScope]*schema.JobMetric)\n   297→\t\t}\n   298→\n   299→\t\tres := mc.Timestep\n   300→\t\tif len(row) > 0 {\n   301→\t\t\tres = row[0].Resolution\n   302→\t\t}\n   303→\n   304→\t\tjobMetric, ok := jobData[metric][scope]\n   305→\t\tif !ok {\n   306→\t\t\tjobMetric = &schema.JobMetric{\n   307→\t\t\t\tUnit:     mc.Unit,\n   308→\t\t\t\tTimestep: res,\n   309→\t\t\t\tSeries:   make([]schema.Series, 0),\n   310→\t\t\t}\n   311→\t\t\tjobData[metric][scope] = jobMetric\n   312→\t\t}\n   313→\n   314→\t\tfor ndx, res := range row {\n   315→\t\t\tif res.Error != nil {\n   316→\t\t\t\t/* Build list for \"partial errors\", if any */\n   317→\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n   318→\t\t\t\tcontinue\n   319→\t\t\t}\n   320→\n   321→\t\t\tid := (*string)(nil)\n   322→\t\t\tif query.Type != nil {\n   323→\t\t\t\t// Check if ndx is within the bounds of TypeIds slice\n   324→\t\t\t\tif ndx < len(query.TypeIds) {\n   325→\t\t\t\t\tid = new(string)\n   326→\t\t\t\t\t*id = query.TypeIds[ndx]\n   327→\t\t\t\t} else {\n   328→\t\t\t\t\t// Log the error but continue processing\n   329→\t\t\t\t\tcclog.Warnf(\"TypeIds index out of range: %d with length %d for metric %s on host %s\",\n   330→\t\t\t\t\t\tndx, len(query.TypeIds), query.Metric, query.Hostname)\n   331→\t\t\t\t}\n   332→\t\t\t}\n   333→\n   334→\t\t\tsanitizeStats(&res.Avg, &res.Min, &res.Max)\n   335→\n   336→\t\t\tjobMetric.Series = append(jobMetric.Series, schema.Series{\n   337→\t\t\t\tHostname: query.Hostname,\n   338→\t\t\t\tID:       id,\n   339→\t\t\t\tStatistics: schema.MetricStatistics{\n   340→\t\t\t\t\tAvg: float64(res.Avg),\n   341→\t\t\t\t\tMin: float64(res.Min),\n   342→\t\t\t\t\tMax: float64(res.Max),\n   343→\t\t\t\t},\n   344→\t\t\t\tData: res.Data,\n   345→\t\t\t})\n   346→\t\t}\n   347→\n   348→\t\t// So that one can later check len(jobData):\n   349→\t\tif len(jobMetric.Series) == 0 {\n   350→\t\t\tdelete(jobData[metric], scope)\n   351→\t\t\tif len(jobData[metric]) == 0 {\n   352→\t\t\t\tdelete(jobData, metric)\n   353→\t\t\t}\n   354→\t\t}\n   355→\t}\n   356→\n   357→\tif len(errors) != 0 {\n   358→\t\t/* Returns list for \"partial errors\" */\n   359→\t\treturn jobData, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n   360→\t}\n   361→\treturn jobData, nil\n   362→}\n   363→\n   364→// LoadStats retrieves min/avg/max statistics for job metrics at node scope.\n   365→// This is faster than LoadData when only statistical summaries are needed (no time series data).\n   366→//\n   367→// Returns statistics organized as: metric -> hostname -> statistics.\n   368→func (ccms *CCMetricStore) LoadStats(\n   369→\tjob *schema.Job,\n   370→\tmetrics []string,\n   371→\tctx context.Context,\n   372→) (map[string]map[string]schema.MetricStatistics, error) {\n   373→\tqueries, _, err := ccms.buildQueries(job, metrics, []schema.MetricScope{schema.MetricScopeNode}, 0) // #166 Add scope shere for analysis view accelerator normalization?\n   374→\tif err != nil {\n   375→\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v: %s\", job.JobID, metrics, err.Error())\n   376→\t\treturn nil, err\n   377→\t}\n   378→\n   379→\treq := APIQueryRequest{\n   380→\t\tCluster:   job.Cluster,\n   381→\t\tFrom:      job.StartTime,\n   382→\t\tTo:        job.StartTime + int64(job.Duration),\n   383→\t\tQueries:   queries,\n   384→\t\tWithStats: true,\n   385→\t\tWithData:  false,\n   386→\t}\n   387→\n   388→\tresBody, err := ccms.doRequest(ctx, &req)\n   389→\tif err != nil {\n   390→\t\tcclog.Errorf(\"Error while performing request for job %d: %s\", job.JobID, err.Error())\n   391→\t\treturn nil, err\n   392→\t}\n   393→\n   394→\tstats := make(map[string]map[string]schema.MetricStatistics, len(metrics))\n   395→\tfor i, res := range resBody.Results {\n   396→\t\tif len(res) == 0 {\n   397→\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n   398→\t\t\tcontinue\n   399→\t\t}\n   400→\t\tquery := req.Queries[i]\n   401→\t\tmetric := query.Metric\n   402→\t\tdata := res[0]\n   403→\t\tif data.Error != nil {\n   404→\t\t\tcclog.Warnf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *data.Error)\n   405→\t\t\tcontinue\n   406→\t\t}\n   407→\n   408→\t\tmetricdata, ok := stats[metric]\n   409→\t\tif !ok {\n   410→\t\t\tmetricdata = make(map[string]schema.MetricStatistics, job.NumNodes)\n   411→\t\t\tstats[metric] = metricdata\n   412→\t\t}\n   413→\n   414→\t\tif hasNaNStats(data.Avg, data.Min, data.Max) {\n   415→\t\t\tcclog.Warnf(\"fetching %s for node %s failed: one of avg/min/max is NaN\", metric, query.Hostname)\n   416→\t\t\tcontinue\n   417→\t\t}\n   418→\n   419→\t\tmetricdata[query.Hostname] = schema.MetricStatistics{\n   420→\t\t\tAvg: float64(data.Avg),\n   421→\t\t\tMin: float64(data.Min),\n   422→\t\t\tMax: float64(data.Max),\n   423→\t\t}\n   424→\t}\n   425→\n   426→\treturn stats, nil\n   427→}\n   428→\n   429→// LoadScopedStats retrieves statistics for job metrics across multiple scopes.\n   430→// Used for the Job-View Statistics Table to display per-scope breakdowns.\n   431→//\n   432→// Returns statistics organized as: metric -> scope -> list of scoped statistics.\n   433→// Each scoped statistic includes hostname, hardware ID (if applicable), and min/avg/max values.\n   434→func (ccms *CCMetricStore) LoadScopedStats(\n   435→\tjob *schema.Job,\n   436→\tmetrics []string,\n   437→\tscopes []schema.MetricScope,\n   438→\tctx context.Context,\n   439→) (schema.ScopedJobStats, error) {\n   440→\tqueries, assignedScope, err := ccms.buildQueries(job, metrics, scopes, 0)\n   441→\tif err != nil {\n   442→\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n   443→\t\treturn nil, err\n   444→\t}\n   445→\n   446→\treq := APIQueryRequest{\n   447→\t\tCluster:   job.Cluster,\n   448→\t\tFrom:      job.StartTime,\n   449→\t\tTo:        job.StartTime + int64(job.Duration),\n   450→\t\tQueries:   queries,\n   451→\t\tWithStats: true,\n   452→\t\tWithData:  false,\n   453→\t}\n   454→\n   455→\tresBody, err := ccms.doRequest(ctx, &req)\n   456→\tif err != nil {\n   457→\t\tcclog.Errorf(\"Error while performing request for job %d: %s\", job.JobID, err.Error())\n   458→\t\treturn nil, err\n   459→\t}\n   460→\n   461→\tvar errors []string\n   462→\tscopedJobStats := make(schema.ScopedJobStats)\n   463→\n   464→\tfor i, row := range resBody.Results {\n   465→\t\tquery := req.Queries[i]\n   466→\t\tmetric := query.Metric\n   467→\t\tscope := assignedScope[i]\n   468→\n   469→\t\tif _, ok := scopedJobStats[metric]; !ok {\n   470→\t\t\tscopedJobStats[metric] = make(map[schema.MetricScope][]*schema.ScopedStats)\n   471→\t\t}\n   472→\n   473→\t\tif _, ok := scopedJobStats[metric][scope]; !ok {\n   474→\t\t\tscopedJobStats[metric][scope] = make([]*schema.ScopedStats, 0)\n   475→\t\t}\n   476→\n   477→\t\tfor ndx, res := range row {\n   478→\t\t\tif res.Error != nil {\n   479→\t\t\t\t/* Build list for \"partial errors\", if any */\n   480→\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n   481→\t\t\t\tcontinue\n   482→\t\t\t}\n   483→\n   484→\t\t\tid := (*string)(nil)\n   485→\t\t\tif query.Type != nil {\n   486→\t\t\t\t// Check if ndx is within the bounds of TypeIds slice\n   487→\t\t\t\tif ndx < len(query.TypeIds) {\n   488→\t\t\t\t\tid = new(string)\n   489→\t\t\t\t\t*id = query.TypeIds[ndx]\n   490→\t\t\t\t} else {\n   491→\t\t\t\t\t// Log the error but continue processing\n   492→\t\t\t\t\tcclog.Warnf(\"TypeIds index out of range: %d with length %d for metric %s on host %s\",\n   493→\t\t\t\t\t\tndx, len(query.TypeIds), query.Metric, query.Hostname)\n   494→\t\t\t\t}\n   495→\t\t\t}\n   496→\n   497→\t\t\tsanitizeStats(&res.Avg, &res.Min, &res.Max)\n   498→\n   499→\t\t\tscopedJobStats[metric][scope] = append(scopedJobStats[metric][scope], &schema.ScopedStats{\n   500→\t\t\t\tHostname: query.Hostname,\n   501→\t\t\t\tID:       id,\n   502→\t\t\t\tData: &schema.MetricStatistics{\n   503→\t\t\t\t\tAvg: float64(res.Avg),\n   504→\t\t\t\t\tMin: float64(res.Min),\n   505→\t\t\t\t\tMax: float64(res.Max),\n   506→\t\t\t\t},\n   507→\t\t\t})\n   508→\t\t}\n   509→\n   510→\t\t// So that one can later check len(scopedJobStats[metric][scope]): Remove from map if empty\n   511→\t\tif len(scopedJobStats[metric][scope]) == 0 {\n   512→\t\t\tdelete(scopedJobStats[metric], scope)\n   513→\t\t\tif len(scopedJobStats[metric]) == 0 {\n   514→\t\t\t\tdelete(scopedJobStats, metric)\n   515→\t\t\t}\n   516→\t\t}\n   517→\t}\n   518→\n   519→\tif len(errors) != 0 {\n   520→\t\t/* Returns list for \"partial errors\" */\n   521→\t\treturn scopedJobStats, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n   522→\t}\n   523→\treturn scopedJobStats, nil\n   524→}\n   525→\n   526→// LoadNodeData retrieves current metric data for specified nodes in a cluster.\n   527→// Used for the Systems-View Node-Overview to display real-time node status.\n   528→//\n   529→// If nodes is nil, queries all metrics for all nodes in the cluster (bulk query).\n   530→// Returns data organized as: hostname -> metric -> list of JobMetric (with time series and stats).\n   531→func (ccms *CCMetricStore) LoadNodeData(\n   532→\tcluster string,\n   533→\tmetrics, nodes []string,\n   534→\tscopes []schema.MetricScope,\n   535→\tfrom, to time.Time,\n   536→\tctx context.Context,\n   537→) (map[string]map[string][]*schema.JobMetric, error) {\n   538→\treq := APIQueryRequest{\n   539→\t\tCluster:   cluster,\n   540→\t\tFrom:      from.Unix(),\n   541→\t\tTo:        to.Unix(),\n   542→\t\tWithStats: true,\n   543→\t\tWithData:  true,\n   544→\t}\n   545→\n   546→\tif nodes == nil {\n   547→\t\treq.ForAllNodes = append(req.ForAllNodes, metrics...)\n   548→\t} else {\n   549→\t\tfor _, node := range nodes {\n   550→\t\t\tfor _, metric := range metrics {\n   551→\t\t\t\treq.Queries = append(req.Queries, APIQuery{\n   552→\t\t\t\t\tHostname:   node,\n   553→\t\t\t\t\tMetric:     metric,\n   554→\t\t\t\t\tResolution: 0, // Default for Node Queries: Will return metric $Timestep Resolution\n   555→\t\t\t\t})\n   556→\t\t\t}\n   557→\t\t}\n   558→\t}\n   559→\n   560→\tresBody, err := ccms.doRequest(ctx, &req)\n   561→\tif err != nil {\n   562→\t\tcclog.Errorf(\"Error while performing request for cluster %s: %s\", cluster, err.Error())\n   563→\t\treturn nil, err\n   564→\t}\n   565→\n   566→\tvar errors []string\n   567→\tdata := make(map[string]map[string][]*schema.JobMetric)\n   568→\tfor i, res := range resBody.Results {\n   569→\t\tif len(res) == 0 {\n   570→\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n   571→\t\t\tcontinue\n   572→\t\t}\n   573→\n   574→\t\tvar query APIQuery\n   575→\t\tif resBody.Queries != nil {\n   576→\t\t\tquery = resBody.Queries[i]\n   577→\t\t} else {\n   578→\t\t\tquery = req.Queries[i]\n   579→\t\t}\n   580→\n   581→\t\tmetric := query.Metric\n   582→\t\tqdata := res[0]\n   583→\t\tif qdata.Error != nil {\n   584→\t\t\terrors = append(errors, fmt.Sprintf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *qdata.Error))\n   585→\t\t}\n   586→\n   587→\t\tsanitizeStats(&qdata.Avg, &qdata.Min, &qdata.Max)\n   588→\n   589→\t\thostdata, ok := data[query.Hostname]\n   590→\t\tif !ok {\n   591→\t\t\thostdata = make(map[string][]*schema.JobMetric)\n   592→\t\t\tdata[query.Hostname] = hostdata\n   593→\t\t}\n   594→\n   595→\t\tmc := archive.GetMetricConfig(cluster, metric)\n   596→\t\thostdata[metric] = append(hostdata[metric], &schema.JobMetric{\n   597→\t\t\tUnit:     mc.Unit,\n   598→\t\t\tTimestep: mc.Timestep,\n   599→\t\t\tSeries: []schema.Series{\n   600→\t\t\t\t{\n   601→\t\t\t\t\tHostname: query.Hostname,\n   602→\t\t\t\t\tData:     qdata.Data,\n   603→\t\t\t\t\tStatistics: schema.MetricStatistics{\n   604→\t\t\t\t\t\tAvg: float64(qdata.Avg),\n   605→\t\t\t\t\t\tMin: float64(qdata.Min),\n   606→\t\t\t\t\t\tMax: float64(qdata.Max),\n   607→\t\t\t\t\t},\n   608→\t\t\t\t},\n   609→\t\t\t},\n   610→\t\t})\n   611→\t}\n   612→\n   613→\tif len(errors) != 0 {\n   614→\t\t/* Returns list of \"partial errors\" */\n   615→\t\treturn data, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n   616→\t}\n   617→\n   618→\treturn data, nil\n   619→}\n   620→\n   621→// LoadNodeListData retrieves paginated node metrics for the Systems-View Node-List.\n   622→//\n   623→// Supports filtering by subcluster and node name pattern. The nodeFilter performs\n   624→// substring matching on hostnames.\n   625→//\n   626→// Returns:\n   627→//   - Node data organized as: hostname -> JobData (metric -> scope -> series)\n   628→//   - Total node count (before pagination)\n   629→//   - HasNextPage flag indicating if more pages are available\n   630→//   - Error (may be partial error with some data returned)\n   631→func (ccms *CCMetricStore) LoadNodeListData(\n   632→\tcluster, subCluster string,\n   633→\tnodes []string,\n   634→\tmetrics []string,\n   635→\tscopes []schema.MetricScope,\n   636→\tresolution int,\n   637→\tfrom, to time.Time,\n   638→\tctx context.Context,\n   639→) (map[string]schema.JobData, error) {\n   640→\tqueries, assignedScope, err := ccms.buildNodeQueries(cluster, subCluster, nodes, metrics, scopes, resolution)\n   641→\tif err != nil {\n   642→\t\tcclog.Errorf(\"Error while building node queries for Cluster %s, SubCluster %s, Metrics %v, Scopes %v: %s\", cluster, subCluster, metrics, scopes, err.Error())\n   643→\t\treturn nil, err\n   644→\t}\n   645→\n   646→\t// Verify assignment is correct - log any inconsistencies for debugging\n   647→\tif len(queries) != len(assignedScope) {\n   648→\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildNodeQueries: %d vs %d\",\n   649→\t\t\tlen(queries), len(assignedScope))\n   650→\t}\n   651→\n   652→\treq := APIQueryRequest{\n   653→\t\tCluster:   cluster,\n   654→\t\tQueries:   queries,\n   655→\t\tFrom:      from.Unix(),\n   656→\t\tTo:        to.Unix(),\n   657→\t\tWithStats: true,\n   658→\t\tWithData:  true,\n   659→\t}\n   660→\n   661→\tresBody, err := ccms.doRequest(ctx, &req)\n   662→\tif err != nil {\n   663→\t\tcclog.Errorf(\"Error while performing request for cluster %s: %s\", cluster, err.Error())\n   664→\t\treturn nil, err\n   665→\t}\n   666→\n   667→\tvar errors []string\n   668→\tdata := make(map[string]schema.JobData)\n   669→\n   670→\t// Add safety check for index out of range issues\n   671→\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n   672→\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n   673→\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n   674→\t\tif len(resBody.Results) > len(req.Queries) {\n   675→\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n   676→\t\t}\n   677→\t\tif len(assignedScope) > len(req.Queries) {\n   678→\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n   679→\t\t}\n   680→\t}\n   681→\n   682→\tfor i, row := range resBody.Results {\n   683→\t\t// Safety check to prevent index out of range errors\n   684→\t\tif i >= len(req.Queries) || i >= len(assignedScope) {\n   685→\t\t\tcclog.Warnf(\"Index out of range prevented: i=%d, queries=%d, assignedScope=%d\",\n   686→\t\t\t\ti, len(req.Queries), len(assignedScope))\n   687→\t\t\tcontinue\n   688→\t\t}\n   689→\n   690→\t\tvar query APIQuery\n   691→\t\tif resBody.Queries != nil {\n   692→\t\t\tif i < len(resBody.Queries) {\n   693→\t\t\t\tquery = resBody.Queries[i]\n   694→\t\t\t} else {\n   695→\t\t\t\tcclog.Warnf(\"Index out of range prevented for resBody.Queries: i=%d, len=%d\",\n   696→\t\t\t\t\ti, len(resBody.Queries))\n   697→\t\t\t\tcontinue\n   698→\t\t\t}\n   699→\t\t} else {\n   700→\t\t\tquery = req.Queries[i]\n   701→\t\t}\n   702→\n   703→\t\tmetric := query.Metric\n   704→\t\tscope := assignedScope[i]\n   705→\t\tmc := archive.GetMetricConfig(cluster, metric)\n   706→\t\tif mc == nil {\n   707→\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, cluster)\n   708→\t\t\tcontinue\n   709→\t\t}\n   710→\n   711→\t\tres := mc.Timestep\n   712→\t\tif len(row) > 0 {\n   713→\t\t\tres = row[0].Resolution\n   714→\t\t}\n   715→\n   716→\t\t// Init Nested Map Data Structures If Not Found\n   717→\t\thostData, ok := data[query.Hostname]\n   718→\t\tif !ok {\n   719→\t\t\thostData = make(schema.JobData)\n   720→\t\t\tdata[query.Hostname] = hostData\n   721→\t\t}\n   722→\n   723→\t\tmetricData, ok := hostData[metric]\n   724→\t\tif !ok {\n   725→\t\t\tmetricData = make(map[schema.MetricScope]*schema.JobMetric)\n   726→\t\t\tdata[query.Hostname][metric] = metricData\n   727→\t\t}\n   728→\n   729→\t\tscopeData, ok := metricData[scope]\n   730→\t\tif !ok {\n   731→\t\t\tscopeData = &schema.JobMetric{\n   732→\t\t\t\tUnit:     mc.Unit,\n   733→\t\t\t\tTimestep: res,\n   734→\t\t\t\tSeries:   make([]schema.Series, 0),\n   735→\t\t\t}\n   736→\t\t\tdata[query.Hostname][metric][scope] = scopeData\n   737→\t\t}\n   738→\n   739→\t\tfor ndx, res := range row {\n   740→\t\t\tif res.Error != nil {\n   741→\t\t\t\t/* Build list for \"partial errors\", if any */\n   742→\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n   743→\t\t\t\tcontinue\n   744→\t\t\t}\n   745→\n   746→\t\t\tid := (*string)(nil)\n   747→\t\t\tif query.Type != nil {\n   748→\t\t\t\t// Check if ndx is within the bounds of TypeIds slice\n   749→\t\t\t\tif ndx < len(query.TypeIds) {\n   750→\t\t\t\t\tid = new(string)\n   751→\t\t\t\t\t*id = query.TypeIds[ndx]\n   752→\t\t\t\t} else {\n   753→\t\t\t\t\t// Log the error but continue processing\n   754→\t\t\t\t\tcclog.Warnf(\"TypeIds index out of range: %d with length %d for metric %s on host %s\",\n   755→\t\t\t\t\t\tndx, len(query.TypeIds), query.Metric, query.Hostname)\n   756→\t\t\t\t}\n   757→\t\t\t}\n   758→\n   759→\t\t\tsanitizeStats(&res.Avg, &res.Min, &res.Max)\n   760→\n   761→\t\t\tscopeData.Series = append(scopeData.Series, schema.Series{\n   762→\t\t\t\tHostname: query.Hostname,\n   763→\t\t\t\tID:       id,\n   764→\t\t\t\tStatistics: schema.MetricStatistics{\n   765→\t\t\t\t\tAvg: float64(res.Avg),\n   766→\t\t\t\t\tMin: float64(res.Min),\n   767→\t\t\t\t\tMax: float64(res.Max),\n   768→\t\t\t\t},\n   769→\t\t\t\tData: res.Data,\n   770→\t\t\t})\n   771→\t\t}\n   772→\t}\n   773→\n   774→\tif len(errors) != 0 {\n   775→\t\t/* Returns list of \"partial errors\" */\n   776→\t\treturn data, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n   777→\t}\n   778→\n   779→\treturn data, nil\n   780→}\n   781→\n   782→// HealthCheck queries the external cc-metric-store's health check endpoint.\n   783→// It sends a HealthCheckReq as the request body to /api/healthcheck and\n   784→// returns the per-node health check results.\n   785→func (ccms *CCMetricStore) HealthCheck(cluster string,\n   786→\tnodes []string, metrics []string,\n   787→) (map[string]metricstore.HealthCheckResult, error) {\n   788→\treq := metricstore.HealthCheckReq{\n   789→\t\tCluster:     cluster,\n   790→\t\tNodes:       nodes,\n   791→\t\tMetricNames: metrics,\n   792→\t}\n   793→\n   794→\tbuf := &bytes.Buffer{}\n   795→\tif err := json.NewEncoder(buf).Encode(req); err != nil {\n   796→\t\tcclog.Errorf(\"Error while encoding health check request body: %s\", err.Error())\n   797→\t\treturn nil, err\n   798→\t}\n   799→\n   800→\tendpoint := fmt.Sprintf(\"%s/api/healthcheck\", ccms.url)\n   801→\thttpReq, err := http.NewRequest(http.MethodGet, endpoint, buf)\n   802→\tif err != nil {\n   803→\t\tcclog.Errorf(\"Error while building health check request: %s\", err.Error())\n   804→\t\treturn nil, err\n   805→\t}\n   806→\tif ccms.jwt != \"\" {\n   807→\t\thttpReq.Header.Add(\"Authorization\", fmt.Sprintf(\"Bearer %s\", ccms.jwt))\n   808→\t}\n   809→\n   810→\tres, err := ccms.client.Do(httpReq)\n   811→\tif err != nil {\n   812→\t\tcclog.Errorf(\"Error while performing health check request: %s\", err.Error())\n   813→\t\treturn nil, err\n   814→\t}\n   815→\tdefer res.Body.Close()\n   816→\n   817→\tif res.StatusCode != http.StatusOK {\n   818→\t\treturn nil, fmt.Errorf(\"'%s': HTTP Status: %s\", endpoint, res.Status)\n   819→\t}\n   820→\n   821→\tvar results map[string]metricstore.HealthCheckResult\n   822→\tif err := json.NewDecoder(bufio.NewReader(res.Body)).Decode(&results); err != nil {\n   823→\t\tcclog.Errorf(\"Error while decoding health check response: %s\", err.Error())\n   824→\t\treturn nil, err\n   825→\t}\n   826→\n   827→\treturn results, nil\n   828→}\n   829→\n   830→// sanitizeStats replaces NaN values in statistics with 0 to enable JSON marshaling.\n   831→// Regular float64 values cannot be JSONed when NaN.\n   832→func sanitizeStats(avg, min, max *schema.Float) {\n   833→\tif avg.IsNaN() || min.IsNaN() || max.IsNaN() {\n   834→\t\t*avg = schema.Float(0)\n   835→\t\t*min = schema.Float(0)\n   836→\t\t*max = schema.Float(0)\n   837→\t}\n   838→}\n   839→\n   840→// hasNaNStats returns true if any of the statistics contain NaN values.\n   841→func hasNaNStats(avg, min, max schema.Float) bool {\n   842→\treturn avg.IsNaN() || min.IsNaN() || max.IsNaN()\n   843→}\n   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{"parentUuid":"bc5497cb-15c6-4b5f-bdde-df1393707042","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019g2hadA3Uk2aYcGV17UrfV","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// Package metricstoreclient - Query Building\n     7→//\n     8→// This file contains the query construction and scope transformation logic for cc-metric-store queries.\n     9→// It handles the complex mapping between requested metric scopes and native hardware topology,\n    10→// automatically aggregating or filtering metrics as needed.\n    11→//\n    12→// # Scope Transformations\n    13→//\n    14→// The buildScopeQueries function implements the core scope transformation algorithm.\n    15→// It handles 25+ different transformation cases, mapping between:\n    16→//   - Accelerator (GPU) scope\n    17→//   - HWThread (hardware thread/SMT) scope\n    18→//   - Core (CPU core) scope\n    19→//   - Socket (CPU package) scope\n    20→//   - MemoryDomain (NUMA domain) scope\n    21→//   - Node (full system) scope\n    22→//\n    23→// Transformations follow these rules:\n    24→//   - Same scope: Return data as-is (e.g., Core → Core)\n    25→//   - Coarser scope: Aggregate data (e.g., Core → Socket with Aggregate=true)\n    26→//   - Finer scope: Error - cannot increase granularity\n    27→//\n    28→// # Query Building\n    29→//\n    30→// buildQueries and buildNodeQueries are the main entry points, handling job-specific\n    31→// and node-specific query construction respectively. They:\n    32→//   - Validate metric configurations\n    33→//   - Handle subcluster-specific metric filtering\n    34→//   - Detect and skip duplicate scope requests\n    35→//   - Call buildScopeQueries for each metric/scope/host combination\n    36→package metricstoreclient\n    37→\n    38→import (\n    39→\t\"fmt\"\n    40→\t\"strconv\"\n    41→\n    42→\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n    43→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    44→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    45→)\n    46→\n    47→// Scope string constants used in API queries.\n    48→// Pre-converted to avoid repeated allocations during query building.\n    49→var (\n    50→\thwthreadString     = string(schema.MetricScopeHWThread)\n    51→\tcoreString         = string(schema.MetricScopeCore)\n    52→\tmemoryDomainString = string(schema.MetricScopeMemoryDomain)\n    53→\tsocketString       = string(schema.MetricScopeSocket)\n    54→\tacceleratorString  = string(schema.MetricScopeAccelerator)\n    55→)\n    56→\n    57→// buildQueries constructs API queries for job-specific metric data.\n    58→// It iterates through metrics, scopes, and job resources to build the complete query set.\n    59→//\n    60→// The function handles:\n    61→//   - Metric configuration validation and subcluster filtering\n    62→//   - Scope deduplication to avoid redundant queries\n    63→//   - Hardware thread list resolution (job-allocated vs full node)\n    64→//   - Delegation to buildScopeQueries for scope transformations\n    65→//\n    66→// Returns queries and their corresponding assigned scopes (which may differ from requested scopes).\n    67→func (ccms *CCMetricStore) buildQueries(\n    68→\tjob *schema.Job,\n    69→\tmetrics []string,\n    70→\tscopes []schema.MetricScope,\n    71→\tresolution int,\n    72→) ([]APIQuery, []schema.MetricScope, error) {\n    73→\t// Initialize both slices together\n    74→\tqueries := make([]APIQuery, 0, len(metrics)*len(scopes)*len(job.Resources))\n    75→\tassignedScope := make([]schema.MetricScope, 0, len(metrics)*len(scopes)*len(job.Resources))\n    76→\n    77→\ttopology, err := ccms.getTopology(job.Cluster, job.SubCluster)\n    78→\tif err != nil {\n    79→\t\tcclog.Errorf(\"could not load cluster %s subCluster %s topology: %s\", job.Cluster, job.SubCluster, err.Error())\n    80→\t\treturn nil, nil, err\n    81→\t}\n    82→\n    83→\tfor _, metric := range metrics {\n    84→\t\tremoteName := metric\n    85→\t\tmc := archive.GetMetricConfig(job.Cluster, metric)\n    86→\t\tif mc == nil {\n    87→\t\t\tcclog.Warnf(\"metric '%s' is not specified for cluster '%s' - skipping\", metric, job.Cluster)\n    88→\t\t\tcontinue\n    89→\t\t}\n    90→\n    91→\t\t// Skip if metric is removed for subcluster\n    92→\t\tif len(mc.SubClusters) != 0 {\n    93→\t\t\tisRemoved := false\n    94→\t\t\tfor _, scConfig := range mc.SubClusters {\n    95→\t\t\t\tif scConfig.Name == job.SubCluster && scConfig.Remove {\n    96→\t\t\t\t\tisRemoved = true\n    97→\t\t\t\t\tbreak\n    98→\t\t\t\t}\n    99→\t\t\t}\n   100→\t\t\tif isRemoved {\n   101→\t\t\t\tcontinue\n   102→\t\t\t}\n   103→\t\t}\n   104→\n   105→\t\t// Avoid duplicates...\n   106→\t\thandledScopes := make([]schema.MetricScope, 0, 3)\n   107→\n   108→\tscopesLoop:\n   109→\t\tfor _, requestedScope := range scopes {\n   110→\t\t\tnativeScope := mc.Scope\n   111→\t\t\tif nativeScope == schema.MetricScopeAccelerator && job.NumAcc == 0 {\n   112→\t\t\t\tcontinue\n   113→\t\t\t}\n   114→\n   115→\t\t\tscope := nativeScope.Max(requestedScope)\n   116→\t\t\tfor _, s := range handledScopes {\n   117→\t\t\t\tif scope == s {\n   118→\t\t\t\t\tcontinue scopesLoop\n   119→\t\t\t\t}\n   120→\t\t\t}\n   121→\t\t\thandledScopes = append(handledScopes, scope)\n   122→\n   123→\t\t\tfor _, host := range job.Resources {\n   124→\t\t\t\thwthreads := host.HWThreads\n   125→\t\t\t\tif hwthreads == nil {\n   126→\t\t\t\t\thwthreads = topology.Node\n   127→\t\t\t\t}\n   128→\n   129→\t\t\t\t// Note: Expected exceptions will return as empty slices -> Continue\n   130→\t\t\t\thostQueries, hostScopes := buildScopeQueries(\n   131→\t\t\t\t\tnativeScope, requestedScope,\n   132→\t\t\t\t\tremoteName, host.Hostname,\n   133→\t\t\t\t\ttopology, hwthreads, host.Accelerators,\n   134→\t\t\t\t\tresolution,\n   135→\t\t\t\t)\n   136→\n   137→\t\t\t\t// Note: Unexpected errors, such as unhandled cases, will return as nils -> Error\n   138→\t\t\t\tif hostQueries == nil && hostScopes == nil {\n   139→\t\t\t\t\treturn nil, nil, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > TODO: unhandled case: native-scope=%s, requested-scope=%s\", nativeScope, requestedScope)\n   140→\t\t\t\t}\n   141→\n   142→\t\t\t\tqueries = append(queries, hostQueries...)\n   143→\t\t\t\tassignedScope = append(assignedScope, hostScopes...)\n   144→\t\t\t}\n   145→\t\t}\n   146→\t}\n   147→\n   148→\treturn queries, assignedScope, nil\n   149→}\n   150→\n   151→// buildNodeQueries constructs API queries for node-specific metric data (Systems View).\n   152→// Similar to buildQueries but uses full node topology instead of job-allocated resources.\n   153→//\n   154→// The function handles:\n   155→//   - SubCluster topology resolution (either pre-loaded or per-node lookup)\n   156→//   - Full node hardware thread lists (not job-specific subsets)\n   157→//   - All accelerators on each node\n   158→//   - Metric configuration validation with subcluster filtering\n   159→//\n   160→// Returns queries and their corresponding assigned scopes.\n   161→func (ccms *CCMetricStore) buildNodeQueries(\n   162→\tcluster string,\n   163→\tsubCluster string,\n   164→\tnodes []string,\n   165→\tmetrics []string,\n   166→\tscopes []schema.MetricScope,\n   167→\tresolution int,\n   168→) ([]APIQuery, []schema.MetricScope, error) {\n   169→\t// Initialize both slices together\n   170→\tqueries := make([]APIQuery, 0, len(metrics)*len(scopes)*len(nodes))\n   171→\tassignedScope := make([]schema.MetricScope, 0, len(metrics)*len(scopes)*len(nodes))\n   172→\n   173→\tfor _, metric := range metrics {\n   174→\t\tremoteName := metric\n   175→\t\tmc := archive.GetMetricConfig(cluster, metric)\n   176→\t\tif mc == nil {\n   177→\t\t\tcclog.Warnf(\"metric '%s' is not specified for cluster '%s'\", metric, cluster)\n   178→\t\t\tcontinue\n   179→\t\t}\n   180→\n   181→\t\t// Skip if metric is removed for subcluster\n   182→\t\tif mc.SubClusters != nil {\n   183→\t\t\tisRemoved := false\n   184→\t\t\tfor _, scConfig := range mc.SubClusters {\n   185→\t\t\t\tif scConfig.Name == subCluster && scConfig.Remove {\n   186→\t\t\t\t\tisRemoved = true\n   187→\t\t\t\t\tbreak\n   188→\t\t\t\t}\n   189→\t\t\t}\n   190→\t\t\tif isRemoved {\n   191→\t\t\t\tcontinue\n   192→\t\t\t}\n   193→\t\t}\n   194→\n   195→\t\t// Avoid duplicates...\n   196→\t\thandledScopes := make([]schema.MetricScope, 0, 3)\n   197→\n   198→\tscopesLoop:\n   199→\t\tfor _, requestedScope := range scopes {\n   200→\t\t\tnativeScope := mc.Scope\n   201→\n   202→\t\t\tscope := nativeScope.Max(requestedScope)\n   203→\t\t\tfor _, s := range handledScopes {\n   204→\t\t\t\tif scope == s {\n   205→\t\t\t\t\tcontinue scopesLoop\n   206→\t\t\t\t}\n   207→\t\t\t}\n   208→\t\t\thandledScopes = append(handledScopes, scope)\n   209→\n   210→\t\t\tfor _, hostname := range nodes {\n   211→\t\t\t\tvar topology *schema.Topology\n   212→\t\t\t\tvar err error\n   213→\n   214→\t\t\t\t// If no subCluster given, get it by node\n   215→\t\t\t\tif subCluster == \"\" {\n   216→\t\t\t\t\ttopology, err = ccms.getTopologyByNode(cluster, hostname)\n   217→\t\t\t\t} else {\n   218→\t\t\t\t\ttopology, err = ccms.getTopology(cluster, subCluster)\n   219→\t\t\t\t}\n   220→\n   221→\t\t\t\tif err != nil {\n   222→\t\t\t\t\treturn nil, nil, err\n   223→\t\t\t\t}\n   224→\n   225→\t\t\t\t// Always full node hwthread id list, no partial queries expected -> Use \"topology.Node\" directly where applicable\n   226→\t\t\t\t// Always full accelerator id list, no partial queries expected -> Use \"acceleratorIds\" directly where applicable\n   227→\t\t\t\tacceleratorIds := topology.GetAcceleratorIDs()\n   228→\n   229→\t\t\t\t// Moved check here if metric matches hardware specs\n   230→\t\t\t\tif nativeScope == schema.MetricScopeAccelerator && len(acceleratorIds) == 0 {\n   231→\t\t\t\t\tcontinue scopesLoop\n   232→\t\t\t\t}\n   233→\n   234→\t\t\t\tnodeQueries, nodeScopes := buildScopeQueries(\n   235→\t\t\t\t\tnativeScope, requestedScope,\n   236→\t\t\t\t\tremoteName, hostname,\n   237→\t\t\t\t\ttopology, topology.Node, acceleratorIds,\n   238→\t\t\t\t\tresolution,\n   239→\t\t\t\t)\n   240→\n   241→\t\t\t\tif len(nodeQueries) == 0 && len(nodeScopes) == 0 {\n   242→\t\t\t\t\treturn nil, nil, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > TODO: unhandled case: native-scope=%s, requested-scope=%s\", nativeScope, requestedScope)\n   243→\t\t\t\t}\n   244→\n   245→\t\t\t\tqueries = append(queries, nodeQueries...)\n   246→\t\t\t\tassignedScope = append(assignedScope, nodeScopes...)\n   247→\t\t\t}\n   248→\t\t}\n   249→\t}\n   250→\n   251→\treturn queries, assignedScope, nil\n   252→}\n   253→\n   254→// buildScopeQueries generates API queries for a given scope transformation.\n   255→// It returns a slice of queries and corresponding assigned scopes.\n   256→// Some transformations (e.g., HWThread -> Core/Socket) may generate multiple queries.\n   257→func buildScopeQueries(\n   258→\tnativeScope, requestedScope schema.MetricScope,\n   259→\tmetric, hostname string,\n   260→\ttopology *schema.Topology,\n   261→\thwthreads []int,\n   262→\taccelerators []string,\n   263→\tresolution int,\n   264→) ([]APIQuery, []schema.MetricScope) {\n   265→\tscope := nativeScope.Max(requestedScope)\n   266→\tqueries := []APIQuery{}\n   267→\tscopes := []schema.MetricScope{}\n   268→\n   269→\thwthreadsStr := intToStringSlice(hwthreads)\n   270→\n   271→\t// Accelerator -> Accelerator (Use \"accelerator\" scope if requested scope is lower than node)\n   272→\tif nativeScope == schema.MetricScopeAccelerator && scope.LT(schema.MetricScopeNode) {\n   273→\t\tif scope != schema.MetricScopeAccelerator {\n   274→\t\t\t// Expected Exception -> Continue -> Return Empty Slices\n   275→\t\t\treturn queries, scopes\n   276→\t\t}\n   277→\n   278→\t\tqueries = append(queries, APIQuery{\n   279→\t\t\tMetric:     metric,\n   280→\t\t\tHostname:   hostname,\n   281→\t\t\tAggregate:  false,\n   282→\t\t\tType:       &acceleratorString,\n   283→\t\t\tTypeIds:    accelerators,\n   284→\t\t\tResolution: resolution,\n   285→\t\t})\n   286→\t\tscopes = append(scopes, schema.MetricScopeAccelerator)\n   287→\t\treturn queries, scopes\n   288→\t}\n   289→\n   290→\t// Accelerator -> Node\n   291→\tif nativeScope == schema.MetricScopeAccelerator && scope == schema.MetricScopeNode {\n   292→\t\tif len(accelerators) == 0 {\n   293→\t\t\t// Expected Exception -> Continue -> Return Empty Slices\n   294→\t\t\treturn queries, scopes\n   295→\t\t}\n   296→\n   297→\t\tqueries = append(queries, APIQuery{\n   298→\t\t\tMetric:     metric,\n   299→\t\t\tHostname:   hostname,\n   300→\t\t\tAggregate:  true,\n   301→\t\t\tType:       &acceleratorString,\n   302→\t\t\tTypeIds:    accelerators,\n   303→\t\t\tResolution: resolution,\n   304→\t\t})\n   305→\t\tscopes = append(scopes, scope)\n   306→\t\treturn queries, scopes\n   307→\t}\n   308→\n   309→\t// HWThread -> HWThread\n   310→\tif nativeScope == schema.MetricScopeHWThread && scope == schema.MetricScopeHWThread {\n   311→\t\tqueries = append(queries, APIQuery{\n   312→\t\t\tMetric:     metric,\n   313→\t\t\tHostname:   hostname,\n   314→\t\t\tAggregate:  false,\n   315→\t\t\tType:       &hwthreadString,\n   316→\t\t\tTypeIds:    hwthreadsStr,\n   317→\t\t\tResolution: resolution,\n   318→\t\t})\n   319→\t\tscopes = append(scopes, scope)\n   320→\t\treturn queries, scopes\n   321→\t}\n   322→\n   323→\t// HWThread -> Core\n   324→\tif nativeScope == schema.MetricScopeHWThread && scope == schema.MetricScopeCore {\n   325→\t\tcores, _ := topology.GetCoresFromHWThreads(hwthreads)\n   326→\t\tfor _, core := range cores {\n   327→\t\t\tqueries = append(queries, APIQuery{\n   328→\t\t\t\tMetric:     metric,\n   329→\t\t\t\tHostname:   hostname,\n   330→\t\t\t\tAggregate:  true,\n   331→\t\t\t\tType:       &hwthreadString,\n   332→\t\t\t\tTypeIds:    intToStringSlice(topology.Core[core]),\n   333→\t\t\t\tResolution: resolution,\n   334→\t\t\t})\n   335→\t\t\tscopes = append(scopes, scope)\n   336→\t\t}\n   337→\t\treturn queries, scopes\n   338→\t}\n   339→\n   340→\t// HWThread -> Socket\n   341→\tif nativeScope == schema.MetricScopeHWThread && scope == schema.MetricScopeSocket {\n   342→\t\tsockets, _ := topology.GetSocketsFromHWThreads(hwthreads)\n   343→\t\tfor _, socket := range sockets {\n   344→\t\t\tqueries = append(queries, APIQuery{\n   345→\t\t\t\tMetric:     metric,\n   346→\t\t\t\tHostname:   hostname,\n   347→\t\t\t\tAggregate:  true,\n   348→\t\t\t\tType:       &hwthreadString,\n   349→\t\t\t\tTypeIds:    intToStringSlice(topology.Socket[socket]),\n   350→\t\t\t\tResolution: resolution,\n   351→\t\t\t})\n   352→\t\t\tscopes = append(scopes, scope)\n   353→\t\t}\n   354→\t\treturn queries, scopes\n   355→\t}\n   356→\n   357→\t// HWThread -> Node\n   358→\tif nativeScope == schema.MetricScopeHWThread && scope == schema.MetricScopeNode {\n   359→\t\tqueries = append(queries, APIQuery{\n   360→\t\t\tMetric:     metric,\n   361→\t\t\tHostname:   hostname,\n   362→\t\t\tAggregate:  true,\n   363→\t\t\tType:       &hwthreadString,\n   364→\t\t\tTypeIds:    hwthreadsStr,\n   365→\t\t\tResolution: resolution,\n   366→\t\t})\n   367→\t\tscopes = append(scopes, scope)\n   368→\t\treturn queries, scopes\n   369→\t}\n   370→\n   371→\t// Core -> Core\n   372→\tif nativeScope == schema.MetricScopeCore && scope == schema.MetricScopeCore {\n   373→\t\tcores, _ := topology.GetCoresFromHWThreads(hwthreads)\n   374→\t\tqueries = append(queries, APIQuery{\n   375→\t\t\tMetric:     metric,\n   376→\t\t\tHostname:   hostname,\n   377→\t\t\tAggregate:  false,\n   378→\t\t\tType:       &coreString,\n   379→\t\t\tTypeIds:    intToStringSlice(cores),\n   380→\t\t\tResolution: resolution,\n   381→\t\t})\n   382→\t\tscopes = append(scopes, scope)\n   383→\t\treturn queries, scopes\n   384→\t}\n   385→\n   386→\t// Core -> Socket\n   387→\tif nativeScope == schema.MetricScopeCore && scope == schema.MetricScopeSocket {\n   388→\t\tsockets, _ := topology.GetSocketsFromCores(hwthreads)\n   389→\t\tfor _, socket := range sockets {\n   390→\t\t\tqueries = append(queries, APIQuery{\n   391→\t\t\t\tMetric:     metric,\n   392→\t\t\t\tHostname:   hostname,\n   393→\t\t\t\tAggregate:  true,\n   394→\t\t\t\tType:       &coreString,\n   395→\t\t\t\tTypeIds:    intToStringSlice(topology.Socket[socket]),\n   396→\t\t\t\tResolution: resolution,\n   397→\t\t\t})\n   398→\t\t\tscopes = append(scopes, scope)\n   399→\t\t}\n   400→\t\treturn queries, scopes\n   401→\t}\n   402→\n   403→\t// Core -> Node\n   404→\tif nativeScope == schema.MetricScopeCore && scope == schema.MetricScopeNode {\n   405→\t\tcores, _ := topology.GetCoresFromHWThreads(hwthreads)\n   406→\t\tqueries = append(queries, APIQuery{\n   407→\t\t\tMetric:     metric,\n   408→\t\t\tHostname:   hostname,\n   409→\t\t\tAggregate:  true,\n   410→\t\t\tType:       &coreString,\n   411→\t\t\tTypeIds:    intToStringSlice(cores),\n   412→\t\t\tResolution: resolution,\n   413→\t\t})\n   414→\t\tscopes = append(scopes, scope)\n   415→\t\treturn queries, scopes\n   416→\t}\n   417→\n   418→\t// MemoryDomain -> MemoryDomain\n   419→\tif nativeScope == schema.MetricScopeMemoryDomain && scope == schema.MetricScopeMemoryDomain {\n   420→\t\tmemDomains, _ := topology.GetMemoryDomainsFromHWThreads(hwthreads)\n   421→\t\tqueries = append(queries, APIQuery{\n   422→\t\t\tMetric:     metric,\n   423→\t\t\tHostname:   hostname,\n   424→\t\t\tAggregate:  false,\n   425→\t\t\tType:       &memoryDomainString,\n   426→\t\t\tTypeIds:    intToStringSlice(memDomains),\n   427→\t\t\tResolution: resolution,\n   428→\t\t})\n   429→\t\tscopes = append(scopes, scope)\n   430→\t\treturn queries, scopes\n   431→\t}\n   432→\n   433→\t// MemoryDomain -> Node\n   434→\tif nativeScope == schema.MetricScopeMemoryDomain && scope == schema.MetricScopeNode {\n   435→\t\tmemDomains, _ := topology.GetMemoryDomainsFromHWThreads(hwthreads)\n   436→\t\tqueries = append(queries, APIQuery{\n   437→\t\t\tMetric:     metric,\n   438→\t\t\tHostname:   hostname,\n   439→\t\t\tAggregate:  true,\n   440→\t\t\tType:       &memoryDomainString,\n   441→\t\t\tTypeIds:    intToStringSlice(memDomains),\n   442→\t\t\tResolution: resolution,\n   443→\t\t})\n   444→\t\tscopes = append(scopes, scope)\n   445→\t\treturn queries, scopes\n   446→\t}\n   447→\n   448→\t// MemoryDomain -> Socket\n   449→\tif nativeScope == schema.MetricScopeMemoryDomain && scope == schema.MetricScopeSocket {\n   450→\t\tmemDomains, _ := topology.GetMemoryDomainsFromHWThreads(hwthreads)\n   451→\t\tsocketToDomains, err := topology.GetMemoryDomainsBySocket(memDomains)\n   452→\t\tif err != nil {\n   453→\t\t\tcclog.Errorf(\"Error mapping memory domains to sockets, return unchanged: %v\", err)\n   454→\t\t\t// Rare Error Case -> Still Continue -> Return Empty Slices\n   455→\t\t\treturn queries, scopes\n   456→\t\t}\n   457→\n   458→\t\t// Create a query for each socket\n   459→\t\tfor _, domains := range socketToDomains {\n   460→\t\t\tqueries = append(queries, APIQuery{\n   461→\t\t\t\tMetric:     metric,\n   462→\t\t\t\tHostname:   hostname,\n   463→\t\t\t\tAggregate:  true,\n   464→\t\t\t\tType:       &memoryDomainString,\n   465→\t\t\t\tTypeIds:    intToStringSlice(domains),\n   466→\t\t\t\tResolution: resolution,\n   467→\t\t\t})\n   468→\t\t\t// Add scope for each query, not just once\n   469→\t\t\tscopes = append(scopes, scope)\n   470→\t\t}\n   471→\t\treturn queries, scopes\n   472→\t}\n   473→\n   474→\t// Socket -> Socket\n   475→\tif nativeScope == schema.MetricScopeSocket && scope == schema.MetricScopeSocket {\n   476→\t\tsockets, _ := topology.GetSocketsFromHWThreads(hwthreads)\n   477→\t\tqueries = append(queries, APIQuery{\n   478→\t\t\tMetric:     metric,\n   479→\t\t\tHostname:   hostname,\n   480→\t\t\tAggregate:  false,\n   481→\t\t\tType:       &socketString,\n   482→\t\t\tTypeIds:    intToStringSlice(sockets),\n   483→\t\t\tResolution: resolution,\n   484→\t\t})\n   485→\t\tscopes = append(scopes, scope)\n   486→\t\treturn queries, scopes\n   487→\t}\n   488→\n   489→\t// Socket -> Node\n   490→\tif nativeScope == schema.MetricScopeSocket && scope == schema.MetricScopeNode {\n   491→\t\tsockets, _ := topology.GetSocketsFromHWThreads(hwthreads)\n   492→\t\tqueries = append(queries, APIQuery{\n   493→\t\t\tMetric:     metric,\n   494→\t\t\tHostname:   hostname,\n   495→\t\t\tAggregate:  true,\n   496→\t\t\tType:       &socketString,\n   497→\t\t\tTypeIds:    intToStringSlice(sockets),\n   498→\t\t\tResolution: resolution,\n   499→\t\t})\n   500→\t\tscopes = append(scopes, scope)\n   501→\t\treturn queries, scopes\n   502→\t}\n   503→\n   504→\t// Node -> Node\n   505→\tif nativeScope == schema.MetricScopeNode && scope == schema.MetricScopeNode {\n   506→\t\tqueries = append(queries, APIQuery{\n   507→\t\t\tMetric:     metric,\n   508→\t\t\tHostname:   hostname,\n   509→\t\t\tResolution: resolution,\n   510→\t\t})\n   511→\t\tscopes = append(scopes, scope)\n   512→\t\treturn queries, scopes\n   513→\t}\n   514→\n   515→\t// Unhandled Case -> Error -> Return nils\n   516→\treturn nil, nil\n   517→}\n   518→\n   519→// intToStringSlice converts a slice of integers to a slice of strings.\n   520→// Used to convert hardware IDs (core IDs, socket IDs, etc.) to the string format required by the API.\n   521→func intToStringSlice(is []int) []string {\n   522→\tss := make([]string, len(is))\n   523→\tfor i, x := range is {\n   524→\t\tss[i] = strconv.Itoa(x)\n   525→\t}\n   526→\treturn ss\n   527→}\n   528→"}]},"uuid":"6327b9c9-4599-466d-95f3-2f569446a89f","timestamp":"2026-03-04T15:23:56.417Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"ac0e18c809f60baa6"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01An2GQTxJmdB2UZE56PWWKf","uuid":"58920bb3-8cd5-4ee7-a2d7-39e29462d746","timestamp":"2026-03-04T15:23:56.419Z"}
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{"parentUuid":"e0feb033-e763-489d-bd4a-c36d54c9428b","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RTNxKDYieuDj3UHRHfsw8M","type":"tool_result","content":"   100→\n   101→\tp.pool = append(p.pool, b)\n   102→}\n   103→\n   104→// GetSize returns the exact number of buffers currently sitting in the pool.\n   105→func (p *PersistentBufferPool) GetSize() int {\n   106→\tp.mu.Lock()\n   107→\tdefer p.mu.Unlock()\n   108→\treturn len(p.pool)\n   109→}\n   110→\n   111→// Clear drains all buffers currently in the pool, allowing the GC to collect them.\n   112→func (p *PersistentBufferPool) Clear() {\n   113→\tp.mu.Lock()\n   114→\tdefer p.mu.Unlock()\n   115→\tfor i := range p.pool {\n   116→\t\tp.pool[i] = nil\n   117→\t}\n   118→\tp.pool = p.pool[:0]\n   119→}\n   120→\n   121→// Clean removes buffers from the pool that haven't been used in the given duration.\n   122→// It uses a simple LRU approach based on the lastUsed timestamp.\n   123→func (p *PersistentBufferPool) Clean(threshold int64) {\n   124→\tp.mu.Lock()\n   125→\tdefer p.mu.Unlock()\n   126→\n   127→\t// Filter in place, retaining only buffers returned to the pool recently enough.\n   128→\tactive := p.pool[:0]\n   129→\tfor _, b := range p.pool {\n   130→\t\tif b.lastUsed >= threshold {\n   131→\t\t\tactive = append(active, b)\n   132→\t\t}\n   133→\t}\n   134→\n   135→\t// Nullify the rest to prevent memory leaks\n   136→\tfor i := len(active); i < len(p.pool); i++ {\n   137→\t\tp.pool[i] = nil\n   138→\t}\n   139→\n   140→\tp.pool = active\n   141→}\n   142→\n   143→var (\n   144→\t// ErrNoData indicates no time-series data exists for the requested metric/level.\n   145→\tErrNoData error = errors.New(\"[METRICSTORE]> no data for this metric/level\")\n   146→\n   147→\t// ErrDataDoesNotAlign indicates that aggregated data from child scopes\n   148→\t// does not align with the parent scope's expected timestamps/intervals.\n   149→\tErrDataDoesNotAlign error = errors.New(\"[METRICSTORE]> data from lower granularities does not align\")\n   150→)\n   151→\n   152→// buffer stores time-series data for a single metric at a specific hierarchical level.\n   153→//\n   154→// Buffers form doubly-linked chains ordered by time. When capacity is reached,\n   155→// a new buffer becomes the head and the old head is linked via prev/next.\n   156→//\n   157→// Fields:\n   158→//   - prev:      Link to older buffer in the chain (nil if this is oldest)\n   159→//   - next:      Link to newer buffer in the chain (nil if this is newest/head)\n   160→//   - data:      Time-series values (schema.Float supports NaN for missing data)\n   161→//   - frequency: Measurement interval in seconds\n   162→//   - start:     Start timestamp (adjusted by -frequency/2 for alignment)\n   163→//   - archived:  True if data has been persisted to disk archive\n   164→//   - closed:    True if buffer is no longer accepting writes\n   165→//\n   166→// Index calculation: index = (timestamp - start) / frequency\n   167→// Actual data timestamp: start + (frequency / 2) + (index * frequency)\n   168→type buffer struct {\n   169→\tprev      *buffer\n   170→\tnext      *buffer\n   171→\tdata      []schema.Float\n   172→\tfrequency int64\n   173→\tstart     int64\n   174→\tarchived  bool\n   175→\tclosed    bool\n   176→\tlastUsed  int64\n   177→}\n   178→\n   179→func newBuffer(ts, freq int64) *buffer {\n   180→\tb := bufferPool.Get()\n   181→\tb.frequency = freq\n   182→\tb.start = ts - (freq / 2)\n   183→\tb.prev = nil\n   184→\tb.next = nil\n   185→\tb.archived = false\n   186→\tb.closed = false\n   187→\tb.data = b.data[:0]\n   188→\treturn b\n   189→}\n   190→\n   191→// write appends a timestamped value to the buffer chain.\n   192→//\n   193→// Returns the head buffer (which may be newly created if capacity was reached).\n   194→// Timestamps older than the buffer's start are rejected. If the calculated index\n   195→// exceeds capacity, a new buffer is allocated and linked as the new head.\n   196→//\n   197→// Missing timestamps are automatically filled with NaN values to maintain alignment.\n   198→// Overwrites are allowed if the index is already within the existing data slice.\n   199→//"}]},"uuid":"50e9dbbe-5018-4937-866b-1ae573351c73","timestamp":"2026-03-04T15:23:57.441Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"390b1de0-304f-4604-bca9-4fb2212be692","timestamp":"2026-03-04T15:23:57.629Z"}
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{"parentUuid":"be3b03a0-c1fd-4802-afe7-2261da6eaf8a","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01ANYQryRJmx7cEViGqYpKQv","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved. This file is part of cc-backend.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// Package metricstore provides config.go: Configuration structures and metric management.\n     7→//\n     8→// # Configuration Hierarchy\n     9→//\n    10→// The metricstore package uses nested configuration structures:\n    11→//\n    12→//\tMetricStoreConfig (Keys)\n    13→//\t├─ NumWorkers: Parallel checkpoint/archive workers\n    14→//\t├─ RetentionInMemory: How long to keep data in RAM (also used as cleanup interval)\n    15→//\t├─ MemoryCap: Memory limit in bytes (triggers forceFree)\n    16→//\t├─ Checkpoints: Persistence configuration\n    17→//\t│  ├─ FileFormat: \"json\" or \"wal\" (default: \"wal\")\n    18→//\t│  └─ RootDir: Checkpoint storage path\n    19→//\t├─ Cleanup: Long-term storage configuration (interval = RetentionInMemory)\n    20→//\t│  ├─ RootDir: Archive storage path (archive mode only)\n    21→//\t│  └─ Mode: \"delete\" or \"archive\"\n    22→//\t├─ Debug: Development/debugging options\n    23→//\t└─ Subscriptions: NATS topic subscriptions for metric ingestion\n    24→//\n    25→// # Metric Configuration\n    26→//\n    27→// Each metric (e.g., \"cpu_load\", \"mem_used\") has a MetricConfig entry in the global\n    28→// Metrics map, defining:\n    29→//\n    30→//   - Frequency: Measurement interval in seconds\n    31→//   - Aggregation: How to combine values (sum/avg/none) when transforming scopes\n    32→//   - offset: Internal index into Level.metrics slice (assigned during Init)\n    33→//\n    34→// # AggregationStrategy\n    35→//\n    36→// Determines how to combine metric values when aggregating from finer to coarser scopes:\n    37→//\n    38→//   - NoAggregation: Do not combine (incompatible scopes)\n    39→//   - SumAggregation: Add values (e.g., power consumption: core→socket)\n    40→//   - AvgAggregation: Average values (e.g., temperature: core→socket)\n    41→package metricstore\n    42→\n    43→import (\n    44→\t\"fmt\"\n    45→\t\"time\"\n    46→\n    47→\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n    48→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    49→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    50→)\n    51→\n    52→const (\n    53→\tDefaultMaxWorkers                 = 10\n    54→\tDefaultBufferCapacity             = 512\n    55→\tDefaultGCTriggerInterval          = 100\n    56→\tDefaultMemoryUsageTrackerInterval = 1 * time.Hour\n    57→)\n    58→\n    59→// Checkpoints configures periodic persistence of in-memory metric data.\n    60→//\n    61→// Fields:\n    62→//   - FileFormat: \"json\" (human-readable, periodic) or \"wal\" (binary snapshot + WAL, crash-safe); default is \"wal\"\n    63→//   - RootDir:    Filesystem path for checkpoint files (created if missing)\n    64→type Checkpoints struct {\n    65→\tFileFormat string `json:\"file-format\"`\n    66→\tRootDir    string `json:\"directory\"`\n    67→}\n    68→\n    69→// Debug provides development and profiling options.\n    70→//\n    71→// Fields:\n    72→//   - DumpToFile: Path to dump checkpoint data for inspection (empty = disabled)\n    73→//   - EnableGops: Enable gops agent for live runtime debugging (https://github.com/google/gops)\n    74→type Debug struct {\n    75→\tDumpToFile string `json:\"dump-to-file\"`\n    76→\tEnableGops bool   `json:\"gops\"`\n    77→}\n    78→\n    79→// Cleanup configures long-term storage of old metric data.\n    80→//\n    81→// Data older than RetentionInMemory is archived to disk or deleted.\n    82→// The cleanup interval is always RetentionInMemory.\n    83→//\n    84→// Fields:\n    85→//   - RootDir: Filesystem path for archived data (used in \"archive\" mode)\n    86→//   - Mode:    \"delete\" (discard old data) or \"archive\" (write to RootDir)\n    87→type Cleanup struct {\n    88→\tRootDir string `json:\"directory\"`\n    89→\tMode    string `json:\"mode\"`\n    90→}\n    91→\n    92→// Subscriptions defines NATS topics to subscribe to for metric ingestion.\n    93→//\n    94→// Each subscription receives metrics via NATS messaging, enabling real-time\n    95→// data collection from compute nodes.\n    96→//\n    97→// Fields:\n    98→//   - SubscribeTo: NATS subject/channel name (e.g., \"metrics.compute.*\")\n    99→//   - ClusterTag:  Default cluster name for metrics without cluster tag (optional)\n   100→type Subscriptions []struct {\n   101→\t// Channel name\n   102→\tSubscribeTo string `json:\"subscribe-to\"`\n   103→\n   104→\t// Allow lines without a cluster tag, use this as default, optional\n   105→\tClusterTag string `json:\"cluster-tag\"`\n   106→}\n   107→\n   108→// MetricStoreConfig defines the main configuration for the metricstore.\n   109→//\n   110→// Loaded from cc-backend's config.json \"metricstore\" section. Controls memory usage,\n   111→// persistence, archiving, and metric ingestion.\n   112→//\n   113→// Fields:\n   114→//   - NumWorkers:        Parallel workers for checkpoint/archive (0 = auto: min(NumCPU/2+1, 10))\n   115→//   - RetentionInMemory: Duration string (e.g., \"48h\") for in-memory data retention\n   116→//   - MemoryCap:         Max bytes for buffer data (0 = unlimited); triggers forceFree when exceeded\n   117→//   - Checkpoints:       Periodic persistence configuration\n   118→//   - Debug:             Development/profiling options (nil = disabled)\n   119→//   - Archive:           Long-term storage configuration (nil = disabled)\n   120→//   - Subscriptions:     NATS topics for metric ingestion (nil = polling only)\n   121→type MetricStoreConfig struct {\n   122→\t// Number of concurrent workers for checkpoint and archive operations.\n   123→\t// If not set or 0, defaults to min(runtime.NumCPU()/2+1, 10)\n   124→\tNumWorkers        int            `json:\"num-workers\"`\n   125→\tRetentionInMemory string         `json:\"retention-in-memory\"`\n   126→\tMemoryCap         int            `json:\"memory-cap\"`\n   127→\tCheckpoints       Checkpoints    `json:\"checkpoints\"`\n   128→\tDebug             *Debug         `json:\"debug\"`\n   129→\tCleanup           *Cleanup       `json:\"cleanup\"`\n   130→\tSubscriptions     *Subscriptions `json:\"nats-subscriptions\"`\n   131→}\n   132→\n   133→// Keys is the global metricstore configuration instance.\n   134→//\n   135→// Initialized with defaults, then overwritten by cc-backend's config.json.\n   136→// Accessed by Init(), Checkpointing(), and other lifecycle functions.\n   137→var Keys MetricStoreConfig = MetricStoreConfig{\n   138→\tCheckpoints: Checkpoints{\n   139→\t\tFileFormat: \"wal\",\n   140→\t\tRootDir:    \"./var/checkpoints\",\n   141→\t},\n   142→\tCleanup: &Cleanup{\n   143→\t\tMode: \"delete\",\n   144→\t},\n   145→}\n   146→\n   147→// AggregationStrategy defines how to combine metric values across hierarchy levels.\n   148→//\n   149→// Used when transforming data from finer-grained scopes (e.g., core) to coarser scopes\n   150→// (e.g., socket). This is SPATIAL aggregation, not TEMPORAL (time-based) aggregation."}]},"uuid":"ec0d682f-5b66-4c00-ba34-647c523c64a2","timestamp":"2026-03-04T15:23:57.858Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"72b8575b-192a-45d3-8eec-507f9a1880f7","timestamp":"2026-03-04T15:23:57.891Z"}
{"parentUuid":"72b8575b-192a-45d3-8eec-507f9a1880f7","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-04T15:23:57.909Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01AVp2HY4hFZrVMLN64u8R7f","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0115cVLTS8EKbCQr3J6QUDT7","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/healthcheck.go"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":6,"cache_creation_input_tokens":6241,"cache_read_input_tokens":31621,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":6241},"output_tokens":3,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYiGfHAdcmfJJCUa79EHT","uuid":"be18132c-8177-426a-9d26-dec50bcf62de"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"a097073a-a3dd-488f-822c-634b274007d1","timestamp":"2026-03-04T15:23:57.909Z"}
{"parentUuid":"a097073a-a3dd-488f-822c-634b274007d1","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0115cVLTS8EKbCQr3J6QUDT7","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved. This file is part of cc-backend.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→package metricstore\n     7→\n     8→import (\n     9→\t\"encoding/json\"\n    10→\t\"fmt\"\n    11→\t\"time\"\n    12→\n    13→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    14→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    15→)\n    16→\n    17→// HealthCheckResponse represents the result of a health check operation.\n    18→type HealthCheckResponse struct {\n    19→\tStatus schema.MonitoringState\n    20→\tError  error\n    21→}\n    22→\n    23→// HealthCheckResult holds the monitoring state and raw JSON health metrics\n    24→// for a single node as determined by HealthCheck.\n    25→type HealthCheckResult struct {\n    26→\tState         schema.MonitoringState\n    27→\tHealthMetrics string // JSON: {\"missing\":[...],\"degraded\":[...]}\n    28→}\n    29→\n    30→// MaxMissingDataPoints is the threshold for stale data detection.\n    31→// A buffer is considered healthy if the gap between its last data point\n    32→// and the current time is within MaxMissingDataPoints * frequency.\n    33→const MaxMissingDataPoints int64 = 5\n    34→\n    35→// bufferExists returns true if the buffer is non-nil and contains data.\n    36→func (b *buffer) bufferExists() bool {\n    37→\tif b == nil || b.data == nil || len(b.data) == 0 {\n    38→\t\treturn false\n    39→\t}\n    40→\n    41→\treturn true\n    42→}\n    43→\n    44→// isBufferHealthy returns true if the buffer has recent data within\n    45→// MaxMissingDataPoints * frequency of the current time.\n    46→func (b *buffer) isBufferHealthy() bool {\n    47→\tbufferEnd := b.start + b.frequency*int64(len(b.data))\n    48→\tt := time.Now().Unix()\n    49→\n    50→\treturn t-bufferEnd <= MaxMissingDataPoints*b.frequency\n    51→}\n    52→\n    53→// collectMetricStatus walks the subtree rooted at l and classifies each\n    54→// expected metric into the healthy or degraded map.\n    55→//\n    56→// Classification rules (evaluated per buffer, pessimistic):\n    57→//   - A single stale buffer marks the metric as degraded permanently.\n    58→//   - A healthy buffer only counts if no stale buffer has been seen.\n    59→//   - Metrics absent from the global config or without any buffer remain\n    60→//     in neither map and are later reported as missing.\n    61→func (l *Level) collectMetricStatus(m *MemoryStore, expectedMetrics []string, healthy, degraded map[string]bool) {\n    62→\tl.lock.RLock()\n    63→\tdefer l.lock.RUnlock()\n    64→\n    65→\tfor _, metricName := range expectedMetrics {\n    66→\t\tif degraded[metricName] {\n    67→\t\t\tcontinue // already degraded, cannot improve\n    68→\t\t}\n    69→\t\tmc := m.Metrics[metricName]\n    70→\t\tb := l.metrics[mc.offset]\n    71→\t\tif b.bufferExists() {\n    72→\t\t\tif !b.isBufferHealthy() {\n    73→\t\t\t\tdegraded[metricName] = true\n    74→\t\t\t\tdelete(healthy, metricName)\n    75→\t\t\t} else if !degraded[metricName] {\n    76→\t\t\t\thealthy[metricName] = true\n    77→\t\t\t}\n    78→\t\t}\n    79→\t}\n    80→\n    81→\tfor _, lvl := range l.children {\n    82→\t\tlvl.collectMetricStatus(m, expectedMetrics, healthy, degraded)\n    83→\t}\n    84→}\n    85→\n    86→// getHealthyMetrics walks the complete subtree rooted at l and classifies\n    87→// each expected metric by comparing the collected status against the\n    88→// expected list.\n    89→//\n    90→// Returns:\n    91→//   - missingList: metrics not found in global config or without any buffer\n    92→//   - degradedList: metrics with at least one stale buffer in the subtree\n    93→func (l *Level) getHealthyMetrics(m *MemoryStore, expectedMetrics []string) ([]string, []string) {\n    94→\thealthy := make(map[string]bool, len(expectedMetrics))\n    95→\tdegraded := make(map[string]bool)\n    96→\n    97→\tl.collectMetricStatus(m, expectedMetrics, healthy, degraded)\n    98→\n    99→\tmissingList := make([]string, 0)\n   100→\tdegradedList := make([]string, 0)\n   101→\n   102→\tfor _, metricName := range expectedMetrics {\n   103→\t\tif healthy[metricName] {\n   104→\t\t\tcontinue\n   105→\t\t}\n   106→\n   107→\t\tif degraded[metricName] {\n   108→\t\t\tdegradedList = append(degradedList, metricName)\n   109→\t\t} else {\n   110→\t\t\tmissingList = append(missingList, metricName)\n   111→\t\t}\n   112→\t}\n   113→\n   114→\treturn degradedList, missingList\n   115→}\n   116→\n   117→// GetHealthyMetrics returns missing and degraded metric lists for a node.\n   118→//\n   119→// It walks the metric tree starting from the node identified by selector\n   120→// and classifies each expected metric:\n   121→//   - Missing: no buffer anywhere in the subtree, or metric not in global config\n   122→//   - Degraded: at least one stale buffer exists in the subtree\n   123→//\n   124→// Metrics present in expectedMetrics but absent from both returned lists\n   125→// are considered fully healthy.\n   126→func (m *MemoryStore) GetHealthyMetrics(selector []string, expectedMetrics []string) ([]string, []string, error) {\n   127→\tlvl := m.root.findLevel(selector)\n   128→\tif lvl == nil {\n   129→\t\treturn nil, nil, fmt.Errorf(\"[METRICSTORE]> GetHealthyMetrics: host not found: %#v\", selector)\n   130→\t}\n   131→\n   132→\tdegradedList, missingList := lvl.getHealthyMetrics(m, expectedMetrics)\n   133→\treturn degradedList, missingList, nil\n   134→}\n   135→\n   136→type HealthCheckReq struct {\n   137→\tCluster     string   `json:\"cluster\" example:\"fritz\"`\n   138→\tNodes       []string `json:\"nodes\"`\n   139→\tMetricNames []string `json:\"metric-names\"`\n   140→}\n   141→\n   142→// HealthCheck evaluates multiple nodes against a set of expected metrics\n   143→// and returns a monitoring state per node.\n   144→//\n   145→// States:\n   146→//   - MonitoringStateFull: all expected metrics are healthy\n   147→//   - MonitoringStatePartial: some metrics are missing or degraded\n   148→//   - MonitoringStateFailed: node not found, or no healthy metrics at all\n   149→func (m *MemoryStore) HealthCheck(cluster string,\n   150→\tnodes []string, expectedMetrics []string,\n   151→) (map[string]HealthCheckResult, error) {\n   152→\tresults := make(map[string]HealthCheckResult, len(nodes))\n   153→\n   154→\tfor _, hostname := range nodes {\n   155→\t\tselector := []string{cluster, hostname}\n   156→\n   157→\t\tdegradedList, missingList, err := m.GetHealthyMetrics(selector, expectedMetrics)\n   158→\t\tif err != nil {\n   159→\t\t\tresults[hostname] = HealthCheckResult{State: schema.MonitoringStateFailed}\n   160→\t\t\tcontinue\n   161→\t\t}\n   162→\n   163→\t\tdegradedCount := len(degradedList)\n   164→\t\tmissingCount := len(missingList)\n   165→\n   166→\t\thealthyCount := len(expectedMetrics) - degradedCount - missingCount\n   167→\n   168→\t\tif degradedCount > 0 {\n   169→\t\t\tcclog.ComponentDebug(\"metricstore\", \"HealthCheck: node \", hostname, \"degraded metrics:\", degradedList)\n   170→\t\t}\n   171→\t\tif missingCount > 0 {\n   172→\t\t\tcclog.ComponentDebug(\"metricstore\", \"HealthCheck: node \", hostname, \"missing metrics:\", missingList)\n   173→\t\t}\n   174→\n   175→\t\tvar state schema.MonitoringState\n   176→\t\tswitch {\n   177→\t\tcase degradedCount == 0 && missingCount == 0:\n   178→\t\t\tstate = schema.MonitoringStateFull\n   179→\t\tcase healthyCount == 0:\n   180→\t\t\tstate = schema.MonitoringStateFailed\n   181→\t\tdefault:\n   182→\t\t\tstate = schema.MonitoringStatePartial\n   183→\t\t}\n   184→\n   185→\t\thm, _ := json.Marshal(map[string][]string{\n   186→\t\t\t\"missing\":  missingList,\n   187→\t\t\t\"degraded\": degradedList,\n   188→\t\t})\n   189→\n   190→\t\tresults[hostname] = HealthCheckResult{\n   191→\t\t\tState:         state,\n   192→\t\t\tHealthMetrics: string(hm),\n   193→\t\t}\n   194→\t}\n   195→\n   196→\treturn results, nil\n   197→}\n   198→"}]},"uuid":"5b560b1e-aad7-431a-9449-aac08c3b908f","timestamp":"2026-03-04T15:23:57.917Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"cc027751-9a4f-4a3e-af5f-94478342e553","timestamp":"2026-03-04T15:23:57.918Z"}
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{"parentUuid":"a483bd99-605a-4291-913b-8c9cb954f364","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JB7fNucWcJjjaFht4JrUxX","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved. This file is part of cc-backend.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// This file implements high-level query functions for loading job metric data\n     7→// with automatic scope transformation and aggregation.\n     8→//\n     9→// Key Concepts:\n    10→//\n    11→// Metric Scopes: Metrics are collected at different granularities (native scope):\n    12→//   - HWThread: Per hardware thread\n    13→//   - Core: Per CPU core\n    14→//   - Socket: Per CPU socket\n    15→//   - MemoryDomain: Per memory domain (NUMA)\n    16→//   - Accelerator: Per GPU/accelerator\n    17→//   - Node: Per compute node\n    18→//\n    19→// Scope Transformation: The buildQueries functions transform between native scope\n    20→// and requested scope by:\n    21→//   - Aggregating finer-grained data (e.g., HWThread → Core → Socket → Node)\n    22→//   - Rejecting requests for finer granularity than available\n    23→//   - Handling special cases (e.g., Accelerator metrics)\n    24→//\n    25→// Query Building: Constructs APIQuery structures with proper selectors (Type, TypeIds)\n    26→// based on cluster topology and job resources.\n    27→package metricstore\n    28→\n    29→import (\n    30→\t\"context\"\n    31→\t\"fmt\"\n    32→\t\"strconv\"\n    33→\t\"strings\"\n    34→\t\"time\"\n    35→\n    36→\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n    37→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    38→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    39→)\n    40→\n    41→type InternalMetricStore struct{}\n    42→\n    43→var MetricStoreHandle *InternalMetricStore\n    44→\n    45→// HealthCheck delegates to the internal MemoryStore's HealthCheck.\n    46→func (ccms *InternalMetricStore) HealthCheck(cluster string,\n    47→\tnodes []string, metrics []string,\n    48→) (map[string]HealthCheckResult, error) {\n    49→\treturn GetMemoryStore().HealthCheck(cluster, nodes, metrics)\n    50→}\n    51→\n    52→// TestLoadDataCallback allows tests to override LoadData behavior for testing purposes.\n    53→// When set to a non-nil function, LoadData will call this function instead of the default implementation.\n    54→var TestLoadDataCallback func(job *schema.Job, metrics []string, scopes []schema.MetricScope, ctx context.Context, resolution int) (schema.JobData, error)\n    55→\n    56→// LoadData loads metric data for a specific job with automatic scope transformation.\n    57→//\n    58→// This is the primary function for retrieving job metric data. It handles:\n    59→//   - Building queries with scope transformation via buildQueries\n    60→//   - Fetching data from the metric store\n    61→//   - Organizing results by metric and scope\n    62→//   - Converting NaN statistics to 0 for JSON compatibility\n    63→//   - Partial error handling (returns data for successful queries even if some fail)\n    64→//\n    65→// Parameters:\n    66→//   - job: Job metadata including cluster, resources, and time range\n    67→//   - metrics: List of metric names to load\n    68→//   - scopes: Requested metric scopes (will be transformed to match native scopes)\n    69→//   - ctx: Context for cancellation (currently unused but reserved for future use)\n    70→//   - resolution: Data resolution in seconds (0 for native resolution)\n    71→//\n    72→// Returns:\n    73→//   - JobData: Map of metric → scope → JobMetric with time-series data and statistics\n    74→//   - Error: Returns error if query building or fetching fails, or partial error listing failed hosts\n    75→//\n    76→// Example:\n    77→//\n    78→//\tjobData, err := LoadData(job, []string{\"cpu_load\", \"mem_used\"}, []schema.MetricScope{schema.MetricScopeNode}, ctx, 60)\n    79→func (ccms *InternalMetricStore) LoadData(\n    80→\tjob *schema.Job,\n    81→\tmetrics []string,\n    82→\tscopes []schema.MetricScope,\n    83→\tctx context.Context,\n    84→\tresolution int,\n    85→) (schema.JobData, error) {\n    86→\tif TestLoadDataCallback != nil {\n    87→\t\treturn TestLoadDataCallback(job, metrics, scopes, ctx, resolution)\n    88→\t}\n    89→\n    90→\tqueries, assignedScope, err := buildQueries(job, metrics, scopes, int64(resolution))\n    91→\tif err != nil {\n    92→\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n    93→\t\treturn nil, err\n    94→\t}\n    95→\n    96→\t// Verify assignment is correct - log any inconsistencies for debugging\n    97→\tif len(queries) != len(assignedScope) {\n    98→\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildQueries: %d vs %d\",\n    99→\t\t\tlen(queries), len(assignedScope))\n   100→\t}\n   101→\n   102→\treq := APIQueryRequest{\n   103→\t\tCluster:   job.Cluster,\n   104→\t\tFrom:      job.StartTime,\n   105→\t\tTo:        job.StartTime + int64(job.Duration),\n   106→\t\tQueries:   queries,\n   107→\t\tWithStats: true,\n   108→\t\tWithData:  true,\n   109→\t}\n   110→\n   111→\tresBody, err := FetchData(req)\n   112→\tif err != nil {\n   113→\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n   114→\t\treturn nil, err\n   115→\t}\n   116→\n   117→\tvar errors []string\n   118→\tjobData := make(schema.JobData)\n   119→\n   120→\t// Add safety check for potential index out of range errors\n   121→\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n   122→\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n   123→\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n   124→\t\tif len(resBody.Results) > len(req.Queries) {\n   125→\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n   126→\t\t}\n   127→\t\tif len(assignedScope) > len(req.Queries) {\n   128→\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n   129→\t\t}\n   130→\t}\n   131→\n   132→\tfor i, row := range resBody.Results {\n   133→\t\t// Safety check to prevent index out of range errors\n   134→\t\tif i >= len(req.Queries) || i >= len(assignedScope) {\n   135→\t\t\tcclog.Warnf(\"Index out of range prevented: i=%d, queries=%d, assignedScope=%d\",\n   136→\t\t\t\ti, len(req.Queries), len(assignedScope))\n   137→\t\t\tcontinue\n   138→\t\t}\n   139→\n   140→\t\tquery := req.Queries[i]\n   141→\t\tmetric := query.Metric\n   142→\t\tscope := assignedScope[i]\n   143→\t\tmc := archive.GetMetricConfig(job.Cluster, metric)\n   144→\n   145→\t\tif mc == nil {\n   146→\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, job.Cluster)\n   147→\t\t\tcontinue\n   148→\t\t}\n   149→\n   150→\t\tif _, ok := jobData[metric]; !ok {\n   151→\t\t\tjobData[metric] = make(map[schema.MetricScope]*schema.JobMetric)\n   152→\t\t}\n   153→\n   154→\t\tres := mc.Timestep\n   155→\t\tif len(row) > 0 {\n   156→\t\t\tres = int(row[0].Resolution)\n   157→\t\t}\n   158→\n   159→\t\tjobMetric, ok := jobData[metric][scope]\n   160→\t\tif !ok {\n   161→\t\t\tjobMetric = &schema.JobMetric{\n   162→\t\t\t\tUnit:     mc.Unit,\n   163→\t\t\t\tTimestep: res,\n   164→\t\t\t\tSeries:   make([]schema.Series, 0),\n   165→\t\t\t}\n   166→\t\t\tjobData[metric][scope] = jobMetric\n   167→\t\t}\n   168→\n   169→\t\tfor ndx, res := range row {\n   170→\t\t\tif res.Error != nil {\n   171→\t\t\t\t/* Build list for \"partial errors\", if any */\n   172→\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n   173→\t\t\t\tcontinue\n   174→\t\t\t}\n   175→\n   176→\t\t\tid := (*string)(nil)\n   177→\t\t\tif query.Type != nil {\n   178→\t\t\t\t// Check if ndx is within the bounds of TypeIds slice\n   179→\t\t\t\tif ndx < len(query.TypeIds) {\n   180→\t\t\t\t\tid = new(string)\n   181→\t\t\t\t\t*id = query.TypeIds[ndx]\n   182→\t\t\t\t} else {\n   183→\t\t\t\t\t// Log the error but continue processing\n   184→\t\t\t\t\tcclog.Warnf(\"TypeIds index out of range: %d with length %d for metric %s on host %s\",\n   185→\t\t\t\t\t\tndx, len(query.TypeIds), query.Metric, query.Hostname)\n   186→\t\t\t\t}\n   187→\t\t\t}\n   188→\n   189→\t\t\tsanitizeStats(&res)\n   190→\n   191→\t\t\tjobMetric.Series = append(jobMetric.Series, schema.Series{\n   192→\t\t\t\tHostname: query.Hostname,\n   193→\t\t\t\tID:       id,\n   194→\t\t\t\tStatistics: schema.MetricStatistics{\n   195→\t\t\t\t\tAvg: float64(res.Avg),\n   196→\t\t\t\t\tMin: float64(res.Min),\n   197→\t\t\t\t\tMax: float64(res.Max),\n   198→\t\t\t\t},\n   199→\t\t\t\tData: res.Data,\n   200→\t\t\t})"}]},"uuid":"5bbae12c-9f0e-44ea-9548-d4a61dadd269","timestamp":"2026-03-04T15:23:58.799Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"ac0e18c809f60baa6"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01An2GQTxJmdB2UZE56PWWKf","uuid":"8e500ebc-d15b-48cd-a0f1-1e4aad28f475","timestamp":"2026-03-04T15:23:58.808Z"}
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{"parentUuid":"714f7563-9b96-4221-bdd4-1788e6954c17","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DAADLyG2rPutY7Uu9HAG16","type":"tool_result","content":"No matches found"}]},"uuid":"a8e4a084-fd8d-4059-afe1-1ffb73147ed5","timestamp":"2026-03-04T15:23:59.530Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"ae13c6665fcb142fa"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_016dkeKh2MTmVpo34xzdvp12","uuid":"c3147494-e18d-4fed-b47e-15a1193110c5","timestamp":"2026-03-04T15:23:59.652Z"}
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{"parentUuid":"03085881-013e-410e-b65a-32c0aa19c0a4","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Ej17VpGtmJNUBe5vzUstdF","type":"tool_result","content":"Found 7 files\npkg/metricstore/query.go\ninternal/metricdispatch/metricdata.go\ninternal/metricstoreclient/cc-metric-store-queries.go\ninternal/metricstoreclient/cc-metric-store.go\ninternal/api/job.go\ninternal/archiver/archiveWorker.go\npkg/metricstore/api.go"}]},"uuid":"7467cf85-8ee7-40c0-a166-d9d85f5dd59c","timestamp":"2026-03-04T15:23:59.965Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"ae13c6665fcb142fa"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_016dkeKh2MTmVpo34xzdvp12","uuid":"be57f293-cd5a-4cd9-adc4-8f7391b7b924","timestamp":"2026-03-04T15:23:59.966Z"}
{"parentUuid":"be57f293-cd5a-4cd9-adc4-8f7391b7b924","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-04T15:24:00.844Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01FXDZZiVCU7KM8ZViGnmHmB","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Uw5wenxRqFd1orK6rZ6HjQ","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/config.go","offset":150,"limit":100},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":6,"cache_creation_input_tokens":6461,"cache_read_input_tokens":37862,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":6461},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYiGfVbzgWsSGSRkDfg3j","uuid":"dd944ab9-0d6b-44cc-9dcf-c54c21e7ce35"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"99f64442-0fff-49d3-843c-d35e089e2628","timestamp":"2026-03-04T15:24:00.844Z"}
{"parentUuid":"99f64442-0fff-49d3-843c-d35e089e2628","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Uw5wenxRqFd1orK6rZ6HjQ","type":"tool_result","content":"   150→// (e.g., socket). This is SPATIAL aggregation, not TEMPORAL (time-based) aggregation.\n   151→//\n   152→// Values:\n   153→//   - NoAggregation:  Do not aggregate (incompatible scopes or non-aggregatable metrics)\n   154→//   - SumAggregation: Add values (e.g., power: sum core power → socket power)\n   155→//   - AvgAggregation: Average values (e.g., temperature: average core temps → socket temp)\n   156→type AggregationStrategy int\n   157→\n   158→const (\n   159→\tNoAggregation  AggregationStrategy = iota // Do not aggregate\n   160→\tSumAggregation                            // Sum values (e.g., power, energy)\n   161→\tAvgAggregation                            // Average values (e.g., temperature, utilization)\n   162→)\n   163→\n   164→// AssignAggregationStrategy parses a string into an AggregationStrategy value.\n   165→//\n   166→// Used when loading metric configurations from JSON/YAML files.\n   167→//\n   168→// Parameters:\n   169→//   - str: \"sum\", \"avg\", or \"\" (empty string for NoAggregation)\n   170→//\n   171→// Returns:\n   172→//   - AggregationStrategy: Parsed value\n   173→//   - error:               Non-nil if str is unrecognized\n   174→func AssignAggregationStrategy(str string) (AggregationStrategy, error) {\n   175→\tswitch str {\n   176→\tcase \"\":\n   177→\t\treturn NoAggregation, nil\n   178→\tcase \"sum\":\n   179→\t\treturn SumAggregation, nil\n   180→\tcase \"avg\":\n   181→\t\treturn AvgAggregation, nil\n   182→\tdefault:\n   183→\t\treturn NoAggregation, fmt.Errorf(\"[METRICSTORE]> unknown aggregation strategy: %s\", str)\n   184→\t}\n   185→}\n   186→\n   187→// MetricConfig defines configuration for a single metric type.\n   188→//\n   189→// Stored in the global Metrics map, keyed by metric name (e.g., \"cpu_load\").\n   190→//\n   191→// Fields:\n   192→//   - Frequency:   Measurement interval in seconds (e.g., 60 for 1-minute granularity)\n   193→//   - Aggregation: How to combine values across hierarchy levels (sum/avg/none)\n   194→//   - offset:      Internal index into Level.metrics slice (assigned during Init)\n   195→type MetricConfig struct {\n   196→\t// Interval in seconds at which measurements are stored\n   197→\tFrequency int64\n   198→\n   199→\t// Can be 'sum', 'avg' or null. Describes how to aggregate metrics from the same timestep over the hierarchy.\n   200→\tAggregation AggregationStrategy\n   201→\n   202→\t// Private, used internally...\n   203→\toffset int\n   204→}\n   205→\n   206→func BuildMetricList() map[string]MetricConfig {\n   207→\tvar metrics map[string]MetricConfig = make(map[string]MetricConfig)\n   208→\n   209→\taddMetric := func(name string, metric MetricConfig) error {\n   210→\t\tif metrics == nil {\n   211→\t\t\tmetrics = make(map[string]MetricConfig, 0)\n   212→\t\t}\n   213→\n   214→\t\tif existingMetric, ok := metrics[name]; ok {\n   215→\t\t\tif existingMetric.Frequency != metric.Frequency {\n   216→\t\t\t\tif existingMetric.Frequency < metric.Frequency {\n   217→\t\t\t\t\texistingMetric.Frequency = metric.Frequency\n   218→\t\t\t\t\tmetrics[name] = existingMetric\n   219→\t\t\t\t}\n   220→\t\t\t}\n   221→\t\t} else {\n   222→\t\t\tmetrics[name] = metric\n   223→\t\t}\n   224→\n   225→\t\treturn nil\n   226→\t}\n   227→\n   228→\t// Helper function to add metric configuration\n   229→\taddMetricConfig := func(mc *schema.MetricConfig) {\n   230→\t\tagg, err := AssignAggregationStrategy(mc.Aggregation)\n   231→\t\tif err != nil {\n   232→\t\t\tcclog.Warnf(\"Could not find aggregation strategy for metric config '%s': %s\", mc.Name, err.Error())\n   233→\t\t}\n   234→\n   235→\t\taddMetric(mc.Name, MetricConfig{\n   236→\t\t\tFrequency:   int64(mc.Timestep),\n   237→\t\t\tAggregation: agg,\n   238→\t\t})\n   239→\t}\n   240→\tfor _, c := range archive.Clusters {\n   241→\t\tfor _, mc := range c.MetricConfig {\n   242→\t\t\taddMetricConfig(mc)\n   243→\t\t}\n   244→\n   245→\t\tfor _, sc := range c.SubClusters {\n   246→\t\t\tfor _, mc := range sc.MetricConfig {\n   247→\t\t\t\taddMetricConfig(mc)\n   248→\t\t\t}\n   249→\t\t}"}]},"uuid":"84fc5884-c485-46c6-a6e8-0266f76c1235","timestamp":"2026-03-04T15:24:00.858Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"81d56123-6228-411e-b0c6-6566cb378a15","timestamp":"2026-03-04T15:24:00.859Z"}
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{"parentUuid":"e3df9816-6f8d-4e57-943f-d4fc8a908eaf","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RfxRNqw8NYncWGzEkQRNZx","type":"tool_result","content":"     314 /Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/api.go\n     277 /Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/archive.go\n     410 /Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/buffer.go\n     662 /Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/checkpoint.go\n     253 /Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/config.go\n      81 /Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/configSchema.go\n     112 /Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/debug.go\n     197 /Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/healthcheck.go\n     393 /Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/level.go\n     366 /Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/lineprotocol.go\n    1019 /Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/metricstore_test.go\n     784 /Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/metricstore.go\n     255 /Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/parquetArchive_test.go\n     213 /Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/parquetArchive.go\n    1357 /Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/query.go\n     125 /Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/stats.go\n     797 /Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/walCheckpoint.go\n    7615 total","is_error":false}]},"uuid":"e998a09d-2153-4ba5-9c2b-9216175d1d90","timestamp":"2026-03-04T15:24:01.040Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"5826aa8d-9390-4d85-a41d-236d8a113e0f","timestamp":"2026-03-04T15:24:01.044Z"}
{"parentUuid":"5826aa8d-9390-4d85-a41d-236d8a113e0f","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-04T15:24:01.199Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01W447n7MhGDwdYdyhw3ogTg","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SyrBRnDohgk46fXGbVHKWG","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/api.go"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":631,"cache_read_input_tokens":15701,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":631},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYiGfeCRdpezLHZDNJkNu","uuid":"d31a88e7-90a3-4517-9483-5f2ca643a50f"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"ae13c6665fcb142fa"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_016dkeKh2MTmVpo34xzdvp12","uuid":"9b44b3a4-adf8-4c24-9119-392103dafbe6","timestamp":"2026-03-04T15:24:01.199Z"}
{"parentUuid":"9b44b3a4-adf8-4c24-9119-392103dafbe6","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SyrBRnDohgk46fXGbVHKWG","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved. This file is part of cc-backend.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// This file contains the API types and data fetching logic for querying metric data\n     7→// from the in-memory metric store. It provides structures for building complex queries\n     8→// with support for aggregation, scaling, padding, and statistics computation.\n     9→package metricstore\n    10→\n    11→import (\n    12→\t\"errors\"\n    13→\t\"fmt\"\n    14→\t\"math\"\n    15→\n    16→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    17→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    18→\t\"github.com/ClusterCockpit/cc-lib/v2/util\"\n    19→)\n    20→\n    21→var (\n    22→\t// ErrNoHostOrMetric is returned when the metric store does not find the host or the metric\n    23→\tErrNoHostOrMetric error = errors.New(\"[METRICSTORE]> metric or host not found\")\n    24→\t// ErrInvalidTimeRange is returned when a query has 'from' >= 'to'\n    25→\tErrInvalidTimeRange = errors.New(\"[METRICSTORE]> invalid time range: 'from' must be before 'to'\")\n    26→\t// ErrEmptyCluster is returned when a query with ForAllNodes has no cluster specified\n    27→\tErrEmptyCluster = errors.New(\"[METRICSTORE]> cluster name cannot be empty\")\n    28→)\n    29→\n    30→// APIMetricData represents the response data for a single metric query.\n    31→//\n    32→// It contains both the time-series data points and computed statistics (avg, min, max).\n    33→// If an error occurred during data retrieval, the Error field will be set and other\n    34→// fields may be incomplete.\n    35→type APIMetricData struct {\n    36→\tError      *string           `json:\"error,omitempty\"`\n    37→\tData       schema.FloatArray `json:\"data,omitempty\"`\n    38→\tFrom       int64             `json:\"from\"`\n    39→\tTo         int64             `json:\"to\"`\n    40→\tResolution int64             `json:\"resolution\"`\n    41→\tAvg        schema.Float      `json:\"avg\"`\n    42→\tMin        schema.Float      `json:\"min\"`\n    43→\tMax        schema.Float      `json:\"max\"`\n    44→}\n    45→\n    46→// APIQueryRequest represents a batch query request for metric data.\n    47→//\n    48→// It supports two modes of operation:\n    49→//  1. Explicit queries via the Queries field\n    50→//  2. Automatic query generation via ForAllNodes (queries all specified metrics for all nodes in the cluster)\n    51→//\n    52→// The request can be customized with flags to include/exclude statistics, raw data, and padding.\n    53→type APIQueryRequest struct {\n    54→\tCluster     string     `json:\"cluster\"`\n    55→\tQueries     []APIQuery `json:\"queries\"`\n    56→\tForAllNodes []string   `json:\"for-all-nodes\"`\n    57→\tFrom        int64      `json:\"from\"`\n    58→\tTo          int64      `json:\"to\"`\n    59→\tWithStats   bool       `json:\"with-stats\"`\n    60→\tWithData    bool       `json:\"with-data\"`\n    61→\tWithPadding bool       `json:\"with-padding\"`\n    62→}\n    63→\n    64→// APIQueryResponse represents the response to an APIQueryRequest.\n    65→//\n    66→// Results is a 2D array where each outer element corresponds to a query,\n    67→// and each inner element corresponds to a selector within that query\n    68→// (e.g., multiple CPUs or cores).\n    69→type APIQueryResponse struct {\n    70→\tQueries []APIQuery        `json:\"queries,omitempty\"`\n    71→\tResults [][]APIMetricData `json:\"results\"`\n    72→}\n    73→\n    74→// APIQuery represents a single metric query with optional hierarchical selectors.\n    75→//\n    76→// The hierarchical selection works as follows:\n    77→//   - Hostname: The node to query\n    78→//   - Type + TypeIds: First level of hierarchy (e.g., \"cpu\" + [\"0\", \"1\", \"2\"])\n    79→//   - SubType + SubTypeIds: Second level of hierarchy (e.g., \"core\" + [\"0\", \"1\"])\n    80→//\n    81→// If Aggregate is true, data from multiple type/subtype IDs will be aggregated according\n    82→// to the metric's aggregation strategy. Otherwise, separate results are returned for each combination.\n    83→type APIQuery struct {\n    84→\tType        *string      `json:\"type,omitempty\"`\n    85→\tSubType     *string      `json:\"subtype,omitempty\"`\n    86→\tMetric      string       `json:\"metric\"`\n    87→\tHostname    string       `json:\"host\"`\n    88→\tResolution  int64        `json:\"resolution\"`\n    89→\tTypeIds     []string     `json:\"type-ids,omitempty\"`\n    90→\tSubTypeIds  []string     `json:\"subtype-ids,omitempty\"`\n    91→\tScaleFactor schema.Float `json:\"scale-by,omitempty\"`\n    92→\tAggregate   bool         `json:\"aggreg\"`\n    93→}\n    94→\n    95→// AddStats computes and populates the Avg, Min, and Max fields from the Data array.\n    96→//\n    97→// NaN values in the data are ignored during computation. If all values are NaN,\n    98→// the statistics fields will be set to NaN.\n    99→//\n   100→// TODO: Optimize this, just like the stats endpoint!\n   101→func (data *APIMetricData) AddStats() {\n   102→\tn := 0\n   103→\tsum, min, max := 0.0, math.MaxFloat64, -math.MaxFloat64\n   104→\tfor _, x := range data.Data {\n   105→\t\tif x.IsNaN() {\n   106→\t\t\tcontinue\n   107→\t\t}\n   108→\n   109→\t\tn += 1\n   110→\t\tsum += float64(x)\n   111→\t\tmin = math.Min(min, float64(x))\n   112→\t\tmax = math.Max(max, float64(x))\n   113→\t}\n   114→\n   115→\tif n > 0 {\n   116→\t\tavg := sum / float64(n)\n   117→\t\tdata.Avg = schema.Float(avg)\n   118→\t\tdata.Min = schema.Float(min)\n   119→\t\tdata.Max = schema.Float(max)\n   120→\t} else {\n   121→\t\tdata.Avg, data.Min, data.Max = schema.NaN, schema.NaN, schema.NaN\n   122→\t}\n   123→}\n   124→\n   125→// ScaleBy multiplies all data points and statistics by the given factor.\n   126→//\n   127→// This is commonly used for unit conversion (e.g., bytes to gigabytes).\n   128→// Scaling by 0 or 1 is a no-op for performance reasons.\n   129→func (data *APIMetricData) ScaleBy(f schema.Float) {\n   130→\tif f == 0 || f == 1 {\n   131→\t\treturn\n   132→\t}\n   133→\n   134→\tdata.Avg *= f\n   135→\tdata.Min *= f\n   136→\tdata.Max *= f\n   137→\tfor i := 0; i < len(data.Data); i++ {\n   138→\t\tdata.Data[i] *= f\n   139→\t}\n   140→}\n   141→\n   142→// PadDataWithNull pads the beginning of the data array with NaN values if needed.\n   143→//\n   144→// This ensures that the data aligns with the requested 'from' timestamp, even if\n   145→// the metric store doesn't have data for the earliest time points. This is useful\n   146→// for maintaining consistent array indexing across multiple queries.\n   147→//\n   148→// Parameters:\n   149→//   - ms: MemoryStore instance to lookup metric configuration\n   150→//   - from: The requested start timestamp\n   151→//   - to: The requested end timestamp (unused but kept for API consistency)\n   152→//   - metric: The metric name to lookup frequency information\n   153→func (data *APIMetricData) PadDataWithNull(ms *MemoryStore, from, to int64, metric string) {\n   154→\tminfo, ok := ms.Metrics[metric]\n   155→\tif !ok {\n   156→\t\treturn\n   157→\t}\n   158→\n   159→\tif (data.From / minfo.Frequency) > (from / minfo.Frequency) {\n   160→\t\tpadfront := int((data.From / minfo.Frequency) - (from / minfo.Frequency))\n   161→\t\tndata := make([]schema.Float, 0, padfront+len(data.Data))\n   162→\t\tfor range padfront {\n   163→\t\t\tndata = append(ndata, schema.NaN)\n   164→\t\t}\n   165→\t\tfor j := 0; j < len(data.Data); j++ {\n   166→\t\t\tndata = append(ndata, data.Data[j])\n   167→\t\t}\n   168→\t\tdata.Data = ndata\n   169→\t}\n   170→}\n   171→\n   172→// FetchData executes a batch metric query request and returns the results.\n   173→//\n   174→// This is the primary API for retrieving metric data from the memory store. It supports:\n   175→//   - Individual queries via req.Queries\n   176→//   - Batch queries for all nodes via req.ForAllNodes\n   177→//   - Hierarchical selector construction (cluster → host → type → subtype)\n   178→//   - Optional statistics computation (avg, min, max)\n   179→//   - Optional data scaling\n   180→//   - Optional data padding with NaN values\n   181→//\n   182→// The function constructs selectors based on the query parameters and calls MemoryStore.Read()\n   183→// for each selector. If a query specifies Aggregate=false with multiple type/subtype IDs,\n   184→// separate results are returned for each combination.\n   185→//\n   186→// Parameters:\n   187→//   - req: The query request containing queries, time range, and options\n   188→//\n   189→// Returns:\n   190→//   - APIQueryResponse containing results for each query, or error if validation fails\n   191→//\n   192→// Errors:\n   193→//   - ErrInvalidTimeRange if req.From > req.To\n   194→//   - ErrEmptyCluster if req.ForAllNodes is used without specifying a cluster\n   195→//   - Error if MemoryStore is not initialized\n   196→//   - Individual query errors are stored in APIMetricData.Error field\n   197→func FetchData(req APIQueryRequest) (*APIQueryResponse, error) {\n   198→\tif req.From > req.To {\n   199→\t\treturn nil, ErrInvalidTimeRange\n   200→\t}\n   201→\tif req.Cluster == \"\" && req.ForAllNodes != nil {\n   202→\t\treturn nil, ErrEmptyCluster\n   203→\t}\n   204→\n   205→\treq.WithData = true\n   206→\tms := GetMemoryStore()\n   207→\tif ms == nil {\n   208→\t\treturn nil, fmt.Errorf(\"[METRICSTORE]> memorystore not initialized\")\n   209→\t}\n   210→\n   211→\tresponse := APIQueryResponse{\n   212→\t\tResults: make([][]APIMetricData, 0, len(req.Queries)),\n   213→\t}\n   214→\tif req.ForAllNodes != nil {\n   215→\t\tnodes := ms.ListChildren([]string{req.Cluster})\n   216→\t\tfor _, node := range nodes {\n   217→\t\t\tfor _, metric := range req.ForAllNodes {\n   218→\t\t\t\tq := APIQuery{\n   219→\t\t\t\t\tMetric:   metric,\n   220→\t\t\t\t\tHostname: node,\n   221→\t\t\t\t}\n   222→\t\t\t\treq.Queries = append(req.Queries, q)\n   223→\t\t\t\tresponse.Queries = append(response.Queries, q)\n   224→\t\t\t}\n   225→\t\t}\n   226→\t}\n   227→\n   228→\tfor _, query := range req.Queries {\n   229→\t\tsels := make([]util.Selector, 0, 1)\n   230→\t\tif query.Aggregate || query.Type == nil {\n   231→\t\t\tsel := util.Selector{{String: req.Cluster}, {String: query.Hostname}}\n   232→\t\t\tif query.Type != nil {\n   233→\t\t\t\tif len(query.TypeIds) == 1 {\n   234→\t\t\t\t\tsel = append(sel, util.SelectorElement{String: *query.Type + query.TypeIds[0]})\n   235→\t\t\t\t} else {\n   236→\t\t\t\t\tids := make([]string, len(query.TypeIds))\n   237→\t\t\t\t\tfor i, id := range query.TypeIds {\n   238→\t\t\t\t\t\tids[i] = *query.Type + id\n   239→\t\t\t\t\t}\n   240→\t\t\t\t\tsel = append(sel, util.SelectorElement{Group: ids})\n   241→\t\t\t\t}\n   242→\n   243→\t\t\t\tif query.SubType != nil {\n   244→\t\t\t\t\tif len(query.SubTypeIds) == 1 {\n   245→\t\t\t\t\t\tsel = append(sel, util.SelectorElement{String: *query.SubType + query.SubTypeIds[0]})\n   246→\t\t\t\t\t} else {\n   247→\t\t\t\t\t\tids := make([]string, len(query.SubTypeIds))\n   248→\t\t\t\t\t\tfor i, id := range query.SubTypeIds {\n   249→\t\t\t\t\t\t\tids[i] = *query.SubType + id\n   250→\t\t\t\t\t\t}\n   251→\t\t\t\t\t\tsel = append(sel, util.SelectorElement{Group: ids})\n   252→\t\t\t\t\t}\n   253→\t\t\t\t}\n   254→\t\t\t}\n   255→\t\t\tsels = append(sels, sel)\n   256→\t\t} else {\n   257→\t\t\tfor _, typeID := range query.TypeIds {\n   258→\t\t\t\tif query.SubType != nil {\n   259→\t\t\t\t\tfor _, subTypeID := range query.SubTypeIds {\n   260→\t\t\t\t\t\tsels = append(sels, util.Selector{\n   261→\t\t\t\t\t\t\t{String: req.Cluster},\n   262→\t\t\t\t\t\t\t{String: query.Hostname},\n   263→\t\t\t\t\t\t\t{String: *query.Type + typeID},\n   264→\t\t\t\t\t\t\t{String: *query.SubType + subTypeID},\n   265→\t\t\t\t\t\t})\n   266→\t\t\t\t\t}\n   267→\t\t\t\t} else {\n   268→\t\t\t\t\tsels = append(sels, util.Selector{\n   269→\t\t\t\t\t\t{String: req.Cluster},\n   270→\t\t\t\t\t\t{String: query.Hostname},\n   271→\t\t\t\t\t\t{String: *query.Type + typeID},\n   272→\t\t\t\t\t})\n   273→\t\t\t\t}\n   274→\t\t\t}\n   275→\t\t}\n   276→\n   277→\t\tvar err error\n   278→\t\tres := make([]APIMetricData, 0, len(sels))\n   279→\t\tfor _, sel := range sels {\n   280→\t\t\tdata := APIMetricData{}\n   281→\n   282→\t\t\tdata.Data, data.From, data.To, data.Resolution, err = ms.Read(sel, query.Metric, req.From, req.To, query.Resolution)\n   283→\t\t\tif err != nil {\n   284→\t\t\t\t// Skip Error If Just Missing Host or Metric, Continue\n   285→\t\t\t\t// Empty Return For Metric Handled Gracefully By Frontend\n   286→\t\t\t\tif err != ErrNoHostOrMetric {\n   287→\t\t\t\t\tmsg := err.Error()\n   288→\t\t\t\t\tdata.Error = &msg\n   289→\t\t\t\t\tres = append(res, data)\n   290→\t\t\t\t} else {\n   291→\t\t\t\t\tcclog.Warnf(\"failed to fetch '%s' from host '%s' (cluster: %s): %s\", query.Metric, query.Hostname, req.Cluster, err.Error())\n   292→\t\t\t\t}\n   293→\t\t\t\tcontinue\n   294→\t\t\t}\n   295→\n   296→\t\t\tif req.WithStats {\n   297→\t\t\t\tdata.AddStats()\n   298→\t\t\t}\n   299→\t\t\tif query.ScaleFactor != 0 {\n   300→\t\t\t\tdata.ScaleBy(query.ScaleFactor)\n   301→\t\t\t}\n   302→\t\t\tif req.WithPadding {\n   303→\t\t\t\tdata.PadDataWithNull(ms, req.From, req.To, query.Metric)\n   304→\t\t\t}\n   305→\t\t\tif !req.WithData {\n   306→\t\t\t\tdata.Data = nil\n   307→\t\t\t}\n   308→\t\t\tres = append(res, data)\n   309→\t\t}\n   310→\t\tresponse.Results = append(response.Results, res)\n   311→\t}\n   312→\n   313→\treturn &response, nil\n   314→}\n   315→"}]},"uuid":"8e49894f-ce95-486f-bca9-d6cf35fc7822","timestamp":"2026-03-04T15:24:01.217Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"ae13c6665fcb142fa"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_016dkeKh2MTmVpo34xzdvp12","uuid":"1032374b-56f3-4be1-947a-922993da7bc4","timestamp":"2026-03-04T15:24:01.427Z"}
{"parentUuid":"1032374b-56f3-4be1-947a-922993da7bc4","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-04T15:24:01.435Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01W447n7MhGDwdYdyhw3ogTg","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01C2DojYmrujwjySjwbpMTHh","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricdispatch/metricdata.go"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":631,"cache_read_input_tokens":15701,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":631},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYiGfeCRdpezLHZDNJkNu","uuid":"58174003-811d-492e-ba24-5812d081d54a"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"ae13c6665fcb142fa"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_016dkeKh2MTmVpo34xzdvp12","uuid":"265a59f7-c0e7-4476-b043-ae43bc9343a1","timestamp":"2026-03-04T15:24:01.435Z"}
{"parentUuid":"265a59f7-c0e7-4476-b043-ae43bc9343a1","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01C2DojYmrujwjySjwbpMTHh","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→package metricdispatch\n     6→\n     7→import (\n     8→\t\"bytes\"\n     9→\t\"context\"\n    10→\t\"encoding/json\"\n    11→\t\"fmt\"\n    12→\t\"time\"\n    13→\n    14→\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n    15→\tccms \"github.com/ClusterCockpit/cc-backend/internal/metricstoreclient\"\n    16→\t\"github.com/ClusterCockpit/cc-backend/pkg/metricstore\"\n    17→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    18→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    19→)\n    20→\n    21→type MetricDataRepository interface {\n    22→\t// Return the JobData for the given job, only with the requested metrics.\n    23→\tLoadData(job *schema.Job,\n    24→\t\tmetrics []string,\n    25→\t\tscopes []schema.MetricScope,\n    26→\t\tctx context.Context,\n    27→\t\tresolution int) (schema.JobData, error)\n    28→\n    29→\t// Return a map of metrics to a map of nodes to the metric statistics of the job. node scope only.\n    30→\tLoadStats(job *schema.Job,\n    31→\t\tmetrics []string,\n    32→\t\tctx context.Context) (map[string]map[string]schema.MetricStatistics, error)\n    33→\n    34→\t// Return a map of metrics to a map of scopes to the scoped metric statistics of the job.\n    35→\tLoadScopedStats(job *schema.Job,\n    36→\t\tmetrics []string,\n    37→\t\tscopes []schema.MetricScope,\n    38→\t\tctx context.Context) (schema.ScopedJobStats, error)\n    39→\n    40→\t// Return a map of hosts to a map of metrics at the requested scopes (currently only node) for that node.\n    41→\tLoadNodeData(cluster string,\n    42→\t\tmetrics, nodes []string,\n    43→\t\tscopes []schema.MetricScope,\n    44→\t\tfrom, to time.Time,\n    45→\t\tctx context.Context) (map[string]map[string][]*schema.JobMetric, error)\n    46→\n    47→\t// Return a map of hosts to a map of metrics to a map of scopes for multiple nodes.\n    48→\tLoadNodeListData(cluster, subCluster string,\n    49→\t\tnodes []string,\n    50→\t\tmetrics []string,\n    51→\t\tscopes []schema.MetricScope,\n    52→\t\tresolution int,\n    53→\t\tfrom, to time.Time,\n    54→\t\tctx context.Context) (map[string]schema.JobData, error)\n    55→\n    56→\t// HealthCheck evaluates the monitoring state for a set of nodes against expected metrics.\n    57→\tHealthCheck(cluster string,\n    58→\t\tnodes []string,\n    59→\t\tmetrics []string) (map[string]metricstore.HealthCheckResult, error)\n    60→}\n    61→\n    62→type CCMetricStoreConfig struct {\n    63→\tScope string `json:\"scope\"`\n    64→\tURL   string `json:\"url\"`\n    65→\tToken string `json:\"token\"`\n    66→}\n    67→\n    68→var metricDataRepos map[string]MetricDataRepository = map[string]MetricDataRepository{}\n    69→\n    70→func Init(rawConfig json.RawMessage) error {\n    71→\tif rawConfig != nil {\n    72→\t\tvar configs []CCMetricStoreConfig\n    73→\t\tconfig.Validate(configSchema, rawConfig)\n    74→\t\tdec := json.NewDecoder(bytes.NewReader(rawConfig))\n    75→\t\tdec.DisallowUnknownFields()\n    76→\t\tif err := dec.Decode(&configs); err != nil {\n    77→\t\t\treturn fmt.Errorf(\"[METRICDISPATCH]> External Metric Store Config Init: Could not decode config file '%s' Error: %s\", rawConfig, err.Error())\n    78→\t\t}\n    79→\n    80→\t\tif len(configs) == 0 {\n    81→\t\t\treturn fmt.Errorf(\"[METRICDISPATCH]> No external metric store configurations found in config file\")\n    82→\t\t}\n    83→\n    84→\t\tfor _, config := range configs {\n    85→\t\t\tmetricDataRepos[config.Scope] = ccms.NewCCMetricStore(config.URL, config.Token)\n    86→\t\t}\n    87→\t}\n    88→\n    89→\treturn nil\n    90→}\n    91→\n    92→func GetMetricDataRepo(cluster string, subcluster string) (MetricDataRepository, error) {\n    93→\tvar repo MetricDataRepository\n    94→\tvar ok bool\n    95→\n    96→\tkey := cluster + \"-\" + subcluster\n    97→\trepo, ok = metricDataRepos[key]\n    98→\n    99→\tif !ok {\n   100→\t\trepo, ok = metricDataRepos[cluster]\n   101→\n   102→\t\tif !ok {\n   103→\t\t\trepo, ok = metricDataRepos[\"*\"]\n   104→\n   105→\t\t\tif !ok {\n   106→\t\t\t\tif metricstore.MetricStoreHandle == nil {\n   107→\t\t\t\t\treturn nil, fmt.Errorf(\"[METRICDISPATCH]> no metric data repository configured '%s'\", key)\n   108→\t\t\t\t}\n   109→\n   110→\t\t\t\trepo = metricstore.MetricStoreHandle\n   111→\t\t\t\tcclog.Debugf(\"[METRICDISPATCH]> Using internal metric data repository for '%s'\", key)\n   112→\t\t\t}\n   113→\t\t}\n   114→\t}\n   115→\n   116→\treturn repo, nil\n   117→}\n   118→\n   119→// GetHealthCheckRepo returns the MetricDataRepository for performing health checks on a cluster.\n   120→// It uses the same fallback logic as GetMetricDataRepo: cluster → wildcard → internal.\n   121→func GetHealthCheckRepo(cluster string) (MetricDataRepository, error) {\n   122→\treturn GetMetricDataRepo(cluster, \"\")\n   123→}\n   124→"}]},"uuid":"c10f112a-e723-4333-9b81-a5af30e55023","timestamp":"2026-03-04T15:24:01.442Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"ae13c6665fcb142fa"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_016dkeKh2MTmVpo34xzdvp12","uuid":"b8acbf78-796a-498a-8fc4-f7d15b20d793","timestamp":"2026-03-04T15:24:01.637Z"}
{"parentUuid":"b8acbf78-796a-498a-8fc4-f7d15b20d793","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-04T15:24:01.656Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01W447n7MhGDwdYdyhw3ogTg","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011ThJhmCw6p1is3X5mSGqwb","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":631,"cache_read_input_tokens":15701,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":631},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYiGfeCRdpezLHZDNJkNu","uuid":"93fa6deb-8393-4d37-8acf-41215e2e3511"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"ae13c6665fcb142fa"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_016dkeKh2MTmVpo34xzdvp12","uuid":"ede61fc8-db35-4651-9af8-abf2b3284ba1","timestamp":"2026-03-04T15:24:01.656Z"}
{"parentUuid":"ede61fc8-db35-4651-9af8-abf2b3284ba1","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011ThJhmCw6p1is3X5mSGqwb","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// Package metricstoreclient provides a client for querying the cc-metric-store time series database.\n     7→//\n     8→// The cc-metric-store is a high-performance time series database optimized for HPC metric data.\n     9→// This client handles HTTP communication, query construction, scope transformations, and data retrieval\n    10→// for job and node metrics across different metric scopes (node, socket, core, hwthread, accelerator).\n    11→//\n    12→// # Architecture\n    13→//\n    14→// The package is split into two main components:\n    15→//   - Client Operations (cc-metric-store.go): HTTP client, request handling, data loading methods\n    16→//   - Query Building (cc-metric-store-queries.go): Query construction and scope transformation logic\n    17→//\n    18→// # Basic Usage\n    19→//\n    20→//\tstore := NewCCMetricStore(\"http://localhost:8080\", \"jwt-token\")\n    21→//\n    22→//\t// Load job data\n    23→//\tjobData, err := store.LoadData(job, metrics, scopes, ctx, resolution)\n    24→//\tif err != nil {\n    25→//\t    log.Fatal(err)\n    26→//\t}\n    27→//\n    28→// # Metric Scopes\n    29→//\n    30→// The client supports hierarchical metric scopes that map to HPC hardware topology:\n    31→//   - MetricScopeAccelerator: GPU/accelerator level metrics\n    32→//   - MetricScopeHWThread: Hardware thread (SMT) level metrics\n    33→//   - MetricScopeCore: CPU core level metrics\n    34→//   - MetricScopeSocket: CPU socket level metrics\n    35→//   - MetricScopeMemoryDomain: NUMA domain level metrics\n    36→//   - MetricScopeNode: Full node level metrics\n    37→//\n    38→// The client automatically handles scope transformations, aggregating finer-grained metrics\n    39→// to coarser scopes when needed (e.g., aggregating core metrics to socket level).\n    40→//\n    41→// # Error Handling\n    42→//\n    43→// The client supports partial errors - if some queries fail, it returns both the successful\n    44→// data and an error listing the failed queries. This allows processing partial results\n    45→// when some nodes or metrics are temporarily unavailable.\n    46→//\n    47→// # API Versioning\n    48→//\n    49→// The client uses cc-metric-store API v2, which includes support for:\n    50→//   - Data resampling for bandwidth optimization\n    51→//   - Multi-scope queries in a single request\n    52→//   - Aggregation across hardware topology levels\n    53→package metricstoreclient\n    54→\n    55→import (\n    56→\t\"bufio\"\n    57→\t\"bytes\"\n    58→\t\"context\"\n    59→\t\"encoding/json\"\n    60→\t\"fmt\"\n    61→\t\"net/http\"\n    62→\t\"strings\"\n    63→\t\"time\"\n    64→\n    65→\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n    66→\t\"github.com/ClusterCockpit/cc-backend/pkg/metricstore\"\n    67→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    68→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    69→)\n    70→\n    71→// CCMetricStore is the HTTP client for communicating with cc-metric-store.\n    72→// It manages connection details, authentication, and provides methods for querying metrics.\n    73→type CCMetricStore struct {\n    74→\tclient        http.Client                 // HTTP client with 10-second timeout\n    75→\tjwt           string                      // JWT Bearer token for authentication\n    76→\turl           string                      // Base URL of cc-metric-store instance\n    77→\tqueryEndpoint string                      // Full URL to query API endpoint\n    78→\ttopologyCache map[string]*schema.Topology // cluster -> topology cache\n    79→}\n    80→\n    81→// APIQueryRequest represents a request to the cc-metric-store query API.\n    82→// It supports both explicit queries and \"for-all-nodes\" bulk queries.\n    83→type APIQueryRequest struct {\n    84→\tCluster     string     `json:\"cluster\"`       // Target cluster name\n    85→\tQueries     []APIQuery `json:\"queries\"`       // Explicit list of metric queries\n    86→\tForAllNodes []string   `json:\"for-all-nodes\"` // Metrics to query for all nodes\n    87→\tFrom        int64      `json:\"from\"`          // Start time (Unix timestamp)\n    88→\tTo          int64      `json:\"to\"`            // End time (Unix timestamp)\n    89→\tWithStats   bool       `json:\"with-stats\"`    // Include min/avg/max statistics\n    90→\tWithData    bool       `json:\"with-data\"`     // Include time series data points\n    91→}\n    92→\n    93→// APIQuery specifies a single metric query with optional scope filtering.\n    94→// Type and TypeIds define the hardware scope (core, socket, accelerator, etc.).\n    95→type APIQuery struct {\n    96→\tType       *string  `json:\"type,omitempty\"`        // Scope type (e.g., \"core\", \"socket\")\n    97→\tSubType    *string  `json:\"subtype,omitempty\"`     // Sub-scope type (reserved for future use)\n    98→\tMetric     string   `json:\"metric\"`                // Metric name\n    99→\tHostname   string   `json:\"host\"`                  // Target hostname\n   100→\tResolution int      `json:\"resolution\"`            // Data resolution in seconds (0 = native)\n   101→\tTypeIds    []string `json:\"type-ids,omitempty\"`    // IDs for the scope type (e.g., core IDs)\n   102→\tSubTypeIds []string `json:\"subtype-ids,omitempty\"` // IDs for sub-scope (reserved)\n   103→\tAggregate  bool     `json:\"aggreg\"`                // Aggregate across TypeIds\n   104→}\n   105→\n   106→// APIQueryResponse contains the results from a cc-metric-store query.\n   107→// Results align with the Queries slice by index.\n   108→type APIQueryResponse struct {\n   109→\tQueries []APIQuery        `json:\"queries,omitempty\"` // Echoed queries (for bulk requests)\n   110→\tResults [][]APIMetricData `json:\"results\"`           // Result data, indexed by query\n   111→}\n   112→\n   113→// APIMetricData represents time series data and statistics for a single metric series.\n   114→// Error is set if this particular series failed to load.\n   115→type APIMetricData struct {\n   116→\tError      *string        `json:\"error\"`      // Error message if query failed\n   117→\tData       []schema.Float `json:\"data\"`       // Time series data points\n   118→\tFrom       int64          `json:\"from\"`       // Actual start time of data\n   119→\tTo         int64          `json:\"to\"`         // Actual end time of data\n   120→\tResolution int            `json:\"resolution\"` // Actual resolution of data in seconds\n   121→\tAvg        schema.Float   `json:\"avg\"`        // Average value across time range\n   122→\tMin        schema.Float   `json:\"min\"`        // Minimum value in time range\n   123→\tMax        schema.Float   `json:\"max\"`        // Maximum value in time range\n   124→}\n   125→\n   126→// NewCCMetricStore creates and initializes a new (external) CCMetricStore client.\n   127→// The url parameter should include the protocol and port (e.g., \"http://localhost:8080\").\n   128→// The token parameter is a JWT used for Bearer authentication; pass empty string if auth is disabled.\n   129→func NewCCMetricStore(url string, token string) *CCMetricStore {\n   130→\treturn &CCMetricStore{\n   131→\t\turl:           url,\n   132→\t\tqueryEndpoint: fmt.Sprintf(\"%s/api/query\", url),\n   133→\t\tjwt:           token,\n   134→\t\tclient: http.Client{\n   135→\t\t\tTimeout: 10 * time.Second,\n   136→\t\t},\n   137→\t\ttopologyCache: make(map[string]*schema.Topology),\n   138→\t}\n   139→}\n   140→\n   141→// doRequest executes an HTTP POST request to the cc-metric-store query API.\n   142→// It handles JSON encoding/decoding, authentication, and API versioning.\n   143→// The request body is automatically closed to prevent resource leaks.\n   144→func (ccms *CCMetricStore) doRequest(\n   145→\tctx context.Context,\n   146→\tbody *APIQueryRequest,\n   147→) (*APIQueryResponse, error) {\n   148→\tbuf := &bytes.Buffer{}\n   149→\tif err := json.NewEncoder(buf).Encode(body); err != nil {\n   150→\t\tcclog.Errorf(\"Error while encoding request body: %s\", err.Error())\n   151→\t\treturn nil, err\n   152→\t}\n   153→\n   154→\treq, err := http.NewRequestWithContext(ctx, http.MethodGet, ccms.queryEndpoint, buf)\n   155→\tif err != nil {\n   156→\t\tcclog.Errorf(\"Error while building request body: %s\", err.Error())\n   157→\t\treturn nil, err\n   158→\t}\n   159→\tif ccms.jwt != \"\" {\n   160→\t\treq.Header.Add(\"Authorization\", fmt.Sprintf(\"Bearer %s\", ccms.jwt))\n   161→\t}\n   162→\n   163→\t// versioning the cc-metric-store query API.\n   164→\t// v2 = data with resampling\n   165→\t// v1 = data without resampling\n   166→\tq := req.URL.Query()\n   167→\tq.Add(\"version\", \"v2\")\n   168→\treq.URL.RawQuery = q.Encode()\n   169→\n   170→\tres, err := ccms.client.Do(req)\n   171→\tif err != nil {\n   172→\t\tcclog.Errorf(\"Error while performing request: %s\", err.Error())\n   173→\t\treturn nil, err\n   174→\t}\n   175→\tdefer res.Body.Close()\n   176→\n   177→\tif res.StatusCode != http.StatusOK {\n   178→\t\treturn nil, fmt.Errorf(\"'%s': HTTP Status: %s\", ccms.queryEndpoint, res.Status)\n   179→\t}\n   180→\n   181→\tvar resBody APIQueryResponse\n   182→\tif err := json.NewDecoder(bufio.NewReader(res.Body)).Decode(&resBody); err != nil {\n   183→\t\tcclog.Errorf(\"Error while decoding result body: %s\", err.Error())\n   184→\t\treturn nil, err\n   185→\t}\n   186→\n   187→\treturn &resBody, nil\n   188→}\n   189→\n   190→// getTopology returns the topology for a given cluster and subcluster, caching it if not already present\n   191→func (ccms *CCMetricStore) getTopology(cluster, subCluster string) (*schema.Topology, error) {\n   192→\tcacheKey := fmt.Sprintf(\"%s:%s\", cluster, subCluster)\n   193→\tif topology, ok := ccms.topologyCache[cacheKey]; ok {\n   194→\t\treturn topology, nil\n   195→\t}\n   196→\n   197→\tsubcluster, err := archive.GetSubCluster(cluster, subCluster)\n   198→\tif err != nil {\n   199→\t\treturn nil, err\n   200→\t}\n   201→\n   202→\tccms.topologyCache[cacheKey] = &subcluster.Topology\n   203→\treturn &subcluster.Topology, nil\n   204→}\n   205→\n   206→// getTopologyByNode returns the topology for a given cluster and node, caching it if not already present\n   207→func (ccms *CCMetricStore) getTopologyByNode(cluster, node string) (*schema.Topology, error) {\n   208→\tsubCluster, err := archive.GetSubClusterByNode(cluster, node)\n   209→\tif err != nil {\n   210→\t\treturn nil, err\n   211→\t}\n   212→\n   213→\treturn ccms.getTopology(cluster, subCluster)\n   214→}\n   215→\n   216→// LoadData retrieves time series data and statistics for the specified job and metrics.\n   217→// It queries data for the job's time range and resources, handling scope transformations automatically.\n   218→//\n   219→// Parameters:\n   220→//   - job: Job metadata including cluster, time range, and allocated resources\n   221→//   - metrics: List of metric names to retrieve\n   222→//   - scopes: Requested metric scopes (node, socket, core, etc.)\n   223→//   - ctx: Context for cancellation and timeouts\n   224→//   - resolution: Data resolution in seconds (0 for native resolution)\n   225→//\n   226→// Returns JobData organized as: metric -> scope -> series list.\n   227→// Supports partial errors: returns available data even if some queries fail.\n   228→func (ccms *CCMetricStore) LoadData(\n   229→\tjob *schema.Job,\n   230→\tmetrics []string,\n   231→\tscopes []schema.MetricScope,\n   232→\tctx context.Context,\n   233→\tresolution int,\n   234→) (schema.JobData, error) {\n   235→\tqueries, assignedScope, err := ccms.buildQueries(job, metrics, scopes, resolution)\n   236→\tif err != nil {\n   237→\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n   238→\t\treturn nil, err\n   239→\t}\n   240→\n   241→\t// Verify assignment is correct - log any inconsistencies for debugging\n   242→\tif len(queries) != len(assignedScope) {\n   243→\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildQueries: %d vs %d\",\n   244→\t\t\tlen(queries), len(assignedScope))\n   245→\t}\n   246→\n   247→\treq := APIQueryRequest{\n   248→\t\tCluster:   job.Cluster,\n   249→\t\tFrom:      job.StartTime,\n   250→\t\tTo:        job.StartTime + int64(job.Duration),\n   251→\t\tQueries:   queries,\n   252→\t\tWithStats: true,\n   253→\t\tWithData:  true,\n   254→\t}\n   255→\n   256→\tresBody, err := ccms.doRequest(ctx, &req)\n   257→\tif err != nil {\n   258→\t\tcclog.Errorf(\"Error while performing request for job %d: %s\", job.JobID, err.Error())\n   259→\t\treturn nil, err\n   260→\t}\n   261→\n   262→\tvar errors []string\n   263→\tjobData := make(schema.JobData)\n   264→\n   265→\t// Add safety check for potential index out of range errors\n   266→\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n   267→\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n   268→\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n   269→\t\tif len(resBody.Results) > len(req.Queries) {\n   270→\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n   271→\t\t}\n   272→\t\tif len(assignedScope) > len(req.Queries) {\n   273→\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n   274→\t\t}\n   275→\t}\n   276→\n   277→\tfor i, row := range resBody.Results {\n   278→\t\t// Safety check to prevent index out of range errors\n   279→\t\tif i >= len(req.Queries) || i >= len(assignedScope) {\n   280→\t\t\tcclog.Warnf(\"Index out of range prevented: i=%d, queries=%d, assignedScope=%d\",\n   281→\t\t\t\ti, len(req.Queries), len(assignedScope))\n   282→\t\t\tcontinue\n   283→\t\t}\n   284→\n   285→\t\tquery := req.Queries[i]\n   286→\t\tmetric := query.Metric\n   287→\t\tscope := assignedScope[i]\n   288→\t\tmc := archive.GetMetricConfig(job.Cluster, metric)\n   289→\n   290→\t\tif mc == nil {\n   291→\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, job.Cluster)\n   292→\t\t\tcontinue\n   293→\t\t}\n   294→\n   295→\t\tif _, ok := jobData[metric]; !ok {\n   296→\t\t\tjobData[metric] = make(map[schema.MetricScope]*schema.JobMetric)\n   297→\t\t}\n   298→\n   299→\t\tres := mc.Timestep\n   300→\t\tif len(row) > 0 {\n   301→\t\t\tres = row[0].Resolution\n   302→\t\t}\n   303→\n   304→\t\tjobMetric, ok := jobData[metric][scope]\n   305→\t\tif !ok {\n   306→\t\t\tjobMetric = &schema.JobMetric{\n   307→\t\t\t\tUnit:     mc.Unit,\n   308→\t\t\t\tTimestep: res,\n   309→\t\t\t\tSeries:   make([]schema.Series, 0),\n   310→\t\t\t}\n   311→\t\t\tjobData[metric][scope] = jobMetric\n   312→\t\t}\n   313→\n   314→\t\tfor ndx, res := range row {\n   315→\t\t\tif res.Error != nil {\n   316→\t\t\t\t/* Build list for \"partial errors\", if any */\n   317→\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n   318→\t\t\t\tcontinue\n   319→\t\t\t}\n   320→\n   321→\t\t\tid := (*string)(nil)\n   322→\t\t\tif query.Type != nil {\n   323→\t\t\t\t// Check if ndx is within the bounds of TypeIds slice\n   324→\t\t\t\tif ndx < len(query.TypeIds) {\n   325→\t\t\t\t\tid = new(string)\n   326→\t\t\t\t\t*id = query.TypeIds[ndx]\n   327→\t\t\t\t} else {\n   328→\t\t\t\t\t// Log the error but continue processing\n   329→\t\t\t\t\tcclog.Warnf(\"TypeIds index out of range: %d with length %d for metric %s on host %s\",\n   330→\t\t\t\t\t\tndx, len(query.TypeIds), query.Metric, query.Hostname)\n   331→\t\t\t\t}\n   332→\t\t\t}\n   333→\n   334→\t\t\tsanitizeStats(&res.Avg, &res.Min, &res.Max)\n   335→\n   336→\t\t\tjobMetric.Series = append(jobMetric.Series, schema.Series{\n   337→\t\t\t\tHostname: query.Hostname,\n   338→\t\t\t\tID:       id,\n   339→\t\t\t\tStatistics: schema.MetricStatistics{\n   340→\t\t\t\t\tAvg: float64(res.Avg),\n   341→\t\t\t\t\tMin: float64(res.Min),\n   342→\t\t\t\t\tMax: float64(res.Max),\n   343→\t\t\t\t},\n   344→\t\t\t\tData: res.Data,\n   345→\t\t\t})\n   346→\t\t}\n   347→\n   348→\t\t// So that one can later check len(jobData):\n   349→\t\tif len(jobMetric.Series) == 0 {\n   350→\t\t\tdelete(jobData[metric], scope)\n   351→\t\t\tif len(jobData[metric]) == 0 {\n   352→\t\t\t\tdelete(jobData, metric)\n   353→\t\t\t}\n   354→\t\t}\n   355→\t}\n   356→\n   357→\tif len(errors) != 0 {\n   358→\t\t/* Returns list for \"partial errors\" */\n   359→\t\treturn jobData, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n   360→\t}\n   361→\treturn jobData, nil\n   362→}\n   363→\n   364→// LoadStats retrieves min/avg/max statistics for job metrics at node scope.\n   365→// This is faster than LoadData when only statistical summaries are needed (no time series data).\n   366→//\n   367→// Returns statistics organized as: metric -> hostname -> statistics.\n   368→func (ccms *CCMetricStore) LoadStats(\n   369→\tjob *schema.Job,\n   370→\tmetrics []string,\n   371→\tctx context.Context,\n   372→) (map[string]map[string]schema.MetricStatistics, error) {\n   373→\tqueries, _, err := ccms.buildQueries(job, metrics, []schema.MetricScope{schema.MetricScopeNode}, 0) // #166 Add scope shere for analysis view accelerator normalization?\n   374→\tif err != nil {\n   375→\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v: %s\", job.JobID, metrics, err.Error())\n   376→\t\treturn nil, err\n   377→\t}\n   378→\n   379→\treq := APIQueryRequest{\n   380→\t\tCluster:   job.Cluster,\n   381→\t\tFrom:      job.StartTime,\n   382→\t\tTo:        job.StartTime + int64(job.Duration),\n   383→\t\tQueries:   queries,\n   384→\t\tWithStats: true,\n   385→\t\tWithData:  false,\n   386→\t}\n   387→\n   388→\tresBody, err := ccms.doRequest(ctx, &req)\n   389→\tif err != nil {\n   390→\t\tcclog.Errorf(\"Error while performing request for job %d: %s\", job.JobID, err.Error())\n   391→\t\treturn nil, err\n   392→\t}\n   393→\n   394→\tstats := make(map[string]map[string]schema.MetricStatistics, len(metrics))\n   395→\tfor i, res := range resBody.Results {\n   396→\t\tif len(res) == 0 {\n   397→\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n   398→\t\t\tcontinue\n   399→\t\t}\n   400→\t\tquery := req.Queries[i]\n   401→\t\tmetric := query.Metric\n   402→\t\tdata := res[0]\n   403→\t\tif data.Error != nil {\n   404→\t\t\tcclog.Warnf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *data.Error)\n   405→\t\t\tcontinue\n   406→\t\t}\n   407→\n   408→\t\tmetricdata, ok := stats[metric]\n   409→\t\tif !ok {\n   410→\t\t\tmetricdata = make(map[string]schema.MetricStatistics, job.NumNodes)\n   411→\t\t\tstats[metric] = metricdata\n   412→\t\t}\n   413→\n   414→\t\tif hasNaNStats(data.Avg, data.Min, data.Max) {\n   415→\t\t\tcclog.Warnf(\"fetching %s for node %s failed: one of avg/min/max is NaN\", metric, query.Hostname)\n   416→\t\t\tcontinue\n   417→\t\t}\n   418→\n   419→\t\tmetricdata[query.Hostname] = schema.MetricStatistics{\n   420→\t\t\tAvg: float64(data.Avg),\n   421→\t\t\tMin: float64(data.Min),\n   422→\t\t\tMax: float64(data.Max),\n   423→\t\t}\n   424→\t}\n   425→\n   426→\treturn stats, nil\n   427→}\n   428→\n   429→// LoadScopedStats retrieves statistics for job metrics across multiple scopes.\n   430→// Used for the Job-View Statistics Table to display per-scope breakdowns.\n   431→//\n   432→// Returns statistics organized as: metric -> scope -> list of scoped statistics.\n   433→// Each scoped statistic includes hostname, hardware ID (if applicable), and min/avg/max values.\n   434→func (ccms *CCMetricStore) LoadScopedStats(\n   435→\tjob *schema.Job,\n   436→\tmetrics []string,\n   437→\tscopes []schema.MetricScope,\n   438→\tctx context.Context,\n   439→) (schema.ScopedJobStats, error) {\n   440→\tqueries, assignedScope, err := ccms.buildQueries(job, metrics, scopes, 0)\n   441→\tif err != nil {\n   442→\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n   443→\t\treturn nil, err\n   444→\t}\n   445→\n   446→\treq := APIQueryRequest{\n   447→\t\tCluster:   job.Cluster,\n   448→\t\tFrom:      job.StartTime,\n   449→\t\tTo:        job.StartTime + int64(job.Duration),\n   450→\t\tQueries:   queries,\n   451→\t\tWithStats: true,\n   452→\t\tWithData:  false,\n   453→\t}\n   454→\n   455→\tresBody, err := ccms.doRequest(ctx, &req)\n   456→\tif err != nil {\n   457→\t\tcclog.Errorf(\"Error while performing request for job %d: %s\", job.JobID, err.Error())\n   458→\t\treturn nil, err\n   459→\t}\n   460→\n   461→\tvar errors []string\n   462→\tscopedJobStats := make(schema.ScopedJobStats)\n   463→\n   464→\tfor i, row := range resBody.Results {\n   465→\t\tquery := req.Queries[i]\n   466→\t\tmetric := query.Metric\n   467→\t\tscope := assignedScope[i]\n   468→\n   469→\t\tif _, ok := scopedJobStats[metric]; !ok {\n   470→\t\t\tscopedJobStats[metric] = make(map[schema.MetricScope][]*schema.ScopedStats)\n   471→\t\t}\n   472→\n   473→\t\tif _, ok := scopedJobStats[metric][scope]; !ok {\n   474→\t\t\tscopedJobStats[metric][scope] = make([]*schema.ScopedStats, 0)\n   475→\t\t}\n   476→\n   477→\t\tfor ndx, res := range row {\n   478→\t\t\tif res.Error != nil {\n   479→\t\t\t\t/* Build list for \"partial errors\", if any */\n   480→\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n   481→\t\t\t\tcontinue\n   482→\t\t\t}\n   483→\n   484→\t\t\tid := (*string)(nil)\n   485→\t\t\tif query.Type != nil {\n   486→\t\t\t\t// Check if ndx is within the bounds of TypeIds slice\n   487→\t\t\t\tif ndx < len(query.TypeIds) {\n   488→\t\t\t\t\tid = new(string)\n   489→\t\t\t\t\t*id = query.TypeIds[ndx]\n   490→\t\t\t\t} else {\n   491→\t\t\t\t\t// Log the error but continue processing\n   492→\t\t\t\t\tcclog.Warnf(\"TypeIds index out of range: %d with length %d for metric %s on host %s\",\n   493→\t\t\t\t\t\tndx, len(query.TypeIds), query.Metric, query.Hostname)\n   494→\t\t\t\t}\n   495→\t\t\t}\n   496→\n   497→\t\t\tsanitizeStats(&res.Avg, &res.Min, &res.Max)\n   498→\n   499→\t\t\tscopedJobStats[metric][scope] = append(scopedJobStats[metric][scope], &schema.ScopedStats{\n   500→\t\t\t\tHostname: query.Hostname,\n   501→\t\t\t\tID:       id,\n   502→\t\t\t\tData: &schema.MetricStatistics{\n   503→\t\t\t\t\tAvg: float64(res.Avg),\n   504→\t\t\t\t\tMin: float64(res.Min),\n   505→\t\t\t\t\tMax: float64(res.Max),\n   506→\t\t\t\t},\n   507→\t\t\t})\n   508→\t\t}\n   509→\n   510→\t\t// So that one can later check len(scopedJobStats[metric][scope]): Remove from map if empty\n   511→\t\tif len(scopedJobStats[metric][scope]) == 0 {\n   512→\t\t\tdelete(scopedJobStats[metric], scope)\n   513→\t\t\tif len(scopedJobStats[metric]) == 0 {\n   514→\t\t\t\tdelete(scopedJobStats, metric)\n   515→\t\t\t}\n   516→\t\t}\n   517→\t}\n   518→\n   519→\tif len(errors) != 0 {\n   520→\t\t/* Returns list for \"partial errors\" */\n   521→\t\treturn scopedJobStats, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n   522→\t}\n   523→\treturn scopedJobStats, nil\n   524→}\n   525→\n   526→// LoadNodeData retrieves current metric data for specified nodes in a cluster.\n   527→// Used for the Systems-View Node-Overview to display real-time node status.\n   528→//\n   529→// If nodes is nil, queries all metrics for all nodes in the cluster (bulk query).\n   530→// Returns data organized as: hostname -> metric -> list of JobMetric (with time series and stats).\n   531→func (ccms *CCMetricStore) LoadNodeData(\n   532→\tcluster string,\n   533→\tmetrics, nodes []string,\n   534→\tscopes []schema.MetricScope,\n   535→\tfrom, to time.Time,\n   536→\tctx context.Context,\n   537→) (map[string]map[string][]*schema.JobMetric, error) {\n   538→\treq := APIQueryRequest{\n   539→\t\tCluster:   cluster,\n   540→\t\tFrom:      from.Unix(),\n   541→\t\tTo:        to.Unix(),\n   542→\t\tWithStats: true,\n   543→\t\tWithData:  true,\n   544→\t}\n   545→\n   546→\tif nodes == nil {\n   547→\t\treq.ForAllNodes = append(req.ForAllNodes, metrics...)\n   548→\t} else {\n   549→\t\tfor _, node := range nodes {\n   550→\t\t\tfor _, metric := range metrics {\n   551→\t\t\t\treq.Queries = append(req.Queries, APIQuery{\n   552→\t\t\t\t\tHostname:   node,\n   553→\t\t\t\t\tMetric:     metric,\n   554→\t\t\t\t\tResolution: 0, // Default for Node Queries: Will return metric $Timestep Resolution\n   555→\t\t\t\t})\n   556→\t\t\t}\n   557→\t\t}\n   558→\t}\n   559→\n   560→\tresBody, err := ccms.doRequest(ctx, &req)\n   561→\tif err != nil {\n   562→\t\tcclog.Errorf(\"Error while performing request for cluster %s: %s\", cluster, err.Error())\n   563→\t\treturn nil, err\n   564→\t}\n   565→\n   566→\tvar errors []string\n   567→\tdata := make(map[string]map[string][]*schema.JobMetric)\n   568→\tfor i, res := range resBody.Results {\n   569→\t\tif len(res) == 0 {\n   570→\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n   571→\t\t\tcontinue\n   572→\t\t}\n   573→\n   574→\t\tvar query APIQuery\n   575→\t\tif resBody.Queries != nil {\n   576→\t\t\tquery = resBody.Queries[i]\n   577→\t\t} else {\n   578→\t\t\tquery = req.Queries[i]\n   579→\t\t}\n   580→\n   581→\t\tmetric := query.Metric\n   582→\t\tqdata := res[0]\n   583→\t\tif qdata.Error != nil {\n   584→\t\t\terrors = append(errors, fmt.Sprintf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *qdata.Error))\n   585→\t\t}\n   586→\n   587→\t\tsanitizeStats(&qdata.Avg, &qdata.Min, &qdata.Max)\n   588→\n   589→\t\thostdata, ok := data[query.Hostname]\n   590→\t\tif !ok {\n   591→\t\t\thostdata = make(map[string][]*schema.JobMetric)\n   592→\t\t\tdata[query.Hostname] = hostdata\n   593→\t\t}\n   594→\n   595→\t\tmc := archive.GetMetricConfig(cluster, metric)\n   596→\t\thostdata[metric] = append(hostdata[metric], &schema.JobMetric{\n   597→\t\t\tUnit:     mc.Unit,\n   598→\t\t\tTimestep: mc.Timestep,\n   599→\t\t\tSeries: []schema.Series{\n   600→\t\t\t\t{\n   601→\t\t\t\t\tHostname: query.Hostname,\n   602→\t\t\t\t\tData:     qdata.Data,\n   603→\t\t\t\t\tStatistics: schema.MetricStatistics{\n   604→\t\t\t\t\t\tAvg: float64(qdata.Avg),\n   605→\t\t\t\t\t\tMin: float64(qdata.Min),\n   606→\t\t\t\t\t\tMax: float64(qdata.Max),\n   607→\t\t\t\t\t},\n   608→\t\t\t\t},\n   609→\t\t\t},\n   610→\t\t})\n   611→\t}\n   612→\n   613→\tif len(errors) != 0 {\n   614→\t\t/* Returns list of \"partial errors\" */\n   615→\t\treturn data, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n   616→\t}\n   617→\n   618→\treturn data, nil\n   619→}\n   620→\n   621→// LoadNodeListData retrieves paginated node metrics for the Systems-View Node-List.\n   622→//\n   623→// Supports filtering by subcluster and node name pattern. The nodeFilter performs\n   624→// substring matching on hostnames.\n   625→//\n   626→// Returns:\n   627→//   - Node data organized as: hostname -> JobData (metric -> scope -> series)\n   628→//   - Total node count (before pagination)\n   629→//   - HasNextPage flag indicating if more pages are available\n   630→//   - Error (may be partial error with some data returned)\n   631→func (ccms *CCMetricStore) LoadNodeListData(\n   632→\tcluster, subCluster string,\n   633→\tnodes []string,\n   634→\tmetrics []string,\n   635→\tscopes []schema.MetricScope,\n   636→\tresolution int,\n   637→\tfrom, to time.Time,\n   638→\tctx context.Context,\n   639→) (map[string]schema.JobData, error) {\n   640→\tqueries, assignedScope, err := ccms.buildNodeQueries(cluster, subCluster, nodes, metrics, scopes, resolution)\n   641→\tif err != nil {\n   642→\t\tcclog.Errorf(\"Error while building node queries for Cluster %s, SubCluster %s, Metrics %v, Scopes %v: %s\", cluster, subCluster, metrics, scopes, err.Error())\n   643→\t\treturn nil, err\n   644→\t}\n   645→\n   646→\t// Verify assignment is correct - log any inconsistencies for debugging\n   647→\tif len(queries) != len(assignedScope) {\n   648→\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildNodeQueries: %d vs %d\",\n   649→\t\t\tlen(queries), len(assignedScope))\n   650→\t}\n   651→\n   652→\treq := APIQueryRequest{\n   653→\t\tCluster:   cluster,\n   654→\t\tQueries:   queries,\n   655→\t\tFrom:      from.Unix(),\n   656→\t\tTo:        to.Unix(),\n   657→\t\tWithStats: true,\n   658→\t\tWithData:  true,\n   659→\t}\n   660→\n   661→\tresBody, err := ccms.doRequest(ctx, &req)\n   662→\tif err != nil {\n   663→\t\tcclog.Errorf(\"Error while performing request for cluster %s: %s\", cluster, err.Error())\n   664→\t\treturn nil, err\n   665→\t}\n   666→\n   667→\tvar errors []string\n   668→\tdata := make(map[string]schema.JobData)\n   669→\n   670→\t// Add safety check for index out of range issues\n   671→\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n   672→\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n   673→\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n   674→\t\tif len(resBody.Results) > len(req.Queries) {\n   675→\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n   676→\t\t}\n   677→\t\tif len(assignedScope) > len(req.Queries) {\n   678→\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n   679→\t\t}\n   680→\t}\n   681→\n   682→\tfor i, row := range resBody.Results {\n   683→\t\t// Safety check to prevent index out of range errors\n   684→\t\tif i >= len(req.Queries) || i >= len(assignedScope) {\n   685→\t\t\tcclog.Warnf(\"Index out of range prevented: i=%d, queries=%d, assignedScope=%d\",\n   686→\t\t\t\ti, len(req.Queries), len(assignedScope))\n   687→\t\t\tcontinue\n   688→\t\t}\n   689→\n   690→\t\tvar query APIQuery\n   691→\t\tif resBody.Queries != nil {\n   692→\t\t\tif i < len(resBody.Queries) {\n   693→\t\t\t\tquery = resBody.Queries[i]\n   694→\t\t\t} else {\n   695→\t\t\t\tcclog.Warnf(\"Index out of range prevented for resBody.Queries: i=%d, len=%d\",\n   696→\t\t\t\t\ti, len(resBody.Queries))\n   697→\t\t\t\tcontinue\n   698→\t\t\t}\n   699→\t\t} else {\n   700→\t\t\tquery = req.Queries[i]\n   701→\t\t}\n   702→\n   703→\t\tmetric := query.Metric\n   704→\t\tscope := assignedScope[i]\n   705→\t\tmc := archive.GetMetricConfig(cluster, metric)\n   706→\t\tif mc == nil {\n   707→\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, cluster)\n   708→\t\t\tcontinue\n   709→\t\t}\n   710→\n   711→\t\tres := mc.Timestep\n   712→\t\tif len(row) > 0 {\n   713→\t\t\tres = row[0].Resolution\n   714→\t\t}\n   715→\n   716→\t\t// Init Nested Map Data Structures If Not Found\n   717→\t\thostData, ok := data[query.Hostname]\n   718→\t\tif !ok {\n   719→\t\t\thostData = make(schema.JobData)\n   720→\t\t\tdata[query.Hostname] = hostData\n   721→\t\t}\n   722→\n   723→\t\tmetricData, ok := hostData[metric]\n   724→\t\tif !ok {\n   725→\t\t\tmetricData = make(map[schema.MetricScope]*schema.JobMetric)\n   726→\t\t\tdata[query.Hostname][metric] = metricData\n   727→\t\t}\n   728→\n   729→\t\tscopeData, ok := metricData[scope]\n   730→\t\tif !ok {\n   731→\t\t\tscopeData = &schema.JobMetric{\n   732→\t\t\t\tUnit:     mc.Unit,\n   733→\t\t\t\tTimestep: res,\n   734→\t\t\t\tSeries:   make([]schema.Series, 0),\n   735→\t\t\t}\n   736→\t\t\tdata[query.Hostname][metric][scope] = scopeData\n   737→\t\t}\n   738→\n   739→\t\tfor ndx, res := range row {\n   740→\t\t\tif res.Error != nil {\n   741→\t\t\t\t/* Build list for \"partial errors\", if any */\n   742→\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n   743→\t\t\t\tcontinue\n   744→\t\t\t}\n   745→\n   746→\t\t\tid := (*string)(nil)\n   747→\t\t\tif query.Type != nil {\n   748→\t\t\t\t// Check if ndx is within the bounds of TypeIds slice\n   749→\t\t\t\tif ndx < len(query.TypeIds) {\n   750→\t\t\t\t\tid = new(string)\n   751→\t\t\t\t\t*id = query.TypeIds[ndx]\n   752→\t\t\t\t} else {\n   753→\t\t\t\t\t// Log the error but continue processing\n   754→\t\t\t\t\tcclog.Warnf(\"TypeIds index out of range: %d with length %d for metric %s on host %s\",\n   755→\t\t\t\t\t\tndx, len(query.TypeIds), query.Metric, query.Hostname)\n   756→\t\t\t\t}\n   757→\t\t\t}\n   758→\n   759→\t\t\tsanitizeStats(&res.Avg, &res.Min, &res.Max)\n   760→\n   761→\t\t\tscopeData.Series = append(scopeData.Series, schema.Series{\n   762→\t\t\t\tHostname: query.Hostname,\n   763→\t\t\t\tID:       id,\n   764→\t\t\t\tStatistics: schema.MetricStatistics{\n   765→\t\t\t\t\tAvg: float64(res.Avg),\n   766→\t\t\t\t\tMin: float64(res.Min),\n   767→\t\t\t\t\tMax: float64(res.Max),\n   768→\t\t\t\t},\n   769→\t\t\t\tData: res.Data,\n   770→\t\t\t})\n   771→\t\t}\n   772→\t}\n   773→\n   774→\tif len(errors) != 0 {\n   775→\t\t/* Returns list of \"partial errors\" */\n   776→\t\treturn data, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n   777→\t}\n   778→\n   779→\treturn data, nil\n   780→}\n   781→\n   782→// HealthCheck queries the external cc-metric-store's health check endpoint.\n   783→// It sends a HealthCheckReq as the request body to /api/healthcheck and\n   784→// returns the per-node health check results.\n   785→func (ccms *CCMetricStore) HealthCheck(cluster string,\n   786→\tnodes []string, metrics []string,\n   787→) (map[string]metricstore.HealthCheckResult, error) {\n   788→\treq := metricstore.HealthCheckReq{\n   789→\t\tCluster:     cluster,\n   790→\t\tNodes:       nodes,\n   791→\t\tMetricNames: metrics,\n   792→\t}\n   793→\n   794→\tbuf := &bytes.Buffer{}\n   795→\tif err := json.NewEncoder(buf).Encode(req); err != nil {\n   796→\t\tcclog.Errorf(\"Error while encoding health check request body: %s\", err.Error())\n   797→\t\treturn nil, err\n   798→\t}\n   799→\n   800→\tendpoint := fmt.Sprintf(\"%s/api/healthcheck\", ccms.url)\n   801→\thttpReq, err := http.NewRequest(http.MethodGet, endpoint, buf)\n   802→\tif err != nil {\n   803→\t\tcclog.Errorf(\"Error while building health check request: %s\", err.Error())\n   804→\t\treturn nil, err\n   805→\t}\n   806→\tif ccms.jwt != \"\" {\n   807→\t\thttpReq.Header.Add(\"Authorization\", fmt.Sprintf(\"Bearer %s\", ccms.jwt))\n   808→\t}\n   809→\n   810→\tres, err := ccms.client.Do(httpReq)\n   811→\tif err != nil {\n   812→\t\tcclog.Errorf(\"Error while performing health check request: %s\", err.Error())\n   813→\t\treturn nil, err\n   814→\t}\n   815→\tdefer res.Body.Close()\n   816→\n   817→\tif res.StatusCode != http.StatusOK {\n   818→\t\treturn nil, fmt.Errorf(\"'%s': HTTP Status: %s\", endpoint, res.Status)\n   819→\t}\n   820→\n   821→\tvar results map[string]metricstore.HealthCheckResult\n   822→\tif err := json.NewDecoder(bufio.NewReader(res.Body)).Decode(&results); err != nil {\n   823→\t\tcclog.Errorf(\"Error while decoding health check response: %s\", err.Error())\n   824→\t\treturn nil, err\n   825→\t}\n   826→\n   827→\treturn results, nil\n   828→}\n   829→\n   830→// sanitizeStats replaces NaN values in statistics with 0 to enable JSON marshaling.\n   831→// Regular float64 values cannot be JSONed when NaN.\n   832→func sanitizeStats(avg, min, max *schema.Float) {\n   833→\tif avg.IsNaN() || min.IsNaN() || max.IsNaN() {\n   834→\t\t*avg = schema.Float(0)\n   835→\t\t*min = schema.Float(0)\n   836→\t\t*max = schema.Float(0)\n   837→\t}\n   838→}\n   839→\n   840→// hasNaNStats returns true if any of the statistics contain NaN values.\n   841→func hasNaNStats(avg, min, max schema.Float) bool {\n   842→\treturn avg.IsNaN() || min.IsNaN() || max.IsNaN()\n   843→}\n   844→"}]},"uuid":"581826ad-963a-48f0-8e81-5fed71753a72","timestamp":"2026-03-04T15:24:01.849Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"ae13c6665fcb142fa"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_016dkeKh2MTmVpo34xzdvp12","uuid":"f0eb5bde-716f-442e-8b40-65f1c92a8cbb","timestamp":"2026-03-04T15:24:01.852Z"}
{"parentUuid":"f0eb5bde-716f-442e-8b40-65f1c92a8cbb","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-04T15:24:02.653Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01ESv9ay3fuebQB7nR3KHMjy","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XWDBtbouUC3fLnCXtGATRC","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/lineprotocol.go","limit":100},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":6,"cache_creation_input_tokens":2414,"cache_read_input_tokens":44323,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":2414},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYiGfirTxxZfbeAbxM1Js","uuid":"e4522080-c6b3-4412-80d4-5c232d5148cd"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"fffe00db-a54a-449f-8a2a-f96d317616e9","timestamp":"2026-03-04T15:24:02.653Z"}
{"parentUuid":"fffe00db-a54a-449f-8a2a-f96d317616e9","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XWDBtbouUC3fLnCXtGATRC","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved. This file is part of cc-backend.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// This file implements ingestion of InfluxDB line-protocol metric data received\n     7→// over NATS. Each line encodes one metric sample with the following structure:\n     8→//\n     9→//\t<measurement>[,cluster=<c>][,hostname=<h>][,type=<t>][,type-id=<id>][,subtype=<s>][,stype-id=<id>] value=<v> [<timestamp>]\n    10→//\n    11→// The measurement name identifies the metric (e.g. \"cpu_load\"). Tags provide\n    12→// routing information (cluster, host) and optional sub-device selectors (type,\n    13→// subtype). Only one field is expected per line: \"value\".\n    14→//\n    15→// After decoding, each sample is:\n    16→//  1. Written to the in-memory store via ms.WriteToLevel.\n    17→//  2. If the checkpoint format is \"wal\", also forwarded to the WAL staging\n    18→//     goroutine via the WALMessages channel for durable write-ahead logging.\n    19→package metricstore\n    20→\n    21→import (\n    22→\t\"bytes\"\n    23→\t\"context\"\n    24→\t\"fmt\"\n    25→\t\"sync\"\n    26→\t\"time\"\n    27→\n    28→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    29→\t\"github.com/ClusterCockpit/cc-lib/v2/nats\"\n    30→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    31→\t\"github.com/ClusterCockpit/cc-line-protocol/v2/lineprotocol\"\n    32→)\n    33→\n    34→// ReceiveNats subscribes to all configured NATS subjects and feeds incoming\n    35→// line-protocol messages into the MemoryStore.\n    36→//\n    37→// When workers > 1 a pool of goroutines drains a shared channel so that\n    38→// multiple messages can be decoded in parallel. With workers == 1 the NATS\n    39→// callback decodes inline (no channel overhead, lower latency).\n    40→//\n    41→// The function blocks until ctx is cancelled and all worker goroutines have\n    42→// finished. It returns nil when the NATS client is not configured; callers\n    43→// should treat that as a no-op rather than an error.\n    44→func ReceiveNats(ms *MemoryStore,\n    45→\tworkers int,\n    46→\tctx context.Context,\n    47→) error {\n    48→\tnc := nats.GetClient()\n    49→\n    50→\tif nc == nil {\n    51→\t\tcclog.Warn(\"NATS client not initialized\")\n    52→\t\treturn nil\n    53→\t}\n    54→\n    55→\tvar wg sync.WaitGroup\n    56→\tmsgs := make(chan []byte, workers*2)\n    57→\n    58→\tfor _, sc := range *Keys.Subscriptions {\n    59→\t\tclusterTag := sc.ClusterTag\n    60→\t\tif workers > 1 {\n    61→\t\t\twg.Add(workers)\n    62→\n    63→\t\t\tfor range workers {\n    64→\t\t\t\tgo func() {\n    65→\t\t\t\t\tdefer wg.Done()\n    66→\t\t\t\t\tfor m := range msgs {\n    67→\t\t\t\t\t\tdec := lineprotocol.NewDecoderWithBytes(m)\n    68→\t\t\t\t\t\tif err := DecodeLine(dec, ms, clusterTag); err != nil {\n    69→\t\t\t\t\t\t\tcclog.Errorf(\"error: %s\", err.Error())\n    70→\t\t\t\t\t\t}\n    71→\t\t\t\t\t}\n    72→\t\t\t\t}()\n    73→\t\t\t}\n    74→\n    75→\t\t\tnc.Subscribe(sc.SubscribeTo, func(subject string, data []byte) {\n    76→\t\t\t\tselect {\n    77→\t\t\t\tcase msgs <- data:\n    78→\t\t\t\tcase <-ctx.Done():\n    79→\t\t\t\t}\n    80→\t\t\t})\n    81→\t\t} else {\n    82→\t\t\tnc.Subscribe(sc.SubscribeTo, func(subject string, data []byte) {\n    83→\t\t\t\tdec := lineprotocol.NewDecoderWithBytes(data)\n    84→\t\t\t\tif err := DecodeLine(dec, ms, clusterTag); err != nil {\n    85→\t\t\t\t\tcclog.Errorf(\"error: %s\", err.Error())\n    86→\t\t\t\t}\n    87→\t\t\t})\n    88→\t\t}\n    89→\t\tcclog.Infof(\"NATS subscription to '%s' established\", sc.SubscribeTo)\n    90→\t}\n    91→\n    92→\tgo func() {\n    93→\t\t<-ctx.Done()\n    94→\t\tclose(msgs)\n    95→\t}()\n    96→\n    97→\twg.Wait()\n    98→\n    99→\treturn nil\n   100→}"}]},"uuid":"b5ecb086-c1c5-4cfd-aca2-4f35dbcf7993","timestamp":"2026-03-04T15:24:02.666Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"cdaad97d-22d3-40ea-b9ab-be6f1f78a7d2","timestamp":"2026-03-04T15:24:02.859Z"}
{"parentUuid":"cdaad97d-22d3-40ea-b9ab-be6f1f78a7d2","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-04T15:24:03.077Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01ESv9ay3fuebQB7nR3KHMjy","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01G3qqXm9o6ws37NHxoQXmAN","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/checkpoint.go","limit":80},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":6,"cache_creation_input_tokens":2414,"cache_read_input_tokens":44323,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":2414},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYiGfirTxxZfbeAbxM1Js","uuid":"6f534607-8c0c-4233-9981-37d4c6b15324"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"69925aa3-5760-4334-b466-c48350b10c5c","timestamp":"2026-03-04T15:24:03.078Z"}
{"parentUuid":"69925aa3-5760-4334-b466-c48350b10c5c","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01G3qqXm9o6ws37NHxoQXmAN","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved. This file is part of cc-backend.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// This file implements checkpoint persistence for the in-memory metric store.\n     7→//\n     8→// Checkpoints enable graceful restarts by periodically saving in-memory metric\n     9→// data to disk in JSON or binary format. The checkpoint system:\n    10→//\n    11→// Key Features:\n    12→//   - Periodic background checkpointing via the Checkpointing() worker\n    13→//   - Two format families: JSON (human-readable) and WAL+binary (compact, crash-safe)\n    14→//   - Parallel checkpoint creation and loading using worker pools\n    15→//   - Hierarchical file organization: checkpoint_dir/cluster/host/timestamp.{json|bin}\n    16→//   - WAL file: checkpoint_dir/cluster/host/current.wal (append-only, per-entry)\n    17→//   - Only saves unarchived data (archived data is already persisted elsewhere)\n    18→//   - GC optimization during loading to prevent excessive heap growth\n    19→//\n    20→// Checkpoint Workflow:\n    21→//  1. Init() loads checkpoints within retention window at startup\n    22→//  2. Checkpointing() worker periodically saves new data\n    23→//  3. Shutdown() writes final checkpoint before exit\n    24→//\n    25→// File Organization:\n    26→//\n    27→//\tcheckpoints/\n    28→//\t  cluster1/\n    29→//\t    host001/\n    30→//\t      1234567890.json  (JSON format: full subtree snapshot)\n    31→//\t      1234567890.bin   (binary format: full subtree snapshot)\n    32→//\t      current.wal      (WAL format: append-only per-entry log)\n    33→//\t    host002/\n    34→//\t      ...\n    35→package metricstore\n    36→\n    37→import (\n    38→\t\"bufio\"\n    39→\t\"context\"\n    40→\t\"encoding/json\"\n    41→\t\"errors\"\n    42→\t\"fmt\"\n    43→\t\"io/fs\"\n    44→\t\"os\"\n    45→\t\"path\"\n    46→\t\"path/filepath\"\n    47→\t\"sort\"\n    48→\t\"strconv\"\n    49→\t\"strings\"\n    50→\t\"sync\"\n    51→\t\"sync/atomic\"\n    52→\t\"time\"\n    53→\n    54→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    55→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    56→)\n    57→\n    58→const (\n    59→\tCheckpointFilePerms = 0o644                    // File permissions for checkpoint files\n    60→\tCheckpointDirPerms  = 0o755                    // Directory permissions for checkpoint directories\n    61→\tGCTriggerInterval   = DefaultGCTriggerInterval // Interval for triggering GC during checkpoint loading\n    62→)\n    63→\n    64→// CheckpointMetrics represents metric data in a checkpoint file.\n    65→// Whenever the structure changes, update MarshalJSON as well!\n    66→type CheckpointMetrics struct {\n    67→\tData      []schema.Float `json:\"data\"`\n    68→\tFrequency int64          `json:\"frequency\"`\n    69→\tStart     int64          `json:\"start\"`\n    70→}\n    71→\n    72→// CheckpointFile represents the hierarchical structure of a checkpoint file.\n    73→// It mirrors the Level tree structure from the MemoryStore.\n    74→type CheckpointFile struct {\n    75→\tMetrics  map[string]*CheckpointMetrics `json:\"metrics\"`\n    76→\tChildren map[string]*CheckpointFile    `json:\"children\"`\n    77→\tFrom     int64                         `json:\"from\"`\n    78→\tTo       int64                         `json:\"to\"`\n    79→}\n    80→"}]},"uuid":"1107e778-5b17-4be5-bf97-58cdccdefb8a","timestamp":"2026-03-04T15:24:03.101Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"67335ef0-64cd-4499-8d6f-753a740ef37c","timestamp":"2026-03-04T15:24:03.332Z"}
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{"parentUuid":"7914c1f4-3fb4-4ab2-8be1-234fdfd5102a","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UiQEsNPDjCUXruZyPhGrbs","type":"tool_result","content":"pkg/metricstore/configSchema.go:8:const configSchema = `{\npkg/metricstore/parquetArchive_test.go:18:func TestParseScopeFromName(t *testing.T) {\npkg/metricstore/parquetArchive_test.go:47:func TestFlattenCheckpointFile(t *testing.T) {\npkg/metricstore/parquetArchive_test.go:106:func TestParquetArchiveRoundtrip(t *testing.T) {\npkg/metricstore/parquetArchive_test.go:214:func TestLoadCheckpointFileFromDisk_JSON(t *testing.T) {\npkg/metricstore/metricstore_test.go:19:func TestBufferPoolGetReuse(t *testing.T) {\npkg/metricstore/metricstore_test.go:44:func TestBufferPoolClear(t *testing.T) {\npkg/metricstore/metricstore_test.go:58:func TestBufferEndFirstWrite(t *testing.T) {\npkg/metricstore/metricstore_test.go:73:func TestBufferWriteNaNFill(t *testing.T) {\npkg/metricstore/metricstore_test.go:98:func TestBufferWriteCapacityOverflow(t *testing.T) {\npkg/metricstore/metricstore_test.go:127:func TestBufferWriteOverwrite(t *testing.T) {\npkg/metricstore/metricstore_test.go:146:func TestBufferReadBeforeFirstWrite(t *testing.T) {\npkg/metricstore/metricstore_test.go:169:func TestBufferReadChain(t *testing.T) {\npkg/metricstore/metricstore_test.go:203:func TestBufferReadIdxAfterSwitch(t *testing.T) {\npkg/metricstore/metricstore_test.go:243:func TestBufferReadNaNValues(t *testing.T) {\npkg/metricstore/metricstore_test.go:270:func TestBufferReadAccumulation(t *testing.T) {\npkg/metricstore/metricstore_test.go:294:func newTestPool(t *testing.T) *PersistentBufferPool {\npkg/metricstore/metricstore_test.go:305:func TestBufferFreeRetention(t *testing.T) {\npkg/metricstore/metricstore_test.go:343:func TestBufferFreeAll(t *testing.T) {\npkg/metricstore/metricstore_test.go:372:func TestForceFreeOldestPoolReturn(t *testing.T) {\npkg/metricstore/metricstore_test.go:406:func TestForceFreeOldestSingleBuffer(t *testing.T) {\npkg/metricstore/metricstore_test.go:423:func TestBufferIterFromToOrder(t *testing.T) {\npkg/metricstore/metricstore_test.go:451:func TestBufferIterFromToFiltered(t *testing.T) {\npkg/metricstore/metricstore_test.go:477:func TestBufferIterFromToNilBuffer(t *testing.T) {\npkg/metricstore/metricstore_test.go:496:func TestBufferCount(t *testing.T) {\npkg/metricstore/metricstore_test.go:518:func TestAssignAggregationStrategy(t *testing.T) {\npkg/metricstore/metricstore_test.go:545:func TestBufferWrite(t *testing.T) {\npkg/metricstore/metricstore_test.go:570:func TestBufferRead(t *testing.T) {\npkg/metricstore/metricstore_test.go:596:func TestHealthCheck(t *testing.T) {\npkg/metricstore/metricstore_test.go:769:func TestGetHealthyMetrics(t *testing.T) {\npkg/metricstore/metricstore_test.go:878:func TestBufferHealthChecks(t *testing.T) {\npkg/metricstore/metricstore_test.go:971:func TestBufferPoolClean(t *testing.T) {\npkg/metricstore/lineprotocol.go:44:func ReceiveNats(ms *MemoryStore,\npkg/metricstore/lineprotocol.go:109:func reorder(buf, prefix []byte) []byte {\npkg/metricstore/lineprotocol.go:129:type decodeState struct {\npkg/metricstore/lineprotocol.go:161:var decodeStatePool = sync.Pool{\npkg/metricstore/lineprotocol.go:194:func DecodeLine(dec *lineprotocol.Decoder,\npkg/metricstore/api.go:21:var (\npkg/metricstore/api.go:35:type APIMetricData struct {\npkg/metricstore/api.go:53:type APIQueryRequest struct {\npkg/metricstore/api.go:69:type APIQueryResponse struct {\npkg/metricstore/api.go:83:type APIQuery struct {\npkg/metricstore/api.go:101:func (data *APIMetricData) AddStats() {\npkg/metricstore/api.go:129:func (data *APIMetricData) ScaleBy(f schema.Float) {\npkg/metricstore/api.go:153:func (data *APIMetricData) PadDataWithNull(ms *MemoryStore, from, to int64, metric string) {\npkg/metricstore/api.go:197:func FetchData(req APIQueryRequest) (*APIQueryResponse, error) {\npkg/metricstore/level.go:62:type Level struct {\npkg/metricstore/level.go:86:func (l *Level) findLevelOrCreate(selector []string, nMetrics int) *Level {\npkg/metricstore/level.go:148:func (l *Level) collectPaths(currentDepth, targetDepth int, currentPath []string, results *[][]string) {\npkg/metricstore/level.go:183:func (l *Level) free(t int64) (int, error) {\npkg/metricstore/level.go:225:func (l *Level) forceFree() (int, error) {\npkg/metricstore/level.go:273:func (l *Level) sizeInBytes() int64 {\npkg/metricstore/level.go:301:func (l *Level) findLevel(selector []string) *Level {\npkg/metricstore/level.go:339:func (l *Level) findBuffers(selector util.Selector, offset int, f func(b *buffer) error) error {\npkg/metricstore/debug.go:14:func (b *buffer) debugDump(buf []byte) []byte {\npkg/metricstore/debug.go:37:func (l *Level) debugDump(m *MemoryStore, w *bufio.Writer, lvlname string, buf []byte, depth int) ([]byte, error) {\npkg/metricstore/debug.go:92:func (m *MemoryStore) DebugDump(w *bufio.Writer, selector []string) error {\npkg/metricstore/buffer.go:55:const BufferCap int = DefaultBufferCapacity\npkg/metricstore/buffer.go:59:var bufferPool = NewPersistentBufferPool()\npkg/metricstore/buffer.go:61:type PersistentBufferPool struct {\npkg/metricstore/buffer.go:67:func NewPersistentBufferPool() *PersistentBufferPool {\npkg/metricstore/buffer.go:73:func (p *PersistentBufferPool) Get() *buffer {\npkg/metricstore/buffer.go:94:func (p *PersistentBufferPool) Put(b *buffer) {\npkg/metricstore/buffer.go:105:func (p *PersistentBufferPool) GetSize() int {\npkg/metricstore/buffer.go:112:func (p *PersistentBufferPool) Clear() {\npkg/metricstore/buffer.go:123:func (p *PersistentBufferPool) Clean(threshold int64) {\npkg/metricstore/buffer.go:143:var (\npkg/metricstore/buffer.go:168:type buffer struct {\npkg/metricstore/buffer.go:179:func newBuffer(ts, freq int64) *buffer {\npkg/metricstore/buffer.go:207:func (b *buffer) write(ts int64, value schema.Float) (*buffer, error) {\npkg/metricstore/buffer.go:237:func (b *buffer) end() int64 {\npkg/metricstore/buffer.go:241:func (b *buffer) firstWrite() int64 {\npkg/metricstore/buffer.go:266:func (b *buffer) read(from, to int64, data []schema.Float) ([]schema.Float, int64, int64, error) {\npkg/metricstore/buffer.go:317:func (b *buffer) free(t int64) (delme bool, n int) {\npkg/metricstore/buffer.go:346:func (b *buffer) forceFreeOldest() (delme bool, n int) {\npkg/metricstore/buffer.go:382:func (b *buffer) iterFromTo(from, to int64, callback func(b *buffer) error) error {\npkg/metricstore/buffer.go:404:func (b *buffer) count() int64 {\npkg/metricstore/stats.go:16:type Stats struct {\npkg/metricstore/stats.go:23:func (b *buffer) stats(from, to int64) (Stats, int64, int64, error) {\npkg/metricstore/stats.go:74:func (m *MemoryStore) Stats(selector util.Selector, metric string, from, to int64) (*Stats, int64, int64, error) {\npkg/metricstore/metricstore.go:42:type GlobalState struct {\npkg/metricstore/metricstore.go:48:var (\npkg/metricstore/metricstore.go:68:type NodeProvider interface {\npkg/metricstore/metricstore.go:82:type Metric struct {\npkg/metricstore/metricstore.go:98:type MemoryStore struct {\npkg/metricstore/metricstore.go:124:func Init(rawConfig json.RawMessage, metrics map[string]MetricConfig, wg *sync.WaitGroup) {\npkg/metricstore/metricstore.go:205:func InitMetrics(metrics map[string]MetricConfig) {\npkg/metricstore/metricstore.go:236:func GetMemoryStore() *MemoryStore {\npkg/metricstore/metricstore.go:244:func (ms *MemoryStore) GetMetricFrequency(metricName string) (int64, error) {\npkg/metricstore/metricstore.go:255:func (ms *MemoryStore) SetNodeProvider(provider NodeProvider) {\npkg/metricstore/metricstore.go:272:func Shutdown() {\npkg/metricstore/metricstore.go:320:func Retention(wg *sync.WaitGroup, ctx context.Context) {\npkg/metricstore/metricstore.go:383:func MemoryUsageTracker(wg *sync.WaitGroup, ctx context.Context) {\npkg/metricstore/metricstore.go:496:func Free(ms *MemoryStore, t time.Time) (int, error) {\npkg/metricstore/metricstore.go:535:func FreeSelected(ms *MemoryStore, selectors [][]string, t time.Time) (int, error) {\npkg/metricstore/metricstore.go:567:func GetSelectors(ms *MemoryStore, excludeSelectors map[string][]string) [][]string {\npkg/metricstore/metricstore.go:599:func (ms *MemoryStore) GetPaths(targetDepth int) [][]string {\npkg/metricstore/metricstore.go:611:func (m *MemoryStore) Write(selector []string, ts int64, metrics []Metric) error {\npkg/metricstore/metricstore.go:627:func (m *MemoryStore) GetLevel(selector []string) *Level {\npkg/metricstore/metricstore.go:632:func (m *MemoryStore) WriteToLevel(l *Level, selector []string, ts int64, metrics []Metric) error {\n\n[Showing results with pagination = limit: 100, offset: 0]"}]},"uuid":"eb024658-3932-4e3c-926e-770c42366522","timestamp":"2026-03-04T15:24:03.905Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"a9c86a3b0a5799ebb"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_01JPUhKVS2cG5n57jLbfbvoS","uuid":"95a00d57-ad21-4d0d-b771-0a98e7b42b1e","timestamp":"2026-03-04T15:24:03.906Z"}
{"parentUuid":"95a00d57-ad21-4d0d-b771-0a98e7b42b1e","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-04T15:24:04.522Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_0196qCLWce7RXbwhoiWUyWAD","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011YAkxwQFEVyqf6NwS98aK1","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/metricstore.go","limit":100},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":7,"cache_creation_input_tokens":18494,"cache_read_input_tokens":16332,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":18494},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYiGfnJpzcu7CNNrreFLv","uuid":"07ef30d6-dbaf-4a6f-a78b-d7a37809d400"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"ae13c6665fcb142fa"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_016dkeKh2MTmVpo34xzdvp12","uuid":"c36be212-f128-473d-a750-9ece64e57b40","timestamp":"2026-03-04T15:24:04.523Z"}
{"parentUuid":"c36be212-f128-473d-a750-9ece64e57b40","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011YAkxwQFEVyqf6NwS98aK1","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved. This file is part of cc-backend.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// Package metricstore provides an efficient in-memory time-series metric storage system\n     7→// with support for hierarchical data organization, checkpointing, and archiving.\n     8→//\n     9→// The package organizes metrics in a tree structure (cluster → host → component) and\n    10→// provides concurrent read/write access to metric data with configurable aggregation strategies.\n    11→// Background goroutines handle periodic checkpointing (JSON or Avro format), archiving old data,\n    12→// and enforcing retention policies.\n    13→//\n    14→// Key features:\n    15→//   - In-memory metric storage with configurable retention\n    16→//   - Hierarchical data organization (selectors)\n    17→//   - Concurrent checkpoint/archive workers\n    18→//   - Support for sum and average aggregation\n    19→//   - NATS integration for metric ingestion\n    20→package metricstore\n    21→\n    22→import (\n    23→\t\"bytes\"\n    24→\t\"context\"\n    25→\t\"encoding/json\"\n    26→\t\"errors\"\n    27→\t\"fmt\"\n    28→\t\"runtime\"\n    29→\t\"runtime/debug\"\n    30→\t\"slices\"\n    31→\t\"sync\"\n    32→\t\"time\"\n    33→\n    34→\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n    35→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    36→\t\"github.com/ClusterCockpit/cc-lib/v2/resampler\"\n    37→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    38→\t\"github.com/ClusterCockpit/cc-lib/v2/util\"\n    39→)\n    40→\n    41→// GlobalState holds the global state for the metric store with thread-safe access.\n    42→type GlobalState struct {\n    43→\tmu                sync.RWMutex\n    44→\tlastRetentionTime int64\n    45→\tselectorsExcluded bool\n    46→}\n    47→\n    48→var (\n    49→\tsingleton  sync.Once\n    50→\tmsInstance *MemoryStore\n    51→\t// shutdownFunc stores the context cancellation function created in Init\n    52→\t// and is called during Shutdown to cancel all background goroutines\n    53→\tshutdownFunc   context.CancelFunc\n    54→\tshutdownFuncMu sync.Mutex // Protects shutdownFunc from concurrent access\n    55→\t// Create a global instance\n    56→\tstate = &GlobalState{}\n    57→)\n    58→\n    59→// NodeProvider provides information about nodes currently in use by running jobs.\n    60→//\n    61→// This interface allows metricstore to query job information without directly\n    62→// depending on the repository package, breaking the import cycle.\n    63→//\n    64→// Implementations should return nodes that are actively processing jobs started\n    65→// before the given timestamp. These nodes will be excluded from retention-based\n    66→// garbage collection to prevent data loss for jobs that are still running or\n    67→// recently completed.\n    68→type NodeProvider interface {\n    69→\t// GetUsedNodes returns a map of cluster names to sorted lists of unique hostnames\n    70→\t// that are currently in use by jobs that started before the given timestamp.\n    71→\t//\n    72→\t// Parameters:\n    73→\t//   - ts: Unix timestamp threshold - returns nodes with jobs started before this time\n    74→\t//\n    75→\t// Returns:\n    76→\t//   - Map of cluster names to lists of node hostnames that should be excluded from garbage collection\n    77→\t//   - Error if the query fails\n    78→\tGetUsedNodes(ts int64) (map[string][]string, error)\n    79→}\n    80→\n    81→// Metric represents a single metric data point to be written to the store.\n    82→type Metric struct {\n    83→\tName  string\n    84→\tValue schema.Float\n    85→\t// MetricConfig contains frequency and aggregation settings for this metric.\n    86→\t// If Frequency is 0, configuration will be looked up from MemoryStore.Metrics during Write().\n    87→\tMetricConfig MetricConfig\n    88→}\n    89→\n    90→// MemoryStore is the main in-memory time-series metric storage implementation.\n    91→//\n    92→// It organizes metrics in a hierarchical tree structure where each level represents\n    93→// a component of the system hierarchy (e.g., cluster → host → CPU). Each level can\n    94→// store multiple metrics as time-series buffers.\n    95→//\n    96→// The store is initialized as a singleton via InitMetrics() and accessed via GetMemoryStore().\n    97→// All public methods are safe for concurrent use.\n    98→type MemoryStore struct {\n    99→\tMetrics      map[string]MetricConfig\n   100→\troot         Level"}]},"uuid":"514d3656-8640-44ec-acef-20ad4f320b90","timestamp":"2026-03-04T15:24:04.541Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"ae13c6665fcb142fa"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_016dkeKh2MTmVpo34xzdvp12","uuid":"8b34d46d-20b5-4b68-996a-5ae883024995","timestamp":"2026-03-04T15:24:04.542Z"}
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{"parentUuid":"38acb11a-0f8f-4fe3-8474-12005b2ec53a","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018cis4eNf2BhuZ3aqrVLQfg","type":"tool_result","content":"/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/healthcheck.go:17:// HealthCheckResponse represents the result of a health check operation.\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/healthcheck.go:18:type HealthCheckResponse struct {\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/healthcheck.go:23:// HealthCheckResult holds the monitoring state and raw JSON health metrics\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/healthcheck.go:24:// for a single node as determined by HealthCheck.\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/healthcheck.go:25:type HealthCheckResult struct {\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/healthcheck.go:136:type HealthCheckReq struct {\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/healthcheck.go:142:// HealthCheck evaluates multiple nodes against a set of expected metrics\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/healthcheck.go:149:func (m *MemoryStore) HealthCheck(cluster string,\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/healthcheck.go:151:) (map[string]HealthCheckResult, error) {\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/healthcheck.go:152:\tresults := make(map[string]HealthCheckResult, len(nodes))\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/healthcheck.go:159:\t\t\tresults[hostname] = HealthCheckResult{State: schema.MonitoringStateFailed}\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/healthcheck.go:169:\t\t\tcclog.ComponentDebug(\"metricstore\", \"HealthCheck: node \", hostname, \"degraded metrics:\", degradedList)\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/healthcheck.go:172:\t\t\tcclog.ComponentDebug(\"metricstore\", \"HealthCheck: node \", hostname, \"missing metrics:\", missingList)\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/healthcheck.go:190:\t\tresults[hostname] = HealthCheckResult{\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/metricstore_test.go:596:func TestHealthCheck(t *testing.T) {\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/metricstore_test.go:738:\t\t\tresults, err := ms.HealthCheck(tt.cluster, tt.nodes, tt.expectedMetrics)\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/metricstore_test.go:740:\t\t\t\tt.Errorf(\"HealthCheck() error = %v\", err)\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/metricstore_test.go:746:\t\t\t\tt.Errorf(\"HealthCheck() returned %d results, want %d\", len(results), len(tt.nodes))\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/metricstore_test.go:753:\t\t\t\t\tt.Errorf(\"HealthCheck() missing result for node %s\", node)\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/metricstore_test.go:760:\t\t\t\t\t\tt.Errorf(\"HealthCheck() node %s status = %v, want %v\", node, state.State, wantStatus)\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/metricstore_test.go:877:// TestBufferHealthChecks tests the buffer-level health check functions\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/metricstore_test.go:878:func TestBufferHealthChecks(t *testing.T) {\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/query.go:45:// HealthCheck delegates to the internal MemoryStore's HealthCheck.\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/query.go:46:func (ccms *InternalMetricStore) HealthCheck(cluster string,\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/query.go:48:) (map[string]HealthCheckResult, error) {\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/query.go:49:\treturn GetMemoryStore().HealthCheck(cluster, nodes, metrics)","is_error":false}]},"uuid":"3fdee468-360d-4598-a5da-3a8e8a784107","timestamp":"2026-03-04T15:24:04.605Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"ae13c6665fcb142fa"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_016dkeKh2MTmVpo34xzdvp12","uuid":"050cfd46-1e38-42b2-9b10-8e1fc4f8a1af","timestamp":"2026-03-04T15:24:04.605Z"}
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{"parentUuid":"aef10bb2-513b-4897-b50d-e4f0a56bc418","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Tpv5NQGj3XaMoAoGqabqVR","type":"tool_result","content":"<persisted-output>\nOutput too large (50.6KB). Full output saved to: /Users/jan/.claude/projects/-Users-jan-prg-ClusterCockpit-cc-backend/eec1fc87-d8f6-42f6-b296-d7a2f79af002/tool-results/toolu_01Tpv5NQGj3XaMoAoGqabqVR.txt\n\nPreview (first 2KB):\n     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved. This file is part of cc-backend.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// This file implements high-level query functions for loading job metric data\n     7→// with automatic scope transformation and aggregation.\n     8→//\n     9→// Key Concepts:\n    10→//\n    11→// Metric Scopes: Metrics are collected at different granularities (native scope):\n    12→//   - HWThread: Per hardware thread\n    13→//   - Core: Per CPU core\n    14→//   - Socket: Per CPU socket\n    15→//   - MemoryDomain: Per memory domain (NUMA)\n    16→//   - Accelerator: Per GPU/accelerator\n    17→//   - Node: Per compute node\n    18→//\n    19→// Scope Transformation: The buildQueries functions transform between native scope\n    20→// and requested scope by:\n    21→//   - Aggregating finer-grained data (e.g., HWThread → Core → Socket → Node)\n    22→//   - Rejecting requests for finer granularity than available\n    23→//   - Handling special cases (e.g., Accelerator metrics)\n    24→//\n    25→// Query Building: Constructs APIQuery structures with proper selectors (Type, TypeIds)\n    26→// based on cluster topology and job resources.\n    27→package metricstore\n    28→\n    29→import (\n    30→\t\"context\"\n    31→\t\"fmt\"\n    32→\t\"strconv\"\n    33→\t\"strings\"\n    34→\t\"time\"\n    35→\n    36→\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n    37→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    38→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    39→)\n    40→\n    41→type InternalMetricStore struct{}\n    42→\n    43→var MetricStoreHandle *InternalMetricStore\n    44→\n    45→// HealthCheck delegates to the internal MemoryStore's HealthCheck.\n    46→func (ccms *InternalMetricStore) HealthCheck(cluster string,\n    47→\tnodes []string, metrics []string,\n    48→) (map[string]HealthCheckResult, error) {\n...\n</persisted-output>"}]},"uuid":"67abe262-fd03-4102-8dd5-3d48a8c53f7c","timestamp":"2026-03-04T15:24:06.310Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"ae13c6665fcb142fa"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_016dkeKh2MTmVpo34xzdvp12","uuid":"b2e05a48-5c17-4b9a-8717-df030ef6c5cf","timestamp":"2026-03-04T15:24:06.312Z"}
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{"parentUuid":"ac5c828b-b7fc-4edf-913e-7060f220b133","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Gh9m1uMTQAqsdYXPKM2hYb","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved. This file is part of cc-backend.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// This file implements high-level query functions for loading job metric data\n     7→// with automatic scope transformation and aggregation.\n     8→//\n     9→// Key Concepts:\n    10→//\n    11→// Metric Scopes: Metrics are collected at different granularities (native scope):\n    12→//   - HWThread: Per hardware thread\n    13→//   - Core: Per CPU core\n    14→//   - Socket: Per CPU socket\n    15→//   - MemoryDomain: Per memory domain (NUMA)\n    16→//   - Accelerator: Per GPU/accelerator\n    17→//   - Node: Per compute node\n    18→//\n    19→// Scope Transformation: The buildQueries functions transform between native scope\n    20→// and requested scope by:\n    21→//   - Aggregating finer-grained data (e.g., HWThread → Core → Socket → Node)\n    22→//   - Rejecting requests for finer granularity than available\n    23→//   - Handling special cases (e.g., Accelerator metrics)\n    24→//\n    25→// Query Building: Constructs APIQuery structures with proper selectors (Type, TypeIds)\n    26→// based on cluster topology and job resources.\n    27→package metricstore\n    28→\n    29→import (\n    30→\t\"context\"\n    31→\t\"fmt\"\n    32→\t\"strconv\"\n    33→\t\"strings\"\n    34→\t\"time\"\n    35→\n    36→\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n    37→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    38→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    39→)\n    40→\n    41→type InternalMetricStore struct{}\n    42→\n    43→var MetricStoreHandle *InternalMetricStore\n    44→\n    45→// HealthCheck delegates to the internal MemoryStore's HealthCheck.\n    46→func (ccms *InternalMetricStore) HealthCheck(cluster string,\n    47→\tnodes []string, metrics []string,\n    48→) (map[string]HealthCheckResult, error) {\n    49→\treturn GetMemoryStore().HealthCheck(cluster, nodes, metrics)\n    50→}\n    51→\n    52→// TestLoadDataCallback allows tests to override LoadData behavior for testing purposes.\n    53→// When set to a non-nil function, LoadData will call this function instead of the default implementation.\n    54→var TestLoadDataCallback func(job *schema.Job, metrics []string, scopes []schema.MetricScope, ctx context.Context, resolution int) (schema.JobData, error)\n    55→\n    56→// LoadData loads metric data for a specific job with automatic scope transformation.\n    57→//\n    58→// This is the primary function for retrieving job metric data. It handles:\n    59→//   - Building queries with scope transformation via buildQueries\n    60→//   - Fetching data from the metric store\n    61→//   - Organizing results by metric and scope\n    62→//   - Converting NaN statistics to 0 for JSON compatibility\n    63→//   - Partial error handling (returns data for successful queries even if some fail)\n    64→//\n    65→// Parameters:\n    66→//   - job: Job metadata including cluster, resources, and time range\n    67→//   - metrics: List of metric names to load\n    68→//   - scopes: Requested metric scopes (will be transformed to match native scopes)\n    69→//   - ctx: Context for cancellation (currently unused but reserved for future use)\n    70→//   - resolution: Data resolution in seconds (0 for native resolution)\n    71→//\n    72→// Returns:\n    73→//   - JobData: Map of metric → scope → JobMetric with time-series data and statistics\n    74→//   - Error: Returns error if query building or fetching fails, or partial error listing failed hosts\n    75→//\n    76→// Example:\n    77→//\n    78→//\tjobData, err := LoadData(job, []string{\"cpu_load\", \"mem_used\"}, []schema.MetricScope{schema.MetricScopeNode}, ctx, 60)\n    79→func (ccms *InternalMetricStore) LoadData(\n    80→\tjob *schema.Job,\n    81→\tmetrics []string,\n    82→\tscopes []schema.MetricScope,\n    83→\tctx context.Context,\n    84→\tresolution int,\n    85→) (schema.JobData, error) {\n    86→\tif TestLoadDataCallback != nil {\n    87→\t\treturn TestLoadDataCallback(job, metrics, scopes, ctx, resolution)\n    88→\t}\n    89→\n    90→\tqueries, assignedScope, err := buildQueries(job, metrics, scopes, int64(resolution))\n    91→\tif err != nil {\n    92→\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n    93→\t\treturn nil, err\n    94→\t}\n    95→\n    96→\t// Verify assignment is correct - log any inconsistencies for debugging\n    97→\tif len(queries) != len(assignedScope) {\n    98→\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildQueries: %d vs %d\",\n    99→\t\t\tlen(queries), len(assignedScope))\n   100→\t}\n   101→\n   102→\treq := APIQueryRequest{\n   103→\t\tCluster:   job.Cluster,\n   104→\t\tFrom:      job.StartTime,\n   105→\t\tTo:        job.StartTime + int64(job.Duration),\n   106→\t\tQueries:   queries,\n   107→\t\tWithStats: true,\n   108→\t\tWithData:  true,\n   109→\t}\n   110→\n   111→\tresBody, err := FetchData(req)\n   112→\tif err != nil {\n   113→\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n   114→\t\treturn nil, err\n   115→\t}\n   116→\n   117→\tvar errors []string\n   118→\tjobData := make(schema.JobData)\n   119→\n   120→\t// Add safety check for potential index out of range errors\n   121→\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n   122→\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n   123→\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n   124→\t\tif len(resBody.Results) > len(req.Queries) {\n   125→\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n   126→\t\t}\n   127→\t\tif len(assignedScope) > len(req.Queries) {\n   128→\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n   129→\t\t}\n   130→\t}\n   131→\n   132→\tfor i, row := range resBody.Results {\n   133→\t\t// Safety check to prevent index out of range errors\n   134→\t\tif i >= len(req.Queries) || i >= len(assignedScope) {\n   135→\t\t\tcclog.Warnf(\"Index out of range prevented: i=%d, queries=%d, assignedScope=%d\",\n   136→\t\t\t\ti, len(req.Queries), len(assignedScope))\n   137→\t\t\tcontinue\n   138→\t\t}\n   139→\n   140→\t\tquery := req.Queries[i]\n   141→\t\tmetric := query.Metric\n   142→\t\tscope := assignedScope[i]\n   143→\t\tmc := archive.GetMetricConfig(job.Cluster, metric)\n   144→\n   145→\t\tif mc == nil {\n   146→\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, job.Cluster)\n   147→\t\t\tcontinue\n   148→\t\t}\n   149→\n   150→\t\tif _, ok := jobData[metric]; !ok {"}]},"uuid":"8f0edff7-cf7e-42a5-87df-1577fbffbeb4","timestamp":"2026-03-04T15:24:09.800Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"ae13c6665fcb142fa"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_016dkeKh2MTmVpo34xzdvp12","uuid":"1a52e77f-409e-4fc8-b1c8-7408d8131cd6","timestamp":"2026-03-04T15:24:10.086Z"}
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{"parentUuid":"7cb7ff79-4396-4c9b-9dd4-a662d978e0d8","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01X7DhoTFRhWWma168B4MYSc","type":"tool_result","content":"   320→\tmscfg := ccconf.GetPackageConfig(\"metric-store\")\n   321→\tif mscfg != nil {\n   322→\t\tmetrics := metricstore.BuildMetricList()\n   323→\t\tmetricstore.Init(mscfg, metrics, &wg)\n   324→\n   325→\t\t// Inject repository as NodeProvider to break import cycle\n   326→\t\tms := metricstore.GetMemoryStore()\n   327→\t\tjobRepo := repository.GetJobRepository()\n   328→\t\tms.SetNodeProvider(jobRepo)\n   329→\t\tmetricstore.MetricStoreHandle = &metricstore.InternalMetricStore{}\n   330→\t\thaveMetricstore = true\n   331→\t} else {\n   332→\t\tmetricstore.MetricStoreHandle = nil\n   333→\t\tcclog.Debug(\"missing internal metricstore configuration\")\n   334→\t}\n   335→\n   336→\t// Initialize external metric stores if configuration is provided\n   337→\tmscfg = ccconf.GetPackageConfig(\"metric-store-external\")\n   338→\tif mscfg != nil {\n   339→\t\terr := metricdispatch.Init(mscfg)\n   340→\n   341→\t\tif err != nil {\n   342→\t\t\tcclog.Debugf(\"error while initializing external metricdispatch: %v\", err)\n   343→\t\t} else {\n   344→\t\t\thaveMetricstore = true\n   345→\t\t}\n   346→\t}\n   347→\n   348→\tif !haveMetricstore {\n   349→\t\treturn fmt.Errorf(\"missing metricstore configuration\")\n   350→\t}\n   351→\n   352→\t// Start archiver and task manager\n   353→\tarchiver.Start(repository.GetJobRepository(), ctx)\n   354→\ttaskmanager.Start(ccconf.GetPackageConfig(\"cron\"), ccconf.GetPackageConfig(\"archive\"))\n   355→\n   356→\t// Initialize web UI\n   357→\tcfg := ccconf.GetPackageConfig(\"ui\")\n   358→\tif err := web.Init(cfg); err != nil {\n   359→\t\treturn fmt.Errorf(\"initializing web UI: %w\", err)\n   360→\t}\n   361→\n   362→\t// Initialize HTTP server\n   363→\tsrv, err := NewServer(version, commit, date)\n   364→\tif err != nil {\n   365→\t\treturn fmt.Errorf(\"creating server: %w\", err)\n   366→\t}\n   367→\n   368→\t// Channel to collect errors from server\n   369→\terrChan := make(chan error, 1)"}]},"uuid":"e01eee03-c11c-46ed-80db-10f81ac3feee","timestamp":"2026-03-04T15:24:14.504Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"ae13c6665fcb142fa"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_016dkeKh2MTmVpo34xzdvp12","uuid":"f10b183d-7147-4064-b53a-989b6bb15da1","timestamp":"2026-03-04T15:24:14.719Z"}
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{"parentUuid":"a5aff984-d086-470e-a6a9-15accf840693","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FtBfz7dh3oBZ2LSNKG7cZF","type":"tool_result","content":"Found 2 files\ninternal/metricstoreclient/cc-metric-store.go\npkg/metricstore/api.go"}]},"uuid":"b6e603ac-c115-41f5-ad61-e5e858f105c2","timestamp":"2026-03-04T15:24:15.198Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"ae13c6665fcb142fa"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_016dkeKh2MTmVpo34xzdvp12","uuid":"794960b0-51fb-43a8-b85c-8ab8ac11c781","timestamp":"2026-03-04T15:24:15.199Z"}
{"parentUuid":"794960b0-51fb-43a8-b85c-8ab8ac11c781","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-04T15:24:17.133Z","message":{"model":"claude-haiku-4-5-20251001","id":"msg_01FGszNWtbah1Bkua9Pdz8F9","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XhpCVBmyWD3V3dhTRL5Neb","name":"Bash","input":{"command":"grep -A 5 \"type APIQuery struct\" /Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/api.go /Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":5,"cache_creation_input_tokens":1022,"cache_read_input_tokens":42464,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1022},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYiGgmMogD5LnyXVyrNRK","uuid":"281becb4-c851-45ed-944e-8df48e5ae2c7"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"ae13c6665fcb142fa"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_016dkeKh2MTmVpo34xzdvp12","uuid":"57a2a98f-e655-4973-bc22-1b71526f2d88","timestamp":"2026-03-04T15:24:17.133Z"}
{"parentUuid":"57a2a98f-e655-4973-bc22-1b71526f2d88","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XhpCVBmyWD3V3dhTRL5Neb","type":"tool_result","content":"/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/api.go:type APIQuery struct {\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/api.go-\tType        *string      `json:\"type,omitempty\"`\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/api.go-\tSubType     *string      `json:\"subtype,omitempty\"`\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/api.go-\tMetric      string       `json:\"metric\"`\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/api.go-\tHostname    string       `json:\"host\"`\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/api.go-\tResolution  int64        `json:\"resolution\"`\n--\n/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go:type APIQuery struct {\n/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go-\tType       *string  `json:\"type,omitempty\"`        // Scope type (e.g., \"core\", \"socket\")\n/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go-\tSubType    *string  `json:\"subtype,omitempty\"`     // Sub-scope type (reserved for future use)\n/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go-\tMetric     string   `json:\"metric\"`                // Metric name\n/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go-\tHostname   string   `json:\"host\"`                  // Target hostname\n/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go-\tResolution int      `json:\"resolution\"`            // Data resolution in seconds (0 = native)","is_error":false}]},"uuid":"c1d46537-7d55-4143-b3ec-0ff3a33a53bd","timestamp":"2026-03-04T15:24:17.158Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"ae13c6665fcb142fa"},"toolUseID":"agent_msg_01T2iXfJxMcDpFuKKEChgtvZ","parentToolUseID":"toolu_016dkeKh2MTmVpo34xzdvp12","uuid":"e1ee102a-32ec-4bf2-ad70-db7865c3d7f2","timestamp":"2026-03-04T15:24:17.159Z"}
{"parentUuid":"e1ee102a-32ec-4bf2-ad70-db7865c3d7f2","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Agent","command":"entire hooks claude-code post-task"},"parentToolUseID":"toolu_016dkeKh2MTmVpo34xzdvp12","toolUseID":"toolu_016dkeKh2MTmVpo34xzdvp12","timestamp":"2026-03-04T15:24:33.068Z","uuid":"3c8c3166-69de-42ea-b857-01f541923292"}
{"parentUuid":"d26acb8a-07a8-46fe-8f14-b4fc2e9aff3d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016dkeKh2MTmVpo34xzdvp12","type":"tool_result","content":[{"type":"text","text":"Perfect! Now I have all the information needed. Let me provide a comprehensive report.\n\n## Summary: Shared Interface & Implementation Pattern\n\nI've found the complete picture of how the two metric store implementations work together. Here are the key findings:\n\n### 1. **Shared Interface: `MetricDataRepository`**\n\n**Location:** `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricdispatch/metricdata.go` (lines 21-60)\n\nThis is the central interface that both implementations must satisfy:\n\n```go\ntype MetricDataRepository interface {\n\t// Load job metric data with specified metrics and scopes\n\tLoadData(job *schema.Job,\n\t\tmetrics []string,\n\t\tscopes []schema.MetricScope,\n\t\tctx context.Context,\n\t\tresolution int) (schema.JobData, error)\n\n\t// Load node-scope statistics only (faster than LoadData)\n\tLoadStats(job *schema.Job,\n\t\tmetrics []string,\n\t\tctx context.Context) (map[string]map[string]schema.MetricStatistics, error)\n\n\t// Load statistics across multiple scopes\n\tLoadScopedStats(job *schema.Job,\n\t\tmetrics []string,\n\t\tscopes []schema.MetricScope,\n\t\tctx context.Context) (schema.ScopedJobStats, error)\n\n\t// Load current metric data for specified nodes (systems view)\n\tLoadNodeData(cluster string,\n\t\tmetrics, nodes []string,\n\t\tscopes []schema.MetricScope,\n\t\tfrom, to time.Time,\n\t\tctx context.Context) (map[string]map[string][]*schema.JobMetric, error)\n\n\t// Load paginated node list data with scope organization\n\tLoadNodeListData(cluster, subCluster string,\n\t\tnodes []string,\n\t\tmetrics []string,\n\t\tscopes []schema.MetricScope,\n\t\tresolution int,\n\t\tfrom, to time.Time,\n\t\tctx context.Context) (map[string]schema.JobData, error)\n\n\t// Health check for cluster monitoring state\n\tHealthCheck(cluster string,\n\t\tnodes []string,\n\t\tmetrics []string) (map[string]metricstore.HealthCheckResult, error)\n}\n```\n\n### 2. **Implementation 1: Internal Metric Store**\n\n**Location:** `/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/query.go` (lines 41-50)\n\n```go\ntype InternalMetricStore struct{}\n\nvar MetricStoreHandle *InternalMetricStore\n\n// Implements all MetricDataRepository methods\nfunc (ccms *InternalMetricStore) LoadData(...) (schema.JobData, error)\nfunc (ccms *InternalMetricStore) LoadStats(...) (map[string]map[string]schema.MetricStatistics, error)\n// ... and others\n```\n\n**Key file:** `/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/query.go` contains all method implementations for `InternalMetricStore`\n\n### 3. **Implementation 2: External CC-Metric-Store Client**\n\n**Location:** `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go` (lines 71-79)\n\n```go\ntype CCMetricStore struct {\n\tclient        http.Client\n\tjwt           string\n\turl           string\n\tqueryEndpoint string\n\ttopologyCache map[string]*schema.Topology\n}\n\n// Implements all MetricDataRepository methods\nfunc (ccms *CCMetricStore) LoadData(...) (schema.JobData, error)\nfunc (ccms *CCMetricStore) LoadStats(...) (map[string]map[string]schema.MetricStatistics, error)\n// ... and others\n```\n\n### 4. **Shared Types - `APIQuery` & `APIMetricData`**\n\nBoth packages define their **own copies** of these types (NOT shared):\n\n**In `pkg/metricstore/api.go` (lines 74-93):**\n```go\ntype APIQuery struct {\n\tType       *string      `json:\"type,omitempty\"`\n\tSubType    *string      `json:\"subtype,omitempty\"`\n\tMetric     string       `json:\"metric\"`\n\tHostname   string       `json:\"host\"`\n\tResolution int64        `json:\"resolution\"`\n\tTypeIds    []string     `json:\"type-ids,omitempty\"`\n\tSubTypeIds []string     `json:\"subtype-ids,omitempty\"`\n\tScaleFactor schema.Float `json:\"scale-by,omitempty\"`\n\tAggregate  bool         `json:\"aggreg\"`\n}\n```\n\n**In `internal/metricstoreclient/cc-metric-store.go` (lines 93-104):**\n```go\ntype APIQuery struct {\n\tType       *string  `json:\"type,omitempty\"`\n\tSubType    *string  `json:\"subtype,omitempty\"`\n\tMetric     string   `json:\"metric\"`\n\tHostname   string   `json:\"host\"`\n\tResolution int      `json:\"resolution\"`\n\tTypeIds    []string `json:\"type-ids,omitempty\"`\n\tSubTypeIds []string `json:\"subtype-ids,omitempty\"`\n\tAggregate  bool     `json:\"aggreg\"`\n}\n```\n\n**Note:** These are separate definitions (not shared) - they're almost identical but used independently by each package.\n\n### 5. **Registration/Initialization**\n\n**Location:** `/Users/jan/prg/ClusterCockpit/cc-backend/cmd/cc-backend/main.go` (lines 320-346)\n\n```go\n// Initialize internal metric store\nmscfg := ccconf.GetPackageConfig(\"metric-store\")\nif mscfg != nil {\n\tmetricstore.Init(mscfg, metrics, &wg)\n\tms := metricstore.GetMemoryStore()\n\tjobRepo := repository.GetJobRepository()\n\tms.SetNodeProvider(jobRepo)\n\tmetricstore.MetricStoreHandle = &metricstore.InternalMetricStore{}\n\thaveMetricstore = true\n}\n\n// Initialize external metric store clients\nmscfg = ccconf.GetPackageConfig(\"metric-store-external\")\nif mscfg != nil {\n\terr := metricdispatch.Init(mscfg)\n\t// ...\n}\n```\n\n### 6. **Runtime Selection via `metricdispatch`**\n\n**Location:** `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricdispatch/metricdata.go` (lines 92-117)\n\nThe `GetMetricDataRepo()` function returns the appropriate implementation based on cluster/subcluster configuration:\n\n```go\nvar metricDataRepos map[string]MetricDataRepository = map[string]MetricDataRepository{}\n\nfunc GetMetricDataRepo(cluster string, subcluster string) (MetricDataRepository, error) {\n\t// Fallback chain:\n\t// 1. Try cluster + subcluster config\n\t// 2. Try cluster config only\n\t// 3. Try wildcard config\n\t// 4. Fall back to internal metric store (if available)\n\t// 5. Return error if none found\n\t\n\tif !ok && metricstore.MetricStoreHandle == nil {\n\t\treturn nil, fmt.Errorf(\"[METRICDISPATCH]> no metric data repository configured\")\n\t}\n\trepo = metricstore.MetricStoreHandle // Use internal as fallback\n}\n```\n\n### Key Architecture Points\n\n1. **Single Interface, Multiple Implementations:** The `MetricDataRepository` interface in `metricdispatch` is the bridge\n2. **Independent Type Definitions:** Each package defines its own `APIQuery` type for internal use\n3. **Dispatcher Pattern:** `internal/metricdispatch` acts as a registry and selector\n4. **Fallback to Internal:** If no external metric store is configured, falls back to `MetricStoreHandle`\n5. **No Shared Types Package:** There's no common `types.go` file - each package is self-contained and defines what it needs\n\n**Files involved:**\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricdispatch/metricdata.go` — Interface definition and registry\n- `/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/query.go` — Internal implementation\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go` — External client implementation\n- `/Users/jan/prg/ClusterCockpit/cc-backend/cmd/cc-backend/main.go` — Initialization"},{"type":"text","text":"agentId: ae13c6665fcb142fa (for resuming to continue this agent's work if needed)\n<usage>total_tokens: 46619\ntool_uses: 21\nduration_ms: 38792</usage>"}]}]},"uuid":"59b93734-24f6-4e65-b55a-b6ab85e03e83","timestamp":"2026-03-04T15:24:33.066Z","toolUseResult":{"status":"completed","prompt":"Find the MetricDataRepository interface or similar interface that both pkg/metricstore and internal/metricstoreclient implement. Search for:\n1. Any shared interface definition (likely in pkg/ or internal/)\n2. How the two metric store implementations are registered/used\n3. Any shared types between the two packages (APIQuery, etc.)\n4. Check if there's a common types file or if each package defines its own\n\nSearch in pkg/metricstore/, internal/metricstoreclient/, and any relevant interface definition files. Also check if there's a metricdata or similar package.","agentId":"ae13c6665fcb142fa","content":[{"type":"text","text":"Perfect! Now I have all the information needed. Let me provide a comprehensive report.\n\n## Summary: Shared Interface & Implementation Pattern\n\nI've found the complete picture of how the two metric store implementations work together. Here are the key findings:\n\n### 1. **Shared Interface: `MetricDataRepository`**\n\n**Location:** `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricdispatch/metricdata.go` (lines 21-60)\n\nThis is the central interface that both implementations must satisfy:\n\n```go\ntype MetricDataRepository interface {\n\t// Load job metric data with specified metrics and scopes\n\tLoadData(job *schema.Job,\n\t\tmetrics []string,\n\t\tscopes []schema.MetricScope,\n\t\tctx context.Context,\n\t\tresolution int) (schema.JobData, error)\n\n\t// Load node-scope statistics only (faster than LoadData)\n\tLoadStats(job *schema.Job,\n\t\tmetrics []string,\n\t\tctx context.Context) (map[string]map[string]schema.MetricStatistics, error)\n\n\t// Load statistics across multiple scopes\n\tLoadScopedStats(job *schema.Job,\n\t\tmetrics []string,\n\t\tscopes []schema.MetricScope,\n\t\tctx context.Context) (schema.ScopedJobStats, error)\n\n\t// Load current metric data for specified nodes (systems view)\n\tLoadNodeData(cluster string,\n\t\tmetrics, nodes []string,\n\t\tscopes []schema.MetricScope,\n\t\tfrom, to time.Time,\n\t\tctx context.Context) (map[string]map[string][]*schema.JobMetric, error)\n\n\t// Load paginated node list data with scope organization\n\tLoadNodeListData(cluster, subCluster string,\n\t\tnodes []string,\n\t\tmetrics []string,\n\t\tscopes []schema.MetricScope,\n\t\tresolution int,\n\t\tfrom, to time.Time,\n\t\tctx context.Context) (map[string]schema.JobData, error)\n\n\t// Health check for cluster monitoring state\n\tHealthCheck(cluster string,\n\t\tnodes []string,\n\t\tmetrics []string) (map[string]metricstore.HealthCheckResult, error)\n}\n```\n\n### 2. **Implementation 1: Internal Metric Store**\n\n**Location:** `/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/query.go` (lines 41-50)\n\n```go\ntype InternalMetricStore struct{}\n\nvar MetricStoreHandle *InternalMetricStore\n\n// Implements all MetricDataRepository methods\nfunc (ccms *InternalMetricStore) LoadData(...) (schema.JobData, error)\nfunc (ccms *InternalMetricStore) LoadStats(...) (map[string]map[string]schema.MetricStatistics, error)\n// ... and others\n```\n\n**Key file:** `/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/query.go` contains all method implementations for `InternalMetricStore`\n\n### 3. **Implementation 2: External CC-Metric-Store Client**\n\n**Location:** `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go` (lines 71-79)\n\n```go\ntype CCMetricStore struct {\n\tclient        http.Client\n\tjwt           string\n\turl           string\n\tqueryEndpoint string\n\ttopologyCache map[string]*schema.Topology\n}\n\n// Implements all MetricDataRepository methods\nfunc (ccms *CCMetricStore) LoadData(...) (schema.JobData, error)\nfunc (ccms *CCMetricStore) LoadStats(...) (map[string]map[string]schema.MetricStatistics, error)\n// ... and others\n```\n\n### 4. **Shared Types - `APIQuery` & `APIMetricData`**\n\nBoth packages define their **own copies** of these types (NOT shared):\n\n**In `pkg/metricstore/api.go` (lines 74-93):**\n```go\ntype APIQuery struct {\n\tType       *string      `json:\"type,omitempty\"`\n\tSubType    *string      `json:\"subtype,omitempty\"`\n\tMetric     string       `json:\"metric\"`\n\tHostname   string       `json:\"host\"`\n\tResolution int64        `json:\"resolution\"`\n\tTypeIds    []string     `json:\"type-ids,omitempty\"`\n\tSubTypeIds []string     `json:\"subtype-ids,omitempty\"`\n\tScaleFactor schema.Float `json:\"scale-by,omitempty\"`\n\tAggregate  bool         `json:\"aggreg\"`\n}\n```\n\n**In `internal/metricstoreclient/cc-metric-store.go` (lines 93-104):**\n```go\ntype APIQuery struct {\n\tType       *string  `json:\"type,omitempty\"`\n\tSubType    *string  `json:\"subtype,omitempty\"`\n\tMetric     string   `json:\"metric\"`\n\tHostname   string   `json:\"host\"`\n\tResolution int      `json:\"resolution\"`\n\tTypeIds    []string `json:\"type-ids,omitempty\"`\n\tSubTypeIds []string `json:\"subtype-ids,omitempty\"`\n\tAggregate  bool     `json:\"aggreg\"`\n}\n```\n\n**Note:** These are separate definitions (not shared) - they're almost identical but used independently by each package.\n\n### 5. **Registration/Initialization**\n\n**Location:** `/Users/jan/prg/ClusterCockpit/cc-backend/cmd/cc-backend/main.go` (lines 320-346)\n\n```go\n// Initialize internal metric store\nmscfg := ccconf.GetPackageConfig(\"metric-store\")\nif mscfg != nil {\n\tmetricstore.Init(mscfg, metrics, &wg)\n\tms := metricstore.GetMemoryStore()\n\tjobRepo := repository.GetJobRepository()\n\tms.SetNodeProvider(jobRepo)\n\tmetricstore.MetricStoreHandle = &metricstore.InternalMetricStore{}\n\thaveMetricstore = true\n}\n\n// Initialize external metric store clients\nmscfg = ccconf.GetPackageConfig(\"metric-store-external\")\nif mscfg != nil {\n\terr := metricdispatch.Init(mscfg)\n\t// ...\n}\n```\n\n### 6. **Runtime Selection via `metricdispatch`**\n\n**Location:** `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricdispatch/metricdata.go` (lines 92-117)\n\nThe `GetMetricDataRepo()` function returns the appropriate implementation based on cluster/subcluster configuration:\n\n```go\nvar metricDataRepos map[string]MetricDataRepository = map[string]MetricDataRepository{}\n\nfunc GetMetricDataRepo(cluster string, subcluster string) (MetricDataRepository, error) {\n\t// Fallback chain:\n\t// 1. Try cluster + subcluster config\n\t// 2. Try cluster config only\n\t// 3. Try wildcard config\n\t// 4. Fall back to internal metric store (if available)\n\t// 5. Return error if none found\n\t\n\tif !ok && metricstore.MetricStoreHandle == nil {\n\t\treturn nil, fmt.Errorf(\"[METRICDISPATCH]> no metric data repository configured\")\n\t}\n\trepo = metricstore.MetricStoreHandle // Use internal as fallback\n}\n```\n\n### Key Architecture Points\n\n1. **Single Interface, Multiple Implementations:** The `MetricDataRepository` interface in `metricdispatch` is the bridge\n2. **Independent Type Definitions:** Each package defines its own `APIQuery` type for internal use\n3. **Dispatcher Pattern:** `internal/metricdispatch` acts as a registry and selector\n4. **Fallback to Internal:** If no external metric store is configured, falls back to `MetricStoreHandle`\n5. **No Shared Types Package:** There's no common `types.go` file - each package is self-contained and defines what it needs\n\n**Files involved:**\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricdispatch/metricdata.go` — Interface definition and registry\n- `/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/query.go` — Internal implementation\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go` — External client implementation\n- `/Users/jan/prg/ClusterCockpit/cc-backend/cmd/cc-backend/main.go` — Initialization"}],"totalDurationMs":38792,"totalTokens":46619,"totalToolUseCount":21,"usage":{"input_tokens":3,"cache_creation_input_tokens":1024,"cache_read_input_tokens":43486,"output_tokens":2106,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1024,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"sourceToolAssistantUUID":"d26acb8a-07a8-46fe-8f14-b4fc2e9aff3d"}
{"parentUuid":"59b93734-24f6-4e65-b55a-b6ab85e03e83","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"type":"hook_progress","hookEvent":"PostToolUse","hookName":"PostToolUse:Agent","command":"entire hooks claude-code post-task"},"parentToolUseID":"toolu_01An2GQTxJmdB2UZE56PWWKf","toolUseID":"toolu_01An2GQTxJmdB2UZE56PWWKf","timestamp":"2026-03-04T15:24:37.771Z","uuid":"a480ef03-2cd8-4395-b23b-46a02f213869"}
{"parentUuid":"87dce091-59a3-440c-aa2e-2f4904c0ff79","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01An2GQTxJmdB2UZE56PWWKf","type":"tool_result","content":[{"type":"text","text":"Excellent! Now I have a comprehensive understanding of the package. Let me create a detailed summary report.\n\n## Comprehensive Exploration Report: `internal/metricstoreclient/` Package\n\n### Overview\n\nThe `internal/metricstoreclient/` package is a **client library for querying cc-metric-store**, an external high-performance time series database optimized for HPC metric data. It mirrors the functionality of `pkg/metricstore/query.go` but communicates with an external cc-metric-store service via HTTP instead of using an in-memory store.\n\n**Files in package:**\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go` (28 KB) — Main client operations\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store-queries.go` (16 KB) — Query building logic\n\n---\n\n### 1. Core Type: `CCMetricStore`\n\n**Location:** `cc-metric-store.go`, lines 71-79\n\n```go\ntype CCMetricStore struct {\n    client        http.Client                 // HTTP client with 10-second timeout\n    jwt           string                      // JWT Bearer token for authentication\n    url           string                      // Base URL of cc-metric-store instance\n    queryEndpoint string                      // Full URL to query API endpoint\n    topologyCache map[string]*schema.Topology // cluster -> topology cache\n}\n```\n\n**Purpose:** HTTP client for communicating with an external cc-metric-store service. Manages connection details, authentication (JWT), and caching of cluster topology information.\n\n**Constructor:**\n```go\nfunc NewCCMetricStore(url string, token string) *CCMetricStore\n```\n- Creates client with 10-second timeout\n- Initializes empty topology cache\n- URL should include protocol and port (e.g., \"http://localhost:8080\")\n- Token is JWT for Bearer authentication; empty string disables auth\n\n---\n\n### 2. API Request/Response Types\n\n#### `APIQueryRequest` (lines 83-91)\nRepresents a request to the cc-metric-store query API:\n```go\ntype APIQueryRequest struct {\n    Cluster     string     // Target cluster name\n    Queries     []APIQuery // Explicit list of metric queries\n    ForAllNodes []string   // Metrics to query for all nodes (bulk query)\n    From        int64      // Start time (Unix timestamp)\n    To          int64      // End time (Unix timestamp)\n    WithStats   bool       // Include min/avg/max statistics\n    WithData    bool       // Include time series data points\n}\n```\n\n#### `APIQuery` (lines 95-104)\nSpecifies a single metric query with optional scope filtering:\n```go\ntype APIQuery struct {\n    Type       *string  // Scope type (e.g., \"core\", \"socket\", \"hwthread\")\n    SubType    *string  // Sub-scope type (reserved for future use)\n    Metric     string   // Metric name\n    Hostname   string   // Target hostname\n    Resolution int      // Data resolution in seconds (0 = native)\n    TypeIds    []string // IDs for the scope type (e.g., [\"0\", \"1\", \"2\"] for core IDs)\n    SubTypeIds []string // IDs for sub-scope (reserved)\n    Aggregate  bool     // Aggregate across TypeIds\n}\n```\n\n#### `APIQueryResponse` (lines 108-111)\nContains results from a cc-metric-store query:\n```go\ntype APIQueryResponse struct {\n    Queries []APIQuery        // Echoed queries (for bulk requests)\n    Results [][]APIMetricData // Result data, indexed by query\n}\n```\n\n#### `APIMetricData` (lines 115-124)\nTime series data and statistics for a single metric series:\n```go\ntype APIMetricData struct {\n    Error      *string        // Error message if query failed\n    Data       []schema.Float // Time series data points\n    From       int64          // Actual start time of data\n    To         int64          // Actual end time of data\n    Resolution int            // Actual resolution of data in seconds\n    Avg        schema.Float   // Average value across time range\n    Min        schema.Float   // Minimum value in time range\n    Max        schema.Float   // Maximum value in time range\n}\n```\n\n---\n\n### 3. Core Methods on `CCMetricStore`\n\n#### `doRequest()` (lines 144-188)\n**Purpose:** Executes HTTP POST request to cc-metric-store query API\n\n**Key behavior:**\n- Encodes request body as JSON\n- Adds JWT Bearer token if configured\n- Adds API versioning (v2 for data with resampling, v1 without)\n- Uses GET method with JSON body (unusual but matches cc-metric-store API)\n- Returns parsed `APIQueryResponse`\n- Properly closes response body to prevent resource leaks\n\n#### `getTopology()` (lines 191-204)\n**Purpose:** Returns topology for given cluster and subcluster, caching results\n\n```go\nfunc (ccms *CCMetricStore) getTopology(cluster, subCluster string) (*schema.Topology, error)\n```\n\n**Implementation:**\n- Uses cache key `\"cluster:subcluster\"`\n- Returns cached topology if available\n- Otherwise loads via `archive.GetSubCluster()` and caches it\n- Returns pointer to `schema.Topology` from subcluster\n\n#### `getTopologyByNode()` (lines 207-214)\n**Purpose:** Returns topology for given cluster and node, caching it\n\n```go\nfunc (ccms *CCMetricStore) getTopologyByNode(cluster, node string) (*schema.Topology, error)\n```\n\n**Implementation:**\n- Uses `archive.GetSubClusterByNode()` to find subcluster for node\n- Delegates to `getTopology()` for cache lookup and loading\n\n---\n\n### 4. Main Data Loading Methods\n\n#### `LoadData()` (lines 228-362)\n**Purpose:** Retrieves time series data and statistics for specified job and metrics\n\n**Signature:**\n```go\nfunc (ccms *CCMetricStore) LoadData(\n    job *schema.Job,\n    metrics []string,\n    scopes []schema.MetricScope,\n    ctx context.Context,\n    resolution int,\n) (schema.JobData, error)\n```\n\n**Returns:** `schema.JobData` organized as: `metric → scope → series list`\n\n**Key steps:**\n1. Calls `buildQueries()` to construct API queries with scope transformations\n2. Creates `APIQueryRequest` with job time range and resources\n3. Calls `doRequest()` to fetch from cc-metric-store\n4. Processes results with safety checks for index bounds\n5. For each result, extracts metric config and builds `schema.Series` objects\n6. Supports partial errors: returns available data even if some queries fail\n7. Sanitizes NaN statistics for JSON compatibility\n\n#### `LoadStats()` (lines 368-427)\n**Purpose:** Retrieves min/avg/max statistics for job metrics at node scope (faster, no time series data)\n\n**Signature:**\n```go\nfunc (ccms *CCMetricStore) LoadStats(\n    job *schema.Job,\n    metrics []string,\n    ctx context.Context,\n) (map[string]map[string]schema.MetricStatistics, error)\n```\n\n**Returns:** Statistics organized as: `metric → hostname → statistics`\n\n**Key differences from LoadData:**\n- Only queries node scope\n- Sets `WithData: false` (no time series)\n- Sets `WithStats: true` (only statistics)\n- Simpler result processing\n\n#### `LoadScopedStats()` (lines 434-524)\n**Purpose:** Retrieves statistics for job metrics across multiple scopes (used for Job-View Statistics Table)\n\n**Signature:**\n```go\nfunc (ccms *CCMetricStore) LoadScopedStats(\n    job *schema.Job,\n    metrics []string,\n    scopes []schema.MetricScope,\n    ctx context.Context,\n) (schema.ScopedJobStats, error)\n```\n\n**Returns:** Statistics organized as: `metric → scope → list of scoped statistics`\n\n**Key features:**\n- Similar structure to `LoadData()` but returns `schema.ScopedStats` instead of full series\n- Each scoped stat includes hostname, hardware ID (if applicable), and min/avg/max\n\n#### `LoadNodeData()` (lines 531-619)\n**Purpose:** Retrieves current metric data for specified nodes in a cluster (Systems-View Node-Overview)\n\n**Signature:**\n```go\nfunc (ccms *CCMetricStore) LoadNodeData(\n    cluster string,\n    metrics, nodes []string,\n    scopes []schema.MetricScope,\n    from, to time.Time,\n    ctx context.Context,\n) (map[string]map[string][]*schema.JobMetric, error)\n```\n\n**Returns:** Data organized as: `hostname → metric → []*JobMetric` (list supports multiple scopes)\n\n**Key features:**\n- If `nodes == nil`: performs bulk query for all metrics on all nodes\n- Otherwise: queries each specified node for each metric\n- Simpler query construction (doesn't call `buildQueries()`)\n- No scope transformations applied\n\n#### `LoadNodeListData()` (lines 631-780)\n**Purpose:** Retrieves paginated node metrics for Systems-View Node-List with filtering\n\n**Signature:**\n```go\nfunc (ccms *CCMetricStore) LoadNodeListData(\n    cluster, subCluster string,\n    nodes []string,\n    metrics []string,\n    scopes []schema.MetricScope,\n    resolution int,\n    from, to time.Time,\n    ctx context.Context,\n) (map[string]schema.JobData, error)\n```\n\n**Returns:** Data organized as: `hostname → JobData (metric → scope → series)`\n\n**Key steps:**\n1. Calls `buildNodeQueries()` for query construction with scope transformations\n2. Uses `getTopology()` or `getTopologyByNode()` depending on subcluster parameter\n3. Similar result processing to `LoadData()` with safety checks\n\n#### `HealthCheck()` (lines 785-828)\n**Purpose:** Queries external cc-metric-store's health check endpoint\n\n**Key features:**\n- Sends `metricstore.HealthCheckReq` with cluster, nodes, metrics\n- Uses `/api/healthcheck` endpoint\n- Returns per-node health check results\n\n---\n\n### 5. Query Building (cc-metric-store-queries.go)\n\n#### Scope String Constants (lines 49-55)\nPre-converted strings to avoid repeated allocations:\n```go\nhwthreadString     = \"hwthread\"\ncoreString         = \"core\"\nmemoryDomainString = \"memory_domain\"\nsocketString       = \"socket\"\nacceleratorString  = \"accelerator\"\n```\n\n#### `buildQueries()` (lines 67-149)\n**Purpose:** Constructs API queries for job-specific metric data\n\n**Signature:**\n```go\nfunc (ccms *CCMetricStore) buildQueries(\n    job *schema.Job,\n    metrics []string,\n    scopes []schema.MetricScope,\n    resolution int,\n) ([]APIQuery, []schema.MetricScope, error)\n```\n\n**Returns:** Both queries and their corresponding assigned scopes (may differ from requested)\n\n**Key logic:**\n1. Validates metric configurations via `archive.GetMetricConfig()`\n2. Skips metrics removed for subcluster\n3. Deduplicates requested scopes to avoid redundant queries\n4. For each host in job resources:\n   - Uses job-allocated HWThreads (or full node if not specified)\n   - Calls `buildScopeQueries()` for scope transformation\n   - Accumulates queries and assigned scopes\n\n**Optimization:** Tracks handled scopes to skip redundant queries\n\n#### `buildNodeQueries()` (lines 161-252)\n**Purpose:** Constructs API queries for node-specific metric data (Systems View)\n\n**Signature:**\n```go\nfunc (ccms *CCMetricStore) buildNodeQueries(\n    cluster string,\n    subCluster string,\n    nodes []string,\n    metrics []string,\n    scopes []schema.MetricScope,\n    resolution int,\n) ([]APIQuery, []schema.MetricScope, error)\n```\n\n**Key differences from `buildQueries()`:**\n- Always uses full node topology (not job-specific subsets)\n- Always uses all accelerators on each node\n- Handles optional subCluster parameter:\n  - If empty: calls `getTopologyByNode()` per node\n  - If provided: calls `getTopology()` once and reuses\n\n#### `buildScopeQueries()` (lines 257-517)\n**Purpose:** Generates API queries for a given scope transformation\n\n**Signature:**\n```go\nfunc buildScopeQueries(\n    nativeScope, requestedScope schema.MetricScope,\n    metric, hostname string,\n    topology *schema.Topology,\n    hwthreads []int,\n    accelerators []string,\n    resolution int,\n) ([]APIQuery, []schema.MetricScope)\n```\n\n**Returns:** Empty slices for expected exceptions; nil for unexpected unhandled cases\n\n**Scope Transformations Implemented (25+ cases):**\n\n| Native → Requested | Behavior | Result Scope |\n|---|---|---|\n| Accelerator → Accelerator | Return as-is | Accelerator |\n| Accelerator → Node | Aggregate all accelerators | Node |\n| HWThread → HWThread | Return as-is | HWThread |\n| HWThread → Core | Aggregate per core | Core (one query per core) |\n| HWThread → Socket | Aggregate per socket | Socket (one query per socket) |\n| HWThread → Node | Aggregate all threads | Node |\n| Core → Core | Return as-is | Core |\n| Core → Socket | Aggregate per socket | Socket (one query per socket) |\n| Core → Node | Aggregate all cores | Node |\n| MemoryDomain → MemoryDomain | Return as-is | MemoryDomain |\n| MemoryDomain → Node | Aggregate all domains | Node |\n| MemoryDomain → Socket | Aggregate domains per socket | Socket (one query per socket) |\n| Socket → Socket | Return as-is | Socket |\n| Socket → Node | Aggregate all sockets | Node |\n| Node → Node | Return as-is | Node |\n\n**Key features:**\n- Uses `Aggregate: true` for aggregations, `false` for direct queries\n- Sets `Type` and `TypeIds` for scope-specific queries\n- Returns nil slices for unhandled cases (triggers error in `buildQueries()`)\n- Uses helper functions like `GetCoresFromHWThreads()`, `GetSocketsFromCores()`, etc. from topology\n\n#### `intToStringSlice()` (lines 521-527)\nHelper function to convert integer hardware IDs to string format required by API:\n```go\nfunc intToStringSlice(is []int) []string\n```\n\n---\n\n### 6. Helper Functions\n\n#### `sanitizeStats()` (lines 832-838)\nReplaces NaN values in statistics with 0 to enable JSON marshaling:\n```go\nfunc sanitizeStats(avg, min, max *schema.Float)\n```\n\n#### `hasNaNStats()` (lines 841-843)\nReturns true if any of the statistics contain NaN values:\n```go\nfunc hasNaNStats(avg, min, max schema.Float) bool\n```\n\n---\n\n### 7. Relationship to `pkg/metricstore/query.go`\n\nBoth packages implement **parallel interfaces** with nearly identical method signatures:\n\n| Method | Location | Purpose |\n|---|---|---|\n| `LoadData()` | Both | Load job metric data with scope transformations |\n| `LoadStats()` | Both | Load statistics at node scope |\n| `LoadScopedStats()` | Both | Load statistics across multiple scopes |\n| `HealthCheck()` | Both | Health check for metric store |\n\n**Key differences:**\n\n| Aspect | `pkg/metricstore/` | `internal/metricstoreclient/` |\n|---|---|---|\n| **Storage** | In-memory store | External cc-metric-store via HTTP |\n| **Data fetching** | `FetchData()` (internal) | `doRequest()` (HTTP to cc-metric-store) |\n| **Query builder** | `buildQueries()` in query.go | `buildQueries()` + `buildScopeQueries()` in cc-metric-store-queries.go |\n| **Authentication** | None (in-process) | JWT Bearer token |\n| **Caching** | No caching | Topology caching |\n| **API versioning** | Not applicable | Uses cc-metric-store v2 API |\n| **Timeout** | Not applicable | 10-second HTTP timeout |\n| **Resolution type** | `int64` | `int` |\n\n**Code Similarity:**\n- Both use same scope transformation logic\n- Both construct queries with metric configs from `archive.GetMetricConfig()`\n- Both handle partial errors (return data for successful queries)\n- Both sanitize NaN statistics\n- Nearly identical result processing loops with identical safety checks\n\n**When to use each:**\n- **`pkg/metricstore/`**: When cc-metric-store is co-located or unavailable\n- **`internal/metricstoreclient/`**: When cc-metric-store is available externally (production preferred for scalability)\n\n---\n\n### 8. API Endpoint Details\n\n**Query Endpoint:**\n- Base: `http://cc-metric-store-host:port`\n- Query: `{base}/api/query?version=v2`\n- Health: `{base}/api/healthcheck`\n\n**Message Format:**\nUses InfluxDB-like line protocol with JSON payloads in `APIQuery` structures. For job events/node updates via NATS (per CLAUDE.md), uses InfluxDB line protocol directly.\n\n---\n\n### 9. Error Handling\n\n- **Partial errors**: Methods return both data and error; error lists failed queries\n- **Index out of range**: Prevented with bounds checking; logged as warnings\n- **Invalid scopes**: Return nil slices from `buildScopeQueries()` triggering error\n- **Metric config missing**: Logged warning, metric skipped\n- **HTTP errors**: Returned from `doRequest()`\n- **Context cancellation**: Supported via context parameter in all load methods\n\n---\n\n### Summary\n\nThe `internal/metricstoreclient/` package is a **complete HTTP client for external cc-metric-store** that mirrors the functionality of the internal metric store while communicating via HTTP API. It handles complex scope transformations, topology caching, and partial error reporting. The query building logic handles 25+ scope transformation cases, enabling flexible metric data requests across different hardware granularities while maintaining compatibility with the in-memory metric store interface."},{"type":"text","text":"agentId: ac0e18c809f60baa6 (for resuming to continue this agent's work if needed)\n<usage>total_tokens: 42376\ntool_uses: 6\nduration_ms: 47035</usage>"}]}]},"uuid":"0dac97f6-1f19-4ba7-88d6-eb2125a33bbd","timestamp":"2026-03-04T15:24:37.770Z","toolUseResult":{"status":"completed","prompt":"Explore the internal/metricstoreclient/ package thoroughly. I need to understand:\n1. All files in this package\n2. The CCMetricStore type and its methods (especially LoadData, LoadStats, LoadScopedStats, LoadNodeData, LoadNodeListData)\n3. How it defines APIQuery and related types\n4. The getTopology and getTopologyByNode helper methods\n5. How this package relates to pkg/metricstore/\n\nFocus on understanding the full structure and how it parallels pkg/metricstore/query.go.","agentId":"ac0e18c809f60baa6","content":[{"type":"text","text":"Excellent! Now I have a comprehensive understanding of the package. Let me create a detailed summary report.\n\n## Comprehensive Exploration Report: `internal/metricstoreclient/` Package\n\n### Overview\n\nThe `internal/metricstoreclient/` package is a **client library for querying cc-metric-store**, an external high-performance time series database optimized for HPC metric data. It mirrors the functionality of `pkg/metricstore/query.go` but communicates with an external cc-metric-store service via HTTP instead of using an in-memory store.\n\n**Files in package:**\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go` (28 KB) — Main client operations\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store-queries.go` (16 KB) — Query building logic\n\n---\n\n### 1. Core Type: `CCMetricStore`\n\n**Location:** `cc-metric-store.go`, lines 71-79\n\n```go\ntype CCMetricStore struct {\n    client        http.Client                 // HTTP client with 10-second timeout\n    jwt           string                      // JWT Bearer token for authentication\n    url           string                      // Base URL of cc-metric-store instance\n    queryEndpoint string                      // Full URL to query API endpoint\n    topologyCache map[string]*schema.Topology // cluster -> topology cache\n}\n```\n\n**Purpose:** HTTP client for communicating with an external cc-metric-store service. Manages connection details, authentication (JWT), and caching of cluster topology information.\n\n**Constructor:**\n```go\nfunc NewCCMetricStore(url string, token string) *CCMetricStore\n```\n- Creates client with 10-second timeout\n- Initializes empty topology cache\n- URL should include protocol and port (e.g., \"http://localhost:8080\")\n- Token is JWT for Bearer authentication; empty string disables auth\n\n---\n\n### 2. API Request/Response Types\n\n#### `APIQueryRequest` (lines 83-91)\nRepresents a request to the cc-metric-store query API:\n```go\ntype APIQueryRequest struct {\n    Cluster     string     // Target cluster name\n    Queries     []APIQuery // Explicit list of metric queries\n    ForAllNodes []string   // Metrics to query for all nodes (bulk query)\n    From        int64      // Start time (Unix timestamp)\n    To          int64      // End time (Unix timestamp)\n    WithStats   bool       // Include min/avg/max statistics\n    WithData    bool       // Include time series data points\n}\n```\n\n#### `APIQuery` (lines 95-104)\nSpecifies a single metric query with optional scope filtering:\n```go\ntype APIQuery struct {\n    Type       *string  // Scope type (e.g., \"core\", \"socket\", \"hwthread\")\n    SubType    *string  // Sub-scope type (reserved for future use)\n    Metric     string   // Metric name\n    Hostname   string   // Target hostname\n    Resolution int      // Data resolution in seconds (0 = native)\n    TypeIds    []string // IDs for the scope type (e.g., [\"0\", \"1\", \"2\"] for core IDs)\n    SubTypeIds []string // IDs for sub-scope (reserved)\n    Aggregate  bool     // Aggregate across TypeIds\n}\n```\n\n#### `APIQueryResponse` (lines 108-111)\nContains results from a cc-metric-store query:\n```go\ntype APIQueryResponse struct {\n    Queries []APIQuery        // Echoed queries (for bulk requests)\n    Results [][]APIMetricData // Result data, indexed by query\n}\n```\n\n#### `APIMetricData` (lines 115-124)\nTime series data and statistics for a single metric series:\n```go\ntype APIMetricData struct {\n    Error      *string        // Error message if query failed\n    Data       []schema.Float // Time series data points\n    From       int64          // Actual start time of data\n    To         int64          // Actual end time of data\n    Resolution int            // Actual resolution of data in seconds\n    Avg        schema.Float   // Average value across time range\n    Min        schema.Float   // Minimum value in time range\n    Max        schema.Float   // Maximum value in time range\n}\n```\n\n---\n\n### 3. Core Methods on `CCMetricStore`\n\n#### `doRequest()` (lines 144-188)\n**Purpose:** Executes HTTP POST request to cc-metric-store query API\n\n**Key behavior:**\n- Encodes request body as JSON\n- Adds JWT Bearer token if configured\n- Adds API versioning (v2 for data with resampling, v1 without)\n- Uses GET method with JSON body (unusual but matches cc-metric-store API)\n- Returns parsed `APIQueryResponse`\n- Properly closes response body to prevent resource leaks\n\n#### `getTopology()` (lines 191-204)\n**Purpose:** Returns topology for given cluster and subcluster, caching results\n\n```go\nfunc (ccms *CCMetricStore) getTopology(cluster, subCluster string) (*schema.Topology, error)\n```\n\n**Implementation:**\n- Uses cache key `\"cluster:subcluster\"`\n- Returns cached topology if available\n- Otherwise loads via `archive.GetSubCluster()` and caches it\n- Returns pointer to `schema.Topology` from subcluster\n\n#### `getTopologyByNode()` (lines 207-214)\n**Purpose:** Returns topology for given cluster and node, caching it\n\n```go\nfunc (ccms *CCMetricStore) getTopologyByNode(cluster, node string) (*schema.Topology, error)\n```\n\n**Implementation:**\n- Uses `archive.GetSubClusterByNode()` to find subcluster for node\n- Delegates to `getTopology()` for cache lookup and loading\n\n---\n\n### 4. Main Data Loading Methods\n\n#### `LoadData()` (lines 228-362)\n**Purpose:** Retrieves time series data and statistics for specified job and metrics\n\n**Signature:**\n```go\nfunc (ccms *CCMetricStore) LoadData(\n    job *schema.Job,\n    metrics []string,\n    scopes []schema.MetricScope,\n    ctx context.Context,\n    resolution int,\n) (schema.JobData, error)\n```\n\n**Returns:** `schema.JobData` organized as: `metric → scope → series list`\n\n**Key steps:**\n1. Calls `buildQueries()` to construct API queries with scope transformations\n2. Creates `APIQueryRequest` with job time range and resources\n3. Calls `doRequest()` to fetch from cc-metric-store\n4. Processes results with safety checks for index bounds\n5. For each result, extracts metric config and builds `schema.Series` objects\n6. Supports partial errors: returns available data even if some queries fail\n7. Sanitizes NaN statistics for JSON compatibility\n\n#### `LoadStats()` (lines 368-427)\n**Purpose:** Retrieves min/avg/max statistics for job metrics at node scope (faster, no time series data)\n\n**Signature:**\n```go\nfunc (ccms *CCMetricStore) LoadStats(\n    job *schema.Job,\n    metrics []string,\n    ctx context.Context,\n) (map[string]map[string]schema.MetricStatistics, error)\n```\n\n**Returns:** Statistics organized as: `metric → hostname → statistics`\n\n**Key differences from LoadData:**\n- Only queries node scope\n- Sets `WithData: false` (no time series)\n- Sets `WithStats: true` (only statistics)\n- Simpler result processing\n\n#### `LoadScopedStats()` (lines 434-524)\n**Purpose:** Retrieves statistics for job metrics across multiple scopes (used for Job-View Statistics Table)\n\n**Signature:**\n```go\nfunc (ccms *CCMetricStore) LoadScopedStats(\n    job *schema.Job,\n    metrics []string,\n    scopes []schema.MetricScope,\n    ctx context.Context,\n) (schema.ScopedJobStats, error)\n```\n\n**Returns:** Statistics organized as: `metric → scope → list of scoped statistics`\n\n**Key features:**\n- Similar structure to `LoadData()` but returns `schema.ScopedStats` instead of full series\n- Each scoped stat includes hostname, hardware ID (if applicable), and min/avg/max\n\n#### `LoadNodeData()` (lines 531-619)\n**Purpose:** Retrieves current metric data for specified nodes in a cluster (Systems-View Node-Overview)\n\n**Signature:**\n```go\nfunc (ccms *CCMetricStore) LoadNodeData(\n    cluster string,\n    metrics, nodes []string,\n    scopes []schema.MetricScope,\n    from, to time.Time,\n    ctx context.Context,\n) (map[string]map[string][]*schema.JobMetric, error)\n```\n\n**Returns:** Data organized as: `hostname → metric → []*JobMetric` (list supports multiple scopes)\n\n**Key features:**\n- If `nodes == nil`: performs bulk query for all metrics on all nodes\n- Otherwise: queries each specified node for each metric\n- Simpler query construction (doesn't call `buildQueries()`)\n- No scope transformations applied\n\n#### `LoadNodeListData()` (lines 631-780)\n**Purpose:** Retrieves paginated node metrics for Systems-View Node-List with filtering\n\n**Signature:**\n```go\nfunc (ccms *CCMetricStore) LoadNodeListData(\n    cluster, subCluster string,\n    nodes []string,\n    metrics []string,\n    scopes []schema.MetricScope,\n    resolution int,\n    from, to time.Time,\n    ctx context.Context,\n) (map[string]schema.JobData, error)\n```\n\n**Returns:** Data organized as: `hostname → JobData (metric → scope → series)`\n\n**Key steps:**\n1. Calls `buildNodeQueries()` for query construction with scope transformations\n2. Uses `getTopology()` or `getTopologyByNode()` depending on subcluster parameter\n3. Similar result processing to `LoadData()` with safety checks\n\n#### `HealthCheck()` (lines 785-828)\n**Purpose:** Queries external cc-metric-store's health check endpoint\n\n**Key features:**\n- Sends `metricstore.HealthCheckReq` with cluster, nodes, metrics\n- Uses `/api/healthcheck` endpoint\n- Returns per-node health check results\n\n---\n\n### 5. Query Building (cc-metric-store-queries.go)\n\n#### Scope String Constants (lines 49-55)\nPre-converted strings to avoid repeated allocations:\n```go\nhwthreadString     = \"hwthread\"\ncoreString         = \"core\"\nmemoryDomainString = \"memory_domain\"\nsocketString       = \"socket\"\nacceleratorString  = \"accelerator\"\n```\n\n#### `buildQueries()` (lines 67-149)\n**Purpose:** Constructs API queries for job-specific metric data\n\n**Signature:**\n```go\nfunc (ccms *CCMetricStore) buildQueries(\n    job *schema.Job,\n    metrics []string,\n    scopes []schema.MetricScope,\n    resolution int,\n) ([]APIQuery, []schema.MetricScope, error)\n```\n\n**Returns:** Both queries and their corresponding assigned scopes (may differ from requested)\n\n**Key logic:**\n1. Validates metric configurations via `archive.GetMetricConfig()`\n2. Skips metrics removed for subcluster\n3. Deduplicates requested scopes to avoid redundant queries\n4. For each host in job resources:\n   - Uses job-allocated HWThreads (or full node if not specified)\n   - Calls `buildScopeQueries()` for scope transformation\n   - Accumulates queries and assigned scopes\n\n**Optimization:** Tracks handled scopes to skip redundant queries\n\n#### `buildNodeQueries()` (lines 161-252)\n**Purpose:** Constructs API queries for node-specific metric data (Systems View)\n\n**Signature:**\n```go\nfunc (ccms *CCMetricStore) buildNodeQueries(\n    cluster string,\n    subCluster string,\n    nodes []string,\n    metrics []string,\n    scopes []schema.MetricScope,\n    resolution int,\n) ([]APIQuery, []schema.MetricScope, error)\n```\n\n**Key differences from `buildQueries()`:**\n- Always uses full node topology (not job-specific subsets)\n- Always uses all accelerators on each node\n- Handles optional subCluster parameter:\n  - If empty: calls `getTopologyByNode()` per node\n  - If provided: calls `getTopology()` once and reuses\n\n#### `buildScopeQueries()` (lines 257-517)\n**Purpose:** Generates API queries for a given scope transformation\n\n**Signature:**\n```go\nfunc buildScopeQueries(\n    nativeScope, requestedScope schema.MetricScope,\n    metric, hostname string,\n    topology *schema.Topology,\n    hwthreads []int,\n    accelerators []string,\n    resolution int,\n) ([]APIQuery, []schema.MetricScope)\n```\n\n**Returns:** Empty slices for expected exceptions; nil for unexpected unhandled cases\n\n**Scope Transformations Implemented (25+ cases):**\n\n| Native → Requested | Behavior | Result Scope |\n|---|---|---|\n| Accelerator → Accelerator | Return as-is | Accelerator |\n| Accelerator → Node | Aggregate all accelerators | Node |\n| HWThread → HWThread | Return as-is | HWThread |\n| HWThread → Core | Aggregate per core | Core (one query per core) |\n| HWThread → Socket | Aggregate per socket | Socket (one query per socket) |\n| HWThread → Node | Aggregate all threads | Node |\n| Core → Core | Return as-is | Core |\n| Core → Socket | Aggregate per socket | Socket (one query per socket) |\n| Core → Node | Aggregate all cores | Node |\n| MemoryDomain → MemoryDomain | Return as-is | MemoryDomain |\n| MemoryDomain → Node | Aggregate all domains | Node |\n| MemoryDomain → Socket | Aggregate domains per socket | Socket (one query per socket) |\n| Socket → Socket | Return as-is | Socket |\n| Socket → Node | Aggregate all sockets | Node |\n| Node → Node | Return as-is | Node |\n\n**Key features:**\n- Uses `Aggregate: true` for aggregations, `false` for direct queries\n- Sets `Type` and `TypeIds` for scope-specific queries\n- Returns nil slices for unhandled cases (triggers error in `buildQueries()`)\n- Uses helper functions like `GetCoresFromHWThreads()`, `GetSocketsFromCores()`, etc. from topology\n\n#### `intToStringSlice()` (lines 521-527)\nHelper function to convert integer hardware IDs to string format required by API:\n```go\nfunc intToStringSlice(is []int) []string\n```\n\n---\n\n### 6. Helper Functions\n\n#### `sanitizeStats()` (lines 832-838)\nReplaces NaN values in statistics with 0 to enable JSON marshaling:\n```go\nfunc sanitizeStats(avg, min, max *schema.Float)\n```\n\n#### `hasNaNStats()` (lines 841-843)\nReturns true if any of the statistics contain NaN values:\n```go\nfunc hasNaNStats(avg, min, max schema.Float) bool\n```\n\n---\n\n### 7. Relationship to `pkg/metricstore/query.go`\n\nBoth packages implement **parallel interfaces** with nearly identical method signatures:\n\n| Method | Location | Purpose |\n|---|---|---|\n| `LoadData()` | Both | Load job metric data with scope transformations |\n| `LoadStats()` | Both | Load statistics at node scope |\n| `LoadScopedStats()` | Both | Load statistics across multiple scopes |\n| `HealthCheck()` | Both | Health check for metric store |\n\n**Key differences:**\n\n| Aspect | `pkg/metricstore/` | `internal/metricstoreclient/` |\n|---|---|---|\n| **Storage** | In-memory store | External cc-metric-store via HTTP |\n| **Data fetching** | `FetchData()` (internal) | `doRequest()` (HTTP to cc-metric-store) |\n| **Query builder** | `buildQueries()` in query.go | `buildQueries()` + `buildScopeQueries()` in cc-metric-store-queries.go |\n| **Authentication** | None (in-process) | JWT Bearer token |\n| **Caching** | No caching | Topology caching |\n| **API versioning** | Not applicable | Uses cc-metric-store v2 API |\n| **Timeout** | Not applicable | 10-second HTTP timeout |\n| **Resolution type** | `int64` | `int` |\n\n**Code Similarity:**\n- Both use same scope transformation logic\n- Both construct queries with metric configs from `archive.GetMetricConfig()`\n- Both handle partial errors (return data for successful queries)\n- Both sanitize NaN statistics\n- Nearly identical result processing loops with identical safety checks\n\n**When to use each:**\n- **`pkg/metricstore/`**: When cc-metric-store is co-located or unavailable\n- **`internal/metricstoreclient/`**: When cc-metric-store is available externally (production preferred for scalability)\n\n---\n\n### 8. API Endpoint Details\n\n**Query Endpoint:**\n- Base: `http://cc-metric-store-host:port`\n- Query: `{base}/api/query?version=v2`\n- Health: `{base}/api/healthcheck`\n\n**Message Format:**\nUses InfluxDB-like line protocol with JSON payloads in `APIQuery` structures. For job events/node updates via NATS (per CLAUDE.md), uses InfluxDB line protocol directly.\n\n---\n\n### 9. Error Handling\n\n- **Partial errors**: Methods return both data and error; error lists failed queries\n- **Index out of range**: Prevented with bounds checking; logged as warnings\n- **Invalid scopes**: Return nil slices from `buildScopeQueries()` triggering error\n- **Metric config missing**: Logged warning, metric skipped\n- **HTTP errors**: Returned from `doRequest()`\n- **Context cancellation**: Supported via context parameter in all load methods\n\n---\n\n### Summary\n\nThe `internal/metricstoreclient/` package is a **complete HTTP client for external cc-metric-store** that mirrors the functionality of the internal metric store while communicating via HTTP API. It handles complex scope transformations, topology caching, and partial error reporting. The query building logic handles 25+ scope transformation cases, enabling flexible metric data requests across different hardware granularities while maintaining compatibility with the in-memory metric store interface."}],"totalDurationMs":47035,"totalTokens":42376,"totalToolUseCount":6,"usage":{"input_tokens":4,"cache_creation_input_tokens":3077,"cache_read_input_tokens":34757,"output_tokens":4538,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":3077,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"sourceToolAssistantUUID":"87dce091-59a3-440c-aa2e-2f4904c0ff79"}
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{"parentUuid":"11e11771-ff9d-464d-948f-664179635a98","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JPUhKVS2cG5n57jLbfbvoS","type":"tool_result","content":[{"type":"text","text":"Perfect! Now I have a comprehensive understanding of the package. Let me compile my findings into a detailed report.\n\n## Comprehensive Exploration of `pkg/metricstore/` Package\n\nI've thoroughly analyzed the `pkg/metricstore/` package. Here's a complete overview:\n\n### Package Overview\nThe `pkg/metricstore/` is a sophisticated in-memory time-series metric storage system for ClusterCockpit, providing:\n- Hierarchical metric organization (cluster → host → component hierarchy)\n- Concurrent read/write access with RWMutex protection\n- Periodic checkpointing (JSON or WAL format) and archiving\n- Memory management with retention policies and emergency freeing\n- NATS integration for real-time metric ingestion\n- Scope transformation for job metric data (HWThread → Core → Socket → Node aggregation)\n\n### Complete File List (17 files, 7,615 lines total)\n\n1. **metricstore.go** (784 lines) - Core singleton instance and lifecycle\n2. **api.go** (314 lines) - API query types and FetchData function\n3. **query.go** (1,357 lines) - Job metric loading with scope transformation\n4. **level.go** (393 lines) - Hierarchical tree structure\n5. **buffer.go** (410 lines) - Time-series data buffers\n6. **config.go** (253 lines) - Configuration structures\n7. **checkpoint.go** (662 lines) - Persistence to disk\n8. **walCheckpoint.go** (797 lines) - WAL format implementation\n9. **lineprotocol.go** (366 lines) - NATS line-protocol ingestion\n10. **healthcheck.go** (197 lines) - Monitoring state detection\n11. **archive.go** (277 lines) - Long-term storage\n12. **parquetArchive.go** (213 lines) - Parquet format support\n13. **stats.go** (125 lines) - Statistics computation\n14. **debug.go** (112 lines) - Debugging utilities\n15. **configSchema.go** (81 lines) - JSON schema validation\n16. **metricstore_test.go** (1,019 lines) - Unit tests\n17. **parquetArchive_test.go** (255 lines) - Parquet tests\n\n---\n\n## 1. TYPE DEFINITIONS\n\n### Core Types\n\n**APIMetricData** (api.go)\n```go\ntype APIMetricData struct {\n    Error      *string           // Error message if retrieval failed\n    Data       schema.FloatArray // Time-series data points\n    From       int64             // Actual start timestamp\n    To         int64             // Actual end timestamp\n    Resolution int64             // Resolution in seconds\n    Avg        schema.Float      // Average of data points\n    Min        schema.Float      // Minimum value\n    Max        schema.Float      // Maximum value\n}\n```\n\n**APIQueryRequest** (api.go)\n```go\ntype APIQueryRequest struct {\n    Cluster     string     // Cluster name\n    Queries     []APIQuery // Individual metric queries\n    ForAllNodes []string   // Auto-generate queries for all nodes in cluster (metric names)\n    From        int64      // Start timestamp\n    To          int64      // End timestamp\n    WithStats   bool       // Compute avg/min/max\n    WithData    bool       // Include data array\n    WithPadding bool       // Pad with NaN at start\n}\n```\n\n**APIQuery** (api.go)\n```go\ntype APIQuery struct {\n    Type        *string      // Scope level (e.g., \"hwthread\", \"core\", \"socket\")\n    SubType     *string      // Sub-level scope (e.g., \"core\" under \"socket\")\n    Metric      string       // Metric name\n    Hostname    string       // Node hostname\n    Resolution  int64        // Requested resolution\n    TypeIds     []string     // IDs for Type level (e.g., [\"0\", \"1\", \"2\"] for cores)\n    SubTypeIds  []string     // IDs for SubType level\n    ScaleFactor schema.Float // Multiplier for unit conversion\n    Aggregate   bool         // Sum/avg across TypeIds/SubTypeIds\n}\n```\n\n**APIQueryResponse** (api.go)\n```go\ntype APIQueryResponse struct {\n    Queries []APIQuery        // Echo of input queries\n    Results [][]APIMetricData // Results: [query_idx][selector_idx]\n}\n```\n\n### Storage Types\n\n**MemoryStore** (metricstore.go)\n```go\ntype MemoryStore struct {\n    Metrics      map[string]MetricConfig // Metric config map (name → MetricConfig)\n    root         Level                   // Root of hierarchical tree\n    nodeProvider NodeProvider            // For job-aware retention\n}\n```\n\n**Level** (level.go) - Hierarchical tree node\n```go\ntype Level struct {\n    children map[string]*Level  // Child levels (e.g., \"node001\" → Level)\n    metrics  []*buffer          // Metric buffers (indexed by MetricConfig.offset)\n    lock     sync.RWMutex       // Protects concurrent access\n}\n```\n\n**buffer** (buffer.go) - Time-series data storage\n```go\ntype buffer struct {\n    prev      *buffer      // Older buffer in chain\n    next      *buffer      // Newer buffer in chain\n    data      []schema.Float // Data points\n    frequency int64        // Measurement interval (seconds)\n    start     int64        // Start timestamp (adjusted by -frequency/2)\n    archived  bool         // Persisted to archive\n    closed    bool         // No longer accepting writes\n    lastUsed  int64        // Last used timestamp (for pool cleanup)\n}\n```\n\n### Configuration Types\n\n**MetricConfig** (config.go)\n```go\ntype MetricConfig struct {\n    Frequency   int64               // Measurement interval in seconds\n    Aggregation AggregationStrategy // How to combine values (sum/avg/none)\n    offset      int                 // Internal index into Level.metrics\n}\n```\n\n**AggregationStrategy** (config.go)\n```go\nconst (\n    NoAggregation  AggregationStrategy = iota // Incompatible scopes\n    SumAggregation                            // Add values (power, energy)\n    AvgAggregation                            // Average values (temperature)\n)\n```\n\n**MetricStoreConfig** (config.go)\n```go\ntype MetricStoreConfig struct {\n    NumWorkers        int           // Workers for checkpoint/archive\n    RetentionInMemory string        // Duration (e.g., \"48h\")\n    MemoryCap         int           // Memory cap in bytes\n    Checkpoints       Checkpoints   // Checkpoint settings\n    Debug             *Debug        // Debug options\n    Cleanup           *Cleanup      // Archive settings\n    Subscriptions     *Subscriptions // NATS topics\n}\n```\n\n### Query Building Types\n\n**InternalMetricStore** (query.go)\n```go\ntype InternalMetricStore struct{}\nvar MetricStoreHandle *InternalMetricStore\n```\n- Provides public API for job data loading\n- Methods: `LoadData()`, `HealthCheck()`\n- Delegates to underlying `MemoryStore`\n\n### Health Check Types\n\n**HealthCheckResult** (healthcheck.go)\n```go\ntype HealthCheckResult struct {\n    State         schema.MonitoringState // Full/Partial/Failed\n    HealthMetrics string                 // JSON: {\"missing\":[...],\"degraded\":[...]}\n}\n```\n\n---\n\n## 2. HOW FetchData WORKS\n\n**Function Signature**: `func FetchData(req APIQueryRequest) (*APIQueryResponse, error)`\n\n**High-Level Flow**:\n\n1. **Validation**\n   - Checks `req.From <= req.To`\n   - Ensures cluster is specified if `ForAllNodes` is used\n   - Verifies MemoryStore is initialized\n\n2. **Query Expansion**\n   - If `req.ForAllNodes` is set: auto-generates queries for all nodes in the cluster\n   - Calls `ms.ListChildren([]string{cluster})` to get node list\n   - Creates one query per (node, metric) combination\n\n3. **Selector Construction** (per query)\n   - Base selector: `[cluster, hostname]`\n   - If `Aggregate=true` or no Type specified:\n     - Creates single aggregated selector\n     - If Type is specified and has multiple TypeIds:\n       - Uses `SelectorElement{Group: ids}` for multi-selection\n     - Similar for SubType\n   - If `Aggregate=false`:\n     - Creates individual selector for each Type/SubType ID combination\n     - Enables non-aggregated per-component results\n\n4. **Data Fetching**\n   - For each selector: calls `ms.Read(selector, metric, from, to, resolution)`\n   - Returns: `(data, from, to, resolution, error)`\n\n5. **Result Processing** (per query result)\n   - **Skips** `ErrNoHostOrMetric` errors (logs warning, continues)\n   - **Includes** other errors in `APIMetricData.Error` field\n   - If `req.WithStats`: calls `data.AddStats()` to compute avg/min/max\n   - If `query.ScaleFactor != 0`: calls `data.ScaleBy()` for unit conversion\n   - If `req.WithPadding`: calls `data.PadDataWithNull()` to align timestamps\n   - If `!req.WithData`: clears the data array (keeps stats)\n\n6. **Response Assembly**\n   - Returns 2D array: `Results[query_idx][selector_idx]`\n   - Each inner element is `APIMetricData` with data/stats\n\n**Example Scenario**:\n```\nRequest: Fetch cpu_load for cluster \"emmy\", node \"node001\", all cores (0-3)\n  Type=\"core\", TypeIds=[\"0\",\"1\",\"2\",\"3\"], Aggregate=false\n\nResult: 4 separate APIMetricData entries, one per core\n  (each with separate data points)\n```\n\n---\n\n## 3. InternalMetricStore & Its Relationship\n\n### Purpose\n`InternalMetricStore` is a **public facade** that abstracts the `MemoryStore` singleton, preventing direct exposure of internal details.\n\n### Key Methods\n\n**`LoadData()`** (query.go)\n```go\nfunc (ccms *InternalMetricStore) LoadData(\n    job *schema.Job,\n    metrics []string,\n    scopes []schema.MetricScope,\n    ctx context.Context,\n    resolution int,\n) (schema.JobData, error)\n```\n\n**Responsibilities**:\n1. Calls `buildQueries()` to construct API queries with scope transformation\n2. Determines target scope for each query (e.g., HWThread native → Socket requested)\n3. Calls `FetchData()` to retrieve metric data\n4. Reorganizes results by metric and scope into `schema.JobData` structure\n5. Returns: `map[metricName]map[scope]*JobMetric`\n\n**Scope Transformation Logic** (in `buildQueries()`):\n- Native scope >= requested scope: **Aggregate** data (Aggregate=true)\n- Native scope < requested scope: **Error** (cannot refine granularity)\n- Handles special cases:\n  - Accelerator metrics (no aggregation)\n  - Memory domain metrics\n  - Multi-node jobs (per-node queries)\n\n**`HealthCheck()`** (query.go)\n```go\nfunc (ccms *InternalMetricStore) HealthCheck(\n    cluster string,\n    nodes []string,\n    metrics []string,\n) (map[string]HealthCheckResult, error)\n```\n- Delegates to `ms.HealthCheck()`\n- Returns monitoring state per node (Full/Partial/Failed)\n\n### Relationship to MemoryStore\n\n| Aspect | InternalMetricStore | MemoryStore |\n|--------|---------------------|------------|\n| **Role** | Public API facade | Internal storage/retrieval |\n| **Instance** | Singleton (var MetricStoreHandle) | Singleton (msInstance) |\n| **Access** | Used by external packages | Only via InternalMetricStore |\n| **Methods** | High-level: LoadData, HealthCheck | Low-level: Write, Read, Free |\n| **Scope** | Job-centric, scope transformation | Metric-centric, raw storage |\n\n---\n\n## 4. KEY DATA STRUCTURES & RELATIONSHIPS\n\n### Hierarchical Tree Organization\n\n```\nMemoryStore\n└── root (Level)\n    └── children[\"emmy\"] (cluster Level)\n        └── metrics [buffer, buffer, ...] (node-level aggregates)\n        └── children[\"node001\"] (host Level)\n            └── metrics [buffer, buffer, ...]\n            └── children[\"socket0\"] (Level)\n                └── metrics [buffer, buffer, ...]\n                └── children[\"core0\"] (Level)\n                    └── metrics [buffer, buffer, ...] (actual data)\n                └── children[\"core1\"]\n                    └── metrics [buffer, buffer, ...]\n```\n\n### Buffer Chain (Linked List)\n\nTime ordering: oldest ← prev -- prev -- newest (head)\n\n```\n[start=1000] ← [start=1512] ← [start=2024] (head)\n data[512]       data[512]      data[42]\n```\n\n### Metrics Indexing\n\n- Each `Level.metrics` is a slice of `*buffer`\n- Index = `MetricConfig.offset` (assigned during `InitMetrics()`)\n- Example:\n  ```\n  Metrics map: {\"cpu_load\": {offset: 0}, \"mem_used\": {offset: 1}}\n  Level.metrics: [*buffer for cpu_load, *buffer for mem_used]\n  ```\n\n---\n\n## 5. LIFECYCLE & BACKGROUND WORKERS\n\n### Initialization (Init)\n1. Decodes JSON config into `Keys`\n2. Calls `InitMetrics()` to create singleton MemoryStore\n3. Loads checkpoints from disk within retention window\n4. Starts 5 background workers:\n   - `Retention()` - Frees old data based on retention policy\n   - `Checkpointing()` - Periodic snapshot to disk\n   - `CleanUp()` - Archives/deletes data older than retention\n   - `WALStaging()` - Writes WAL entries to disk (if WAL format)\n   - `MemoryUsageTracker()` - Monitors memory, triggers emergency freeing\n\n### Shutdown\n1. Cancels all background workers\n2. Closes WAL messages channel (if WAL format)\n3. Writes final checkpoint to disk\n\n---\n\n## 6. CHECKPOINT FORMATS\n\n### JSON Format (Legacy)\n```\ncheckpoints/\n  cluster1/\n    host001/\n      1234567890.json\n```\n\nStructure:\n```go\ntype CheckpointFile struct {\n    Metrics  map[string]*CheckpointMetrics\n    Children map[string]*CheckpointFile\n    From     int64\n    To       int64\n}\n```\n\n### WAL Format (New, Recommended)\n```\ncheckpoints/\n  cluster1/\n    host001/\n      1234567890.bin    (full binary snapshot)\n      current.wal       (append-only write-ahead log)\n```\n\n- **Binary snapshot**: Complete tree dump at a point in time\n- **WAL file**: Per-write entries, replayed on startup for crash recovery\n- **Rotation**: After checkpoint, WAL is truncated\n\n---\n\n## 7. CONCURRENCY STRATEGY\n\n### RWMutex Usage\n- **Read-heavy**: Metric reads during `ms.Read()` use `RLock`\n- **Write-rare**: Level creation and buffer writes use `Lock`\n- **Double-checked locking**: In `findLevelOrCreate()` prevents race conditions\n\n### Channel-based Communication\n- `WALMessages` channel: Queues WAL entries for disk write\n- `WALStaging()` goroutine: Drains channel, batches to disk\n\n### Buffer Pool\n- Sync.Pool reduces GC pressure\n- `PersistentBufferPool` with LRU cleanup (based on lastUsed timestamp)\n\n---\n\n## 8. RETENTION & MEMORY MANAGEMENT\n\n### Retention Policy\n- Time-based: Data older than `RetentionInMemory` is freed\n- **NodeProvider integration**: Preserves data for nodes with active jobs\n- Runs at half the retention interval\n\n### Memory Emergency\n- Monitors actual heap usage via `runtime.MemStats`\n- If exceeds `MemoryCap`: triggers `ForceFree()` up to 100 iterations\n- `ForceFree()` removes oldest buffer from each metric chain\n\n### Free Mechanisms\n- **Free()**: Removes buffers older than threshold, respecting active nodes\n- **FreeSelected()**: Frees specific selectors (excludes others)\n- **ForceFree()**: Unconditionally removes oldest buffers\n\n---\n\n## Summary\n\nThe `pkg/metricstore/` package is a production-grade time-series storage system with:\n- **Hierarchical organization** for efficient per-component metric storage\n- **Flexible API** supporting both raw queries and job-centric scope transformation\n- **Robust persistence** via JSON or WAL+binary checkpoint formats\n- **Intelligent memory management** with retention, emergency freeing, and pooling\n- **Real-time ingestion** via NATS line-protocol\n- **Health monitoring** with degraded metric detection\n\nThe **InternalMetricStore** abstraction provides a clean public interface for job metric loading, handling scope transformation automatically."},{"type":"text","text":"agentId: a9c86a3b0a5799ebb (for resuming to continue this agent's work if needed)\n<usage>total_tokens: 57113\ntool_uses: 15\nduration_ms: 57023</usage>"}]}]},"uuid":"930d1650-6c74-4867-b077-2998ea7c705f","timestamp":"2026-03-04T15:24:44.622Z","toolUseResult":{"status":"completed","prompt":"Explore the pkg/metricstore/ package thoroughly. I need to understand:\n1. What types are defined (APIQuery, APIQueryRequest, etc.) - check metricstore.go and any type definition files\n2. How FetchData works\n3. What InternalMetricStore is and how it relates to the metric store interface\n4. The full list of files in this package\n\nFocus on understanding the package's role and its types/interfaces.","agentId":"a9c86a3b0a5799ebb","content":[{"type":"text","text":"Perfect! Now I have a comprehensive understanding of the package. Let me compile my findings into a detailed report.\n\n## Comprehensive Exploration of `pkg/metricstore/` Package\n\nI've thoroughly analyzed the `pkg/metricstore/` package. Here's a complete overview:\n\n### Package Overview\nThe `pkg/metricstore/` is a sophisticated in-memory time-series metric storage system for ClusterCockpit, providing:\n- Hierarchical metric organization (cluster → host → component hierarchy)\n- Concurrent read/write access with RWMutex protection\n- Periodic checkpointing (JSON or WAL format) and archiving\n- Memory management with retention policies and emergency freeing\n- NATS integration for real-time metric ingestion\n- Scope transformation for job metric data (HWThread → Core → Socket → Node aggregation)\n\n### Complete File List (17 files, 7,615 lines total)\n\n1. **metricstore.go** (784 lines) - Core singleton instance and lifecycle\n2. **api.go** (314 lines) - API query types and FetchData function\n3. **query.go** (1,357 lines) - Job metric loading with scope transformation\n4. **level.go** (393 lines) - Hierarchical tree structure\n5. **buffer.go** (410 lines) - Time-series data buffers\n6. **config.go** (253 lines) - Configuration structures\n7. **checkpoint.go** (662 lines) - Persistence to disk\n8. **walCheckpoint.go** (797 lines) - WAL format implementation\n9. **lineprotocol.go** (366 lines) - NATS line-protocol ingestion\n10. **healthcheck.go** (197 lines) - Monitoring state detection\n11. **archive.go** (277 lines) - Long-term storage\n12. **parquetArchive.go** (213 lines) - Parquet format support\n13. **stats.go** (125 lines) - Statistics computation\n14. **debug.go** (112 lines) - Debugging utilities\n15. **configSchema.go** (81 lines) - JSON schema validation\n16. **metricstore_test.go** (1,019 lines) - Unit tests\n17. **parquetArchive_test.go** (255 lines) - Parquet tests\n\n---\n\n## 1. TYPE DEFINITIONS\n\n### Core Types\n\n**APIMetricData** (api.go)\n```go\ntype APIMetricData struct {\n    Error      *string           // Error message if retrieval failed\n    Data       schema.FloatArray // Time-series data points\n    From       int64             // Actual start timestamp\n    To         int64             // Actual end timestamp\n    Resolution int64             // Resolution in seconds\n    Avg        schema.Float      // Average of data points\n    Min        schema.Float      // Minimum value\n    Max        schema.Float      // Maximum value\n}\n```\n\n**APIQueryRequest** (api.go)\n```go\ntype APIQueryRequest struct {\n    Cluster     string     // Cluster name\n    Queries     []APIQuery // Individual metric queries\n    ForAllNodes []string   // Auto-generate queries for all nodes in cluster (metric names)\n    From        int64      // Start timestamp\n    To          int64      // End timestamp\n    WithStats   bool       // Compute avg/min/max\n    WithData    bool       // Include data array\n    WithPadding bool       // Pad with NaN at start\n}\n```\n\n**APIQuery** (api.go)\n```go\ntype APIQuery struct {\n    Type        *string      // Scope level (e.g., \"hwthread\", \"core\", \"socket\")\n    SubType     *string      // Sub-level scope (e.g., \"core\" under \"socket\")\n    Metric      string       // Metric name\n    Hostname    string       // Node hostname\n    Resolution  int64        // Requested resolution\n    TypeIds     []string     // IDs for Type level (e.g., [\"0\", \"1\", \"2\"] for cores)\n    SubTypeIds  []string     // IDs for SubType level\n    ScaleFactor schema.Float // Multiplier for unit conversion\n    Aggregate   bool         // Sum/avg across TypeIds/SubTypeIds\n}\n```\n\n**APIQueryResponse** (api.go)\n```go\ntype APIQueryResponse struct {\n    Queries []APIQuery        // Echo of input queries\n    Results [][]APIMetricData // Results: [query_idx][selector_idx]\n}\n```\n\n### Storage Types\n\n**MemoryStore** (metricstore.go)\n```go\ntype MemoryStore struct {\n    Metrics      map[string]MetricConfig // Metric config map (name → MetricConfig)\n    root         Level                   // Root of hierarchical tree\n    nodeProvider NodeProvider            // For job-aware retention\n}\n```\n\n**Level** (level.go) - Hierarchical tree node\n```go\ntype Level struct {\n    children map[string]*Level  // Child levels (e.g., \"node001\" → Level)\n    metrics  []*buffer          // Metric buffers (indexed by MetricConfig.offset)\n    lock     sync.RWMutex       // Protects concurrent access\n}\n```\n\n**buffer** (buffer.go) - Time-series data storage\n```go\ntype buffer struct {\n    prev      *buffer      // Older buffer in chain\n    next      *buffer      // Newer buffer in chain\n    data      []schema.Float // Data points\n    frequency int64        // Measurement interval (seconds)\n    start     int64        // Start timestamp (adjusted by -frequency/2)\n    archived  bool         // Persisted to archive\n    closed    bool         // No longer accepting writes\n    lastUsed  int64        // Last used timestamp (for pool cleanup)\n}\n```\n\n### Configuration Types\n\n**MetricConfig** (config.go)\n```go\ntype MetricConfig struct {\n    Frequency   int64               // Measurement interval in seconds\n    Aggregation AggregationStrategy // How to combine values (sum/avg/none)\n    offset      int                 // Internal index into Level.metrics\n}\n```\n\n**AggregationStrategy** (config.go)\n```go\nconst (\n    NoAggregation  AggregationStrategy = iota // Incompatible scopes\n    SumAggregation                            // Add values (power, energy)\n    AvgAggregation                            // Average values (temperature)\n)\n```\n\n**MetricStoreConfig** (config.go)\n```go\ntype MetricStoreConfig struct {\n    NumWorkers        int           // Workers for checkpoint/archive\n    RetentionInMemory string        // Duration (e.g., \"48h\")\n    MemoryCap         int           // Memory cap in bytes\n    Checkpoints       Checkpoints   // Checkpoint settings\n    Debug             *Debug        // Debug options\n    Cleanup           *Cleanup      // Archive settings\n    Subscriptions     *Subscriptions // NATS topics\n}\n```\n\n### Query Building Types\n\n**InternalMetricStore** (query.go)\n```go\ntype InternalMetricStore struct{}\nvar MetricStoreHandle *InternalMetricStore\n```\n- Provides public API for job data loading\n- Methods: `LoadData()`, `HealthCheck()`\n- Delegates to underlying `MemoryStore`\n\n### Health Check Types\n\n**HealthCheckResult** (healthcheck.go)\n```go\ntype HealthCheckResult struct {\n    State         schema.MonitoringState // Full/Partial/Failed\n    HealthMetrics string                 // JSON: {\"missing\":[...],\"degraded\":[...]}\n}\n```\n\n---\n\n## 2. HOW FetchData WORKS\n\n**Function Signature**: `func FetchData(req APIQueryRequest) (*APIQueryResponse, error)`\n\n**High-Level Flow**:\n\n1. **Validation**\n   - Checks `req.From <= req.To`\n   - Ensures cluster is specified if `ForAllNodes` is used\n   - Verifies MemoryStore is initialized\n\n2. **Query Expansion**\n   - If `req.ForAllNodes` is set: auto-generates queries for all nodes in the cluster\n   - Calls `ms.ListChildren([]string{cluster})` to get node list\n   - Creates one query per (node, metric) combination\n\n3. **Selector Construction** (per query)\n   - Base selector: `[cluster, hostname]`\n   - If `Aggregate=true` or no Type specified:\n     - Creates single aggregated selector\n     - If Type is specified and has multiple TypeIds:\n       - Uses `SelectorElement{Group: ids}` for multi-selection\n     - Similar for SubType\n   - If `Aggregate=false`:\n     - Creates individual selector for each Type/SubType ID combination\n     - Enables non-aggregated per-component results\n\n4. **Data Fetching**\n   - For each selector: calls `ms.Read(selector, metric, from, to, resolution)`\n   - Returns: `(data, from, to, resolution, error)`\n\n5. **Result Processing** (per query result)\n   - **Skips** `ErrNoHostOrMetric` errors (logs warning, continues)\n   - **Includes** other errors in `APIMetricData.Error` field\n   - If `req.WithStats`: calls `data.AddStats()` to compute avg/min/max\n   - If `query.ScaleFactor != 0`: calls `data.ScaleBy()` for unit conversion\n   - If `req.WithPadding`: calls `data.PadDataWithNull()` to align timestamps\n   - If `!req.WithData`: clears the data array (keeps stats)\n\n6. **Response Assembly**\n   - Returns 2D array: `Results[query_idx][selector_idx]`\n   - Each inner element is `APIMetricData` with data/stats\n\n**Example Scenario**:\n```\nRequest: Fetch cpu_load for cluster \"emmy\", node \"node001\", all cores (0-3)\n  Type=\"core\", TypeIds=[\"0\",\"1\",\"2\",\"3\"], Aggregate=false\n\nResult: 4 separate APIMetricData entries, one per core\n  (each with separate data points)\n```\n\n---\n\n## 3. InternalMetricStore & Its Relationship\n\n### Purpose\n`InternalMetricStore` is a **public facade** that abstracts the `MemoryStore` singleton, preventing direct exposure of internal details.\n\n### Key Methods\n\n**`LoadData()`** (query.go)\n```go\nfunc (ccms *InternalMetricStore) LoadData(\n    job *schema.Job,\n    metrics []string,\n    scopes []schema.MetricScope,\n    ctx context.Context,\n    resolution int,\n) (schema.JobData, error)\n```\n\n**Responsibilities**:\n1. Calls `buildQueries()` to construct API queries with scope transformation\n2. Determines target scope for each query (e.g., HWThread native → Socket requested)\n3. Calls `FetchData()` to retrieve metric data\n4. Reorganizes results by metric and scope into `schema.JobData` structure\n5. Returns: `map[metricName]map[scope]*JobMetric`\n\n**Scope Transformation Logic** (in `buildQueries()`):\n- Native scope >= requested scope: **Aggregate** data (Aggregate=true)\n- Native scope < requested scope: **Error** (cannot refine granularity)\n- Handles special cases:\n  - Accelerator metrics (no aggregation)\n  - Memory domain metrics\n  - Multi-node jobs (per-node queries)\n\n**`HealthCheck()`** (query.go)\n```go\nfunc (ccms *InternalMetricStore) HealthCheck(\n    cluster string,\n    nodes []string,\n    metrics []string,\n) (map[string]HealthCheckResult, error)\n```\n- Delegates to `ms.HealthCheck()`\n- Returns monitoring state per node (Full/Partial/Failed)\n\n### Relationship to MemoryStore\n\n| Aspect | InternalMetricStore | MemoryStore |\n|--------|---------------------|------------|\n| **Role** | Public API facade | Internal storage/retrieval |\n| **Instance** | Singleton (var MetricStoreHandle) | Singleton (msInstance) |\n| **Access** | Used by external packages | Only via InternalMetricStore |\n| **Methods** | High-level: LoadData, HealthCheck | Low-level: Write, Read, Free |\n| **Scope** | Job-centric, scope transformation | Metric-centric, raw storage |\n\n---\n\n## 4. KEY DATA STRUCTURES & RELATIONSHIPS\n\n### Hierarchical Tree Organization\n\n```\nMemoryStore\n└── root (Level)\n    └── children[\"emmy\"] (cluster Level)\n        └── metrics [buffer, buffer, ...] (node-level aggregates)\n        └── children[\"node001\"] (host Level)\n            └── metrics [buffer, buffer, ...]\n            └── children[\"socket0\"] (Level)\n                └── metrics [buffer, buffer, ...]\n                └── children[\"core0\"] (Level)\n                    └── metrics [buffer, buffer, ...] (actual data)\n                └── children[\"core1\"]\n                    └── metrics [buffer, buffer, ...]\n```\n\n### Buffer Chain (Linked List)\n\nTime ordering: oldest ← prev -- prev -- newest (head)\n\n```\n[start=1000] ← [start=1512] ← [start=2024] (head)\n data[512]       data[512]      data[42]\n```\n\n### Metrics Indexing\n\n- Each `Level.metrics` is a slice of `*buffer`\n- Index = `MetricConfig.offset` (assigned during `InitMetrics()`)\n- Example:\n  ```\n  Metrics map: {\"cpu_load\": {offset: 0}, \"mem_used\": {offset: 1}}\n  Level.metrics: [*buffer for cpu_load, *buffer for mem_used]\n  ```\n\n---\n\n## 5. LIFECYCLE & BACKGROUND WORKERS\n\n### Initialization (Init)\n1. Decodes JSON config into `Keys`\n2. Calls `InitMetrics()` to create singleton MemoryStore\n3. Loads checkpoints from disk within retention window\n4. Starts 5 background workers:\n   - `Retention()` - Frees old data based on retention policy\n   - `Checkpointing()` - Periodic snapshot to disk\n   - `CleanUp()` - Archives/deletes data older than retention\n   - `WALStaging()` - Writes WAL entries to disk (if WAL format)\n   - `MemoryUsageTracker()` - Monitors memory, triggers emergency freeing\n\n### Shutdown\n1. Cancels all background workers\n2. Closes WAL messages channel (if WAL format)\n3. Writes final checkpoint to disk\n\n---\n\n## 6. CHECKPOINT FORMATS\n\n### JSON Format (Legacy)\n```\ncheckpoints/\n  cluster1/\n    host001/\n      1234567890.json\n```\n\nStructure:\n```go\ntype CheckpointFile struct {\n    Metrics  map[string]*CheckpointMetrics\n    Children map[string]*CheckpointFile\n    From     int64\n    To       int64\n}\n```\n\n### WAL Format (New, Recommended)\n```\ncheckpoints/\n  cluster1/\n    host001/\n      1234567890.bin    (full binary snapshot)\n      current.wal       (append-only write-ahead log)\n```\n\n- **Binary snapshot**: Complete tree dump at a point in time\n- **WAL file**: Per-write entries, replayed on startup for crash recovery\n- **Rotation**: After checkpoint, WAL is truncated\n\n---\n\n## 7. CONCURRENCY STRATEGY\n\n### RWMutex Usage\n- **Read-heavy**: Metric reads during `ms.Read()` use `RLock`\n- **Write-rare**: Level creation and buffer writes use `Lock`\n- **Double-checked locking**: In `findLevelOrCreate()` prevents race conditions\n\n### Channel-based Communication\n- `WALMessages` channel: Queues WAL entries for disk write\n- `WALStaging()` goroutine: Drains channel, batches to disk\n\n### Buffer Pool\n- Sync.Pool reduces GC pressure\n- `PersistentBufferPool` with LRU cleanup (based on lastUsed timestamp)\n\n---\n\n## 8. RETENTION & MEMORY MANAGEMENT\n\n### Retention Policy\n- Time-based: Data older than `RetentionInMemory` is freed\n- **NodeProvider integration**: Preserves data for nodes with active jobs\n- Runs at half the retention interval\n\n### Memory Emergency\n- Monitors actual heap usage via `runtime.MemStats`\n- If exceeds `MemoryCap`: triggers `ForceFree()` up to 100 iterations\n- `ForceFree()` removes oldest buffer from each metric chain\n\n### Free Mechanisms\n- **Free()**: Removes buffers older than threshold, respecting active nodes\n- **FreeSelected()**: Frees specific selectors (excludes others)\n- **ForceFree()**: Unconditionally removes oldest buffers\n\n---\n\n## Summary\n\nThe `pkg/metricstore/` package is a production-grade time-series storage system with:\n- **Hierarchical organization** for efficient per-component metric storage\n- **Flexible API** supporting both raw queries and job-centric scope transformation\n- **Robust persistence** via JSON or WAL+binary checkpoint formats\n- **Intelligent memory management** with retention, emergency freeing, and pooling\n- **Real-time ingestion** via NATS line-protocol\n- **Health monitoring** with degraded metric detection\n\nThe **InternalMetricStore** abstraction provides a clean public interface for job metric loading, handling scope transformation automatically."}],"totalDurationMs":57023,"totalTokens":57113,"totalToolUseCount":15,"usage":{"input_tokens":6,"cache_creation_input_tokens":6113,"cache_read_input_tokens":46737,"output_tokens":4257,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":6113,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"sourceToolAssistantUUID":"11e11771-ff9d-464d-948f-664179635a98"}
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{"parentUuid":"762e46e2-62ee-4fa1-810e-4127301fc634","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VMW4GepkJSzi7dwQa6Z6w1","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved. This file is part of cc-backend.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// This file contains the API types and data fetching logic for querying metric data\n     7→// from the in-memory metric store. It provides structures for building complex queries\n     8→// with support for aggregation, scaling, padding, and statistics computation.\n     9→package metricstore\n    10→\n    11→import (\n    12→\t\"errors\"\n    13→\t\"fmt\"\n    14→\t\"math\"\n    15→\n    16→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    17→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    18→\t\"github.com/ClusterCockpit/cc-lib/v2/util\"\n    19→)\n    20→\n    21→var (\n    22→\t// ErrNoHostOrMetric is returned when the metric store does not find the host or the metric\n    23→\tErrNoHostOrMetric error = errors.New(\"[METRICSTORE]> metric or host not found\")\n    24→\t// ErrInvalidTimeRange is returned when a query has 'from' >= 'to'\n    25→\tErrInvalidTimeRange = errors.New(\"[METRICSTORE]> invalid time range: 'from' must be before 'to'\")\n    26→\t// ErrEmptyCluster is returned when a query with ForAllNodes has no cluster specified\n    27→\tErrEmptyCluster = errors.New(\"[METRICSTORE]> cluster name cannot be empty\")\n    28→)\n    29→\n    30→// APIMetricData represents the response data for a single metric query.\n    31→//\n    32→// It contains both the time-series data points and computed statistics (avg, min, max).\n    33→// If an error occurred during data retrieval, the Error field will be set and other\n    34→// fields may be incomplete.\n    35→type APIMetricData struct {\n    36→\tError      *string           `json:\"error,omitempty\"`\n    37→\tData       schema.FloatArray `json:\"data,omitempty\"`\n    38→\tFrom       int64             `json:\"from\"`\n    39→\tTo         int64             `json:\"to\"`\n    40→\tResolution int64             `json:\"resolution\"`\n    41→\tAvg        schema.Float      `json:\"avg\"`\n    42→\tMin        schema.Float      `json:\"min\"`\n    43→\tMax        schema.Float      `json:\"max\"`\n    44→}\n    45→\n    46→// APIQueryRequest represents a batch query request for metric data.\n    47→//\n    48→// It supports two modes of operation:\n    49→//  1. Explicit queries via the Queries field\n    50→//  2. Automatic query generation via ForAllNodes (queries all specified metrics for all nodes in the cluster)\n    51→//\n    52→// The request can be customized with flags to include/exclude statistics, raw data, and padding.\n    53→type APIQueryRequest struct {\n    54→\tCluster     string     `json:\"cluster\"`\n    55→\tQueries     []APIQuery `json:\"queries\"`\n    56→\tForAllNodes []string   `json:\"for-all-nodes\"`\n    57→\tFrom        int64      `json:\"from\"`\n    58→\tTo          int64      `json:\"to\"`\n    59→\tWithStats   bool       `json:\"with-stats\"`\n    60→\tWithData    bool       `json:\"with-data\"`\n    61→\tWithPadding bool       `json:\"with-padding\"`\n    62→}\n    63→\n    64→// APIQueryResponse represents the response to an APIQueryRequest.\n    65→//\n    66→// Results is a 2D array where each outer element corresponds to a query,\n    67→// and each inner element corresponds to a selector within that query\n    68→// (e.g., multiple CPUs or cores).\n    69→type APIQueryResponse struct {\n    70→\tQueries []APIQuery        `json:\"queries,omitempty\"`\n    71→\tResults [][]APIMetricData `json:\"results\"`\n    72→}\n    73→\n    74→// APIQuery represents a single metric query with optional hierarchical selectors.\n    75→//\n    76→// The hierarchical selection works as follows:\n    77→//   - Hostname: The node to query\n    78→//   - Type + TypeIds: First level of hierarchy (e.g., \"cpu\" + [\"0\", \"1\", \"2\"])\n    79→//   - SubType + SubTypeIds: Second level of hierarchy (e.g., \"core\" + [\"0\", \"1\"])\n    80→//\n    81→// If Aggregate is true, data from multiple type/subtype IDs will be aggregated according\n    82→// to the metric's aggregation strategy. Otherwise, separate results are returned for each combination.\n    83→type APIQuery struct {\n    84→\tType        *string      `json:\"type,omitempty\"`\n    85→\tSubType     *string      `json:\"subtype,omitempty\"`\n    86→\tMetric      string       `json:\"metric\"`\n    87→\tHostname    string       `json:\"host\"`\n    88→\tResolution  int64        `json:\"resolution\"`\n    89→\tTypeIds     []string     `json:\"type-ids,omitempty\"`\n    90→\tSubTypeIds  []string     `json:\"subtype-ids,omitempty\"`\n    91→\tScaleFactor schema.Float `json:\"scale-by,omitempty\"`\n    92→\tAggregate   bool         `json:\"aggreg\"`\n    93→}\n    94→\n    95→// AddStats computes and populates the Avg, Min, and Max fields from the Data array.\n    96→//\n    97→// NaN values in the data are ignored during computation. If all values are NaN,\n    98→// the statistics fields will be set to NaN.\n    99→//\n   100→// TODO: Optimize this, just like the stats endpoint!\n   101→func (data *APIMetricData) AddStats() {\n   102→\tn := 0\n   103→\tsum, min, max := 0.0, math.MaxFloat64, -math.MaxFloat64\n   104→\tfor _, x := range data.Data {\n   105→\t\tif x.IsNaN() {\n   106→\t\t\tcontinue\n   107→\t\t}\n   108→\n   109→\t\tn += 1\n   110→\t\tsum += float64(x)"}]},"uuid":"86e0fe8b-cd0b-4c73-a537-19e3d56ab4c4","timestamp":"2026-03-04T15:24:49.254Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/api.go","content":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved. This file is part of cc-backend.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// This file contains the API types and data fetching logic for querying metric data\n// from the in-memory metric store. It provides structures for building complex queries\n// with support for aggregation, scaling, padding, and statistics computation.\npackage metricstore\n\nimport (\n\t\"errors\"\n\t\"fmt\"\n\t\"math\"\n\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/util\"\n)\n\nvar (\n\t// ErrNoHostOrMetric is returned when the metric store does not find the host or the metric\n\tErrNoHostOrMetric error = errors.New(\"[METRICSTORE]> metric or host not found\")\n\t// ErrInvalidTimeRange is returned when a query has 'from' >= 'to'\n\tErrInvalidTimeRange = errors.New(\"[METRICSTORE]> invalid time range: 'from' must be before 'to'\")\n\t// ErrEmptyCluster is returned when a query with ForAllNodes has no cluster specified\n\tErrEmptyCluster = errors.New(\"[METRICSTORE]> cluster name cannot be empty\")\n)\n\n// APIMetricData represents the response data for a single metric query.\n//\n// It contains both the time-series data points and computed statistics (avg, min, max).\n// If an error occurred during data retrieval, the Error field will be set and other\n// fields may be incomplete.\ntype APIMetricData struct {\n\tError      *string           `json:\"error,omitempty\"`\n\tData       schema.FloatArray `json:\"data,omitempty\"`\n\tFrom       int64             `json:\"from\"`\n\tTo         int64             `json:\"to\"`\n\tResolution int64             `json:\"resolution\"`\n\tAvg        schema.Float      `json:\"avg\"`\n\tMin        schema.Float      `json:\"min\"`\n\tMax        schema.Float      `json:\"max\"`\n}\n\n// APIQueryRequest represents a batch query request for metric data.\n//\n// It supports two modes of operation:\n//  1. Explicit queries via the Queries field\n//  2. Automatic query generation via ForAllNodes (queries all specified metrics for all nodes in the cluster)\n//\n// The request can be customized with flags to include/exclude statistics, raw data, and padding.\ntype APIQueryRequest struct {\n\tCluster     string     `json:\"cluster\"`\n\tQueries     []APIQuery `json:\"queries\"`\n\tForAllNodes []string   `json:\"for-all-nodes\"`\n\tFrom        int64      `json:\"from\"`\n\tTo          int64      `json:\"to\"`\n\tWithStats   bool       `json:\"with-stats\"`\n\tWithData    bool       `json:\"with-data\"`\n\tWithPadding bool       `json:\"with-padding\"`\n}\n\n// APIQueryResponse represents the response to an APIQueryRequest.\n//\n// Results is a 2D array where each outer element corresponds to a query,\n// and each inner element corresponds to a selector within that query\n// (e.g., multiple CPUs or cores).\ntype APIQueryResponse struct {\n\tQueries []APIQuery        `json:\"queries,omitempty\"`\n\tResults [][]APIMetricData `json:\"results\"`\n}\n\n// APIQuery represents a single metric query with optional hierarchical selectors.\n//\n// The hierarchical selection works as follows:\n//   - Hostname: The node to query\n//   - Type + TypeIds: First level of hierarchy (e.g., \"cpu\" + [\"0\", \"1\", \"2\"])\n//   - SubType + SubTypeIds: Second level of hierarchy (e.g., \"core\" + [\"0\", \"1\"])\n//\n// If Aggregate is true, data from multiple type/subtype IDs will be aggregated according\n// to the metric's aggregation strategy. Otherwise, separate results are returned for each combination.\ntype APIQuery struct {\n\tType        *string      `json:\"type,omitempty\"`\n\tSubType     *string      `json:\"subtype,omitempty\"`\n\tMetric      string       `json:\"metric\"`\n\tHostname    string       `json:\"host\"`\n\tResolution  int64        `json:\"resolution\"`\n\tTypeIds     []string     `json:\"type-ids,omitempty\"`\n\tSubTypeIds  []string     `json:\"subtype-ids,omitempty\"`\n\tScaleFactor schema.Float `json:\"scale-by,omitempty\"`\n\tAggregate   bool         `json:\"aggreg\"`\n}\n\n// AddStats computes and populates the Avg, Min, and Max fields from the Data array.\n//\n// NaN values in the data are ignored during computation. If all values are NaN,\n// the statistics fields will be set to NaN.\n//\n// TODO: Optimize this, just like the stats endpoint!\nfunc (data *APIMetricData) AddStats() {\n\tn := 0\n\tsum, min, max := 0.0, math.MaxFloat64, -math.MaxFloat64\n\tfor _, x := range data.Data {\n\t\tif x.IsNaN() {\n\t\t\tcontinue\n\t\t}\n\n\t\tn += 1\n\t\tsum += float64(x)","numLines":110,"startLine":1,"totalLines":315}},"sourceToolAssistantUUID":"762e46e2-62ee-4fa1-810e-4127301fc634"}
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{"parentUuid":"e0ab2599-40a2-42ab-ad81-b1adb647a086","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019Gb7CdgGiZrkXScgsJg84R","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// Package metricstoreclient provides a client for querying the cc-metric-store time series database.\n     7→//\n     8→// The cc-metric-store is a high-performance time series database optimized for HPC metric data.\n     9→// This client handles HTTP communication, query construction, scope transformations, and data retrieval\n    10→// for job and node metrics across different metric scopes (node, socket, core, hwthread, accelerator).\n    11→//\n    12→// # Architecture\n    13→//\n    14→// The package is split into two main components:\n    15→//   - Client Operations (cc-metric-store.go): HTTP client, request handling, data loading methods\n    16→//   - Query Building (cc-metric-store-queries.go): Query construction and scope transformation logic\n    17→//\n    18→// # Basic Usage\n    19→//\n    20→//\tstore := NewCCMetricStore(\"http://localhost:8080\", \"jwt-token\")\n    21→//\n    22→//\t// Load job data\n    23→//\tjobData, err := store.LoadData(job, metrics, scopes, ctx, resolution)\n    24→//\tif err != nil {\n    25→//\t    log.Fatal(err)\n    26→//\t}\n    27→//\n    28→// # Metric Scopes\n    29→//\n    30→// The client supports hierarchical metric scopes that map to HPC hardware topology:\n    31→//   - MetricScopeAccelerator: GPU/accelerator level metrics\n    32→//   - MetricScopeHWThread: Hardware thread (SMT) level metrics\n    33→//   - MetricScopeCore: CPU core level metrics\n    34→//   - MetricScopeSocket: CPU socket level metrics\n    35→//   - MetricScopeMemoryDomain: NUMA domain level metrics\n    36→//   - MetricScopeNode: Full node level metrics\n    37→//\n    38→// The client automatically handles scope transformations, aggregating finer-grained metrics\n    39→// to coarser scopes when needed (e.g., aggregating core metrics to socket level).\n    40→//\n    41→// # Error Handling\n    42→//\n    43→// The client supports partial errors - if some queries fail, it returns both the successful\n    44→// data and an error listing the failed queries. This allows processing partial results\n    45→// when some nodes or metrics are temporarily unavailable.\n    46→//\n    47→// # API Versioning\n    48→//\n    49→// The client uses cc-metric-store API v2, which includes support for:\n    50→//   - Data resampling for bandwidth optimization\n    51→//   - Multi-scope queries in a single request\n    52→//   - Aggregation across hardware topology levels\n    53→package metricstoreclient\n    54→\n    55→import (\n    56→\t\"bufio\"\n    57→\t\"bytes\"\n    58→\t\"context\"\n    59→\t\"encoding/json\"\n    60→\t\"fmt\"\n    61→\t\"net/http\"\n    62→\t\"strings\"\n    63→\t\"time\"\n    64→\n    65→\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n    66→\t\"github.com/ClusterCockpit/cc-backend/pkg/metricstore\"\n    67→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    68→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    69→)\n    70→\n    71→// CCMetricStore is the HTTP client for communicating with cc-metric-store.\n    72→// It manages connection details, authentication, and provides methods for querying metrics.\n    73→type CCMetricStore struct {\n    74→\tclient        http.Client                 // HTTP client with 10-second timeout\n    75→\tjwt           string                      // JWT Bearer token for authentication\n    76→\turl           string                      // Base URL of cc-metric-store instance\n    77→\tqueryEndpoint string                      // Full URL to query API endpoint\n    78→\ttopologyCache map[string]*schema.Topology // cluster -> topology cache\n    79→}\n    80→\n    81→// APIQueryRequest represents a request to the cc-metric-store query API.\n    82→// It supports both explicit queries and \"for-all-nodes\" bulk queries.\n    83→type APIQueryRequest struct {\n    84→\tCluster     string     `json:\"cluster\"`       // Target cluster name\n    85→\tQueries     []APIQuery `json:\"queries\"`       // Explicit list of metric queries\n    86→\tForAllNodes []string   `json:\"for-all-nodes\"` // Metrics to query for all nodes\n    87→\tFrom        int64      `json:\"from\"`          // Start time (Unix timestamp)\n    88→\tTo          int64      `json:\"to\"`            // End time (Unix timestamp)\n    89→\tWithStats   bool       `json:\"with-stats\"`    // Include min/avg/max statistics\n    90→\tWithData    bool       `json:\"with-data\"`     // Include time series data points\n    91→}\n    92→\n    93→// APIQuery specifies a single metric query with optional scope filtering.\n    94→// Type and TypeIds define the hardware scope (core, socket, accelerator, etc.).\n    95→type APIQuery struct {\n    96→\tType       *string  `json:\"type,omitempty\"`        // Scope type (e.g., \"core\", \"socket\")\n    97→\tSubType    *string  `json:\"subtype,omitempty\"`     // Sub-scope type (reserved for future use)\n    98→\tMetric     string   `json:\"metric\"`                // Metric name\n    99→\tHostname   string   `json:\"host\"`                  // Target hostname\n   100→\tResolution int      `json:\"resolution\"`            // Data resolution in seconds (0 = native)\n   101→\tTypeIds    []string `json:\"type-ids,omitempty\"`    // IDs for the scope type (e.g., core IDs)\n   102→\tSubTypeIds []string `json:\"subtype-ids,omitempty\"` // IDs for sub-scope (reserved)\n   103→\tAggregate  bool     `json:\"aggreg\"`                // Aggregate across TypeIds\n   104→}\n   105→\n   106→// APIQueryResponse contains the results from a cc-metric-store query.\n   107→// Results align with the Queries slice by index.\n   108→type APIQueryResponse struct {\n   109→\tQueries []APIQuery        `json:\"queries,omitempty\"` // Echoed queries (for bulk requests)\n   110→\tResults [][]APIMetricData `json:\"results\"`           // Result data, indexed by query\n   111→}\n   112→\n   113→// APIMetricData represents time series data and statistics for a single metric series.\n   114→// Error is set if this particular series failed to load.\n   115→type APIMetricData struct {\n   116→\tError      *string        `json:\"error\"`      // Error message if query failed\n   117→\tData       []schema.Float `json:\"data\"`       // Time series data points\n   118→\tFrom       int64          `json:\"from\"`       // Actual start time of data\n   119→\tTo         int64          `json:\"to\"`         // Actual end time of data\n   120→\tResolution int            `json:\"resolution\"` // Actual resolution of data in seconds\n   121→\tAvg        schema.Float   `json:\"avg\"`        // Average value across time range\n   122→\tMin        schema.Float   `json:\"min\"`        // Minimum value in time range\n   123→\tMax        schema.Float   `json:\"max\"`        // Maximum value in time range\n   124→}\n   125→\n   126→// NewCCMetricStore creates and initializes a new (external) CCMetricStore client.\n   127→// The url parameter should include the protocol and port (e.g., \"http://localhost:8080\").\n   128→// The token parameter is a JWT used for Bearer authentication; pass empty string if auth is disabled.\n   129→func NewCCMetricStore(url string, token string) *CCMetricStore {\n   130→\treturn &CCMetricStore{"}]},"uuid":"54ad7905-f641-4fd1-962b-027b4449380b","timestamp":"2026-03-04T15:24:49.742Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go","content":"// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n// All rights reserved.\n// Use of this source code is governed by a MIT-style\n// license that can be found in the LICENSE file.\n\n// Package metricstoreclient provides a client for querying the cc-metric-store time series database.\n//\n// The cc-metric-store is a high-performance time series database optimized for HPC metric data.\n// This client handles HTTP communication, query construction, scope transformations, and data retrieval\n// for job and node metrics across different metric scopes (node, socket, core, hwthread, accelerator).\n//\n// # Architecture\n//\n// The package is split into two main components:\n//   - Client Operations (cc-metric-store.go): HTTP client, request handling, data loading methods\n//   - Query Building (cc-metric-store-queries.go): Query construction and scope transformation logic\n//\n// # Basic Usage\n//\n//\tstore := NewCCMetricStore(\"http://localhost:8080\", \"jwt-token\")\n//\n//\t// Load job data\n//\tjobData, err := store.LoadData(job, metrics, scopes, ctx, resolution)\n//\tif err != nil {\n//\t    log.Fatal(err)\n//\t}\n//\n// # Metric Scopes\n//\n// The client supports hierarchical metric scopes that map to HPC hardware topology:\n//   - MetricScopeAccelerator: GPU/accelerator level metrics\n//   - MetricScopeHWThread: Hardware thread (SMT) level metrics\n//   - MetricScopeCore: CPU core level metrics\n//   - MetricScopeSocket: CPU socket level metrics\n//   - MetricScopeMemoryDomain: NUMA domain level metrics\n//   - MetricScopeNode: Full node level metrics\n//\n// The client automatically handles scope transformations, aggregating finer-grained metrics\n// to coarser scopes when needed (e.g., aggregating core metrics to socket level).\n//\n// # Error Handling\n//\n// The client supports partial errors - if some queries fail, it returns both the successful\n// data and an error listing the failed queries. This allows processing partial results\n// when some nodes or metrics are temporarily unavailable.\n//\n// # API Versioning\n//\n// The client uses cc-metric-store API v2, which includes support for:\n//   - Data resampling for bandwidth optimization\n//   - Multi-scope queries in a single request\n//   - Aggregation across hardware topology levels\npackage metricstoreclient\n\nimport (\n\t\"bufio\"\n\t\"bytes\"\n\t\"context\"\n\t\"encoding/json\"\n\t\"fmt\"\n\t\"net/http\"\n\t\"strings\"\n\t\"time\"\n\n\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n\t\"github.com/ClusterCockpit/cc-backend/pkg/metricstore\"\n\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n)\n\n// CCMetricStore is the HTTP client for communicating with cc-metric-store.\n// It manages connection details, authentication, and provides methods for querying metrics.\ntype CCMetricStore struct {\n\tclient        http.Client                 // HTTP client with 10-second timeout\n\tjwt           string                      // JWT Bearer token for authentication\n\turl           string                      // Base URL of cc-metric-store instance\n\tqueryEndpoint string                      // Full URL to query API endpoint\n\ttopologyCache map[string]*schema.Topology // cluster -> topology cache\n}\n\n// APIQueryRequest represents a request to the cc-metric-store query API.\n// It supports both explicit queries and \"for-all-nodes\" bulk queries.\ntype APIQueryRequest struct {\n\tCluster     string     `json:\"cluster\"`       // Target cluster name\n\tQueries     []APIQuery `json:\"queries\"`       // Explicit list of metric queries\n\tForAllNodes []string   `json:\"for-all-nodes\"` // Metrics to query for all nodes\n\tFrom        int64      `json:\"from\"`          // Start time (Unix timestamp)\n\tTo          int64      `json:\"to\"`            // End time (Unix timestamp)\n\tWithStats   bool       `json:\"with-stats\"`    // Include min/avg/max statistics\n\tWithData    bool       `json:\"with-data\"`     // Include time series data points\n}\n\n// APIQuery specifies a single metric query with optional scope filtering.\n// Type and TypeIds define the hardware scope (core, socket, accelerator, etc.).\ntype APIQuery struct {\n\tType       *string  `json:\"type,omitempty\"`        // Scope type (e.g., \"core\", \"socket\")\n\tSubType    *string  `json:\"subtype,omitempty\"`     // Sub-scope type (reserved for future use)\n\tMetric     string   `json:\"metric\"`                // Metric name\n\tHostname   string   `json:\"host\"`                  // Target hostname\n\tResolution int      `json:\"resolution\"`            // Data resolution in seconds (0 = native)\n\tTypeIds    []string `json:\"type-ids,omitempty\"`    // IDs for the scope type (e.g., core IDs)\n\tSubTypeIds []string `json:\"subtype-ids,omitempty\"` // IDs for sub-scope (reserved)\n\tAggregate  bool     `json:\"aggreg\"`                // Aggregate across TypeIds\n}\n\n// APIQueryResponse contains the results from a cc-metric-store query.\n// Results align with the Queries slice by index.\ntype APIQueryResponse struct {\n\tQueries []APIQuery        `json:\"queries,omitempty\"` // Echoed queries (for bulk requests)\n\tResults [][]APIMetricData `json:\"results\"`           // Result data, indexed by query\n}\n\n// APIMetricData represents time series data and statistics for a single metric series.\n// Error is set if this particular series failed to load.\ntype APIMetricData struct {\n\tError      *string        `json:\"error\"`      // Error message if query failed\n\tData       []schema.Float `json:\"data\"`       // Time series data points\n\tFrom       int64          `json:\"from\"`       // Actual start time of data\n\tTo         int64          `json:\"to\"`         // Actual end time of data\n\tResolution int            `json:\"resolution\"` // Actual resolution of data in seconds\n\tAvg        schema.Float   `json:\"avg\"`        // Average value across time range\n\tMin        schema.Float   `json:\"min\"`        // Minimum value in time range\n\tMax        schema.Float   `json:\"max\"`        // Maximum value in time range\n}\n\n// NewCCMetricStore creates and initializes a new (external) CCMetricStore client.\n// The url parameter should include the protocol and port (e.g., \"http://localhost:8080\").\n// The token parameter is a JWT used for Bearer authentication; pass empty string if auth is disabled.\nfunc NewCCMetricStore(url string, token string) *CCMetricStore {\n\treturn &CCMetricStore{","numLines":130,"startLine":1,"totalLines":844}},"sourceToolAssistantUUID":"e0ab2599-40a2-42ab-ad81-b1adb647a086"}
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{"parentUuid":"ce898af0-e64f-4e27-843d-9303a963bbe1","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Et6kN3xMwpyWFNmv3Y1dbL","type":"tool_result","content":"   130→\treturn &CCMetricStore{\n   131→\t\turl:           url,\n   132→\t\tqueryEndpoint: fmt.Sprintf(\"%s/api/query\", url),\n   133→\t\tjwt:           token,\n   134→\t\tclient: http.Client{\n   135→\t\t\tTimeout: 10 * time.Second,\n   136→\t\t},\n   137→\t\ttopologyCache: make(map[string]*schema.Topology),\n   138→\t}\n   139→}\n   140→\n   141→// doRequest executes an HTTP POST request to the cc-metric-store query API.\n   142→// It handles JSON encoding/decoding, authentication, and API versioning.\n   143→// The request body is automatically closed to prevent resource leaks.\n   144→func (ccms *CCMetricStore) doRequest(\n   145→\tctx context.Context,\n   146→\tbody *APIQueryRequest,\n   147→) (*APIQueryResponse, error) {\n   148→\tbuf := &bytes.Buffer{}\n   149→\tif err := json.NewEncoder(buf).Encode(body); err != nil {\n   150→\t\tcclog.Errorf(\"Error while encoding request body: %s\", err.Error())\n   151→\t\treturn nil, err\n   152→\t}\n   153→\n   154→\treq, err := http.NewRequestWithContext(ctx, http.MethodGet, ccms.queryEndpoint, buf)\n   155→\tif err != nil {\n   156→\t\tcclog.Errorf(\"Error while building request body: %s\", err.Error())\n   157→\t\treturn nil, err\n   158→\t}\n   159→\tif ccms.jwt != \"\" {\n   160→\t\treq.Header.Add(\"Authorization\", fmt.Sprintf(\"Bearer %s\", ccms.jwt))\n   161→\t}\n   162→\n   163→\t// versioning the cc-metric-store query API.\n   164→\t// v2 = data with resampling\n   165→\t// v1 = data without resampling\n   166→\tq := req.URL.Query()\n   167→\tq.Add(\"version\", \"v2\")\n   168→\treq.URL.RawQuery = q.Encode()\n   169→\n   170→\tres, err := ccms.client.Do(req)\n   171→\tif err != nil {\n   172→\t\tcclog.Errorf(\"Error while performing request: %s\", err.Error())\n   173→\t\treturn nil, err\n   174→\t}\n   175→\tdefer res.Body.Close()\n   176→\n   177→\tif res.StatusCode != http.StatusOK {\n   178→\t\treturn nil, fmt.Errorf(\"'%s': HTTP Status: %s\", ccms.queryEndpoint, res.Status)\n   179→\t}\n   180→\n   181→\tvar resBody APIQueryResponse\n   182→\tif err := json.NewDecoder(bufio.NewReader(res.Body)).Decode(&resBody); err != nil {\n   183→\t\tcclog.Errorf(\"Error while decoding result body: %s\", err.Error())\n   184→\t\treturn nil, err\n   185→\t}\n   186→\n   187→\treturn &resBody, nil\n   188→}\n   189→\n   190→// getTopology returns the topology for a given cluster and subcluster, caching it if not already present\n   191→func (ccms *CCMetricStore) getTopology(cluster, subCluster string) (*schema.Topology, error) {\n   192→\tcacheKey := fmt.Sprintf(\"%s:%s\", cluster, subCluster)\n   193→\tif topology, ok := ccms.topologyCache[cacheKey]; ok {\n   194→\t\treturn topology, nil\n   195→\t}\n   196→\n   197→\tsubcluster, err := archive.GetSubCluster(cluster, subCluster)\n   198→\tif err != nil {\n   199→\t\treturn nil, err\n   200→\t}\n   201→\n   202→\tccms.topologyCache[cacheKey] = &subcluster.Topology\n   203→\treturn &subcluster.Topology, nil\n   204→}\n   205→\n   206→// getTopologyByNode returns the topology for a given cluster and node, caching it if not already present\n   207→func (ccms *CCMetricStore) getTopologyByNode(cluster, node string) (*schema.Topology, error) {\n   208→\tsubCluster, err := archive.GetSubClusterByNode(cluster, node)\n   209→\tif err != nil {\n   210→\t\treturn nil, err\n   211→\t}\n   212→\n   213→\treturn ccms.getTopology(cluster, subCluster)\n   214→}\n   215→\n   216→// LoadData retrieves time series data and statistics for the specified job and metrics.\n   217→// It queries data for the job's time range and resources, handling scope transformations automatically.\n   218→//\n   219→// Parameters:\n   220→//   - job: Job metadata including cluster, time range, and allocated resources\n   221→//   - metrics: List of metric names to retrieve\n   222→//   - scopes: Requested metric scopes (node, socket, core, etc.)\n   223→//   - ctx: Context for cancellation and timeouts\n   224→//   - resolution: Data resolution in seconds (0 for native resolution)\n   225→//\n   226→// Returns JobData organized as: metric -> scope -> series list.\n   227→// Supports partial errors: returns available data even if some queries fail.\n   228→func (ccms *CCMetricStore) LoadData(\n   229→\tjob *schema.Job,\n   230→\tmetrics []string,\n   231→\tscopes []schema.MetricScope,\n   232→\tctx context.Context,\n   233→\tresolution int,\n   234→) (schema.JobData, error) {\n   235→\tqueries, assignedScope, err := ccms.buildQueries(job, metrics, scopes, resolution)\n   236→\tif err != nil {\n   237→\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n   238→\t\treturn nil, err\n   239→\t}\n   240→\n   241→\t// Verify assignment is correct - log any inconsistencies for debugging\n   242→\tif len(queries) != len(assignedScope) {\n   243→\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildQueries: %d vs %d\",\n   244→\t\t\tlen(queries), len(assignedScope))\n   245→\t}\n   246→\n   247→\treq := APIQueryRequest{\n   248→\t\tCluster:   job.Cluster,\n   249→\t\tFrom:      job.StartTime,\n   250→\t\tTo:        job.StartTime + int64(job.Duration),\n   251→\t\tQueries:   queries,\n   252→\t\tWithStats: true,\n   253→\t\tWithData:  true,\n   254→\t}\n   255→\n   256→\tresBody, err := ccms.doRequest(ctx, &req)\n   257→\tif err != nil {\n   258→\t\tcclog.Errorf(\"Error while performing request for job %d: %s\", job.JobID, err.Error())\n   259→\t\treturn nil, err\n   260→\t}\n   261→\n   262→\tvar errors []string\n   263→\tjobData := make(schema.JobData)\n   264→\n   265→\t// Add safety check for potential index out of range errors\n   266→\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n   267→\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n   268→\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n   269→\t\tif len(resBody.Results) > len(req.Queries) {\n   270→\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n   271→\t\t}\n   272→\t\tif len(assignedScope) > len(req.Queries) {\n   273→\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n   274→\t\t}\n   275→\t}\n   276→\n   277→\tfor i, row := range resBody.Results {\n   278→\t\t// Safety check to prevent index out of range errors\n   279→\t\tif i >= len(req.Queries) || i >= len(assignedScope) {\n   280→\t\t\tcclog.Warnf(\"Index out of range prevented: i=%d, queries=%d, assignedScope=%d\",\n   281→\t\t\t\ti, len(req.Queries), len(assignedScope))\n   282→\t\t\tcontinue\n   283→\t\t}\n   284→\n   285→\t\tquery := req.Queries[i]\n   286→\t\tmetric := query.Metric\n   287→\t\tscope := assignedScope[i]\n   288→\t\tmc := archive.GetMetricConfig(job.Cluster, metric)\n   289→\n   290→\t\tif mc == nil {\n   291→\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, job.Cluster)\n   292→\t\t\tcontinue\n   293→\t\t}\n   294→\n   295→\t\tif _, ok := jobData[metric]; !ok {\n   296→\t\t\tjobData[metric] = make(map[schema.MetricScope]*schema.JobMetric)\n   297→\t\t}\n   298→\n   299→\t\tres := mc.Timestep\n   300→\t\tif len(row) > 0 {\n   301→\t\t\tres = row[0].Resolution\n   302→\t\t}\n   303→\n   304→\t\tjobMetric, ok := jobData[metric][scope]\n   305→\t\tif !ok {\n   306→\t\t\tjobMetric = &schema.JobMetric{\n   307→\t\t\t\tUnit:     mc.Unit,\n   308→\t\t\t\tTimestep: res,\n   309→\t\t\t\tSeries:   make([]schema.Series, 0),\n   310→\t\t\t}\n   311→\t\t\tjobData[metric][scope] = jobMetric\n   312→\t\t}\n   313→\n   314→\t\tfor ndx, res := range row {\n   315→\t\t\tif res.Error != nil {\n   316→\t\t\t\t/* Build list for \"partial errors\", if any */\n   317→\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n   318→\t\t\t\tcontinue\n   319→\t\t\t}\n   320→\n   321→\t\t\tid := (*string)(nil)\n   322→\t\t\tif query.Type != nil {\n   323→\t\t\t\t// Check if ndx is within the bounds of TypeIds slice\n   324→\t\t\t\tif ndx < len(query.TypeIds) {\n   325→\t\t\t\t\tid = new(string)\n   326→\t\t\t\t\t*id = query.TypeIds[ndx]\n   327→\t\t\t\t} else {\n   328→\t\t\t\t\t// Log the error but continue processing\n   329→\t\t\t\t\tcclog.Warnf(\"TypeIds index out of range: %d with length %d for metric %s on host %s\",\n   330→\t\t\t\t\t\tndx, len(query.TypeIds), query.Metric, query.Hostname)\n   331→\t\t\t\t}\n   332→\t\t\t}\n   333→\n   334→\t\t\tsanitizeStats(&res.Avg, &res.Min, &res.Max)\n   335→\n   336→\t\t\tjobMetric.Series = append(jobMetric.Series, schema.Series{\n   337→\t\t\t\tHostname: query.Hostname,\n   338→\t\t\t\tID:       id,\n   339→\t\t\t\tStatistics: schema.MetricStatistics{\n   340→\t\t\t\t\tAvg: float64(res.Avg),\n   341→\t\t\t\t\tMin: float64(res.Min),\n   342→\t\t\t\t\tMax: float64(res.Max),\n   343→\t\t\t\t},\n   344→\t\t\t\tData: res.Data,\n   345→\t\t\t})\n   346→\t\t}\n   347→\n   348→\t\t// So that one can later check len(jobData):\n   349→\t\tif len(jobMetric.Series) == 0 {\n   350→\t\t\tdelete(jobData[metric], scope)\n   351→\t\t\tif len(jobData[metric]) == 0 {\n   352→\t\t\t\tdelete(jobData, metric)\n   353→\t\t\t}\n   354→\t\t}\n   355→\t}\n   356→\n   357→\tif len(errors) != 0 {\n   358→\t\t/* Returns list for \"partial errors\" */\n   359→\t\treturn jobData, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n   360→\t}\n   361→\treturn jobData, nil\n   362→}\n   363→\n   364→// LoadStats retrieves min/avg/max statistics for job metrics at node scope.\n   365→// This is faster than LoadData when only statistical summaries are needed (no time series data).\n   366→//\n   367→// Returns statistics organized as: metric -> hostname -> statistics.\n   368→func (ccms *CCMetricStore) LoadStats(\n   369→\tjob *schema.Job,\n   370→\tmetrics []string,\n   371→\tctx context.Context,\n   372→) (map[string]map[string]schema.MetricStatistics, error) {\n   373→\tqueries, _, err := ccms.buildQueries(job, metrics, []schema.MetricScope{schema.MetricScopeNode}, 0) // #166 Add scope shere for analysis view accelerator normalization?\n   374→\tif err != nil {\n   375→\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v: %s\", job.JobID, metrics, err.Error())\n   376→\t\treturn nil, err\n   377→\t}\n   378→\n   379→\treq := APIQueryRequest{\n   380→\t\tCluster:   job.Cluster,\n   381→\t\tFrom:      job.StartTime,\n   382→\t\tTo:        job.StartTime + int64(job.Duration),\n   383→\t\tQueries:   queries,\n   384→\t\tWithStats: true,\n   385→\t\tWithData:  false,\n   386→\t}\n   387→\n   388→\tresBody, err := ccms.doRequest(ctx, &req)\n   389→\tif err != nil {\n   390→\t\tcclog.Errorf(\"Error while performing request for job %d: %s\", job.JobID, err.Error())\n   391→\t\treturn nil, err\n   392→\t}\n   393→\n   394→\tstats := make(map[string]map[string]schema.MetricStatistics, len(metrics))\n   395→\tfor i, res := range resBody.Results {\n   396→\t\tif len(res) == 0 {\n   397→\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n   398→\t\t\tcontinue\n   399→\t\t}\n   400→\t\tquery := req.Queries[i]\n   401→\t\tmetric := query.Metric\n   402→\t\tdata := res[0]\n   403→\t\tif data.Error != nil {\n   404→\t\t\tcclog.Warnf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *data.Error)\n   405→\t\t\tcontinue\n   406→\t\t}\n   407→\n   408→\t\tmetricdata, ok := stats[metric]\n   409→\t\tif !ok {\n   410→\t\t\tmetricdata = make(map[string]schema.MetricStatistics, job.NumNodes)\n   411→\t\t\tstats[metric] = metricdata\n   412→\t\t}\n   413→\n   414→\t\tif hasNaNStats(data.Avg, data.Min, data.Max) {\n   415→\t\t\tcclog.Warnf(\"fetching %s for node %s failed: one of avg/min/max is NaN\", metric, query.Hostname)\n   416→\t\t\tcontinue\n   417→\t\t}\n   418→\n   419→\t\tmetricdata[query.Hostname] = schema.MetricStatistics{\n   420→\t\t\tAvg: float64(data.Avg),\n   421→\t\t\tMin: float64(data.Min),\n   422→\t\t\tMax: float64(data.Max),\n   423→\t\t}\n   424→\t}\n   425→\n   426→\treturn stats, nil\n   427→}\n   428→\n   429→// LoadScopedStats retrieves statistics for job metrics across multiple scopes.\n   430→// Used for the Job-View Statistics Table to display per-scope breakdowns.\n   431→//\n   432→// Returns statistics organized as: metric -> scope -> list of scoped statistics.\n   433→// Each scoped statistic includes hostname, hardware ID (if applicable), and min/avg/max values.\n   434→func (ccms *CCMetricStore) LoadScopedStats(\n   435→\tjob *schema.Job,\n   436→\tmetrics []string,\n   437→\tscopes []schema.MetricScope,\n   438→\tctx context.Context,\n   439→) (schema.ScopedJobStats, error) {\n   440→\tqueries, assignedScope, err := ccms.buildQueries(job, metrics, scopes, 0)\n   441→\tif err != nil {\n   442→\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n   443→\t\treturn nil, err\n   444→\t}\n   445→\n   446→\treq := APIQueryRequest{\n   447→\t\tCluster:   job.Cluster,\n   448→\t\tFrom:      job.StartTime,\n   449→\t\tTo:        job.StartTime + int64(job.Duration),\n   450→\t\tQueries:   queries,\n   451→\t\tWithStats: true,\n   452→\t\tWithData:  false,\n   453→\t}\n   454→\n   455→\tresBody, err := ccms.doRequest(ctx, &req)\n   456→\tif err != nil {\n   457→\t\tcclog.Errorf(\"Error while performing request for job %d: %s\", job.JobID, err.Error())\n   458→\t\treturn nil, err\n   459→\t}\n   460→\n   461→\tvar errors []string\n   462→\tscopedJobStats := make(schema.ScopedJobStats)\n   463→\n   464→\tfor i, row := range resBody.Results {\n   465→\t\tquery := req.Queries[i]\n   466→\t\tmetric := query.Metric\n   467→\t\tscope := assignedScope[i]\n   468→\n   469→\t\tif _, ok := scopedJobStats[metric]; !ok {\n   470→\t\t\tscopedJobStats[metric] = make(map[schema.MetricScope][]*schema.ScopedStats)\n   471→\t\t}\n   472→\n   473→\t\tif _, ok := scopedJobStats[metric][scope]; !ok {\n   474→\t\t\tscopedJobStats[metric][scope] = make([]*schema.ScopedStats, 0)\n   475→\t\t}\n   476→\n   477→\t\tfor ndx, res := range row {\n   478→\t\t\tif res.Error != nil {\n   479→\t\t\t\t/* Build list for \"partial errors\", if any */\n   480→\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n   481→\t\t\t\tcontinue\n   482→\t\t\t}\n   483→\n   484→\t\t\tid := (*string)(nil)\n   485→\t\t\tif query.Type != nil {\n   486→\t\t\t\t// Check if ndx is within the bounds of TypeIds slice\n   487→\t\t\t\tif ndx < len(query.TypeIds) {\n   488→\t\t\t\t\tid = new(string)\n   489→\t\t\t\t\t*id = query.TypeIds[ndx]\n   490→\t\t\t\t} else {\n   491→\t\t\t\t\t// Log the error but continue processing\n   492→\t\t\t\t\tcclog.Warnf(\"TypeIds index out of range: %d with length %d for metric %s on host %s\",\n   493→\t\t\t\t\t\tndx, len(query.TypeIds), query.Metric, query.Hostname)\n   494→\t\t\t\t}\n   495→\t\t\t}\n   496→\n   497→\t\t\tsanitizeStats(&res.Avg, &res.Min, &res.Max)\n   498→\n   499→\t\t\tscopedJobStats[metric][scope] = append(scopedJobStats[metric][scope], &schema.ScopedStats{\n   500→\t\t\t\tHostname: query.Hostname,\n   501→\t\t\t\tID:       id,\n   502→\t\t\t\tData: &schema.MetricStatistics{\n   503→\t\t\t\t\tAvg: float64(res.Avg),\n   504→\t\t\t\t\tMin: float64(res.Min),\n   505→\t\t\t\t\tMax: float64(res.Max),\n   506→\t\t\t\t},\n   507→\t\t\t})\n   508→\t\t}\n   509→\n   510→\t\t// So that one can later check len(scopedJobStats[metric][scope]): Remove from map if empty\n   511→\t\tif len(scopedJobStats[metric][scope]) == 0 {\n   512→\t\t\tdelete(scopedJobStats[metric], scope)\n   513→\t\t\tif len(scopedJobStats[metric]) == 0 {\n   514→\t\t\t\tdelete(scopedJobStats, metric)\n   515→\t\t\t}\n   516→\t\t}\n   517→\t}\n   518→\n   519→\tif len(errors) != 0 {\n   520→\t\t/* Returns list for \"partial errors\" */\n   521→\t\treturn scopedJobStats, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n   522→\t}\n   523→\treturn scopedJobStats, nil\n   524→}\n   525→\n   526→// LoadNodeData retrieves current metric data for specified nodes in a cluster.\n   527→// Used for the Systems-View Node-Overview to display real-time node status.\n   528→//\n   529→// If nodes is nil, queries all metrics for all nodes in the cluster (bulk query).\n   530→// Returns data organized as: hostname -> metric -> list of JobMetric (with time series and stats).\n   531→func (ccms *CCMetricStore) LoadNodeData(\n   532→\tcluster string,\n   533→\tmetrics, nodes []string,\n   534→\tscopes []schema.MetricScope,\n   535→\tfrom, to time.Time,\n   536→\tctx context.Context,\n   537→) (map[string]map[string][]*schema.JobMetric, error) {\n   538→\treq := APIQueryRequest{\n   539→\t\tCluster:   cluster,\n   540→\t\tFrom:      from.Unix(),\n   541→\t\tTo:        to.Unix(),\n   542→\t\tWithStats: true,\n   543→\t\tWithData:  true,\n   544→\t}\n   545→\n   546→\tif nodes == nil {\n   547→\t\treq.ForAllNodes = append(req.ForAllNodes, metrics...)\n   548→\t} else {\n   549→\t\tfor _, node := range nodes {\n   550→\t\t\tfor _, metric := range metrics {\n   551→\t\t\t\treq.Queries = append(req.Queries, APIQuery{\n   552→\t\t\t\t\tHostname:   node,\n   553→\t\t\t\t\tMetric:     metric,\n   554→\t\t\t\t\tResolution: 0, // Default for Node Queries: Will return metric $Timestep Resolution\n   555→\t\t\t\t})\n   556→\t\t\t}\n   557→\t\t}\n   558→\t}\n   559→\n   560→\tresBody, err := ccms.doRequest(ctx, &req)\n   561→\tif err != nil {\n   562→\t\tcclog.Errorf(\"Error while performing request for cluster %s: %s\", cluster, err.Error())\n   563→\t\treturn nil, err\n   564→\t}\n   565→\n   566→\tvar errors []string\n   567→\tdata := make(map[string]map[string][]*schema.JobMetric)\n   568→\tfor i, res := range resBody.Results {\n   569→\t\tif len(res) == 0 {\n   570→\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n   571→\t\t\tcontinue\n   572→\t\t}\n   573→\n   574→\t\tvar query APIQuery\n   575→\t\tif resBody.Queries != nil {\n   576→\t\t\tquery = resBody.Queries[i]\n   577→\t\t} else {\n   578→\t\t\tquery = req.Queries[i]\n   579→\t\t}\n   580→\n   581→\t\tmetric := query.Metric\n   582→\t\tqdata := res[0]\n   583→\t\tif qdata.Error != nil {\n   584→\t\t\terrors = append(errors, fmt.Sprintf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *qdata.Error))\n   585→\t\t}\n   586→\n   587→\t\tsanitizeStats(&qdata.Avg, &qdata.Min, &qdata.Max)\n   588→\n   589→\t\thostdata, ok := data[query.Hostname]\n   590→\t\tif !ok {\n   591→\t\t\thostdata = make(map[string][]*schema.JobMetric)\n   592→\t\t\tdata[query.Hostname] = hostdata\n   593→\t\t}\n   594→\n   595→\t\tmc := archive.GetMetricConfig(cluster, metric)\n   596→\t\thostdata[metric] = append(hostdata[metric], &schema.JobMetric{\n   597→\t\t\tUnit:     mc.Unit,\n   598→\t\t\tTimestep: mc.Timestep,\n   599→\t\t\tSeries: []schema.Series{\n   600→\t\t\t\t{\n   601→\t\t\t\t\tHostname: query.Hostname,\n   602→\t\t\t\t\tData:     qdata.Data,\n   603→\t\t\t\t\tStatistics: schema.MetricStatistics{\n   604→\t\t\t\t\t\tAvg: float64(qdata.Avg),\n   605→\t\t\t\t\t\tMin: float64(qdata.Min),\n   606→\t\t\t\t\t\tMax: float64(qdata.Max),\n   607→\t\t\t\t\t},\n   608→\t\t\t\t},\n   609→\t\t\t},\n   610→\t\t})\n   611→\t}\n   612→\n   613→\tif len(errors) != 0 {\n   614→\t\t/* Returns list of \"partial errors\" */\n   615→\t\treturn data, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n   616→\t}\n   617→\n   618→\treturn data, nil\n   619→}\n   620→\n   621→// LoadNodeListData retrieves paginated node metrics for the Systems-View Node-List.\n   622→//\n   623→// Supports filtering by subcluster and node name pattern. The nodeFilter performs\n   624→// substring matching on hostnames.\n   625→//\n   626→// Returns:\n   627→//   - Node data organized as: hostname -> JobData (metric -> scope -> series)\n   628→//   - Total node count (before pagination)\n   629→//   - HasNextPage flag indicating if more pages are available\n   630→//   - Error (may be partial error with some data returned)\n   631→func (ccms *CCMetricStore) LoadNodeListData(\n   632→\tcluster, subCluster string,\n   633→\tnodes []string,\n   634→\tmetrics []string,\n   635→\tscopes []schema.MetricScope,\n   636→\tresolution int,\n   637→\tfrom, to time.Time,\n   638→\tctx context.Context,\n   639→) (map[string]schema.JobData, error) {\n   640→\tqueries, assignedScope, err := ccms.buildNodeQueries(cluster, subCluster, nodes, metrics, scopes, resolution)\n   641→\tif err != nil {\n   642→\t\tcclog.Errorf(\"Error while building node queries for Cluster %s, SubCluster %s, Metrics %v, Scopes %v: %s\", cluster, subCluster, metrics, scopes, err.Error())\n   643→\t\treturn nil, err\n   644→\t}\n   645→\n   646→\t// Verify assignment is correct - log any inconsistencies for debugging\n   647→\tif len(queries) != len(assignedScope) {\n   648→\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildNodeQueries: %d vs %d\",\n   649→\t\t\tlen(queries), len(assignedScope))\n   650→\t}\n   651→\n   652→\treq := APIQueryRequest{\n   653→\t\tCluster:   cluster,\n   654→\t\tQueries:   queries,\n   655→\t\tFrom:      from.Unix(),\n   656→\t\tTo:        to.Unix(),\n   657→\t\tWithStats: true,\n   658→\t\tWithData:  true,\n   659→\t}\n   660→\n   661→\tresBody, err := ccms.doRequest(ctx, &req)\n   662→\tif err != nil {\n   663→\t\tcclog.Errorf(\"Error while performing request for cluster %s: %s\", cluster, err.Error())\n   664→\t\treturn nil, err\n   665→\t}\n   666→\n   667→\tvar errors []string\n   668→\tdata := make(map[string]schema.JobData)\n   669→\n   670→\t// Add safety check for index out of range issues\n   671→\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n   672→\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n   673→\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n   674→\t\tif len(resBody.Results) > len(req.Queries) {\n   675→\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n   676→\t\t}\n   677→\t\tif len(assignedScope) > len(req.Queries) {\n   678→\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n   679→\t\t}\n   680→\t}\n   681→\n   682→\tfor i, row := range resBody.Results {\n   683→\t\t// Safety check to prevent index out of range errors\n   684→\t\tif i >= len(req.Queries) || i >= len(assignedScope) {\n   685→\t\t\tcclog.Warnf(\"Index out of range prevented: i=%d, queries=%d, assignedScope=%d\",\n   686→\t\t\t\ti, len(req.Queries), len(assignedScope))\n   687→\t\t\tcontinue\n   688→\t\t}\n   689→\n   690→\t\tvar query APIQuery\n   691→\t\tif resBody.Queries != nil {\n   692→\t\t\tif i < len(resBody.Queries) {\n   693→\t\t\t\tquery = resBody.Queries[i]\n   694→\t\t\t} else {\n   695→\t\t\t\tcclog.Warnf(\"Index out of range prevented for resBody.Queries: i=%d, len=%d\",\n   696→\t\t\t\t\ti, len(resBody.Queries))\n   697→\t\t\t\tcontinue\n   698→\t\t\t}\n   699→\t\t} else {\n   700→\t\t\tquery = req.Queries[i]\n   701→\t\t}\n   702→\n   703→\t\tmetric := query.Metric\n   704→\t\tscope := assignedScope[i]\n   705→\t\tmc := archive.GetMetricConfig(cluster, metric)\n   706→\t\tif mc == nil {\n   707→\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, cluster)\n   708→\t\t\tcontinue\n   709→\t\t}\n   710→\n   711→\t\tres := mc.Timestep\n   712→\t\tif len(row) > 0 {\n   713→\t\t\tres = row[0].Resolution\n   714→\t\t}\n   715→\n   716→\t\t// Init Nested Map Data Structures If Not Found\n   717→\t\thostData, ok := data[query.Hostname]\n   718→\t\tif !ok {\n   719→\t\t\thostData = make(schema.JobData)\n   720→\t\t\tdata[query.Hostname] = hostData\n   721→\t\t}\n   722→\n   723→\t\tmetricData, ok := hostData[metric]\n   724→\t\tif !ok {\n   725→\t\t\tmetricData = make(map[schema.MetricScope]*schema.JobMetric)\n   726→\t\t\tdata[query.Hostname][metric] = metricData\n   727→\t\t}\n   728→\n   729→\t\tscopeData, ok := metricData[scope]\n   730→\t\tif !ok {\n   731→\t\t\tscopeData = &schema.JobMetric{\n   732→\t\t\t\tUnit:     mc.Unit,\n   733→\t\t\t\tTimestep: res,\n   734→\t\t\t\tSeries:   make([]schema.Series, 0),\n   735→\t\t\t}\n   736→\t\t\tdata[query.Hostname][metric][scope] = scopeData\n   737→\t\t}\n   738→\n   739→\t\tfor ndx, res := range row {\n   740→\t\t\tif res.Error != nil {\n   741→\t\t\t\t/* Build list for \"partial errors\", if any */\n   742→\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n   743→\t\t\t\tcontinue\n   744→\t\t\t}\n   745→\n   746→\t\t\tid := (*string)(nil)\n   747→\t\t\tif query.Type != nil {\n   748→\t\t\t\t// Check if ndx is within the bounds of TypeIds slice\n   749→\t\t\t\tif ndx < len(query.TypeIds) {\n   750→\t\t\t\t\tid = new(string)\n   751→\t\t\t\t\t*id = query.TypeIds[ndx]\n   752→\t\t\t\t} else {\n   753→\t\t\t\t\t// Log the error but continue processing\n   754→\t\t\t\t\tcclog.Warnf(\"TypeIds index out of range: %d with length %d for metric %s on host %s\",\n   755→\t\t\t\t\t\tndx, len(query.TypeIds), query.Metric, query.Hostname)\n   756→\t\t\t\t}\n   757→\t\t\t}\n   758→\n   759→\t\t\tsanitizeStats(&res.Avg, &res.Min, &res.Max)\n   760→\n   761→\t\t\tscopeData.Series = append(scopeData.Series, schema.Series{\n   762→\t\t\t\tHostname: query.Hostname,\n   763→\t\t\t\tID:       id,\n   764→\t\t\t\tStatistics: schema.MetricStatistics{\n   765→\t\t\t\t\tAvg: float64(res.Avg),\n   766→\t\t\t\t\tMin: float64(res.Min),\n   767→\t\t\t\t\tMax: float64(res.Max),\n   768→\t\t\t\t},\n   769→\t\t\t\tData: res.Data,\n   770→\t\t\t})\n   771→\t\t}\n   772→\t}\n   773→\n   774→\tif len(errors) != 0 {\n   775→\t\t/* Returns list of \"partial errors\" */\n   776→\t\treturn data, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n   777→\t}\n   778→\n   779→\treturn data, nil\n   780→}\n   781→\n   782→// HealthCheck queries the external cc-metric-store's health check endpoint.\n   783→// It sends a HealthCheckReq as the request body to /api/healthcheck and\n   784→// returns the per-node health check results.\n   785→func (ccms *CCMetricStore) HealthCheck(cluster string,\n   786→\tnodes []string, metrics []string,\n   787→) (map[string]metricstore.HealthCheckResult, error) {\n   788→\treq := metricstore.HealthCheckReq{\n   789→\t\tCluster:     cluster,\n   790→\t\tNodes:       nodes,\n   791→\t\tMetricNames: metrics,\n   792→\t}\n   793→\n   794→\tbuf := &bytes.Buffer{}\n   795→\tif err := json.NewEncoder(buf).Encode(req); err != nil {\n   796→\t\tcclog.Errorf(\"Error while encoding health check request body: %s\", err.Error())\n   797→\t\treturn nil, err\n   798→\t}\n   799→\n   800→\tendpoint := fmt.Sprintf(\"%s/api/healthcheck\", ccms.url)\n   801→\thttpReq, err := http.NewRequest(http.MethodGet, endpoint, buf)\n   802→\tif err != nil {\n   803→\t\tcclog.Errorf(\"Error while building health check request: %s\", err.Error())\n   804→\t\treturn nil, err\n   805→\t}\n   806→\tif ccms.jwt != \"\" {\n   807→\t\thttpReq.Header.Add(\"Authorization\", fmt.Sprintf(\"Bearer %s\", ccms.jwt))\n   808→\t}\n   809→\n   810→\tres, err := ccms.client.Do(httpReq)\n   811→\tif err != nil {\n   812→\t\tcclog.Errorf(\"Error while performing health check request: %s\", err.Error())\n   813→\t\treturn nil, err\n   814→\t}\n   815→\tdefer res.Body.Close()\n   816→\n   817→\tif res.StatusCode != http.StatusOK {\n   818→\t\treturn nil, fmt.Errorf(\"'%s': HTTP Status: %s\", endpoint, res.Status)\n   819→\t}\n   820→\n   821→\tvar results map[string]metricstore.HealthCheckResult\n   822→\tif err := json.NewDecoder(bufio.NewReader(res.Body)).Decode(&results); err != nil {\n   823→\t\tcclog.Errorf(\"Error while decoding health check response: %s\", err.Error())\n   824→\t\treturn nil, err\n   825→\t}\n   826→\n   827→\treturn results, nil\n   828→}\n   829→\n   830→// sanitizeStats replaces NaN values in statistics with 0 to enable JSON marshaling.\n   831→// Regular float64 values cannot be JSONed when NaN.\n   832→func sanitizeStats(avg, min, max *schema.Float) {\n   833→\tif avg.IsNaN() || min.IsNaN() || max.IsNaN() {\n   834→\t\t*avg = schema.Float(0)\n   835→\t\t*min = schema.Float(0)\n   836→\t\t*max = schema.Float(0)\n   837→\t}\n   838→}\n   839→\n   840→// hasNaNStats returns true if any of the statistics contain NaN values.\n   841→func hasNaNStats(avg, min, max schema.Float) bool {\n   842→\treturn avg.IsNaN() || min.IsNaN() || max.IsNaN()\n   843→}\n   844→"}]},"uuid":"e77c40ab-a723-4510-9166-98857c7b7c85","timestamp":"2026-03-04T15:24:52.947Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go","content":"\treturn &CCMetricStore{\n\t\turl:           url,\n\t\tqueryEndpoint: fmt.Sprintf(\"%s/api/query\", url),\n\t\tjwt:           token,\n\t\tclient: http.Client{\n\t\t\tTimeout: 10 * time.Second,\n\t\t},\n\t\ttopologyCache: make(map[string]*schema.Topology),\n\t}\n}\n\n// doRequest executes an HTTP POST request to the cc-metric-store query API.\n// It handles JSON encoding/decoding, authentication, and API versioning.\n// The request body is automatically closed to prevent resource leaks.\nfunc (ccms *CCMetricStore) doRequest(\n\tctx context.Context,\n\tbody *APIQueryRequest,\n) (*APIQueryResponse, error) {\n\tbuf := &bytes.Buffer{}\n\tif err := json.NewEncoder(buf).Encode(body); err != nil {\n\t\tcclog.Errorf(\"Error while encoding request body: %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\treq, err := http.NewRequestWithContext(ctx, http.MethodGet, ccms.queryEndpoint, buf)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building request body: %s\", err.Error())\n\t\treturn nil, err\n\t}\n\tif ccms.jwt != \"\" {\n\t\treq.Header.Add(\"Authorization\", fmt.Sprintf(\"Bearer %s\", ccms.jwt))\n\t}\n\n\t// versioning the cc-metric-store query API.\n\t// v2 = data with resampling\n\t// v1 = data without resampling\n\tq := req.URL.Query()\n\tq.Add(\"version\", \"v2\")\n\treq.URL.RawQuery = q.Encode()\n\n\tres, err := ccms.client.Do(req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while performing request: %s\", err.Error())\n\t\treturn nil, err\n\t}\n\tdefer res.Body.Close()\n\n\tif res.StatusCode != http.StatusOK {\n\t\treturn nil, fmt.Errorf(\"'%s': HTTP Status: %s\", ccms.queryEndpoint, res.Status)\n\t}\n\n\tvar resBody APIQueryResponse\n\tif err := json.NewDecoder(bufio.NewReader(res.Body)).Decode(&resBody); err != nil {\n\t\tcclog.Errorf(\"Error while decoding result body: %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\treturn &resBody, nil\n}\n\n// getTopology returns the topology for a given cluster and subcluster, caching it if not already present\nfunc (ccms *CCMetricStore) getTopology(cluster, subCluster string) (*schema.Topology, error) {\n\tcacheKey := fmt.Sprintf(\"%s:%s\", cluster, subCluster)\n\tif topology, ok := ccms.topologyCache[cacheKey]; ok {\n\t\treturn topology, nil\n\t}\n\n\tsubcluster, err := archive.GetSubCluster(cluster, subCluster)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\tccms.topologyCache[cacheKey] = &subcluster.Topology\n\treturn &subcluster.Topology, nil\n}\n\n// getTopologyByNode returns the topology for a given cluster and node, caching it if not already present\nfunc (ccms *CCMetricStore) getTopologyByNode(cluster, node string) (*schema.Topology, error) {\n\tsubCluster, err := archive.GetSubClusterByNode(cluster, node)\n\tif err != nil {\n\t\treturn nil, err\n\t}\n\n\treturn ccms.getTopology(cluster, subCluster)\n}\n\n// LoadData retrieves time series data and statistics for the specified job and metrics.\n// It queries data for the job's time range and resources, handling scope transformations automatically.\n//\n// Parameters:\n//   - job: Job metadata including cluster, time range, and allocated resources\n//   - metrics: List of metric names to retrieve\n//   - scopes: Requested metric scopes (node, socket, core, etc.)\n//   - ctx: Context for cancellation and timeouts\n//   - resolution: Data resolution in seconds (0 for native resolution)\n//\n// Returns JobData organized as: metric -> scope -> series list.\n// Supports partial errors: returns available data even if some queries fail.\nfunc (ccms *CCMetricStore) LoadData(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n\tresolution int,\n) (schema.JobData, error) {\n\tqueries, assignedScope, err := ccms.buildQueries(job, metrics, scopes, resolution)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n\t\treturn nil, err\n\t}\n\n\t// Verify assignment is correct - log any inconsistencies for debugging\n\tif len(queries) != len(assignedScope) {\n\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildQueries: %d vs %d\",\n\t\t\tlen(queries), len(assignedScope))\n\t}\n\n\treq := APIQueryRequest{\n\t\tCluster:   job.Cluster,\n\t\tFrom:      job.StartTime,\n\t\tTo:        job.StartTime + int64(job.Duration),\n\t\tQueries:   queries,\n\t\tWithStats: true,\n\t\tWithData:  true,\n\t}\n\n\tresBody, err := ccms.doRequest(ctx, &req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while performing request for job %d: %s\", job.JobID, err.Error())\n\t\treturn nil, err\n\t}\n\n\tvar errors []string\n\tjobData := make(schema.JobData)\n\n\t// Add safety check for potential index out of range errors\n\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n\t\tif len(resBody.Results) > len(req.Queries) {\n\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n\t\t}\n\t\tif len(assignedScope) > len(req.Queries) {\n\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n\t\t}\n\t}\n\n\tfor i, row := range resBody.Results {\n\t\t// Safety check to prevent index out of range errors\n\t\tif i >= len(req.Queries) || i >= len(assignedScope) {\n\t\t\tcclog.Warnf(\"Index out of range prevented: i=%d, queries=%d, assignedScope=%d\",\n\t\t\t\ti, len(req.Queries), len(assignedScope))\n\t\t\tcontinue\n\t\t}\n\n\t\tquery := req.Queries[i]\n\t\tmetric := query.Metric\n\t\tscope := assignedScope[i]\n\t\tmc := archive.GetMetricConfig(job.Cluster, metric)\n\n\t\tif mc == nil {\n\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, job.Cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\tif _, ok := jobData[metric]; !ok {\n\t\t\tjobData[metric] = make(map[schema.MetricScope]*schema.JobMetric)\n\t\t}\n\n\t\tres := mc.Timestep\n\t\tif len(row) > 0 {\n\t\t\tres = row[0].Resolution\n\t\t}\n\n\t\tjobMetric, ok := jobData[metric][scope]\n\t\tif !ok {\n\t\t\tjobMetric = &schema.JobMetric{\n\t\t\t\tUnit:     mc.Unit,\n\t\t\t\tTimestep: res,\n\t\t\t\tSeries:   make([]schema.Series, 0),\n\t\t\t}\n\t\t\tjobData[metric][scope] = jobMetric\n\t\t}\n\n\t\tfor ndx, res := range row {\n\t\t\tif res.Error != nil {\n\t\t\t\t/* Build list for \"partial errors\", if any */\n\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tid := (*string)(nil)\n\t\t\tif query.Type != nil {\n\t\t\t\t// Check if ndx is within the bounds of TypeIds slice\n\t\t\t\tif ndx < len(query.TypeIds) {\n\t\t\t\t\tid = new(string)\n\t\t\t\t\t*id = query.TypeIds[ndx]\n\t\t\t\t} else {\n\t\t\t\t\t// Log the error but continue processing\n\t\t\t\t\tcclog.Warnf(\"TypeIds index out of range: %d with length %d for metric %s on host %s\",\n\t\t\t\t\t\tndx, len(query.TypeIds), query.Metric, query.Hostname)\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tsanitizeStats(&res.Avg, &res.Min, &res.Max)\n\n\t\t\tjobMetric.Series = append(jobMetric.Series, schema.Series{\n\t\t\t\tHostname: query.Hostname,\n\t\t\t\tID:       id,\n\t\t\t\tStatistics: schema.MetricStatistics{\n\t\t\t\t\tAvg: float64(res.Avg),\n\t\t\t\t\tMin: float64(res.Min),\n\t\t\t\t\tMax: float64(res.Max),\n\t\t\t\t},\n\t\t\t\tData: res.Data,\n\t\t\t})\n\t\t}\n\n\t\t// So that one can later check len(jobData):\n\t\tif len(jobMetric.Series) == 0 {\n\t\t\tdelete(jobData[metric], scope)\n\t\t\tif len(jobData[metric]) == 0 {\n\t\t\t\tdelete(jobData, metric)\n\t\t\t}\n\t\t}\n\t}\n\n\tif len(errors) != 0 {\n\t\t/* Returns list for \"partial errors\" */\n\t\treturn jobData, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n\t}\n\treturn jobData, nil\n}\n\n// LoadStats retrieves min/avg/max statistics for job metrics at node scope.\n// This is faster than LoadData when only statistical summaries are needed (no time series data).\n//\n// Returns statistics organized as: metric -> hostname -> statistics.\nfunc (ccms *CCMetricStore) LoadStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tctx context.Context,\n) (map[string]map[string]schema.MetricStatistics, error) {\n\tqueries, _, err := ccms.buildQueries(job, metrics, []schema.MetricScope{schema.MetricScopeNode}, 0) // #166 Add scope shere for analysis view accelerator normalization?\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v: %s\", job.JobID, metrics, err.Error())\n\t\treturn nil, err\n\t}\n\n\treq := APIQueryRequest{\n\t\tCluster:   job.Cluster,\n\t\tFrom:      job.StartTime,\n\t\tTo:        job.StartTime + int64(job.Duration),\n\t\tQueries:   queries,\n\t\tWithStats: true,\n\t\tWithData:  false,\n\t}\n\n\tresBody, err := ccms.doRequest(ctx, &req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while performing request for job %d: %s\", job.JobID, err.Error())\n\t\treturn nil, err\n\t}\n\n\tstats := make(map[string]map[string]schema.MetricStatistics, len(metrics))\n\tfor i, res := range resBody.Results {\n\t\tif len(res) == 0 {\n\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n\t\t\tcontinue\n\t\t}\n\t\tquery := req.Queries[i]\n\t\tmetric := query.Metric\n\t\tdata := res[0]\n\t\tif data.Error != nil {\n\t\t\tcclog.Warnf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *data.Error)\n\t\t\tcontinue\n\t\t}\n\n\t\tmetricdata, ok := stats[metric]\n\t\tif !ok {\n\t\t\tmetricdata = make(map[string]schema.MetricStatistics, job.NumNodes)\n\t\t\tstats[metric] = metricdata\n\t\t}\n\n\t\tif hasNaNStats(data.Avg, data.Min, data.Max) {\n\t\t\tcclog.Warnf(\"fetching %s for node %s failed: one of avg/min/max is NaN\", metric, query.Hostname)\n\t\t\tcontinue\n\t\t}\n\n\t\tmetricdata[query.Hostname] = schema.MetricStatistics{\n\t\t\tAvg: float64(data.Avg),\n\t\t\tMin: float64(data.Min),\n\t\t\tMax: float64(data.Max),\n\t\t}\n\t}\n\n\treturn stats, nil\n}\n\n// LoadScopedStats retrieves statistics for job metrics across multiple scopes.\n// Used for the Job-View Statistics Table to display per-scope breakdowns.\n//\n// Returns statistics organized as: metric -> scope -> list of scoped statistics.\n// Each scoped statistic includes hostname, hardware ID (if applicable), and min/avg/max values.\nfunc (ccms *CCMetricStore) LoadScopedStats(\n\tjob *schema.Job,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tctx context.Context,\n) (schema.ScopedJobStats, error) {\n\tqueries, assignedScope, err := ccms.buildQueries(job, metrics, scopes, 0)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n\t\treturn nil, err\n\t}\n\n\treq := APIQueryRequest{\n\t\tCluster:   job.Cluster,\n\t\tFrom:      job.StartTime,\n\t\tTo:        job.StartTime + int64(job.Duration),\n\t\tQueries:   queries,\n\t\tWithStats: true,\n\t\tWithData:  false,\n\t}\n\n\tresBody, err := ccms.doRequest(ctx, &req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while performing request for job %d: %s\", job.JobID, err.Error())\n\t\treturn nil, err\n\t}\n\n\tvar errors []string\n\tscopedJobStats := make(schema.ScopedJobStats)\n\n\tfor i, row := range resBody.Results {\n\t\tquery := req.Queries[i]\n\t\tmetric := query.Metric\n\t\tscope := assignedScope[i]\n\n\t\tif _, ok := scopedJobStats[metric]; !ok {\n\t\t\tscopedJobStats[metric] = make(map[schema.MetricScope][]*schema.ScopedStats)\n\t\t}\n\n\t\tif _, ok := scopedJobStats[metric][scope]; !ok {\n\t\t\tscopedJobStats[metric][scope] = make([]*schema.ScopedStats, 0)\n\t\t}\n\n\t\tfor ndx, res := range row {\n\t\t\tif res.Error != nil {\n\t\t\t\t/* Build list for \"partial errors\", if any */\n\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tid := (*string)(nil)\n\t\t\tif query.Type != nil {\n\t\t\t\t// Check if ndx is within the bounds of TypeIds slice\n\t\t\t\tif ndx < len(query.TypeIds) {\n\t\t\t\t\tid = new(string)\n\t\t\t\t\t*id = query.TypeIds[ndx]\n\t\t\t\t} else {\n\t\t\t\t\t// Log the error but continue processing\n\t\t\t\t\tcclog.Warnf(\"TypeIds index out of range: %d with length %d for metric %s on host %s\",\n\t\t\t\t\t\tndx, len(query.TypeIds), query.Metric, query.Hostname)\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tsanitizeStats(&res.Avg, &res.Min, &res.Max)\n\n\t\t\tscopedJobStats[metric][scope] = append(scopedJobStats[metric][scope], &schema.ScopedStats{\n\t\t\t\tHostname: query.Hostname,\n\t\t\t\tID:       id,\n\t\t\t\tData: &schema.MetricStatistics{\n\t\t\t\t\tAvg: float64(res.Avg),\n\t\t\t\t\tMin: float64(res.Min),\n\t\t\t\t\tMax: float64(res.Max),\n\t\t\t\t},\n\t\t\t})\n\t\t}\n\n\t\t// So that one can later check len(scopedJobStats[metric][scope]): Remove from map if empty\n\t\tif len(scopedJobStats[metric][scope]) == 0 {\n\t\t\tdelete(scopedJobStats[metric], scope)\n\t\t\tif len(scopedJobStats[metric]) == 0 {\n\t\t\t\tdelete(scopedJobStats, metric)\n\t\t\t}\n\t\t}\n\t}\n\n\tif len(errors) != 0 {\n\t\t/* Returns list for \"partial errors\" */\n\t\treturn scopedJobStats, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n\t}\n\treturn scopedJobStats, nil\n}\n\n// LoadNodeData retrieves current metric data for specified nodes in a cluster.\n// Used for the Systems-View Node-Overview to display real-time node status.\n//\n// If nodes is nil, queries all metrics for all nodes in the cluster (bulk query).\n// Returns data organized as: hostname -> metric -> list of JobMetric (with time series and stats).\nfunc (ccms *CCMetricStore) LoadNodeData(\n\tcluster string,\n\tmetrics, nodes []string,\n\tscopes []schema.MetricScope,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]map[string][]*schema.JobMetric, error) {\n\treq := APIQueryRequest{\n\t\tCluster:   cluster,\n\t\tFrom:      from.Unix(),\n\t\tTo:        to.Unix(),\n\t\tWithStats: true,\n\t\tWithData:  true,\n\t}\n\n\tif nodes == nil {\n\t\treq.ForAllNodes = append(req.ForAllNodes, metrics...)\n\t} else {\n\t\tfor _, node := range nodes {\n\t\t\tfor _, metric := range metrics {\n\t\t\t\treq.Queries = append(req.Queries, APIQuery{\n\t\t\t\t\tHostname:   node,\n\t\t\t\t\tMetric:     metric,\n\t\t\t\t\tResolution: 0, // Default for Node Queries: Will return metric $Timestep Resolution\n\t\t\t\t})\n\t\t\t}\n\t\t}\n\t}\n\n\tresBody, err := ccms.doRequest(ctx, &req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while performing request for cluster %s: %s\", cluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tvar errors []string\n\tdata := make(map[string]map[string][]*schema.JobMetric)\n\tfor i, res := range resBody.Results {\n\t\tif len(res) == 0 {\n\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n\t\t\tcontinue\n\t\t}\n\n\t\tvar query APIQuery\n\t\tif resBody.Queries != nil {\n\t\t\tquery = resBody.Queries[i]\n\t\t} else {\n\t\t\tquery = req.Queries[i]\n\t\t}\n\n\t\tmetric := query.Metric\n\t\tqdata := res[0]\n\t\tif qdata.Error != nil {\n\t\t\terrors = append(errors, fmt.Sprintf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *qdata.Error))\n\t\t}\n\n\t\tsanitizeStats(&qdata.Avg, &qdata.Min, &qdata.Max)\n\n\t\thostdata, ok := data[query.Hostname]\n\t\tif !ok {\n\t\t\thostdata = make(map[string][]*schema.JobMetric)\n\t\t\tdata[query.Hostname] = hostdata\n\t\t}\n\n\t\tmc := archive.GetMetricConfig(cluster, metric)\n\t\thostdata[metric] = append(hostdata[metric], &schema.JobMetric{\n\t\t\tUnit:     mc.Unit,\n\t\t\tTimestep: mc.Timestep,\n\t\t\tSeries: []schema.Series{\n\t\t\t\t{\n\t\t\t\t\tHostname: query.Hostname,\n\t\t\t\t\tData:     qdata.Data,\n\t\t\t\t\tStatistics: schema.MetricStatistics{\n\t\t\t\t\t\tAvg: float64(qdata.Avg),\n\t\t\t\t\t\tMin: float64(qdata.Min),\n\t\t\t\t\t\tMax: float64(qdata.Max),\n\t\t\t\t\t},\n\t\t\t\t},\n\t\t\t},\n\t\t})\n\t}\n\n\tif len(errors) != 0 {\n\t\t/* Returns list of \"partial errors\" */\n\t\treturn data, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n\t}\n\n\treturn data, nil\n}\n\n// LoadNodeListData retrieves paginated node metrics for the Systems-View Node-List.\n//\n// Supports filtering by subcluster and node name pattern. The nodeFilter performs\n// substring matching on hostnames.\n//\n// Returns:\n//   - Node data organized as: hostname -> JobData (metric -> scope -> series)\n//   - Total node count (before pagination)\n//   - HasNextPage flag indicating if more pages are available\n//   - Error (may be partial error with some data returned)\nfunc (ccms *CCMetricStore) LoadNodeListData(\n\tcluster, subCluster string,\n\tnodes []string,\n\tmetrics []string,\n\tscopes []schema.MetricScope,\n\tresolution int,\n\tfrom, to time.Time,\n\tctx context.Context,\n) (map[string]schema.JobData, error) {\n\tqueries, assignedScope, err := ccms.buildNodeQueries(cluster, subCluster, nodes, metrics, scopes, resolution)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building node queries for Cluster %s, SubCluster %s, Metrics %v, Scopes %v: %s\", cluster, subCluster, metrics, scopes, err.Error())\n\t\treturn nil, err\n\t}\n\n\t// Verify assignment is correct - log any inconsistencies for debugging\n\tif len(queries) != len(assignedScope) {\n\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildNodeQueries: %d vs %d\",\n\t\t\tlen(queries), len(assignedScope))\n\t}\n\n\treq := APIQueryRequest{\n\t\tCluster:   cluster,\n\t\tQueries:   queries,\n\t\tFrom:      from.Unix(),\n\t\tTo:        to.Unix(),\n\t\tWithStats: true,\n\t\tWithData:  true,\n\t}\n\n\tresBody, err := ccms.doRequest(ctx, &req)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while performing request for cluster %s: %s\", cluster, err.Error())\n\t\treturn nil, err\n\t}\n\n\tvar errors []string\n\tdata := make(map[string]schema.JobData)\n\n\t// Add safety check for index out of range issues\n\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n\t\tif len(resBody.Results) > len(req.Queries) {\n\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n\t\t}\n\t\tif len(assignedScope) > len(req.Queries) {\n\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n\t\t}\n\t}\n\n\tfor i, row := range resBody.Results {\n\t\t// Safety check to prevent index out of range errors\n\t\tif i >= len(req.Queries) || i >= len(assignedScope) {\n\t\t\tcclog.Warnf(\"Index out of range prevented: i=%d, queries=%d, assignedScope=%d\",\n\t\t\t\ti, len(req.Queries), len(assignedScope))\n\t\t\tcontinue\n\t\t}\n\n\t\tvar query APIQuery\n\t\tif resBody.Queries != nil {\n\t\t\tif i < len(resBody.Queries) {\n\t\t\t\tquery = resBody.Queries[i]\n\t\t\t} else {\n\t\t\t\tcclog.Warnf(\"Index out of range prevented for resBody.Queries: i=%d, len=%d\",\n\t\t\t\t\ti, len(resBody.Queries))\n\t\t\t\tcontinue\n\t\t\t}\n\t\t} else {\n\t\t\tquery = req.Queries[i]\n\t\t}\n\n\t\tmetric := query.Metric\n\t\tscope := assignedScope[i]\n\t\tmc := archive.GetMetricConfig(cluster, metric)\n\t\tif mc == nil {\n\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, cluster)\n\t\t\tcontinue\n\t\t}\n\n\t\tres := mc.Timestep\n\t\tif len(row) > 0 {\n\t\t\tres = row[0].Resolution\n\t\t}\n\n\t\t// Init Nested Map Data Structures If Not Found\n\t\thostData, ok := data[query.Hostname]\n\t\tif !ok {\n\t\t\thostData = make(schema.JobData)\n\t\t\tdata[query.Hostname] = hostData\n\t\t}\n\n\t\tmetricData, ok := hostData[metric]\n\t\tif !ok {\n\t\t\tmetricData = make(map[schema.MetricScope]*schema.JobMetric)\n\t\t\tdata[query.Hostname][metric] = metricData\n\t\t}\n\n\t\tscopeData, ok := metricData[scope]\n\t\tif !ok {\n\t\t\tscopeData = &schema.JobMetric{\n\t\t\t\tUnit:     mc.Unit,\n\t\t\t\tTimestep: res,\n\t\t\t\tSeries:   make([]schema.Series, 0),\n\t\t\t}\n\t\t\tdata[query.Hostname][metric][scope] = scopeData\n\t\t}\n\n\t\tfor ndx, res := range row {\n\t\t\tif res.Error != nil {\n\t\t\t\t/* Build list for \"partial errors\", if any */\n\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n\t\t\t\tcontinue\n\t\t\t}\n\n\t\t\tid := (*string)(nil)\n\t\t\tif query.Type != nil {\n\t\t\t\t// Check if ndx is within the bounds of TypeIds slice\n\t\t\t\tif ndx < len(query.TypeIds) {\n\t\t\t\t\tid = new(string)\n\t\t\t\t\t*id = query.TypeIds[ndx]\n\t\t\t\t} else {\n\t\t\t\t\t// Log the error but continue processing\n\t\t\t\t\tcclog.Warnf(\"TypeIds index out of range: %d with length %d for metric %s on host %s\",\n\t\t\t\t\t\tndx, len(query.TypeIds), query.Metric, query.Hostname)\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tsanitizeStats(&res.Avg, &res.Min, &res.Max)\n\n\t\t\tscopeData.Series = append(scopeData.Series, schema.Series{\n\t\t\t\tHostname: query.Hostname,\n\t\t\t\tID:       id,\n\t\t\t\tStatistics: schema.MetricStatistics{\n\t\t\t\t\tAvg: float64(res.Avg),\n\t\t\t\t\tMin: float64(res.Min),\n\t\t\t\t\tMax: float64(res.Max),\n\t\t\t\t},\n\t\t\t\tData: res.Data,\n\t\t\t})\n\t\t}\n\t}\n\n\tif len(errors) != 0 {\n\t\t/* Returns list of \"partial errors\" */\n\t\treturn data, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n\t}\n\n\treturn data, nil\n}\n\n// HealthCheck queries the external cc-metric-store's health check endpoint.\n// It sends a HealthCheckReq as the request body to /api/healthcheck and\n// returns the per-node health check results.\nfunc (ccms *CCMetricStore) HealthCheck(cluster string,\n\tnodes []string, metrics []string,\n) (map[string]metricstore.HealthCheckResult, error) {\n\treq := metricstore.HealthCheckReq{\n\t\tCluster:     cluster,\n\t\tNodes:       nodes,\n\t\tMetricNames: metrics,\n\t}\n\n\tbuf := &bytes.Buffer{}\n\tif err := json.NewEncoder(buf).Encode(req); err != nil {\n\t\tcclog.Errorf(\"Error while encoding health check request body: %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\tendpoint := fmt.Sprintf(\"%s/api/healthcheck\", ccms.url)\n\thttpReq, err := http.NewRequest(http.MethodGet, endpoint, buf)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while building health check request: %s\", err.Error())\n\t\treturn nil, err\n\t}\n\tif ccms.jwt != \"\" {\n\t\thttpReq.Header.Add(\"Authorization\", fmt.Sprintf(\"Bearer %s\", ccms.jwt))\n\t}\n\n\tres, err := ccms.client.Do(httpReq)\n\tif err != nil {\n\t\tcclog.Errorf(\"Error while performing health check request: %s\", err.Error())\n\t\treturn nil, err\n\t}\n\tdefer res.Body.Close()\n\n\tif res.StatusCode != http.StatusOK {\n\t\treturn nil, fmt.Errorf(\"'%s': HTTP Status: %s\", endpoint, res.Status)\n\t}\n\n\tvar results map[string]metricstore.HealthCheckResult\n\tif err := json.NewDecoder(bufio.NewReader(res.Body)).Decode(&results); err != nil {\n\t\tcclog.Errorf(\"Error while decoding health check response: %s\", err.Error())\n\t\treturn nil, err\n\t}\n\n\treturn results, nil\n}\n\n// sanitizeStats replaces NaN values in statistics with 0 to enable JSON marshaling.\n// Regular float64 values cannot be JSONed when NaN.\nfunc sanitizeStats(avg, min, max *schema.Float) {\n\tif avg.IsNaN() || min.IsNaN() || max.IsNaN() {\n\t\t*avg = schema.Float(0)\n\t\t*min = schema.Float(0)\n\t\t*max = schema.Float(0)\n\t}\n}\n\n// hasNaNStats returns true if any of the statistics contain NaN values.\nfunc hasNaNStats(avg, min, max schema.Float) bool {\n\treturn avg.IsNaN() || min.IsNaN() || max.IsNaN()\n}\n","numLines":715,"startLine":130,"totalLines":844}},"sourceToolAssistantUUID":"ce898af0-e64f-4e27-843d-9303a963bbe1"}
{"parentUuid":"e77c40ab-a723-4510-9166-98857c7b7c85","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","message":{"model":"claude-opus-4-6","id":"msg_017PhdenzLz5rCHUntWZksNg","type":"message","role":"assistant","content":[{"type":"text","text":"Now I have a comprehensive understanding. Let me design the plan."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":9784,"cache_read_input_tokens":76005,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":9784},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYiGjYnbpHAtgS5Jo1cYg","type":"assistant","uuid":"34e3de8b-1459-4dce-b405-62c6e1d9dab9","timestamp":"2026-03-04T15:24:56.558Z"}
{"parentUuid":"34e3de8b-1459-4dce-b405-62c6e1d9dab9","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","message":{"model":"claude-opus-4-6","id":"msg_017PhdenzLz5rCHUntWZksNg","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TXANL4MzLEvewR2e42CBCn","name":"Agent","input":{"description":"Design consolidation plan","prompt":"I need to design a plan to synchronize and consolidate the query-building logic between two packages that implement the same MetricDataRepository interface:\n\n## Context\n\n1. **`pkg/metricstore/query.go`** (InternalMetricStore) — in-memory metric store\n2. **`internal/metricstoreclient/cc-metric-store-queries.go`** (CCMetricStore) — HTTP client to external cc-metric-store\n\nBoth implement the `MetricDataRepository` interface from `internal/metricdispatch/metricdata.go`.\n\n## Key Differences Found\n\n### buildQueries differences:\n- **pkg/metricstore**: Inline scope transformation (long chain of if-statements in the inner loop). Uses `map[schema.MetricScope]bool` for dedup. Resolution is `int64`.\n- **metricstoreclient**: Extracted `buildScopeQueries()` helper function. Uses labeled loop + `[]schema.MetricScope` slice for dedup. Resolution is `int`. Has topology caching via `getTopology()`.\n\n### buildScopeQueries (only in metricstoreclient):\n- Clean standalone function handling all scope transformations\n- Returns empty slices for expected exceptions, nil for unhandled cases\n- Includes **MemoryDomain → Socket** transformation (MISSING from pkg/metricstore)\n\n### buildNodeQueries differences:\n- **pkg/metricstore**: Gets topology inline per node. Uses `map[schema.MetricScope]bool` dedup. Has inline scope transformation.\n- **metricstoreclient**: Uses `getTopology()`/`getTopologyByNode()` with caching. Uses labeled loop dedup. Delegates to `buildScopeQueries()`.\n\n### APIQuery type differences:\n- **pkg/metricstore**: `Resolution int64`, has `ScaleFactor schema.Float`\n- **metricstoreclient**: `Resolution int`, no ScaleFactor\n\n### sanitizeStats differences:\n- **pkg/metricstore**: `sanitizeStats(data *APIMetricData)` — zeros each independently\n- **metricstoreclient**: `sanitizeStats(avg, min, max *schema.Float)` — zeros all if any is NaN. Also has `hasNaNStats()` helper.\n\n### LoadData/LoadStats/LoadScopedStats/LoadNodeData/LoadNodeListData:\n- Nearly identical result-processing logic\n- Different data fetching: `FetchData(req)` vs `ccms.doRequest(ctx, &req)`  \n- Internal store has `TestLoadDataCallback` test hook\n- External client has topology caching\n\n### Missing from pkg/metricstore:\n- **MemoryDomain → Socket** scope transformation (present in metricstoreclient)\n\n### Missing from metricstoreclient:\n- Nothing significant missing from metricstoreclient\n\n## Consolidation Approach\n\nThe user said metricstoreclient is the cleaner implementation. I agree — `buildScopeQueries()` as a standalone function is much cleaner than the inline if-chain.\n\nDesign a plan to:\n1. Extract `buildScopeQueries()` as a shared helper (possibly in a shared package or as a package-level function in metricstore)\n2. Synchronize the scope transformations (add MemoryDomain → Socket to internal)\n3. Evaluate if result-processing logic can be shared\n4. Consider type differences (int vs int64 Resolution)\n\nKey constraints:\n- The two packages have separate APIQuery types (different Resolution types, internal has ScaleFactor)\n- The two packages use different data fetching mechanisms\n- Must not break the MetricDataRepository interface contract\n- Keep changes minimal and focused\n\nProvide a detailed implementation plan.","subagent_type":"Plan"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":9784,"cache_read_input_tokens":76005,"output_tokens":980,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":9784,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYiGjYnbpHAtgS5Jo1cYg","type":"assistant","uuid":"ff830631-3e76-4c5a-9a43-d0ae540820df","timestamp":"2026-03-04T15:25:16.164Z"}
{"parentUuid":"ff830631-3e76-4c5a-9a43-d0ae540820df","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"type":"hook_progress","hookEvent":"PreToolUse","hookName":"PreToolUse:Agent","command":"entire hooks claude-code pre-task"},"toolUseID":"toolu_01TXANL4MzLEvewR2e42CBCn","parentToolUseID":"toolu_01TXANL4MzLEvewR2e42CBCn","uuid":"74a998f6-b087-4ca5-89e7-a8cdcc14d07b","timestamp":"2026-03-04T15:25:16.166Z"}
{"parentUuid":"74a998f6-b087-4ca5-89e7-a8cdcc14d07b","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"type":"text","text":"I need to design a plan to synchronize and consolidate the query-building logic between two packages that implement the same MetricDataRepository interface:\n\n## Context\n\n1. **`pkg/metricstore/query.go`** (InternalMetricStore) — in-memory metric store\n2. **`internal/metricstoreclient/cc-metric-store-queries.go`** (CCMetricStore) — HTTP client to external cc-metric-store\n\nBoth implement the `MetricDataRepository` interface from `internal/metricdispatch/metricdata.go`.\n\n## Key Differences Found\n\n### buildQueries differences:\n- **pkg/metricstore**: Inline scope transformation (long chain of if-statements in the inner loop). Uses `map[schema.MetricScope]bool` for dedup. Resolution is `int64`.\n- **metricstoreclient**: Extracted `buildScopeQueries()` helper function. Uses labeled loop + `[]schema.MetricScope` slice for dedup. Resolution is `int`. Has topology caching via `getTopology()`.\n\n### buildScopeQueries (only in metricstoreclient):\n- Clean standalone function handling all scope transformations\n- Returns empty slices for expected exceptions, nil for unhandled cases\n- Includes **MemoryDomain → Socket** transformation (MISSING from pkg/metricstore)\n\n### buildNodeQueries differences:\n- **pkg/metricstore**: Gets topology inline per node. Uses `map[schema.MetricScope]bool` dedup. Has inline scope transformation.\n- **metricstoreclient**: Uses `getTopology()`/`getTopologyByNode()` with caching. Uses labeled loop dedup. Delegates to `buildScopeQueries()`.\n\n### APIQuery type differences:\n- **pkg/metricstore**: `Resolution int64`, has `ScaleFactor schema.Float`\n- **metricstoreclient**: `Resolution int`, no ScaleFactor\n\n### sanitizeStats differences:\n- **pkg/metricstore**: `sanitizeStats(data *APIMetricData)` — zeros each independently\n- **metricstoreclient**: `sanitizeStats(avg, min, max *schema.Float)` — zeros all if any is NaN. Also has `hasNaNStats()` helper.\n\n### LoadData/LoadStats/LoadScopedStats/LoadNodeData/LoadNodeListData:\n- Nearly identical result-processing logic\n- Different data fetching: `FetchData(req)` vs `ccms.doRequest(ctx, &req)`  \n- Internal store has `TestLoadDataCallback` test hook\n- External client has topology caching\n\n### Missing from pkg/metricstore:\n- **MemoryDomain → Socket** scope transformation (present in metricstoreclient)\n\n### Missing from metricstoreclient:\n- Nothing significant missing from metricstoreclient\n\n## Consolidation Approach\n\nThe user said metricstoreclient is the cleaner implementation. I agree — `buildScopeQueries()` as a standalone function is much cleaner than the inline if-chain.\n\nDesign a plan to:\n1. Extract `buildScopeQueries()` as a shared helper (possibly in a shared package or as a package-level function in metricstore)\n2. Synchronize the scope transformations (add MemoryDomain → Socket to internal)\n3. Evaluate if result-processing logic can be shared\n4. Consider type differences (int vs int64 Resolution)\n\nKey constraints:\n- The two packages have separate APIQuery types (different Resolution types, internal has ScaleFactor)\n- The two packages use different data fetching mechanisms\n- Must not break the MetricDataRepository interface contract\n- Keep changes minimal and focused\n\nProvide a detailed implementation plan."}]},"uuid":"25526290-9301-4373-a999-7acfe57133d6","timestamp":"2026-03-04T15:25:16.470Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"I need to design a plan to synchronize and consolidate the query-building logic between two packages that implement the same MetricDataRepository interface:\n\n## Context\n\n1. **`pkg/metricstore/query.go`** (InternalMetricStore) — in-memory metric store\n2. **`internal/metricstoreclient/cc-metric-store-queries.go`** (CCMetricStore) — HTTP client to external cc-metric-store\n\nBoth implement the `MetricDataRepository` interface from `internal/metricdispatch/metricdata.go`.\n\n## Key Differences Found\n\n### buildQueries differences:\n- **pkg/metricstore**: Inline scope transformation (long chain of if-statements in the inner loop). Uses `map[schema.MetricScope]bool` for dedup. Resolution is `int64`.\n- **metricstoreclient**: Extracted `buildScopeQueries()` helper function. Uses labeled loop + `[]schema.MetricScope` slice for dedup. Resolution is `int`. Has topology caching via `getTopology()`.\n\n### buildScopeQueries (only in metricstoreclient):\n- Clean standalone function handling all scope transformations\n- Returns empty slices for expected exceptions, nil for unhandled cases\n- Includes **MemoryDomain → Socket** transformation (MISSING from pkg/metricstore)\n\n### buildNodeQueries differences:\n- **pkg/metricstore**: Gets topology inline per node. Uses `map[schema.MetricScope]bool` dedup. Has inline scope transformation.\n- **metricstoreclient**: Uses `getTopology()`/`getTopologyByNode()` with caching. Uses labeled loop dedup. Delegates to `buildScopeQueries()`.\n\n### APIQuery type differences:\n- **pkg/metricstore**: `Resolution int64`, has `ScaleFactor schema.Float`\n- **metricstoreclient**: `Resolution int`, no ScaleFactor\n\n### sanitizeStats differences:\n- **pkg/metricstore**: `sanitizeStats(data *APIMetricData)` — zeros each independently\n- **metricstoreclient**: `sanitizeStats(avg, min, max *schema.Float)` — zeros all if any is NaN. Also has `hasNaNStats()` helper.\n\n### LoadData/LoadStats/LoadScopedStats/LoadNodeData/LoadNodeListData:\n- Nearly identical result-processing logic\n- Different data fetching: `FetchData(req)` vs `ccms.doRequest(ctx, &req)`  \n- Internal store has `TestLoadDataCallback` test hook\n- External client has topology caching\n\n### Missing from pkg/metricstore:\n- **MemoryDomain → Socket** scope transformation (present in metricstoreclient)\n\n### Missing from metricstoreclient:\n- Nothing significant missing from metricstoreclient\n\n## Consolidation Approach\n\nThe user said metricstoreclient is the cleaner implementation. I agree — `buildScopeQueries()` as a standalone function is much cleaner than the inline if-chain.\n\nDesign a plan to:\n1. Extract `buildScopeQueries()` as a shared helper (possibly in a shared package or as a package-level function in metricstore)\n2. Synchronize the scope transformations (add MemoryDomain → Socket to internal)\n3. Evaluate if result-processing logic can be shared\n4. Consider type differences (int vs int64 Resolution)\n\nKey constraints:\n- The two packages have separate APIQuery types (different Resolution types, internal has ScaleFactor)\n- The two packages use different data fetching mechanisms\n- Must not break the MetricDataRepository interface contract\n- Keep changes minimal and focused\n\nProvide a detailed implementation plan.","agentId":"aeb62a7c5fea57dd2"},"toolUseID":"agent_msg_017PhdenzLz5rCHUntWZksNg","parentToolUseID":"toolu_01TXANL4MzLEvewR2e42CBCn","uuid":"c1032b1c-a5a6-4ce2-881a-3be6ab1152ac","timestamp":"2026-03-04T15:25:16.470Z"}
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{"parentUuid":"c6f9b9bf-997a-4cb1-b77d-d6f77414d204","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SJwjDjjHUssBPJtycj9NVd","type":"tool_result","content":"<persisted-output>\nOutput too large (50.6KB). Full output saved to: /Users/jan/.claude/projects/-Users-jan-prg-ClusterCockpit-cc-backend/eec1fc87-d8f6-42f6-b296-d7a2f79af002/tool-results/toolu_01SJwjDjjHUssBPJtycj9NVd.txt\n\nPreview (first 2KB):\n     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved. This file is part of cc-backend.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// This file implements high-level query functions for loading job metric data\n     7→// with automatic scope transformation and aggregation.\n     8→//\n     9→// Key Concepts:\n    10→//\n    11→// Metric Scopes: Metrics are collected at different granularities (native scope):\n    12→//   - HWThread: Per hardware thread\n    13→//   - Core: Per CPU core\n    14→//   - Socket: Per CPU socket\n    15→//   - MemoryDomain: Per memory domain (NUMA)\n    16→//   - Accelerator: Per GPU/accelerator\n    17→//   - Node: Per compute node\n    18→//\n    19→// Scope Transformation: The buildQueries functions transform between native scope\n    20→// and requested scope by:\n    21→//   - Aggregating finer-grained data (e.g., HWThread → Core → Socket → Node)\n    22→//   - Rejecting requests for finer granularity than available\n    23→//   - Handling special cases (e.g., Accelerator metrics)\n    24→//\n    25→// Query Building: Constructs APIQuery structures with proper selectors (Type, TypeIds)\n    26→// based on cluster topology and job resources.\n    27→package metricstore\n    28→\n    29→import (\n    30→\t\"context\"\n    31→\t\"fmt\"\n    32→\t\"strconv\"\n    33→\t\"strings\"\n    34→\t\"time\"\n    35→\n    36→\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n    37→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    38→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    39→)\n    40→\n    41→type InternalMetricStore struct{}\n    42→\n    43→var MetricStoreHandle *InternalMetricStore\n    44→\n    45→// HealthCheck delegates to the internal MemoryStore's HealthCheck.\n    46→func (ccms *InternalMetricStore) HealthCheck(cluster string,\n    47→\tnodes []string, metrics []string,\n    48→) (map[string]HealthCheckResult, error) {\n...\n</persisted-output>"}]},"uuid":"97bd7c6c-0277-4e42-ad4c-8f80a4eba1c7","timestamp":"2026-03-04T15:25:20.397Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"aeb62a7c5fea57dd2"},"toolUseID":"agent_msg_017PhdenzLz5rCHUntWZksNg","parentToolUseID":"toolu_01TXANL4MzLEvewR2e42CBCn","uuid":"1e4cdacc-d0b6-4c59-baab-5287978b4e85","timestamp":"2026-03-04T15:25:20.618Z"}
{"parentUuid":"1e4cdacc-d0b6-4c59-baab-5287978b4e85","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-04T15:25:20.627Z","message":{"model":"claude-opus-4-6","id":"msg_01UscNw3RC26MHxogRhyE9yx","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012hzwApUE7TpyjxixvELiz9","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store-queries.go"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":15754,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":15754},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYiGmHJz4EMGgyi39svas","uuid":"476edbf4-08dd-4416-8f60-343330f343c1"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"aeb62a7c5fea57dd2"},"toolUseID":"agent_msg_017PhdenzLz5rCHUntWZksNg","parentToolUseID":"toolu_01TXANL4MzLEvewR2e42CBCn","uuid":"103ba252-6057-4f3e-93bf-0c0d6e420d9d","timestamp":"2026-03-04T15:25:20.627Z"}
{"parentUuid":"103ba252-6057-4f3e-93bf-0c0d6e420d9d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012hzwApUE7TpyjxixvELiz9","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// Package metricstoreclient - Query Building\n     7→//\n     8→// This file contains the query construction and scope transformation logic for cc-metric-store queries.\n     9→// It handles the complex mapping between requested metric scopes and native hardware topology,\n    10→// automatically aggregating or filtering metrics as needed.\n    11→//\n    12→// # Scope Transformations\n    13→//\n    14→// The buildScopeQueries function implements the core scope transformation algorithm.\n    15→// It handles 25+ different transformation cases, mapping between:\n    16→//   - Accelerator (GPU) scope\n    17→//   - HWThread (hardware thread/SMT) scope\n    18→//   - Core (CPU core) scope\n    19→//   - Socket (CPU package) scope\n    20→//   - MemoryDomain (NUMA domain) scope\n    21→//   - Node (full system) scope\n    22→//\n    23→// Transformations follow these rules:\n    24→//   - Same scope: Return data as-is (e.g., Core → Core)\n    25→//   - Coarser scope: Aggregate data (e.g., Core → Socket with Aggregate=true)\n    26→//   - Finer scope: Error - cannot increase granularity\n    27→//\n    28→// # Query Building\n    29→//\n    30→// buildQueries and buildNodeQueries are the main entry points, handling job-specific\n    31→// and node-specific query construction respectively. They:\n    32→//   - Validate metric configurations\n    33→//   - Handle subcluster-specific metric filtering\n    34→//   - Detect and skip duplicate scope requests\n    35→//   - Call buildScopeQueries for each metric/scope/host combination\n    36→package metricstoreclient\n    37→\n    38→import (\n    39→\t\"fmt\"\n    40→\t\"strconv\"\n    41→\n    42→\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n    43→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    44→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    45→)\n    46→\n    47→// Scope string constants used in API queries.\n    48→// Pre-converted to avoid repeated allocations during query building.\n    49→var (\n    50→\thwthreadString     = string(schema.MetricScopeHWThread)\n    51→\tcoreString         = string(schema.MetricScopeCore)\n    52→\tmemoryDomainString = string(schema.MetricScopeMemoryDomain)\n    53→\tsocketString       = string(schema.MetricScopeSocket)\n    54→\tacceleratorString  = string(schema.MetricScopeAccelerator)\n    55→)\n    56→\n    57→// buildQueries constructs API queries for job-specific metric data.\n    58→// It iterates through metrics, scopes, and job resources to build the complete query set.\n    59→//\n    60→// The function handles:\n    61→//   - Metric configuration validation and subcluster filtering\n    62→//   - Scope deduplication to avoid redundant queries\n    63→//   - Hardware thread list resolution (job-allocated vs full node)\n    64→//   - Delegation to buildScopeQueries for scope transformations\n    65→//\n    66→// Returns queries and their corresponding assigned scopes (which may differ from requested scopes).\n    67→func (ccms *CCMetricStore) buildQueries(\n    68→\tjob *schema.Job,\n    69→\tmetrics []string,\n    70→\tscopes []schema.MetricScope,\n    71→\tresolution int,\n    72→) ([]APIQuery, []schema.MetricScope, error) {\n    73→\t// Initialize both slices together\n    74→\tqueries := make([]APIQuery, 0, len(metrics)*len(scopes)*len(job.Resources))\n    75→\tassignedScope := make([]schema.MetricScope, 0, len(metrics)*len(scopes)*len(job.Resources))\n    76→\n    77→\ttopology, err := ccms.getTopology(job.Cluster, job.SubCluster)\n    78→\tif err != nil {\n    79→\t\tcclog.Errorf(\"could not load cluster %s subCluster %s topology: %s\", job.Cluster, job.SubCluster, err.Error())\n    80→\t\treturn nil, nil, err\n    81→\t}\n    82→\n    83→\tfor _, metric := range metrics {\n    84→\t\tremoteName := metric\n    85→\t\tmc := archive.GetMetricConfig(job.Cluster, metric)\n    86→\t\tif mc == nil {\n    87→\t\t\tcclog.Warnf(\"metric '%s' is not specified for cluster '%s' - skipping\", metric, job.Cluster)\n    88→\t\t\tcontinue\n    89→\t\t}\n    90→\n    91→\t\t// Skip if metric is removed for subcluster\n    92→\t\tif len(mc.SubClusters) != 0 {\n    93→\t\t\tisRemoved := false\n    94→\t\t\tfor _, scConfig := range mc.SubClusters {\n    95→\t\t\t\tif scConfig.Name == job.SubCluster && scConfig.Remove {\n    96→\t\t\t\t\tisRemoved = true\n    97→\t\t\t\t\tbreak\n    98→\t\t\t\t}\n    99→\t\t\t}\n   100→\t\t\tif isRemoved {\n   101→\t\t\t\tcontinue\n   102→\t\t\t}\n   103→\t\t}\n   104→\n   105→\t\t// Avoid duplicates...\n   106→\t\thandledScopes := make([]schema.MetricScope, 0, 3)\n   107→\n   108→\tscopesLoop:\n   109→\t\tfor _, requestedScope := range scopes {\n   110→\t\t\tnativeScope := mc.Scope\n   111→\t\t\tif nativeScope == schema.MetricScopeAccelerator && job.NumAcc == 0 {\n   112→\t\t\t\tcontinue\n   113→\t\t\t}\n   114→\n   115→\t\t\tscope := nativeScope.Max(requestedScope)\n   116→\t\t\tfor _, s := range handledScopes {\n   117→\t\t\t\tif scope == s {\n   118→\t\t\t\t\tcontinue scopesLoop\n   119→\t\t\t\t}\n   120→\t\t\t}\n   121→\t\t\thandledScopes = append(handledScopes, scope)\n   122→\n   123→\t\t\tfor _, host := range job.Resources {\n   124→\t\t\t\thwthreads := host.HWThreads\n   125→\t\t\t\tif hwthreads == nil {\n   126→\t\t\t\t\thwthreads = topology.Node\n   127→\t\t\t\t}\n   128→\n   129→\t\t\t\t// Note: Expected exceptions will return as empty slices -> Continue\n   130→\t\t\t\thostQueries, hostScopes := buildScopeQueries(\n   131→\t\t\t\t\tnativeScope, requestedScope,\n   132→\t\t\t\t\tremoteName, host.Hostname,\n   133→\t\t\t\t\ttopology, hwthreads, host.Accelerators,\n   134→\t\t\t\t\tresolution,\n   135→\t\t\t\t)\n   136→\n   137→\t\t\t\t// Note: Unexpected errors, such as unhandled cases, will return as nils -> Error\n   138→\t\t\t\tif hostQueries == nil && hostScopes == nil {\n   139→\t\t\t\t\treturn nil, nil, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > TODO: unhandled case: native-scope=%s, requested-scope=%s\", nativeScope, requestedScope)\n   140→\t\t\t\t}\n   141→\n   142→\t\t\t\tqueries = append(queries, hostQueries...)\n   143→\t\t\t\tassignedScope = append(assignedScope, hostScopes...)\n   144→\t\t\t}\n   145→\t\t}\n   146→\t}\n   147→\n   148→\treturn queries, assignedScope, nil\n   149→}\n   150→\n   151→// buildNodeQueries constructs API queries for node-specific metric data (Systems View).\n   152→// Similar to buildQueries but uses full node topology instead of job-allocated resources.\n   153→//\n   154→// The function handles:\n   155→//   - SubCluster topology resolution (either pre-loaded or per-node lookup)\n   156→//   - Full node hardware thread lists (not job-specific subsets)\n   157→//   - All accelerators on each node\n   158→//   - Metric configuration validation with subcluster filtering\n   159→//\n   160→// Returns queries and their corresponding assigned scopes.\n   161→func (ccms *CCMetricStore) buildNodeQueries(\n   162→\tcluster string,\n   163→\tsubCluster string,\n   164→\tnodes []string,\n   165→\tmetrics []string,\n   166→\tscopes []schema.MetricScope,\n   167→\tresolution int,\n   168→) ([]APIQuery, []schema.MetricScope, error) {\n   169→\t// Initialize both slices together\n   170→\tqueries := make([]APIQuery, 0, len(metrics)*len(scopes)*len(nodes))\n   171→\tassignedScope := make([]schema.MetricScope, 0, len(metrics)*len(scopes)*len(nodes))\n   172→\n   173→\tfor _, metric := range metrics {\n   174→\t\tremoteName := metric\n   175→\t\tmc := archive.GetMetricConfig(cluster, metric)\n   176→\t\tif mc == nil {\n   177→\t\t\tcclog.Warnf(\"metric '%s' is not specified for cluster '%s'\", metric, cluster)\n   178→\t\t\tcontinue\n   179→\t\t}\n   180→\n   181→\t\t// Skip if metric is removed for subcluster\n   182→\t\tif mc.SubClusters != nil {\n   183→\t\t\tisRemoved := false\n   184→\t\t\tfor _, scConfig := range mc.SubClusters {\n   185→\t\t\t\tif scConfig.Name == subCluster && scConfig.Remove {\n   186→\t\t\t\t\tisRemoved = true\n   187→\t\t\t\t\tbreak\n   188→\t\t\t\t}\n   189→\t\t\t}\n   190→\t\t\tif isRemoved {\n   191→\t\t\t\tcontinue\n   192→\t\t\t}\n   193→\t\t}\n   194→\n   195→\t\t// Avoid duplicates...\n   196→\t\thandledScopes := make([]schema.MetricScope, 0, 3)\n   197→\n   198→\tscopesLoop:\n   199→\t\tfor _, requestedScope := range scopes {\n   200→\t\t\tnativeScope := mc.Scope\n   201→\n   202→\t\t\tscope := nativeScope.Max(requestedScope)\n   203→\t\t\tfor _, s := range handledScopes {\n   204→\t\t\t\tif scope == s {\n   205→\t\t\t\t\tcontinue scopesLoop\n   206→\t\t\t\t}\n   207→\t\t\t}\n   208→\t\t\thandledScopes = append(handledScopes, scope)\n   209→\n   210→\t\t\tfor _, hostname := range nodes {\n   211→\t\t\t\tvar topology *schema.Topology\n   212→\t\t\t\tvar err error\n   213→\n   214→\t\t\t\t// If no subCluster given, get it by node\n   215→\t\t\t\tif subCluster == \"\" {\n   216→\t\t\t\t\ttopology, err = ccms.getTopologyByNode(cluster, hostname)\n   217→\t\t\t\t} else {\n   218→\t\t\t\t\ttopology, err = ccms.getTopology(cluster, subCluster)\n   219→\t\t\t\t}\n   220→\n   221→\t\t\t\tif err != nil {\n   222→\t\t\t\t\treturn nil, nil, err\n   223→\t\t\t\t}\n   224→\n   225→\t\t\t\t// Always full node hwthread id list, no partial queries expected -> Use \"topology.Node\" directly where applicable\n   226→\t\t\t\t// Always full accelerator id list, no partial queries expected -> Use \"acceleratorIds\" directly where applicable\n   227→\t\t\t\tacceleratorIds := topology.GetAcceleratorIDs()\n   228→\n   229→\t\t\t\t// Moved check here if metric matches hardware specs\n   230→\t\t\t\tif nativeScope == schema.MetricScopeAccelerator && len(acceleratorIds) == 0 {\n   231→\t\t\t\t\tcontinue scopesLoop\n   232→\t\t\t\t}\n   233→\n   234→\t\t\t\tnodeQueries, nodeScopes := buildScopeQueries(\n   235→\t\t\t\t\tnativeScope, requestedScope,\n   236→\t\t\t\t\tremoteName, hostname,\n   237→\t\t\t\t\ttopology, topology.Node, acceleratorIds,\n   238→\t\t\t\t\tresolution,\n   239→\t\t\t\t)\n   240→\n   241→\t\t\t\tif len(nodeQueries) == 0 && len(nodeScopes) == 0 {\n   242→\t\t\t\t\treturn nil, nil, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > TODO: unhandled case: native-scope=%s, requested-scope=%s\", nativeScope, requestedScope)\n   243→\t\t\t\t}\n   244→\n   245→\t\t\t\tqueries = append(queries, nodeQueries...)\n   246→\t\t\t\tassignedScope = append(assignedScope, nodeScopes...)\n   247→\t\t\t}\n   248→\t\t}\n   249→\t}\n   250→\n   251→\treturn queries, assignedScope, nil\n   252→}\n   253→\n   254→// buildScopeQueries generates API queries for a given scope transformation.\n   255→// It returns a slice of queries and corresponding assigned scopes.\n   256→// Some transformations (e.g., HWThread -> Core/Socket) may generate multiple queries.\n   257→func buildScopeQueries(\n   258→\tnativeScope, requestedScope schema.MetricScope,\n   259→\tmetric, hostname string,\n   260→\ttopology *schema.Topology,\n   261→\thwthreads []int,\n   262→\taccelerators []string,\n   263→\tresolution int,\n   264→) ([]APIQuery, []schema.MetricScope) {\n   265→\tscope := nativeScope.Max(requestedScope)\n   266→\tqueries := []APIQuery{}\n   267→\tscopes := []schema.MetricScope{}\n   268→\n   269→\thwthreadsStr := intToStringSlice(hwthreads)\n   270→\n   271→\t// Accelerator -> Accelerator (Use \"accelerator\" scope if requested scope is lower than node)\n   272→\tif nativeScope == schema.MetricScopeAccelerator && scope.LT(schema.MetricScopeNode) {\n   273→\t\tif scope != schema.MetricScopeAccelerator {\n   274→\t\t\t// Expected Exception -> Continue -> Return Empty Slices\n   275→\t\t\treturn queries, scopes\n   276→\t\t}\n   277→\n   278→\t\tqueries = append(queries, APIQuery{\n   279→\t\t\tMetric:     metric,\n   280→\t\t\tHostname:   hostname,\n   281→\t\t\tAggregate:  false,\n   282→\t\t\tType:       &acceleratorString,\n   283→\t\t\tTypeIds:    accelerators,\n   284→\t\t\tResolution: resolution,\n   285→\t\t})\n   286→\t\tscopes = append(scopes, schema.MetricScopeAccelerator)\n   287→\t\treturn queries, scopes\n   288→\t}\n   289→\n   290→\t// Accelerator -> Node\n   291→\tif nativeScope == schema.MetricScopeAccelerator && scope == schema.MetricScopeNode {\n   292→\t\tif len(accelerators) == 0 {\n   293→\t\t\t// Expected Exception -> Continue -> Return Empty Slices\n   294→\t\t\treturn queries, scopes\n   295→\t\t}\n   296→\n   297→\t\tqueries = append(queries, APIQuery{\n   298→\t\t\tMetric:     metric,\n   299→\t\t\tHostname:   hostname,\n   300→\t\t\tAggregate:  true,\n   301→\t\t\tType:       &acceleratorString,\n   302→\t\t\tTypeIds:    accelerators,\n   303→\t\t\tResolution: resolution,\n   304→\t\t})\n   305→\t\tscopes = append(scopes, scope)\n   306→\t\treturn queries, scopes\n   307→\t}\n   308→\n   309→\t// HWThread -> HWThread\n   310→\tif nativeScope == schema.MetricScopeHWThread && scope == schema.MetricScopeHWThread {\n   311→\t\tqueries = append(queries, APIQuery{\n   312→\t\t\tMetric:     metric,\n   313→\t\t\tHostname:   hostname,\n   314→\t\t\tAggregate:  false,\n   315→\t\t\tType:       &hwthreadString,\n   316→\t\t\tTypeIds:    hwthreadsStr,\n   317→\t\t\tResolution: resolution,\n   318→\t\t})\n   319→\t\tscopes = append(scopes, scope)\n   320→\t\treturn queries, scopes\n   321→\t}\n   322→\n   323→\t// HWThread -> Core\n   324→\tif nativeScope == schema.MetricScopeHWThread && scope == schema.MetricScopeCore {\n   325→\t\tcores, _ := topology.GetCoresFromHWThreads(hwthreads)\n   326→\t\tfor _, core := range cores {\n   327→\t\t\tqueries = append(queries, APIQuery{\n   328→\t\t\t\tMetric:     metric,\n   329→\t\t\t\tHostname:   hostname,\n   330→\t\t\t\tAggregate:  true,\n   331→\t\t\t\tType:       &hwthreadString,\n   332→\t\t\t\tTypeIds:    intToStringSlice(topology.Core[core]),\n   333→\t\t\t\tResolution: resolution,\n   334→\t\t\t})\n   335→\t\t\tscopes = append(scopes, scope)\n   336→\t\t}\n   337→\t\treturn queries, scopes\n   338→\t}\n   339→\n   340→\t// HWThread -> Socket\n   341→\tif nativeScope == schema.MetricScopeHWThread && scope == schema.MetricScopeSocket {\n   342→\t\tsockets, _ := topology.GetSocketsFromHWThreads(hwthreads)\n   343→\t\tfor _, socket := range sockets {\n   344→\t\t\tqueries = append(queries, APIQuery{\n   345→\t\t\t\tMetric:     metric,\n   346→\t\t\t\tHostname:   hostname,\n   347→\t\t\t\tAggregate:  true,\n   348→\t\t\t\tType:       &hwthreadString,\n   349→\t\t\t\tTypeIds:    intToStringSlice(topology.Socket[socket]),\n   350→\t\t\t\tResolution: resolution,\n   351→\t\t\t})\n   352→\t\t\tscopes = append(scopes, scope)\n   353→\t\t}\n   354→\t\treturn queries, scopes\n   355→\t}\n   356→\n   357→\t// HWThread -> Node\n   358→\tif nativeScope == schema.MetricScopeHWThread && scope == schema.MetricScopeNode {\n   359→\t\tqueries = append(queries, APIQuery{\n   360→\t\t\tMetric:     metric,\n   361→\t\t\tHostname:   hostname,\n   362→\t\t\tAggregate:  true,\n   363→\t\t\tType:       &hwthreadString,\n   364→\t\t\tTypeIds:    hwthreadsStr,\n   365→\t\t\tResolution: resolution,\n   366→\t\t})\n   367→\t\tscopes = append(scopes, scope)\n   368→\t\treturn queries, scopes\n   369→\t}\n   370→\n   371→\t// Core -> Core\n   372→\tif nativeScope == schema.MetricScopeCore && scope == schema.MetricScopeCore {\n   373→\t\tcores, _ := topology.GetCoresFromHWThreads(hwthreads)\n   374→\t\tqueries = append(queries, APIQuery{\n   375→\t\t\tMetric:     metric,\n   376→\t\t\tHostname:   hostname,\n   377→\t\t\tAggregate:  false,\n   378→\t\t\tType:       &coreString,\n   379→\t\t\tTypeIds:    intToStringSlice(cores),\n   380→\t\t\tResolution: resolution,\n   381→\t\t})\n   382→\t\tscopes = append(scopes, scope)\n   383→\t\treturn queries, scopes\n   384→\t}\n   385→\n   386→\t// Core -> Socket\n   387→\tif nativeScope == schema.MetricScopeCore && scope == schema.MetricScopeSocket {\n   388→\t\tsockets, _ := topology.GetSocketsFromCores(hwthreads)\n   389→\t\tfor _, socket := range sockets {\n   390→\t\t\tqueries = append(queries, APIQuery{\n   391→\t\t\t\tMetric:     metric,\n   392→\t\t\t\tHostname:   hostname,\n   393→\t\t\t\tAggregate:  true,\n   394→\t\t\t\tType:       &coreString,\n   395→\t\t\t\tTypeIds:    intToStringSlice(topology.Socket[socket]),\n   396→\t\t\t\tResolution: resolution,\n   397→\t\t\t})\n   398→\t\t\tscopes = append(scopes, scope)\n   399→\t\t}\n   400→\t\treturn queries, scopes\n   401→\t}\n   402→\n   403→\t// Core -> Node\n   404→\tif nativeScope == schema.MetricScopeCore && scope == schema.MetricScopeNode {\n   405→\t\tcores, _ := topology.GetCoresFromHWThreads(hwthreads)\n   406→\t\tqueries = append(queries, APIQuery{\n   407→\t\t\tMetric:     metric,\n   408→\t\t\tHostname:   hostname,\n   409→\t\t\tAggregate:  true,\n   410→\t\t\tType:       &coreString,\n   411→\t\t\tTypeIds:    intToStringSlice(cores),\n   412→\t\t\tResolution: resolution,\n   413→\t\t})\n   414→\t\tscopes = append(scopes, scope)\n   415→\t\treturn queries, scopes\n   416→\t}\n   417→\n   418→\t// MemoryDomain -> MemoryDomain\n   419→\tif nativeScope == schema.MetricScopeMemoryDomain && scope == schema.MetricScopeMemoryDomain {\n   420→\t\tmemDomains, _ := topology.GetMemoryDomainsFromHWThreads(hwthreads)\n   421→\t\tqueries = append(queries, APIQuery{\n   422→\t\t\tMetric:     metric,\n   423→\t\t\tHostname:   hostname,\n   424→\t\t\tAggregate:  false,\n   425→\t\t\tType:       &memoryDomainString,\n   426→\t\t\tTypeIds:    intToStringSlice(memDomains),\n   427→\t\t\tResolution: resolution,\n   428→\t\t})\n   429→\t\tscopes = append(scopes, scope)\n   430→\t\treturn queries, scopes\n   431→\t}\n   432→\n   433→\t// MemoryDomain -> Node\n   434→\tif nativeScope == schema.MetricScopeMemoryDomain && scope == schema.MetricScopeNode {\n   435→\t\tmemDomains, _ := topology.GetMemoryDomainsFromHWThreads(hwthreads)\n   436→\t\tqueries = append(queries, APIQuery{\n   437→\t\t\tMetric:     metric,\n   438→\t\t\tHostname:   hostname,\n   439→\t\t\tAggregate:  true,\n   440→\t\t\tType:       &memoryDomainString,\n   441→\t\t\tTypeIds:    intToStringSlice(memDomains),\n   442→\t\t\tResolution: resolution,\n   443→\t\t})\n   444→\t\tscopes = append(scopes, scope)\n   445→\t\treturn queries, scopes\n   446→\t}\n   447→\n   448→\t// MemoryDomain -> Socket\n   449→\tif nativeScope == schema.MetricScopeMemoryDomain && scope == schema.MetricScopeSocket {\n   450→\t\tmemDomains, _ := topology.GetMemoryDomainsFromHWThreads(hwthreads)\n   451→\t\tsocketToDomains, err := topology.GetMemoryDomainsBySocket(memDomains)\n   452→\t\tif err != nil {\n   453→\t\t\tcclog.Errorf(\"Error mapping memory domains to sockets, return unchanged: %v\", err)\n   454→\t\t\t// Rare Error Case -> Still Continue -> Return Empty Slices\n   455→\t\t\treturn queries, scopes\n   456→\t\t}\n   457→\n   458→\t\t// Create a query for each socket\n   459→\t\tfor _, domains := range socketToDomains {\n   460→\t\t\tqueries = append(queries, APIQuery{\n   461→\t\t\t\tMetric:     metric,\n   462→\t\t\t\tHostname:   hostname,\n   463→\t\t\t\tAggregate:  true,\n   464→\t\t\t\tType:       &memoryDomainString,\n   465→\t\t\t\tTypeIds:    intToStringSlice(domains),\n   466→\t\t\t\tResolution: resolution,\n   467→\t\t\t})\n   468→\t\t\t// Add scope for each query, not just once\n   469→\t\t\tscopes = append(scopes, scope)\n   470→\t\t}\n   471→\t\treturn queries, scopes\n   472→\t}\n   473→\n   474→\t// Socket -> Socket\n   475→\tif nativeScope == schema.MetricScopeSocket && scope == schema.MetricScopeSocket {\n   476→\t\tsockets, _ := topology.GetSocketsFromHWThreads(hwthreads)\n   477→\t\tqueries = append(queries, APIQuery{\n   478→\t\t\tMetric:     metric,\n   479→\t\t\tHostname:   hostname,\n   480→\t\t\tAggregate:  false,\n   481→\t\t\tType:       &socketString,\n   482→\t\t\tTypeIds:    intToStringSlice(sockets),\n   483→\t\t\tResolution: resolution,\n   484→\t\t})\n   485→\t\tscopes = append(scopes, scope)\n   486→\t\treturn queries, scopes\n   487→\t}\n   488→\n   489→\t// Socket -> Node\n   490→\tif nativeScope == schema.MetricScopeSocket && scope == schema.MetricScopeNode {\n   491→\t\tsockets, _ := topology.GetSocketsFromHWThreads(hwthreads)\n   492→\t\tqueries = append(queries, APIQuery{\n   493→\t\t\tMetric:     metric,\n   494→\t\t\tHostname:   hostname,\n   495→\t\t\tAggregate:  true,\n   496→\t\t\tType:       &socketString,\n   497→\t\t\tTypeIds:    intToStringSlice(sockets),\n   498→\t\t\tResolution: resolution,\n   499→\t\t})\n   500→\t\tscopes = append(scopes, scope)\n   501→\t\treturn queries, scopes\n   502→\t}\n   503→\n   504→\t// Node -> Node\n   505→\tif nativeScope == schema.MetricScopeNode && scope == schema.MetricScopeNode {\n   506→\t\tqueries = append(queries, APIQuery{\n   507→\t\t\tMetric:     metric,\n   508→\t\t\tHostname:   hostname,\n   509→\t\t\tResolution: resolution,\n   510→\t\t})\n   511→\t\tscopes = append(scopes, scope)\n   512→\t\treturn queries, scopes\n   513→\t}\n   514→\n   515→\t// Unhandled Case -> Error -> Return nils\n   516→\treturn nil, nil\n   517→}\n   518→\n   519→// intToStringSlice converts a slice of integers to a slice of strings.\n   520→// Used to convert hardware IDs (core IDs, socket IDs, etc.) to the string format required by the API.\n   521→func intToStringSlice(is []int) []string {\n   522→\tss := make([]string, len(is))\n   523→\tfor i, x := range is {\n   524→\t\tss[i] = strconv.Itoa(x)\n   525→\t}\n   526→\treturn ss\n   527→}\n   528→"}]},"uuid":"5276817a-6dcb-4d1b-805e-df0c67a34a44","timestamp":"2026-03-04T15:25:20.651Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"aeb62a7c5fea57dd2"},"toolUseID":"agent_msg_017PhdenzLz5rCHUntWZksNg","parentToolUseID":"toolu_01TXANL4MzLEvewR2e42CBCn","uuid":"129b30dc-38cb-411d-bd88-0f77dc7c107a","timestamp":"2026-03-04T15:25:21.091Z"}
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{"parentUuid":"a66c80dc-9f4d-4bbc-a3a7-006a93181c9e","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012W68jfP2TS81pMWMsth8mg","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→package metricdispatch\n     6→\n     7→import (\n     8→\t\"bytes\"\n     9→\t\"context\"\n    10→\t\"encoding/json\"\n    11→\t\"fmt\"\n    12→\t\"time\"\n    13→\n    14→\t\"github.com/ClusterCockpit/cc-backend/internal/config\"\n    15→\tccms \"github.com/ClusterCockpit/cc-backend/internal/metricstoreclient\"\n    16→\t\"github.com/ClusterCockpit/cc-backend/pkg/metricstore\"\n    17→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    18→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    19→)\n    20→\n    21→type MetricDataRepository interface {\n    22→\t// Return the JobData for the given job, only with the requested metrics.\n    23→\tLoadData(job *schema.Job,\n    24→\t\tmetrics []string,\n    25→\t\tscopes []schema.MetricScope,\n    26→\t\tctx context.Context,\n    27→\t\tresolution int) (schema.JobData, error)\n    28→\n    29→\t// Return a map of metrics to a map of nodes to the metric statistics of the job. node scope only.\n    30→\tLoadStats(job *schema.Job,\n    31→\t\tmetrics []string,\n    32→\t\tctx context.Context) (map[string]map[string]schema.MetricStatistics, error)\n    33→\n    34→\t// Return a map of metrics to a map of scopes to the scoped metric statistics of the job.\n    35→\tLoadScopedStats(job *schema.Job,\n    36→\t\tmetrics []string,\n    37→\t\tscopes []schema.MetricScope,\n    38→\t\tctx context.Context) (schema.ScopedJobStats, error)\n    39→\n    40→\t// Return a map of hosts to a map of metrics at the requested scopes (currently only node) for that node.\n    41→\tLoadNodeData(cluster string,\n    42→\t\tmetrics, nodes []string,\n    43→\t\tscopes []schema.MetricScope,\n    44→\t\tfrom, to time.Time,\n    45→\t\tctx context.Context) (map[string]map[string][]*schema.JobMetric, error)\n    46→\n    47→\t// Return a map of hosts to a map of metrics to a map of scopes for multiple nodes.\n    48→\tLoadNodeListData(cluster, subCluster string,\n    49→\t\tnodes []string,\n    50→\t\tmetrics []string,\n    51→\t\tscopes []schema.MetricScope,\n    52→\t\tresolution int,\n    53→\t\tfrom, to time.Time,\n    54→\t\tctx context.Context) (map[string]schema.JobData, error)\n    55→\n    56→\t// HealthCheck evaluates the monitoring state for a set of nodes against expected metrics.\n    57→\tHealthCheck(cluster string,\n    58→\t\tnodes []string,\n    59→\t\tmetrics []string) (map[string]metricstore.HealthCheckResult, error)\n    60→}\n    61→\n    62→type CCMetricStoreConfig struct {\n    63→\tScope string `json:\"scope\"`\n    64→\tURL   string `json:\"url\"`\n    65→\tToken string `json:\"token\"`\n    66→}\n    67→\n    68→var metricDataRepos map[string]MetricDataRepository = map[string]MetricDataRepository{}\n    69→\n    70→func Init(rawConfig json.RawMessage) error {\n    71→\tif rawConfig != nil {\n    72→\t\tvar configs []CCMetricStoreConfig\n    73→\t\tconfig.Validate(configSchema, rawConfig)\n    74→\t\tdec := json.NewDecoder(bytes.NewReader(rawConfig))\n    75→\t\tdec.DisallowUnknownFields()\n    76→\t\tif err := dec.Decode(&configs); err != nil {\n    77→\t\t\treturn fmt.Errorf(\"[METRICDISPATCH]> External Metric Store Config Init: Could not decode config file '%s' Error: %s\", rawConfig, err.Error())\n    78→\t\t}\n    79→\n    80→\t\tif len(configs) == 0 {\n    81→\t\t\treturn fmt.Errorf(\"[METRICDISPATCH]> No external metric store configurations found in config file\")\n    82→\t\t}\n    83→\n    84→\t\tfor _, config := range configs {\n    85→\t\t\tmetricDataRepos[config.Scope] = ccms.NewCCMetricStore(config.URL, config.Token)\n    86→\t\t}\n    87→\t}\n    88→\n    89→\treturn nil\n    90→}\n    91→\n    92→func GetMetricDataRepo(cluster string, subcluster string) (MetricDataRepository, error) {\n    93→\tvar repo MetricDataRepository\n    94→\tvar ok bool\n    95→\n    96→\tkey := cluster + \"-\" + subcluster\n    97→\trepo, ok = metricDataRepos[key]\n    98→\n    99→\tif !ok {\n   100→\t\trepo, ok = metricDataRepos[cluster]\n   101→\n   102→\t\tif !ok {\n   103→\t\t\trepo, ok = metricDataRepos[\"*\"]\n   104→\n   105→\t\t\tif !ok {\n   106→\t\t\t\tif metricstore.MetricStoreHandle == nil {\n   107→\t\t\t\t\treturn nil, fmt.Errorf(\"[METRICDISPATCH]> no metric data repository configured '%s'\", key)\n   108→\t\t\t\t}\n   109→\n   110→\t\t\t\trepo = metricstore.MetricStoreHandle\n   111→\t\t\t\tcclog.Debugf(\"[METRICDISPATCH]> Using internal metric data repository for '%s'\", key)\n   112→\t\t\t}\n   113→\t\t}\n   114→\t}\n   115→\n   116→\treturn repo, nil\n   117→}\n   118→\n   119→// GetHealthCheckRepo returns the MetricDataRepository for performing health checks on a cluster.\n   120→// It uses the same fallback logic as GetMetricDataRepo: cluster → wildcard → internal.\n   121→func GetHealthCheckRepo(cluster string) (MetricDataRepository, error) {\n   122→\treturn GetMetricDataRepo(cluster, \"\")\n   123→}\n   124→"}]},"uuid":"2313ff3f-42c1-432b-8326-a529516d2e34","timestamp":"2026-03-04T15:25:21.125Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"aeb62a7c5fea57dd2"},"toolUseID":"agent_msg_017PhdenzLz5rCHUntWZksNg","parentToolUseID":"toolu_01TXANL4MzLEvewR2e42CBCn","uuid":"3a52b372-aa2c-41ba-8a45-cc34528f2352","timestamp":"2026-03-04T15:25:21.126Z"}
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{"parentUuid":"f5b877ff-e484-4467-add5-693af8a67ebf","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Mg4MWwhkX8KtX6f2GZWRL1","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved. This file is part of cc-backend.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// This file implements high-level query functions for loading job metric data\n     7→// with automatic scope transformation and aggregation.\n     8→//\n     9→// Key Concepts:\n    10→//\n    11→// Metric Scopes: Metrics are collected at different granularities (native scope):\n    12→//   - HWThread: Per hardware thread\n    13→//   - Core: Per CPU core\n    14→//   - Socket: Per CPU socket\n    15→//   - MemoryDomain: Per memory domain (NUMA)\n    16→//   - Accelerator: Per GPU/accelerator\n    17→//   - Node: Per compute node\n    18→//\n    19→// Scope Transformation: The buildQueries functions transform between native scope\n    20→// and requested scope by:\n    21→//   - Aggregating finer-grained data (e.g., HWThread → Core → Socket → Node)\n    22→//   - Rejecting requests for finer granularity than available\n    23→//   - Handling special cases (e.g., Accelerator metrics)\n    24→//\n    25→// Query Building: Constructs APIQuery structures with proper selectors (Type, TypeIds)\n    26→// based on cluster topology and job resources.\n    27→package metricstore\n    28→\n    29→import (\n    30→\t\"context\"\n    31→\t\"fmt\"\n    32→\t\"strconv\"\n    33→\t\"strings\"\n    34→\t\"time\"\n    35→\n    36→\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n    37→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    38→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    39→)\n    40→\n    41→type InternalMetricStore struct{}\n    42→\n    43→var MetricStoreHandle *InternalMetricStore\n    44→\n    45→// HealthCheck delegates to the internal MemoryStore's HealthCheck.\n    46→func (ccms *InternalMetricStore) HealthCheck(cluster string,\n    47→\tnodes []string, metrics []string,\n    48→) (map[string]HealthCheckResult, error) {\n    49→\treturn GetMemoryStore().HealthCheck(cluster, nodes, metrics)\n    50→}\n    51→\n    52→// TestLoadDataCallback allows tests to override LoadData behavior for testing purposes.\n    53→// When set to a non-nil function, LoadData will call this function instead of the default implementation.\n    54→var TestLoadDataCallback func(job *schema.Job, metrics []string, scopes []schema.MetricScope, ctx context.Context, resolution int) (schema.JobData, error)\n    55→\n    56→// LoadData loads metric data for a specific job with automatic scope transformation.\n    57→//\n    58→// This is the primary function for retrieving job metric data. It handles:\n    59→//   - Building queries with scope transformation via buildQueries\n    60→//   - Fetching data from the metric store\n    61→//   - Organizing results by metric and scope\n    62→//   - Converting NaN statistics to 0 for JSON compatibility\n    63→//   - Partial error handling (returns data for successful queries even if some fail)\n    64→//\n    65→// Parameters:\n    66→//   - job: Job metadata including cluster, resources, and time range\n    67→//   - metrics: List of metric names to load\n    68→//   - scopes: Requested metric scopes (will be transformed to match native scopes)\n    69→//   - ctx: Context for cancellation (currently unused but reserved for future use)\n    70→//   - resolution: Data resolution in seconds (0 for native resolution)\n    71→//\n    72→// Returns:\n    73→//   - JobData: Map of metric → scope → JobMetric with time-series data and statistics\n    74→//   - Error: Returns error if query building or fetching fails, or partial error listing failed hosts\n    75→//\n    76→// Example:\n    77→//\n    78→//\tjobData, err := LoadData(job, []string{\"cpu_load\", \"mem_used\"}, []schema.MetricScope{schema.MetricScopeNode}, ctx, 60)\n    79→func (ccms *InternalMetricStore) LoadData(\n    80→\tjob *schema.Job,\n    81→\tmetrics []string,\n    82→\tscopes []schema.MetricScope,\n    83→\tctx context.Context,\n    84→\tresolution int,\n    85→) (schema.JobData, error) {\n    86→\tif TestLoadDataCallback != nil {\n    87→\t\treturn TestLoadDataCallback(job, metrics, scopes, ctx, resolution)\n    88→\t}\n    89→\n    90→\tqueries, assignedScope, err := buildQueries(job, metrics, scopes, int64(resolution))\n    91→\tif err != nil {\n    92→\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n    93→\t\treturn nil, err\n    94→\t}\n    95→\n    96→\t// Verify assignment is correct - log any inconsistencies for debugging\n    97→\tif len(queries) != len(assignedScope) {\n    98→\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildQueries: %d vs %d\",\n    99→\t\t\tlen(queries), len(assignedScope))\n   100→\t}\n   101→\n   102→\treq := APIQueryRequest{\n   103→\t\tCluster:   job.Cluster,\n   104→\t\tFrom:      job.StartTime,\n   105→\t\tTo:        job.StartTime + int64(job.Duration),\n   106→\t\tQueries:   queries,\n   107→\t\tWithStats: true,\n   108→\t\tWithData:  true,\n   109→\t}\n   110→\n   111→\tresBody, err := FetchData(req)\n   112→\tif err != nil {\n   113→\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n   114→\t\treturn nil, err\n   115→\t}\n   116→\n   117→\tvar errors []string\n   118→\tjobData := make(schema.JobData)\n   119→\n   120→\t// Add safety check for potential index out of range errors\n   121→\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n   122→\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n   123→\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n   124→\t\tif len(resBody.Results) > len(req.Queries) {\n   125→\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n   126→\t\t}\n   127→\t\tif len(assignedScope) > len(req.Queries) {\n   128→\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n   129→\t\t}\n   130→\t}\n   131→\n   132→\tfor i, row := range resBody.Results {\n   133→\t\t// Safety check to prevent index out of range errors\n   134→\t\tif i >= len(req.Queries) || i >= len(assignedScope) {\n   135→\t\t\tcclog.Warnf(\"Index out of range prevented: i=%d, queries=%d, assignedScope=%d\",\n   136→\t\t\t\ti, len(req.Queries), len(assignedScope))\n   137→\t\t\tcontinue\n   138→\t\t}\n   139→\n   140→\t\tquery := req.Queries[i]\n   141→\t\tmetric := query.Metric\n   142→\t\tscope := assignedScope[i]\n   143→\t\tmc := archive.GetMetricConfig(job.Cluster, metric)\n   144→\n   145→\t\tif mc == nil {\n   146→\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, job.Cluster)\n   147→\t\t\tcontinue\n   148→\t\t}\n   149→\n   150→\t\tif _, ok := jobData[metric]; !ok {\n   151→\t\t\tjobData[metric] = make(map[schema.MetricScope]*schema.JobMetric)\n   152→\t\t}\n   153→\n   154→\t\tres := mc.Timestep\n   155→\t\tif len(row) > 0 {\n   156→\t\t\tres = int(row[0].Resolution)\n   157→\t\t}\n   158→\n   159→\t\tjobMetric, ok := jobData[metric][scope]\n   160→\t\tif !ok {\n   161→\t\t\tjobMetric = &schema.JobMetric{\n   162→\t\t\t\tUnit:     mc.Unit,\n   163→\t\t\t\tTimestep: res,\n   164→\t\t\t\tSeries:   make([]schema.Series, 0),\n   165→\t\t\t}\n   166→\t\t\tjobData[metric][scope] = jobMetric\n   167→\t\t}\n   168→\n   169→\t\tfor ndx, res := range row {\n   170→\t\t\tif res.Error != nil {\n   171→\t\t\t\t/* Build list for \"partial errors\", if any */\n   172→\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n   173→\t\t\t\tcontinue\n   174→\t\t\t}\n   175→\n   176→\t\t\tid := (*string)(nil)\n   177→\t\t\tif query.Type != nil {\n   178→\t\t\t\t// Check if ndx is within the bounds of TypeIds slice\n   179→\t\t\t\tif ndx < len(query.TypeIds) {\n   180→\t\t\t\t\tid = new(string)\n   181→\t\t\t\t\t*id = query.TypeIds[ndx]\n   182→\t\t\t\t} else {\n   183→\t\t\t\t\t// Log the error but continue processing\n   184→\t\t\t\t\tcclog.Warnf(\"TypeIds index out of range: %d with length %d for metric %s on host %s\",\n   185→\t\t\t\t\t\tndx, len(query.TypeIds), query.Metric, query.Hostname)\n   186→\t\t\t\t}\n   187→\t\t\t}\n   188→\n   189→\t\t\tsanitizeStats(&res)\n   190→\n   191→\t\t\tjobMetric.Series = append(jobMetric.Series, schema.Series{\n   192→\t\t\t\tHostname: query.Hostname,\n   193→\t\t\t\tID:       id,\n   194→\t\t\t\tStatistics: schema.MetricStatistics{\n   195→\t\t\t\t\tAvg: float64(res.Avg),\n   196→\t\t\t\t\tMin: float64(res.Min),\n   197→\t\t\t\t\tMax: float64(res.Max),\n   198→\t\t\t\t},\n   199→\t\t\t\tData: res.Data,\n   200→\t\t\t})"}]},"uuid":"3befe58c-197c-42f4-acdf-8b5a7633aedb","timestamp":"2026-03-04T15:25:26.744Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"aeb62a7c5fea57dd2"},"toolUseID":"agent_msg_017PhdenzLz5rCHUntWZksNg","parentToolUseID":"toolu_01TXANL4MzLEvewR2e42CBCn","uuid":"0dfbdb2b-f122-4bf9-9859-ae787e8dff0d","timestamp":"2026-03-04T15:25:27.130Z"}
{"parentUuid":"0dfbdb2b-f122-4bf9-9859-ae787e8dff0d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-04T15:25:27.207Z","message":{"model":"claude-opus-4-6","id":"msg_01P2Qpqo4h9epyvwiMRzNQif","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019BeoghPbgyXTUd9Ejf1nRd","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store-queries.go","offset":517},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":9867,"cache_read_input_tokens":15754,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":9867},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYiGmdD4GbJnBEXLJvfLC","uuid":"ac10a6da-6722-4e65-88a0-90c211512dde"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"aeb62a7c5fea57dd2"},"toolUseID":"agent_msg_017PhdenzLz5rCHUntWZksNg","parentToolUseID":"toolu_01TXANL4MzLEvewR2e42CBCn","uuid":"d4d19cb5-75a3-4314-b58f-77547a931fec","timestamp":"2026-03-04T15:25:27.207Z"}
{"parentUuid":"d4d19cb5-75a3-4314-b58f-77547a931fec","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019BeoghPbgyXTUd9Ejf1nRd","type":"tool_result","content":"   517→}\n   518→\n   519→// intToStringSlice converts a slice of integers to a slice of strings.\n   520→// Used to convert hardware IDs (core IDs, socket IDs, etc.) to the string format required by the API.\n   521→func intToStringSlice(is []int) []string {\n   522→\tss := make([]string, len(is))\n   523→\tfor i, x := range is {\n   524→\t\tss[i] = strconv.Itoa(x)\n   525→\t}\n   526→\treturn ss\n   527→}\n   528→"}]},"uuid":"01b22f13-f810-4538-94db-6fff38913c23","timestamp":"2026-03-04T15:25:27.230Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"aeb62a7c5fea57dd2"},"toolUseID":"agent_msg_017PhdenzLz5rCHUntWZksNg","parentToolUseID":"toolu_01TXANL4MzLEvewR2e42CBCn","uuid":"73eb46a0-6f45-478c-897b-7f3c40840bac","timestamp":"2026-03-04T15:25:27.230Z"}
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{"parentUuid":"74bb71af-1788-4bbf-bee9-9aea1c9040ce","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Mbh7fdbrMX6G431BYAQNKx","type":"tool_result","content":"   200→\t\t\t})\n   201→\t\t}\n   202→\n   203→\t\t// So that one can later check len(jobData):\n   204→\t\tif len(jobMetric.Series) == 0 {\n   205→\t\t\tdelete(jobData[metric], scope)\n   206→\t\t\tif len(jobData[metric]) == 0 {\n   207→\t\t\t\tdelete(jobData, metric)\n   208→\t\t\t}\n   209→\t\t}\n   210→\t}\n   211→\n   212→\tif len(errors) != 0 {\n   213→\t\t/* Returns list for \"partial errors\" */\n   214→\t\treturn jobData, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n   215→\t}\n   216→\treturn jobData, nil\n   217→}\n   218→\n   219→// Pre-converted scope strings avoid repeated string(MetricScope) allocations during\n   220→// query construction. These are used in APIQuery.Type field throughout buildQueries\n   221→// and buildNodeQueries functions. Converting once at package initialization improves\n   222→// performance for high-volume query building.\n   223→var (\n   224→\thwthreadString     = string(schema.MetricScopeHWThread)\n   225→\tcoreString         = string(schema.MetricScopeCore)\n   226→\tmemoryDomainString = string(schema.MetricScopeMemoryDomain)\n   227→\tsocketString       = string(schema.MetricScopeSocket)\n   228→\tacceleratorString  = string(schema.MetricScopeAccelerator)\n   229→)\n   230→\n   231→// buildQueries constructs APIQuery structures with automatic scope transformation for a job.\n   232→//\n   233→// This function implements the core scope transformation logic, handling all combinations of\n   234→// native metric scopes and requested scopes. It uses the cluster topology to determine which\n   235→// hardware IDs to include in each query.\n   236→//\n   237→// Scope Transformation Rules:\n   238→//   - If native scope >= requested scope: Aggregates data (Aggregate=true in APIQuery)\n   239→//   - If native scope < requested scope: Returns error (cannot increase granularity)\n   240→//   - Special handling for Accelerator scope (independent of CPU hierarchy)\n   241→//\n   242→// The function generates one or more APIQuery per (metric, scope, host) combination:\n   243→//   - For non-aggregated queries: One query with all relevant IDs\n   244→//   - For aggregated queries: May generate multiple queries (e.g., one per socket/core)\n   245→//\n   246→// Parameters:\n   247→//   - job: Job metadata including cluster, subcluster, and resource allocation\n   248→//   - metrics: List of metrics to query\n   249→//   - scopes: Requested scopes for each metric\n   250→//   - resolution: Data resolution in seconds\n   251→//\n   252→// Returns:\n   253→//   - []APIQuery: List of queries to execute\n   254→//   - []schema.MetricScope: Assigned scope for each query (after transformation)\n   255→//   - error: Returns error if topology lookup fails or unhandled scope combination encountered\n   256→func buildQueries(\n   257→\tjob *schema.Job,\n   258→\tmetrics []string,\n   259→\tscopes []schema.MetricScope,\n   260→\tresolution int64,\n   261→) ([]APIQuery, []schema.MetricScope, error) {\n   262→\tif len(job.Resources) == 0 {\n   263→\t\treturn nil, nil, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > no resources allocated for job %d\", job.JobID)\n   264→\t}\n   265→\n   266→\tqueries := make([]APIQuery, 0, len(metrics)*len(scopes)*len(job.Resources))\n   267→\tassignedScope := []schema.MetricScope{}\n   268→\n   269→\tsubcluster, scerr := archive.GetSubCluster(job.Cluster, job.SubCluster)\n   270→\tif scerr != nil {\n   271→\t\treturn nil, nil, scerr\n   272→\t}\n   273→\ttopology := subcluster.Topology\n   274→\n   275→\tfor _, metric := range metrics {\n   276→\t\tmc := archive.GetMetricConfig(job.Cluster, metric)\n   277→\t\tif mc == nil {\n   278→\t\t\tcclog.Warnf(\"metric '%s' is not specified for cluster '%s'\", metric, job.Cluster)\n   279→\t\t\tcontinue\n   280→\t\t}\n   281→\n   282→\t\t// Skip if metric is removed for subcluster\n   283→\t\tif len(mc.SubClusters) != 0 {\n   284→\t\t\tisRemoved := false\n   285→\t\t\tfor _, scConfig := range mc.SubClusters {\n   286→\t\t\t\tif scConfig.Name == job.SubCluster && scConfig.Remove {\n   287→\t\t\t\t\tisRemoved = true\n   288→\t\t\t\t\tbreak\n   289→\t\t\t\t}\n   290→\t\t\t}\n   291→\t\t\tif isRemoved {\n   292→\t\t\t\tcontinue\n   293→\t\t\t}\n   294→\t\t}\n   295→\n   296→\t\t// Avoid duplicates using map for O(1) lookup\n   297→\t\thandledScopes := make(map[schema.MetricScope]bool, 3)\n   298→\n   299→\t\tfor _, requestedScope := range scopes {\n   300→\t\t\tnativeScope := mc.Scope\n   301→\t\t\tif nativeScope == schema.MetricScopeAccelerator && job.NumAcc == 0 {\n   302→\t\t\t\tcontinue\n   303→\t\t\t}\n   304→\n   305→\t\t\tscope := nativeScope.Max(requestedScope)\n   306→\t\t\tif handledScopes[scope] {\n   307→\t\t\t\tcontinue\n   308→\t\t\t}\n   309→\t\t\thandledScopes[scope] = true\n   310→\n   311→\t\t\tfor _, host := range job.Resources {\n   312→\t\t\t\thwthreads := host.HWThreads\n   313→\t\t\t\tif hwthreads == nil {\n   314→\t\t\t\t\thwthreads = topology.Node\n   315→\t\t\t\t}\n   316→\n   317→\t\t\t\t// Accelerator -> Accelerator (Use \"accelerator\" scope if requested scope is lower than node)\n   318→\t\t\t\tif nativeScope == schema.MetricScopeAccelerator && scope.LT(schema.MetricScopeNode) {\n   319→\t\t\t\t\tif scope != schema.MetricScopeAccelerator {\n   320→\t\t\t\t\t\t// Skip all other catched cases\n   321→\t\t\t\t\t\tcontinue\n   322→\t\t\t\t\t}\n   323→\n   324→\t\t\t\t\tqueries = append(queries, APIQuery{\n   325→\t\t\t\t\t\tMetric:     metric,\n   326→\t\t\t\t\t\tHostname:   host.Hostname,\n   327→\t\t\t\t\t\tAggregate:  false,\n   328→\t\t\t\t\t\tType:       &acceleratorString,\n   329→\t\t\t\t\t\tTypeIds:    host.Accelerators,\n   330→\t\t\t\t\t\tResolution: resolution,\n   331→\t\t\t\t\t})\n   332→\t\t\t\t\tassignedScope = append(assignedScope, schema.MetricScopeAccelerator)\n   333→\t\t\t\t\tcontinue\n   334→\t\t\t\t}\n   335→\n   336→\t\t\t\t// Accelerator -> Node\n   337→\t\t\t\tif nativeScope == schema.MetricScopeAccelerator && scope == schema.MetricScopeNode {\n   338→\t\t\t\t\tif len(host.Accelerators) == 0 {\n   339→\t\t\t\t\t\tcontinue\n   340→\t\t\t\t\t}\n   341→\n   342→\t\t\t\t\tqueries = append(queries, APIQuery{\n   343→\t\t\t\t\t\tMetric:     metric,\n   344→\t\t\t\t\t\tHostname:   host.Hostname,\n   345→\t\t\t\t\t\tAggregate:  true,\n   346→\t\t\t\t\t\tType:       &acceleratorString,\n   347→\t\t\t\t\t\tTypeIds:    host.Accelerators,\n   348→\t\t\t\t\t\tResolution: resolution,\n   349→\t\t\t\t\t})\n   350→\t\t\t\t\tassignedScope = append(assignedScope, scope)\n   351→\t\t\t\t\tcontinue\n   352→\t\t\t\t}\n   353→\n   354→\t\t\t\t// HWThread -> HWThread\n   355→\t\t\t\tif nativeScope == schema.MetricScopeHWThread && scope == schema.MetricScopeHWThread {\n   356→\t\t\t\t\tqueries = append(queries, APIQuery{\n   357→\t\t\t\t\t\tMetric:     metric,\n   358→\t\t\t\t\t\tHostname:   host.Hostname,\n   359→\t\t\t\t\t\tAggregate:  false,\n   360→\t\t\t\t\t\tType:       &hwthreadString,\n   361→\t\t\t\t\t\tTypeIds:    intToStringSlice(hwthreads),\n   362→\t\t\t\t\t\tResolution: resolution,\n   363→\t\t\t\t\t})\n   364→\t\t\t\t\tassignedScope = append(assignedScope, scope)\n   365→\t\t\t\t\tcontinue\n   366→\t\t\t\t}\n   367→\n   368→\t\t\t\t// HWThread -> Core\n   369→\t\t\t\tif nativeScope == schema.MetricScopeHWThread && scope == schema.MetricScopeCore {\n   370→\t\t\t\t\tcores, _ := topology.GetCoresFromHWThreads(hwthreads)\n   371→\t\t\t\t\tfor _, core := range cores {\n   372→\t\t\t\t\t\tqueries = append(queries, APIQuery{\n   373→\t\t\t\t\t\t\tMetric:     metric,\n   374→\t\t\t\t\t\t\tHostname:   host.Hostname,\n   375→\t\t\t\t\t\t\tAggregate:  true,\n   376→\t\t\t\t\t\t\tType:       &hwthreadString,\n   377→\t\t\t\t\t\t\tTypeIds:    intToStringSlice(topology.Core[core]),\n   378→\t\t\t\t\t\t\tResolution: resolution,\n   379→\t\t\t\t\t\t})\n   380→\t\t\t\t\t\tassignedScope = append(assignedScope, scope)\n   381→\t\t\t\t\t}\n   382→\t\t\t\t\tcontinue\n   383→\t\t\t\t}\n   384→\n   385→\t\t\t\t// HWThread -> Socket\n   386→\t\t\t\tif nativeScope == schema.MetricScopeHWThread && scope == schema.MetricScopeSocket {\n   387→\t\t\t\t\tsockets, _ := topology.GetSocketsFromHWThreads(hwthreads)\n   388→\t\t\t\t\tfor _, socket := range sockets {\n   389→\t\t\t\t\t\tqueries = append(queries, APIQuery{\n   390→\t\t\t\t\t\t\tMetric:     metric,\n   391→\t\t\t\t\t\t\tHostname:   host.Hostname,\n   392→\t\t\t\t\t\t\tAggregate:  true,\n   393→\t\t\t\t\t\t\tType:       &hwthreadString,\n   394→\t\t\t\t\t\t\tTypeIds:    intToStringSlice(topology.Socket[socket]),\n   395→\t\t\t\t\t\t\tResolution: resolution,\n   396→\t\t\t\t\t\t})\n   397→\t\t\t\t\t\tassignedScope = append(assignedScope, scope)\n   398→\t\t\t\t\t}\n   399→\t\t\t\t\tcontinue\n   400→\t\t\t\t}\n   401→\n   402→\t\t\t\t// HWThread -> Node\n   403→\t\t\t\tif nativeScope == schema.MetricScopeHWThread && scope == schema.MetricScopeNode {\n   404→\t\t\t\t\tqueries = append(queries, APIQuery{\n   405→\t\t\t\t\t\tMetric:     metric,\n   406→\t\t\t\t\t\tHostname:   host.Hostname,\n   407→\t\t\t\t\t\tAggregate:  true,\n   408→\t\t\t\t\t\tType:       &hwthreadString,\n   409→\t\t\t\t\t\tTypeIds:    intToStringSlice(hwthreads),\n   410→\t\t\t\t\t\tResolution: resolution,\n   411→\t\t\t\t\t})\n   412→\t\t\t\t\tassignedScope = append(assignedScope, scope)\n   413→\t\t\t\t\tcontinue\n   414→\t\t\t\t}\n   415→\n   416→\t\t\t\t// Core -> Core\n   417→\t\t\t\tif nativeScope == schema.MetricScopeCore && scope == schema.MetricScopeCore {\n   418→\t\t\t\t\tcores, _ := topology.GetCoresFromHWThreads(hwthreads)\n   419→\t\t\t\t\tqueries = append(queries, APIQuery{\n   420→\t\t\t\t\t\tMetric:     metric,\n   421→\t\t\t\t\t\tHostname:   host.Hostname,\n   422→\t\t\t\t\t\tAggregate:  false,\n   423→\t\t\t\t\t\tType:       &coreString,\n   424→\t\t\t\t\t\tTypeIds:    intToStringSlice(cores),\n   425→\t\t\t\t\t\tResolution: resolution,\n   426→\t\t\t\t\t})\n   427→\t\t\t\t\tassignedScope = append(assignedScope, scope)\n   428→\t\t\t\t\tcontinue\n   429→\t\t\t\t}\n   430→\n   431→\t\t\t\t// Core -> Socket\n   432→\t\t\t\tif nativeScope == schema.MetricScopeCore && scope == schema.MetricScopeSocket {\n   433→\t\t\t\t\tsockets, _ := topology.GetSocketsFromCores(hwthreads)\n   434→\t\t\t\t\tfor _, socket := range sockets {\n   435→\t\t\t\t\t\tqueries = append(queries, APIQuery{\n   436→\t\t\t\t\t\t\tMetric:     metric,\n   437→\t\t\t\t\t\t\tHostname:   host.Hostname,\n   438→\t\t\t\t\t\t\tAggregate:  true,\n   439→\t\t\t\t\t\t\tType:       &coreString,\n   440→\t\t\t\t\t\t\tTypeIds:    intToStringSlice(topology.Socket[socket]),\n   441→\t\t\t\t\t\t\tResolution: resolution,\n   442→\t\t\t\t\t\t})\n   443→\t\t\t\t\t\tassignedScope = append(assignedScope, scope)\n   444→\t\t\t\t\t}\n   445→\t\t\t\t\tcontinue\n   446→\t\t\t\t}\n   447→\n   448→\t\t\t\t// Core -> Node\n   449→\t\t\t\tif nativeScope == schema.MetricScopeCore && scope == schema.MetricScopeNode {\n   450→\t\t\t\t\tcores, _ := topology.GetCoresFromHWThreads(hwthreads)\n   451→\t\t\t\t\tqueries = append(queries, APIQuery{\n   452→\t\t\t\t\t\tMetric:     metric,\n   453→\t\t\t\t\t\tHostname:   host.Hostname,\n   454→\t\t\t\t\t\tAggregate:  true,\n   455→\t\t\t\t\t\tType:       &coreString,\n   456→\t\t\t\t\t\tTypeIds:    intToStringSlice(cores),\n   457→\t\t\t\t\t\tResolution: resolution,\n   458→\t\t\t\t\t})\n   459→\t\t\t\t\tassignedScope = append(assignedScope, scope)\n   460→\t\t\t\t\tcontinue\n   461→\t\t\t\t}\n   462→\n   463→\t\t\t\t// MemoryDomain -> MemoryDomain\n   464→\t\t\t\tif nativeScope == schema.MetricScopeMemoryDomain && scope == schema.MetricScopeMemoryDomain {\n   465→\t\t\t\t\tsockets, _ := topology.GetMemoryDomainsFromHWThreads(hwthreads)\n   466→\t\t\t\t\tqueries = append(queries, APIQuery{\n   467→\t\t\t\t\t\tMetric:     metric,\n   468→\t\t\t\t\t\tHostname:   host.Hostname,\n   469→\t\t\t\t\t\tAggregate:  false,\n   470→\t\t\t\t\t\tType:       &memoryDomainString,\n   471→\t\t\t\t\t\tTypeIds:    intToStringSlice(sockets),\n   472→\t\t\t\t\t\tResolution: resolution,\n   473→\t\t\t\t\t})\n   474→\t\t\t\t\tassignedScope = append(assignedScope, scope)\n   475→\t\t\t\t\tcontinue\n   476→\t\t\t\t}\n   477→\n   478→\t\t\t\t// MemoryDomain -> Node\n   479→\t\t\t\tif nativeScope == schema.MetricScopeMemoryDomain && scope == schema.MetricScopeNode {\n   480→\t\t\t\t\tsockets, _ := topology.GetMemoryDomainsFromHWThreads(hwthreads)\n   481→\t\t\t\t\tqueries = append(queries, APIQuery{\n   482→\t\t\t\t\t\tMetric:     metric,\n   483→\t\t\t\t\t\tHostname:   host.Hostname,\n   484→\t\t\t\t\t\tAggregate:  true,\n   485→\t\t\t\t\t\tType:       &memoryDomainString,\n   486→\t\t\t\t\t\tTypeIds:    intToStringSlice(sockets),\n   487→\t\t\t\t\t\tResolution: resolution,\n   488→\t\t\t\t\t})\n   489→\t\t\t\t\tassignedScope = append(assignedScope, scope)\n   490→\t\t\t\t\tcontinue\n   491→\t\t\t\t}\n   492→\n   493→\t\t\t\t// Socket -> Socket\n   494→\t\t\t\tif nativeScope == schema.MetricScopeSocket && scope == schema.MetricScopeSocket {\n   495→\t\t\t\t\tsockets, _ := topology.GetSocketsFromHWThreads(hwthreads)\n   496→\t\t\t\t\tqueries = append(queries, APIQuery{\n   497→\t\t\t\t\t\tMetric:     metric,\n   498→\t\t\t\t\t\tHostname:   host.Hostname,\n   499→\t\t\t\t\t\tAggregate:  false,\n   500→\t\t\t\t\t\tType:       &socketString,\n   501→\t\t\t\t\t\tTypeIds:    intToStringSlice(sockets),\n   502→\t\t\t\t\t\tResolution: resolution,\n   503→\t\t\t\t\t})\n   504→\t\t\t\t\tassignedScope = append(assignedScope, scope)\n   505→\t\t\t\t\tcontinue\n   506→\t\t\t\t}\n   507→\n   508→\t\t\t\t// Socket -> Node\n   509→\t\t\t\tif nativeScope == schema.MetricScopeSocket && scope == schema.MetricScopeNode {\n   510→\t\t\t\t\tsockets, _ := topology.GetSocketsFromHWThreads(hwthreads)\n   511→\t\t\t\t\tqueries = append(queries, APIQuery{\n   512→\t\t\t\t\t\tMetric:     metric,\n   513→\t\t\t\t\t\tHostname:   host.Hostname,\n   514→\t\t\t\t\t\tAggregate:  true,\n   515→\t\t\t\t\t\tType:       &socketString,\n   516→\t\t\t\t\t\tTypeIds:    intToStringSlice(sockets),\n   517→\t\t\t\t\t\tResolution: resolution,\n   518→\t\t\t\t\t})\n   519→\t\t\t\t\tassignedScope = append(assignedScope, scope)\n   520→\t\t\t\t\tcontinue\n   521→\t\t\t\t}\n   522→\n   523→\t\t\t\t// Node -> Node\n   524→\t\t\t\tif nativeScope == schema.MetricScopeNode && scope == schema.MetricScopeNode {\n   525→\t\t\t\t\tqueries = append(queries, APIQuery{\n   526→\t\t\t\t\t\tMetric:     metric,\n   527→\t\t\t\t\t\tHostname:   host.Hostname,\n   528→\t\t\t\t\t\tResolution: resolution,\n   529→\t\t\t\t\t})\n   530→\t\t\t\t\tassignedScope = append(assignedScope, scope)\n   531→\t\t\t\t\tcontinue\n   532→\t\t\t\t}\n   533→\n   534→\t\t\t\treturn nil, nil, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > TODO: unhandled case: native-scope=%s, requested-scope=%s\", nativeScope, requestedScope)\n   535→\t\t\t}\n   536→\t\t}\n   537→\t}\n   538→\n   539→\treturn queries, assignedScope, nil\n   540→}\n   541→\n   542→// LoadStats loads only metric statistics (avg/min/max) for a job at node scope.\n   543→//\n   544→// This is an optimized version of LoadData that fetches only statistics without\n   545→// time-series data, reducing bandwidth and memory usage. Always queries at node scope.\n   546→//\n   547→// Parameters:\n   548→//   - job: Job metadata\n   549→//   - metrics: List of metric names\n   550→//   - ctx: Context (currently unused)\n   551→//\n   552→// Returns:\n   553→//   - Map of metric → hostname → statistics\n   554→//   - Error on query building or fetching failure\n   555→func (ccms *InternalMetricStore) LoadStats(\n   556→\tjob *schema.Job,\n   557→\tmetrics []string,\n   558→\tctx context.Context,\n   559→) (map[string]map[string]schema.MetricStatistics, error) {\n   560→\t// TODO(#166): Add scope parameter for analysis view accelerator normalization\n   561→\tqueries, _, err := buildQueries(job, metrics, []schema.MetricScope{schema.MetricScopeNode}, 0)\n   562→\tif err != nil {\n   563→\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v: %s\", job.JobID, metrics, err.Error())\n   564→\t\treturn nil, err\n   565→\t}\n   566→\n   567→\treq := APIQueryRequest{\n   568→\t\tCluster:   job.Cluster,\n   569→\t\tFrom:      job.StartTime,\n   570→\t\tTo:        job.StartTime + int64(job.Duration),\n   571→\t\tQueries:   queries,\n   572→\t\tWithStats: true,\n   573→\t\tWithData:  false,\n   574→\t}\n   575→\n   576→\tresBody, err := FetchData(req)\n   577→\tif err != nil {\n   578→\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n   579→\t\treturn nil, err\n   580→\t}\n   581→\n   582→\tstats := make(map[string]map[string]schema.MetricStatistics, len(metrics))\n   583→\tfor i, res := range resBody.Results {\n   584→\t\tif len(res) == 0 {\n   585→\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n   586→\t\t\tcontinue\n   587→\t\t}\n   588→\t\tquery := req.Queries[i]\n   589→\t\tmetric := query.Metric\n   590→\t\tdata := res[0]\n   591→\t\tif data.Error != nil {\n   592→\t\t\tcclog.Warnf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *data.Error)\n   593→\t\t\tcontinue\n   594→\t\t}\n   595→\n   596→\t\tmetricdata, ok := stats[metric]\n   597→\t\tif !ok {\n   598→\t\t\tmetricdata = make(map[string]schema.MetricStatistics, job.NumNodes)\n   599→\t\t\tstats[metric] = metricdata\n   600→\t\t}\n   601→\n   602→\t\tif data.Avg.IsNaN() || data.Min.IsNaN() || data.Max.IsNaN() {\n   603→\t\t\tcclog.Warnf(\"fetching %s for node %s failed: one of avg/min/max is NaN\", metric, query.Hostname)\n   604→\t\t\tcontinue\n   605→\t\t}\n   606→\n   607→\t\tmetricdata[query.Hostname] = schema.MetricStatistics{\n   608→\t\t\tAvg: float64(data.Avg),\n   609→\t\t\tMin: float64(data.Min),\n   610→\t\t\tMax: float64(data.Max),\n   611→\t\t}\n   612→\t}\n   613→\n   614→\treturn stats, nil\n   615→}\n   616→\n   617→// LoadScopedStats loads metric statistics for a job with scope-aware grouping.\n   618→//\n   619→// Similar to LoadStats but supports multiple scopes and returns statistics grouped\n   620→// by scope with hardware IDs (e.g., per-core, per-socket statistics).\n   621→//\n   622→// Parameters:\n   623→//   - job: Job metadata\n   624→//   - metrics: List of metric names\n   625→//   - scopes: Requested metric scopes\n   626→//   - ctx: Context (currently unused)\n   627→//\n   628→// Returns:\n   629→//   - ScopedJobStats: Map of metric → scope → []ScopedStats (with hostname and ID)\n   630→//   - Error or partial error listing failed queries\n   631→func (ccms *InternalMetricStore) LoadScopedStats(\n   632→\tjob *schema.Job,\n   633→\tmetrics []string,\n   634→\tscopes []schema.MetricScope,\n   635→\tctx context.Context,\n   636→) (schema.ScopedJobStats, error) {\n   637→\tqueries, assignedScope, err := buildQueries(job, metrics, scopes, 0)\n   638→\tif err != nil {\n   639→\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n   640→\t\treturn nil, err\n   641→\t}\n   642→\n   643→\treq := APIQueryRequest{\n   644→\t\tCluster:   job.Cluster,\n   645→\t\tFrom:      job.StartTime,\n   646→\t\tTo:        job.StartTime + int64(job.Duration),\n   647→\t\tQueries:   queries,\n   648→\t\tWithStats: true,\n   649→\t\tWithData:  false,\n   650→\t}\n   651→\n   652→\tresBody, err := FetchData(req)\n   653→\tif err != nil {\n   654→\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n   655→\t\treturn nil, err\n   656→\t}\n   657→\n   658→\tvar errors []string\n   659→\tscopedJobStats := make(schema.ScopedJobStats)\n   660→\n   661→\tfor i, row := range resBody.Results {\n   662→\t\tif len(row) == 0 {\n   663→\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n   664→\t\t\tcontinue\n   665→\t\t}\n   666→\t\tquery := req.Queries[i]\n   667→\t\tmetric := query.Metric\n   668→\t\tscope := assignedScope[i]\n   669→\n   670→\t\tif _, ok := scopedJobStats[metric]; !ok {\n   671→\t\t\tscopedJobStats[metric] = make(map[schema.MetricScope][]*schema.ScopedStats)\n   672→\t\t}\n   673→\n   674→\t\tif _, ok := scopedJobStats[metric][scope]; !ok {\n   675→\t\t\tscopedJobStats[metric][scope] = make([]*schema.ScopedStats, 0)\n   676→\t\t}\n   677→\n   678→\t\tfor ndx, res := range row {\n   679→\t\t\tif res.Error != nil {\n   680→\t\t\t\t/* Build list for \"partial errors\", if any */\n   681→\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n   682→\t\t\t\tcontinue\n   683→\t\t\t}\n   684→\n   685→\t\t\tid := (*string)(nil)\n   686→\t\t\tif query.Type != nil {\n   687→\t\t\t\t// Check if ndx is within the bounds of TypeIds slice\n   688→\t\t\t\tif ndx < len(query.TypeIds) {\n   689→\t\t\t\t\tid = new(string)\n   690→\t\t\t\t\t*id = query.TypeIds[ndx]\n   691→\t\t\t\t} else {\n   692→\t\t\t\t\t// Log the error but continue processing\n   693→\t\t\t\t\tcclog.Warnf(\"TypeIds index out of range: %d with length %d for metric %s on host %s\",\n   694→\t\t\t\t\t\tndx, len(query.TypeIds), query.Metric, query.Hostname)\n   695→\t\t\t\t}\n   696→\t\t\t}\n   697→\n   698→\t\t\tsanitizeStats(&res)\n   699→\n   700→\t\t\tscopedJobStats[metric][scope] = append(scopedJobStats[metric][scope], &schema.ScopedStats{\n   701→\t\t\t\tHostname: query.Hostname,\n   702→\t\t\t\tID:       id,\n   703→\t\t\t\tData: &schema.MetricStatistics{\n   704→\t\t\t\t\tAvg: float64(res.Avg),\n   705→\t\t\t\t\tMin: float64(res.Min),\n   706→\t\t\t\t\tMax: float64(res.Max),\n   707→\t\t\t\t},\n   708→\t\t\t})\n   709→\t\t}\n   710→\n   711→\t\t// So that one can later check len(scopedJobStats[metric][scope]): Remove from map if empty\n   712→\t\tif len(scopedJobStats[metric][scope]) == 0 {\n   713→\t\t\tdelete(scopedJobStats[metric], scope)\n   714→\t\t\tif len(scopedJobStats[metric]) == 0 {\n   715→\t\t\t\tdelete(scopedJobStats, metric)\n   716→\t\t\t}\n   717→\t\t}\n   718→\t}\n   719→\n   720→\tif len(errors) != 0 {\n   721→\t\t/* Returns list for \"partial errors\" */\n   722→\t\treturn scopedJobStats, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n   723→\t}\n   724→\treturn scopedJobStats, nil\n   725→}\n   726→\n   727→// LoadNodeData loads metric data for specific nodes in a cluster over a time range.\n   728→//\n   729→// Unlike LoadData which operates on job resources, this function queries arbitrary nodes\n   730→// directly. Useful for system monitoring and node status views.\n   731→//\n   732→// Parameters:\n   733→//   - cluster: Cluster name\n   734→//   - metrics: List of metric names\n   735→//   - nodes: List of node hostnames (nil = all nodes in cluster via ForAllNodes)\n   736→//   - scopes: Requested metric scopes (currently unused - always node scope)\n   737→//   - from, to: Time range\n   738→//   - ctx: Context (currently unused)\n   739→//\n   740→// Returns:\n   741→//   - Map of hostname → metric → []JobMetric\n   742→//   - Error or partial error listing failed queries\n   743→func (ccms *InternalMetricStore) LoadNodeData(\n   744→\tcluster string,\n   745→\tmetrics, nodes []string,\n   746→\tscopes []schema.MetricScope,\n   747→\tfrom, to time.Time,\n   748→\tctx context.Context,\n   749→) (map[string]map[string][]*schema.JobMetric, error) {\n   750→\treq := APIQueryRequest{\n   751→\t\tCluster:   cluster,\n   752→\t\tFrom:      from.Unix(),\n   753→\t\tTo:        to.Unix(),\n   754→\t\tWithStats: true,\n   755→\t\tWithData:  true,\n   756→\t}\n   757→\n   758→\tif nodes == nil {\n   759→\t\treq.ForAllNodes = append(req.ForAllNodes, metrics...)\n   760→\t} else {\n   761→\t\tfor _, node := range nodes {\n   762→\t\t\tfor _, metric := range metrics {\n   763→\t\t\t\treq.Queries = append(req.Queries, APIQuery{\n   764→\t\t\t\t\tHostname:   node,\n   765→\t\t\t\t\tMetric:     metric,\n   766→\t\t\t\t\tResolution: 0, // Default for Node Queries: Will return metric $Timestep Resolution\n   767→\t\t\t\t})\n   768→\t\t\t}\n   769→\t\t}\n   770→\t}\n   771→\n   772→\tresBody, err := FetchData(req)\n   773→\tif err != nil {\n   774→\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n   775→\t\treturn nil, err\n   776→\t}\n   777→\n   778→\tvar errors []string\n   779→\tdata := make(map[string]map[string][]*schema.JobMetric)\n   780→\tfor i, res := range resBody.Results {\n   781→\t\tif len(res) == 0 {\n   782→\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n   783→\t\t\tcontinue\n   784→\t\t}\n   785→\n   786→\t\tvar query APIQuery\n   787→\t\tif resBody.Queries != nil {\n   788→\t\t\tquery = resBody.Queries[i]\n   789→\t\t} else {\n   790→\t\t\tquery = req.Queries[i]\n   791→\t\t}\n   792→\n   793→\t\tmetric := query.Metric\n   794→\t\tqdata := res[0]\n   795→\t\tif qdata.Error != nil {\n   796→\t\t\terrors = append(errors, fmt.Sprintf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *qdata.Error))\n   797→\t\t}\n   798→\n   799→\t\tsanitizeStats(&qdata)\n   800→\n   801→\t\thostdata, ok := data[query.Hostname]\n   802→\t\tif !ok {\n   803→\t\t\thostdata = make(map[string][]*schema.JobMetric)\n   804→\t\t\tdata[query.Hostname] = hostdata\n   805→\t\t}\n   806→\n   807→\t\tmc := archive.GetMetricConfig(cluster, metric)\n   808→\t\thostdata[metric] = append(hostdata[metric], &schema.JobMetric{\n   809→\t\t\tUnit:     mc.Unit,\n   810→\t\t\tTimestep: mc.Timestep,\n   811→\t\t\tSeries: []schema.Series{\n   812→\t\t\t\t{\n   813→\t\t\t\t\tHostname: query.Hostname,\n   814→\t\t\t\t\tData:     qdata.Data,\n   815→\t\t\t\t\tStatistics: schema.MetricStatistics{\n   816→\t\t\t\t\t\tAvg: float64(qdata.Avg),\n   817→\t\t\t\t\t\tMin: float64(qdata.Min),\n   818→\t\t\t\t\t\tMax: float64(qdata.Max),\n   819→\t\t\t\t\t},\n   820→\t\t\t\t},\n   821→\t\t\t},\n   822→\t\t})\n   823→\t}\n   824→\n   825→\tif len(errors) != 0 {\n   826→\t\t/* Returns list of \"partial errors\" */\n   827→\t\treturn data, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n   828→\t}\n   829→\n   830→\treturn data, nil\n   831→}\n   832→\n   833→// LoadNodeListData loads metric data for a list of nodes with full scope transformation support.\n   834→//\n   835→// This is the most flexible node data loading function, supporting arbitrary scopes and\n   836→// resolution. Uses buildNodeQueries for proper scope transformation based on topology.\n   837→//\n   838→// Parameters:\n   839→//   - cluster: Cluster name\n   840→//   - subCluster: SubCluster name (empty string to infer from node names)\n   841→//   - nodes: List of node hostnames\n   842→//   - metrics: List of metric names\n   843→//   - scopes: Requested metric scopes\n   844→//   - resolution: Data resolution in seconds\n   845→//   - from, to: Time range\n   846→//   - ctx: Context (currently unused)\n   847→//\n   848→// Returns:\n   849→//   - Map of hostname → JobData (metric → scope → JobMetric)\n   850→//   - Error or partial error listing failed queries\n   851→func (ccms *InternalMetricStore) LoadNodeListData(\n   852→\tcluster, subCluster string,\n   853→\tnodes []string,\n   854→\tmetrics []string,\n   855→\tscopes []schema.MetricScope,\n   856→\tresolution int,\n   857→\tfrom, to time.Time,\n   858→\tctx context.Context,\n   859→) (map[string]schema.JobData, error) {\n   860→\t// Note: Order of node data is not guaranteed after this point\n   861→\tqueries, assignedScope, err := buildNodeQueries(cluster, subCluster, nodes, metrics, scopes, int64(resolution))\n   862→\tif err != nil {\n   863→\t\tcclog.Errorf(\"Error while building node queries for Cluster %s, SubCLuster %s, Metrics %v, Scopes %v: %s\", cluster, subCluster, metrics, scopes, err.Error())\n   864→\t\treturn nil, err\n   865→\t}\n   866→\n   867→\t// Verify assignment is correct - log any inconsistencies for debugging\n   868→\tif len(queries) != len(assignedScope) {\n   869→\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildNodeQueries: %d vs %d\",\n   870→\t\t\tlen(queries), len(assignedScope))\n   871→\t}\n   872→\n   873→\treq := APIQueryRequest{\n   874→\t\tCluster:   cluster,\n   875→\t\tQueries:   queries,\n   876→\t\tFrom:      from.Unix(),\n   877→\t\tTo:        to.Unix(),\n   878→\t\tWithStats: true,\n   879→\t\tWithData:  true,\n   880→\t}\n   881→\n   882→\tresBody, err := FetchData(req)\n   883→\tif err != nil {\n   884→\t\tcclog.Errorf(\"Error while fetching data : %s\", err.Error())\n   885→\t\treturn nil, err\n   886→\t}\n   887→\n   888→\tvar errors []string\n   889→\tdata := make(map[string]schema.JobData)\n   890→\n   891→\t// Add safety check for index out of range issues\n   892→\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n   893→\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n   894→\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n   895→\t\tif len(resBody.Results) > len(req.Queries) {\n   896→\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n   897→\t\t}\n   898→\t\tif len(assignedScope) > len(req.Queries) {\n   899→\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n   900→\t\t}\n   901→\t}\n   902→\n   903→\tfor i, row := range resBody.Results {\n   904→\t\t// Safety check to prevent index out of range errors\n   905→\t\tif i >= len(req.Queries) || i >= len(assignedScope) {\n   906→\t\t\tcclog.Warnf(\"Index out of range prevented: i=%d, queries=%d, assignedScope=%d\",\n   907→\t\t\t\ti, len(req.Queries), len(assignedScope))\n   908→\t\t\tcontinue\n   909→\t\t}\n   910→\n   911→\t\tvar query APIQuery\n   912→\t\tif resBody.Queries != nil {\n   913→\t\t\tif i < len(resBody.Queries) {\n   914→\t\t\t\tquery = resBody.Queries[i]\n   915→\t\t\t} else {\n   916→\t\t\t\tcclog.Warnf(\"Index out of range prevented for resBody.Queries: i=%d, len=%d\",\n   917→\t\t\t\t\ti, len(resBody.Queries))\n   918→\t\t\t\tcontinue\n   919→\t\t\t}\n   920→\t\t} else {\n   921→\t\t\tquery = req.Queries[i]\n   922→\t\t}\n   923→\n   924→\t\tmetric := query.Metric\n   925→\t\tscope := assignedScope[i]\n   926→\t\tmc := archive.GetMetricConfig(cluster, metric)\n   927→\t\tif mc == nil {\n   928→\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, cluster)\n   929→\t\t\tcontinue\n   930→\t\t}\n   931→\n   932→\t\tres := mc.Timestep\n   933→\t\tif len(row) > 0 {\n   934→\t\t\tres = int(row[0].Resolution)\n   935→\t\t}\n   936→\n   937→\t\t// Init Nested Map Data Structures If Not Found\n   938→\t\thostData, ok := data[query.Hostname]\n   939→\t\tif !ok {\n   940→\t\t\thostData = make(schema.JobData)\n   941→\t\t\tdata[query.Hostname] = hostData\n   942→\t\t}\n   943→\n   944→\t\tmetricData, ok := hostData[metric]\n   945→\t\tif !ok {\n   946→\t\t\tmetricData = make(map[schema.MetricScope]*schema.JobMetric)\n   947→\t\t\tdata[query.Hostname][metric] = metricData\n   948→\t\t}\n   949→\n   950→\t\tscopeData, ok := metricData[scope]\n   951→\t\tif !ok {\n   952→\t\t\tscopeData = &schema.JobMetric{\n   953→\t\t\t\tUnit:     mc.Unit,\n   954→\t\t\t\tTimestep: res,\n   955→\t\t\t\tSeries:   make([]schema.Series, 0),\n   956→\t\t\t}\n   957→\t\t\tdata[query.Hostname][metric][scope] = scopeData\n   958→\t\t}\n   959→\n   960→\t\tfor ndx, res := range row {\n   961→\t\t\tif res.Error != nil {\n   962→\t\t\t\t/* Build list for \"partial errors\", if any */\n   963→\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n   964→\t\t\t\tcontinue\n   965→\t\t\t}\n   966→\n   967→\t\t\tid := (*string)(nil)\n   968→\t\t\tif query.Type != nil {\n   969→\t\t\t\t// Check if ndx is within the bounds of TypeIds slice\n   970→\t\t\t\tif ndx < len(query.TypeIds) {\n   971→\t\t\t\t\tid = new(string)\n   972→\t\t\t\t\t*id = query.TypeIds[ndx]\n   973→\t\t\t\t} else {\n   974→\t\t\t\t\t// Log the error but continue processing\n   975→\t\t\t\t\tcclog.Warnf(\"TypeIds index out of range: %d with length %d for metric %s on host %s\",\n   976→\t\t\t\t\t\tndx, len(query.TypeIds), query.Metric, query.Hostname)\n   977→\t\t\t\t}\n   978→\t\t\t}\n   979→\n   980→\t\t\tsanitizeStats(&res)\n   981→\n   982→\t\t\tscopeData.Series = append(scopeData.Series, schema.Series{\n   983→\t\t\t\tHostname: query.Hostname,\n   984→\t\t\t\tID:       id,\n   985→\t\t\t\tStatistics: schema.MetricStatistics{\n   986→\t\t\t\t\tAvg: float64(res.Avg),\n   987→\t\t\t\t\tMin: float64(res.Min),\n   988→\t\t\t\t\tMax: float64(res.Max),\n   989→\t\t\t\t},\n   990→\t\t\t\tData: res.Data,\n   991→\t\t\t})\n   992→\t\t}\n   993→\t}\n   994→\n   995→\tif len(errors) != 0 {\n   996→\t\t/* Returns list of \"partial errors\" */\n   997→\t\treturn data, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n   998→\t}\n   999→\n  1000→\treturn data, nil\n  1001→}\n  1002→\n  1003→// buildNodeQueries constructs APIQuery structures for node-based queries with scope transformation.\n  1004→//\n  1005→// Similar to buildQueries but operates on node lists rather than job resources.\n  1006→// Supports dynamic subcluster lookup when subCluster parameter is empty.\n  1007→//\n  1008→// Parameters:\n  1009→//   - cluster: Cluster name\n  1010→//   - subCluster: SubCluster name (empty = infer from node hostnames)\n  1011→//   - nodes: List of node hostnames\n  1012→//   - metrics: List of metric names\n  1013→//   - scopes: Requested metric scopes\n  1014→//   - resolution: Data resolution in seconds\n  1015→//\n  1016→// Returns:\n  1017→//   - []APIQuery: List of queries to execute\n  1018→//   - []schema.MetricScope: Assigned scope for each query\n  1019→//   - error: Returns error if topology lookup fails or unhandled scope combination\n  1020→func buildNodeQueries(\n  1021→\tcluster string,\n  1022→\tsubCluster string,\n  1023→\tnodes []string,\n  1024→\tmetrics []string,\n  1025→\tscopes []schema.MetricScope,\n  1026→\tresolution int64,\n  1027→) ([]APIQuery, []schema.MetricScope, error) {\n  1028→\tqueries := make([]APIQuery, 0, len(metrics)*len(scopes)*len(nodes))\n  1029→\tassignedScope := []schema.MetricScope{}\n  1030→\n  1031→\t// Get Topol before loop if subCluster given\n  1032→\tvar subClusterTopol *schema.SubCluster\n  1033→\tvar scterr error\n  1034→\tif subCluster != \"\" {\n  1035→\t\tsubClusterTopol, scterr = archive.GetSubCluster(cluster, subCluster)\n  1036→\t\tif scterr != nil {\n  1037→\t\t\tcclog.Errorf(\"could not load cluster %s subCluster %s topology: %s\", cluster, subCluster, scterr.Error())\n  1038→\t\t\treturn nil, nil, scterr\n  1039→\t\t}\n  1040→\t}\n  1041→\n  1042→\tfor _, metric := range metrics {\n  1043→\t\tmc := archive.GetMetricConfig(cluster, metric)\n  1044→\t\tif mc == nil {\n  1045→\t\t\tcclog.Warnf(\"metric '%s' is not specified for cluster '%s'\", metric, cluster)\n  1046→\t\t\tcontinue\n  1047→\t\t}\n  1048→\n  1049→\t\t// Skip if metric is removed for subcluster\n  1050→\t\tif mc.SubClusters != nil {\n  1051→\t\t\tisRemoved := false\n  1052→\t\t\tfor _, scConfig := range mc.SubClusters {\n  1053→\t\t\t\tif scConfig.Name == subCluster && scConfig.Remove {\n  1054→\t\t\t\t\tisRemoved = true\n  1055→\t\t\t\t\tbreak\n  1056→\t\t\t\t}\n  1057→\t\t\t}\n  1058→\t\t\tif isRemoved {\n  1059→\t\t\t\tcontinue\n  1060→\t\t\t}\n  1061→\t\t}\n  1062→\n  1063→\t\t// Avoid duplicates using map for O(1) lookup\n  1064→\t\thandledScopes := make(map[schema.MetricScope]bool, 3)\n  1065→\n  1066→\t\tfor _, requestedScope := range scopes {\n  1067→\t\t\tnativeScope := mc.Scope\n  1068→\n  1069→\t\t\tscope := nativeScope.Max(requestedScope)\n  1070→\t\t\tif handledScopes[scope] {\n  1071→\t\t\t\tcontinue\n  1072→\t\t\t}\n  1073→\t\t\thandledScopes[scope] = true\n  1074→\n  1075→\t\t\tfor _, hostname := range nodes {\n  1076→\n  1077→\t\t\t\t// If no subCluster given, get it by node\n  1078→\t\t\t\tif subCluster == \"\" {\n  1079→\t\t\t\t\tsubClusterName, scnerr := archive.GetSubClusterByNode(cluster, hostname)\n  1080→\t\t\t\t\tif scnerr != nil {\n  1081→\t\t\t\t\t\treturn nil, nil, scnerr\n  1082→\t\t\t\t\t}\n  1083→\t\t\t\t\tsubClusterTopol, scterr = archive.GetSubCluster(cluster, subClusterName)\n  1084→\t\t\t\t\tif scterr != nil {\n  1085→\t\t\t\t\t\treturn nil, nil, scterr\n  1086→\t\t\t\t\t}\n  1087→\t\t\t\t}\n  1088→\n  1089→\t\t\t\t// Always full node hwthread id list, no partial queries expected -> Use \"topology.Node\" directly where applicable\n  1090→\t\t\t\t// Always full accelerator id list, no partial queries expected -> Use \"acceleratorIds\" directly where applicable\n  1091→\t\t\t\ttopology := subClusterTopol.Topology\n  1092→\t\t\t\tacceleratorIds := topology.GetAcceleratorIDs()\n  1093→\n  1094→\t\t\t\t// Moved check here if metric matches hardware specs\n  1095→\t\t\t\tif nativeScope == schema.MetricScopeAccelerator && len(acceleratorIds) == 0 {\n  1096→\t\t\t\t\tcontinue\n  1097→\t\t\t\t}\n  1098→\n  1099→\t\t\t\t// Accelerator -> Accelerator (Use \"accelerator\" scope if requested scope is lower than node)\n  1100→\t\t\t\tif nativeScope == schema.MetricScopeAccelerator && scope.LT(schema.MetricScopeNode) {\n  1101→\t\t\t\t\tif scope != schema.MetricScopeAccelerator {\n  1102→\t\t\t\t\t\t// Skip all other catched cases\n  1103→\t\t\t\t\t\tcontinue\n  1104→\t\t\t\t\t}\n  1105→\n  1106→\t\t\t\t\tqueries = append(queries, APIQuery{\n  1107→\t\t\t\t\t\tMetric:     metric,\n  1108→\t\t\t\t\t\tHostname:   hostname,\n  1109→\t\t\t\t\t\tAggregate:  false,\n  1110→\t\t\t\t\t\tType:       &acceleratorString,\n  1111→\t\t\t\t\t\tTypeIds:    acceleratorIds,\n  1112→\t\t\t\t\t\tResolution: resolution,\n  1113→\t\t\t\t\t})\n  1114→\t\t\t\t\tassignedScope = append(assignedScope, schema.MetricScopeAccelerator)\n  1115→\t\t\t\t\tcontinue\n  1116→\t\t\t\t}\n  1117→\n  1118→\t\t\t\t// Accelerator -> Node\n  1119→\t\t\t\tif nativeScope == schema.MetricScopeAccelerator && scope == schema.MetricScopeNode {\n  1120→\t\t\t\t\tif len(acceleratorIds) == 0 {\n  1121→\t\t\t\t\t\tcontinue\n  1122→\t\t\t\t\t}\n  1123→\n  1124→\t\t\t\t\tqueries = append(queries, APIQuery{\n  1125→\t\t\t\t\t\tMetric:     metric,\n  1126→\t\t\t\t\t\tHostname:   hostname,\n  1127→\t\t\t\t\t\tAggregate:  true,\n  1128→\t\t\t\t\t\tType:       &acceleratorString,\n  1129→\t\t\t\t\t\tTypeIds:    acceleratorIds,\n  1130→\t\t\t\t\t\tResolution: resolution,\n  1131→\t\t\t\t\t})\n  1132→\t\t\t\t\tassignedScope = append(assignedScope, scope)\n  1133→\t\t\t\t\tcontinue\n  1134→\t\t\t\t}\n  1135→\n  1136→\t\t\t\t// HWThread -> HWThread\n  1137→\t\t\t\tif nativeScope == schema.MetricScopeHWThread && scope == schema.MetricScopeHWThread {\n  1138→\t\t\t\t\tqueries = append(queries, APIQuery{\n  1139→\t\t\t\t\t\tMetric:     metric,\n  1140→\t\t\t\t\t\tHostname:   hostname,\n  1141→\t\t\t\t\t\tAggregate:  false,\n  1142→\t\t\t\t\t\tType:       &hwthreadString,\n  1143→\t\t\t\t\t\tTypeIds:    intToStringSlice(topology.Node),\n  1144→\t\t\t\t\t\tResolution: resolution,\n  1145→\t\t\t\t\t})\n  1146→\t\t\t\t\tassignedScope = append(assignedScope, scope)\n  1147→\t\t\t\t\tcontinue\n  1148→\t\t\t\t}\n  1149→\n  1150→\t\t\t\t// HWThread -> Core\n  1151→\t\t\t\tif nativeScope == schema.MetricScopeHWThread && scope == schema.MetricScopeCore {\n  1152→\t\t\t\t\tcores, _ := topology.GetCoresFromHWThreads(topology.Node)\n  1153→\t\t\t\t\tfor _, core := range cores {\n  1154→\t\t\t\t\t\tqueries = append(queries, APIQuery{\n  1155→\t\t\t\t\t\t\tMetric:     metric,\n  1156→\t\t\t\t\t\t\tHostname:   hostname,\n  1157→\t\t\t\t\t\t\tAggregate:  true,\n  1158→\t\t\t\t\t\t\tType:       &hwthreadString,\n  1159→\t\t\t\t\t\t\tTypeIds:    intToStringSlice(topology.Core[core]),\n  1160→\t\t\t\t\t\t\tResolution: resolution,\n  1161→\t\t\t\t\t\t})\n  1162→\t\t\t\t\t\tassignedScope = append(assignedScope, scope)\n  1163→\t\t\t\t\t}\n  1164→\t\t\t\t\tcontinue\n  1165→\t\t\t\t}\n  1166→\n  1167→\t\t\t\t// HWThread -> Socket\n  1168→\t\t\t\tif nativeScope == schema.MetricScopeHWThread && scope == schema.MetricScopeSocket {\n  1169→\t\t\t\t\tsockets, _ := topology.GetSocketsFromHWThreads(topology.Node)\n  1170→\t\t\t\t\tfor _, socket := range sockets {\n  1171→\t\t\t\t\t\tqueries = append(queries, APIQuery{\n  1172→\t\t\t\t\t\t\tMetric:     metric,\n  1173→\t\t\t\t\t\t\tHostname:   hostname,\n  1174→\t\t\t\t\t\t\tAggregate:  true,\n  1175→\t\t\t\t\t\t\tType:       &hwthreadString,\n  1176→\t\t\t\t\t\t\tTypeIds:    intToStringSlice(topology.Socket[socket]),\n  1177→\t\t\t\t\t\t\tResolution: resolution,\n  1178→\t\t\t\t\t\t})\n  1179→\t\t\t\t\t\tassignedScope = append(assignedScope, scope)\n  1180→\t\t\t\t\t}\n  1181→\t\t\t\t\tcontinue\n  1182→\t\t\t\t}\n  1183→\n  1184→\t\t\t\t// HWThread -> Node\n  1185→\t\t\t\tif nativeScope == schema.MetricScopeHWThread && scope == schema.MetricScopeNode {\n  1186→\t\t\t\t\tqueries = append(queries, APIQuery{\n  1187→\t\t\t\t\t\tMetric:     metric,\n  1188→\t\t\t\t\t\tHostname:   hostname,\n  1189→\t\t\t\t\t\tAggregate:  true,\n  1190→\t\t\t\t\t\tType:       &hwthreadString,\n  1191→\t\t\t\t\t\tTypeIds:    intToStringSlice(topology.Node),\n  1192→\t\t\t\t\t\tResolution: resolution,\n  1193→\t\t\t\t\t})\n  1194→\t\t\t\t\tassignedScope = append(assignedScope, scope)\n  1195→\t\t\t\t\tcontinue\n  1196→\t\t\t\t}\n  1197→\n  1198→\t\t\t\t// Core -> Core\n  1199→\t\t\t\tif nativeScope == schema.MetricScopeCore && scope == schema.MetricScopeCore {\n  1200→\t\t\t\t\tcores, _ := topology.GetCoresFromHWThreads(topology.Node)\n  1201→\t\t\t\t\tqueries = append(queries, APIQuery{\n  1202→\t\t\t\t\t\tMetric:     metric,\n  1203→\t\t\t\t\t\tHostname:   hostname,\n  1204→\t\t\t\t\t\tAggregate:  false,\n  1205→\t\t\t\t\t\tType:       &coreString,\n  1206→\t\t\t\t\t\tTypeIds:    intToStringSlice(cores),\n  1207→\t\t\t\t\t\tResolution: resolution,\n  1208→\t\t\t\t\t})\n  1209→\t\t\t\t\tassignedScope = append(assignedScope, scope)\n  1210→\t\t\t\t\tcontinue\n  1211→\t\t\t\t}\n  1212→\n  1213→\t\t\t\t// Core -> Socket\n  1214→\t\t\t\tif nativeScope == schema.MetricScopeCore && scope == schema.MetricScopeSocket {\n  1215→\t\t\t\t\tsockets, _ := topology.GetSocketsFromCores(topology.Node)\n  1216→\t\t\t\t\tfor _, socket := range sockets {\n  1217→\t\t\t\t\t\tqueries = append(queries, APIQuery{\n  1218→\t\t\t\t\t\t\tMetric:     metric,\n  1219→\t\t\t\t\t\t\tHostname:   hostname,\n  1220→\t\t\t\t\t\t\tAggregate:  true,\n  1221→\t\t\t\t\t\t\tType:       &coreString,\n  1222→\t\t\t\t\t\t\tTypeIds:    intToStringSlice(topology.Socket[socket]),\n  1223→\t\t\t\t\t\t\tResolution: resolution,\n  1224→\t\t\t\t\t\t})\n  1225→\t\t\t\t\t\tassignedScope = append(assignedScope, scope)\n  1226→\t\t\t\t\t}\n  1227→\t\t\t\t\tcontinue\n  1228→\t\t\t\t}\n  1229→\n  1230→\t\t\t\t// Core -> Node\n  1231→\t\t\t\tif nativeScope == schema.MetricScopeCore && scope == schema.MetricScopeNode {\n  1232→\t\t\t\t\tcores, _ := topology.GetCoresFromHWThreads(topology.Node)\n  1233→\t\t\t\t\tqueries = append(queries, APIQuery{\n  1234→\t\t\t\t\t\tMetric:     metric,\n  1235→\t\t\t\t\t\tHostname:   hostname,\n  1236→\t\t\t\t\t\tAggregate:  true,\n  1237→\t\t\t\t\t\tType:       &coreString,\n  1238→\t\t\t\t\t\tTypeIds:    intToStringSlice(cores),\n  1239→\t\t\t\t\t\tResolution: resolution,\n  1240→\t\t\t\t\t})\n  1241→\t\t\t\t\tassignedScope = append(assignedScope, scope)\n  1242→\t\t\t\t\tcontinue\n  1243→\t\t\t\t}\n  1244→\n  1245→\t\t\t\t// MemoryDomain -> MemoryDomain\n  1246→\t\t\t\tif nativeScope == schema.MetricScopeMemoryDomain && scope == schema.MetricScopeMemoryDomain {\n  1247→\t\t\t\t\tsockets, _ := topology.GetMemoryDomainsFromHWThreads(topology.Node)\n  1248→\t\t\t\t\tqueries = append(queries, APIQuery{\n  1249→\t\t\t\t\t\tMetric:     metric,\n  1250→\t\t\t\t\t\tHostname:   hostname,\n  1251→\t\t\t\t\t\tAggregate:  false,\n  1252→\t\t\t\t\t\tType:       &memoryDomainString,\n  1253→\t\t\t\t\t\tTypeIds:    intToStringSlice(sockets),\n  1254→\t\t\t\t\t\tResolution: resolution,\n  1255→\t\t\t\t\t})\n  1256→\t\t\t\t\tassignedScope = append(assignedScope, scope)\n  1257→\t\t\t\t\tcontinue\n  1258→\t\t\t\t}\n  1259→\n  1260→\t\t\t\t// MemoryDomain -> Node\n  1261→\t\t\t\tif nativeScope == schema.MetricScopeMemoryDomain && scope == schema.MetricScopeNode {\n  1262→\t\t\t\t\tsockets, _ := topology.GetMemoryDomainsFromHWThreads(topology.Node)\n  1263→\t\t\t\t\tqueries = append(queries, APIQuery{\n  1264→\t\t\t\t\t\tMetric:     metric,\n  1265→\t\t\t\t\t\tHostname:   hostname,\n  1266→\t\t\t\t\t\tAggregate:  true,\n  1267→\t\t\t\t\t\tType:       &memoryDomainString,\n  1268→\t\t\t\t\t\tTypeIds:    intToStringSlice(sockets),\n  1269→\t\t\t\t\t\tResolution: resolution,\n  1270→\t\t\t\t\t})\n  1271→\t\t\t\t\tassignedScope = append(assignedScope, scope)\n  1272→\t\t\t\t\tcontinue\n  1273→\t\t\t\t}\n  1274→\n  1275→\t\t\t\t// Socket -> Socket\n  1276→\t\t\t\tif nativeScope == schema.MetricScopeSocket && scope == schema.MetricScopeSocket {\n  1277→\t\t\t\t\tsockets, _ := topology.GetSocketsFromHWThreads(topology.Node)\n  1278→\t\t\t\t\tqueries = append(queries, APIQuery{\n  1279→\t\t\t\t\t\tMetric:     metric,\n  1280→\t\t\t\t\t\tHostname:   hostname,\n  1281→\t\t\t\t\t\tAggregate:  false,\n  1282→\t\t\t\t\t\tType:       &socketString,\n  1283→\t\t\t\t\t\tTypeIds:    intToStringSlice(sockets),\n  1284→\t\t\t\t\t\tResolution: resolution,\n  1285→\t\t\t\t\t})\n  1286→\t\t\t\t\tassignedScope = append(assignedScope, scope)\n  1287→\t\t\t\t\tcontinue\n  1288→\t\t\t\t}\n  1289→\n  1290→\t\t\t\t// Socket -> Node\n  1291→\t\t\t\tif nativeScope == schema.MetricScopeSocket && scope == schema.MetricScopeNode {\n  1292→\t\t\t\t\tsockets, _ := topology.GetSocketsFromHWThreads(topology.Node)\n  1293→\t\t\t\t\tqueries = append(queries, APIQuery{\n  1294→\t\t\t\t\t\tMetric:     metric,\n  1295→\t\t\t\t\t\tHostname:   hostname,\n  1296→\t\t\t\t\t\tAggregate:  true,\n  1297→\t\t\t\t\t\tType:       &socketString,\n  1298→\t\t\t\t\t\tTypeIds:    intToStringSlice(sockets),\n  1299→\t\t\t\t\t\tResolution: resolution,\n  1300→\t\t\t\t\t})\n  1301→\t\t\t\t\tassignedScope = append(assignedScope, scope)\n  1302→\t\t\t\t\tcontinue\n  1303→\t\t\t\t}\n  1304→\n  1305→\t\t\t\t// Node -> Node\n  1306→\t\t\t\tif nativeScope == schema.MetricScopeNode && scope == schema.MetricScopeNode {\n  1307→\t\t\t\t\tqueries = append(queries, APIQuery{\n  1308→\t\t\t\t\t\tMetric:     metric,\n  1309→\t\t\t\t\t\tHostname:   hostname,\n  1310→\t\t\t\t\t\tResolution: resolution,\n  1311→\t\t\t\t\t})\n  1312→\t\t\t\t\tassignedScope = append(assignedScope, scope)\n  1313→\t\t\t\t\tcontinue\n  1314→\t\t\t\t}\n  1315→\n  1316→\t\t\t\treturn nil, nil, fmt.Errorf(\"METRICDATA/INTERNAL-CCMS > TODO: unhandled case: native-scope=%s, requested-scope=%s\", nativeScope, requestedScope)\n  1317→\t\t\t}\n  1318→\t\t}\n  1319→\t}\n  1320→\n  1321→\treturn queries, assignedScope, nil\n  1322→}\n  1323→\n  1324→// sanitizeStats converts NaN statistics to zero for JSON compatibility.\n  1325→//\n  1326→// schema.Float with NaN values cannot be properly JSON-encoded, so we convert\n  1327→// NaN to 0. This loses the distinction between \"no data\" and \"zero value\",\n  1328→// but maintains API compatibility.\n  1329→func sanitizeStats(data *APIMetricData) {\n  1330→\tif data.Avg.IsNaN() {\n  1331→\t\tdata.Avg = schema.Float(0)\n  1332→\t}\n  1333→\tif data.Min.IsNaN() {\n  1334→\t\tdata.Min = schema.Float(0)\n  1335→\t}\n  1336→\tif data.Max.IsNaN() {\n  1337→\t\tdata.Max = schema.Float(0)\n  1338→\t}\n  1339→}\n  1340→\n  1341→// intToStringSlice converts a slice of integers to a slice of strings.\n  1342→// Used to convert hardware thread/core/socket IDs from topology (int) to APIQuery TypeIds (string).\n  1343→//\n  1344→// Optimized to reuse a byte buffer for string conversion, reducing allocations.\n  1345→func intToStringSlice(is []int) []string {\n  1346→\tif len(is) == 0 {\n  1347→\t\treturn nil\n  1348→\t}\n  1349→\n  1350→\tss := make([]string, len(is))\n  1351→\tbuf := make([]byte, 0, 16) // Reusable buffer for integer conversion\n  1352→\tfor i, x := range is {\n  1353→\t\tbuf = strconv.AppendInt(buf[:0], int64(x), 10)\n  1354→\t\tss[i] = string(buf)\n  1355→\t}\n  1356→\treturn ss\n  1357→}\n  1358→"}]},"uuid":"c7c49656-c8d1-4f2c-a019-bfbccec9b68e","timestamp":"2026-03-04T15:25:31.612Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"aeb62a7c5fea57dd2"},"toolUseID":"agent_msg_017PhdenzLz5rCHUntWZksNg","parentToolUseID":"toolu_01TXANL4MzLEvewR2e42CBCn","uuid":"d557f337-3f1f-46b0-a36d-cd69230b6753","timestamp":"2026-03-04T15:25:31.690Z"}
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{"parentUuid":"5ade5383-4ea7-4b92-8db3-005c9a333d2a","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JNKRXrtxqVGAJLWKjF7ajA","type":"tool_result","content":"     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.\n     2→// All rights reserved.\n     3→// Use of this source code is governed by a MIT-style\n     4→// license that can be found in the LICENSE file.\n     5→\n     6→// Package metricstoreclient provides a client for querying the cc-metric-store time series database.\n     7→//\n     8→// The cc-metric-store is a high-performance time series database optimized for HPC metric data.\n     9→// This client handles HTTP communication, query construction, scope transformations, and data retrieval\n    10→// for job and node metrics across different metric scopes (node, socket, core, hwthread, accelerator).\n    11→//\n    12→// # Architecture\n    13→//\n    14→// The package is split into two main components:\n    15→//   - Client Operations (cc-metric-store.go): HTTP client, request handling, data loading methods\n    16→//   - Query Building (cc-metric-store-queries.go): Query construction and scope transformation logic\n    17→//\n    18→// # Basic Usage\n    19→//\n    20→//\tstore := NewCCMetricStore(\"http://localhost:8080\", \"jwt-token\")\n    21→//\n    22→//\t// Load job data\n    23→//\tjobData, err := store.LoadData(job, metrics, scopes, ctx, resolution)\n    24→//\tif err != nil {\n    25→//\t    log.Fatal(err)\n    26→//\t}\n    27→//\n    28→// # Metric Scopes\n    29→//\n    30→// The client supports hierarchical metric scopes that map to HPC hardware topology:\n    31→//   - MetricScopeAccelerator: GPU/accelerator level metrics\n    32→//   - MetricScopeHWThread: Hardware thread (SMT) level metrics\n    33→//   - MetricScopeCore: CPU core level metrics\n    34→//   - MetricScopeSocket: CPU socket level metrics\n    35→//   - MetricScopeMemoryDomain: NUMA domain level metrics\n    36→//   - MetricScopeNode: Full node level metrics\n    37→//\n    38→// The client automatically handles scope transformations, aggregating finer-grained metrics\n    39→// to coarser scopes when needed (e.g., aggregating core metrics to socket level).\n    40→//\n    41→// # Error Handling\n    42→//\n    43→// The client supports partial errors - if some queries fail, it returns both the successful\n    44→// data and an error listing the failed queries. This allows processing partial results\n    45→// when some nodes or metrics are temporarily unavailable.\n    46→//\n    47→// # API Versioning\n    48→//\n    49→// The client uses cc-metric-store API v2, which includes support for:\n    50→//   - Data resampling for bandwidth optimization\n    51→//   - Multi-scope queries in a single request\n    52→//   - Aggregation across hardware topology levels\n    53→package metricstoreclient\n    54→\n    55→import (\n    56→\t\"bufio\"\n    57→\t\"bytes\"\n    58→\t\"context\"\n    59→\t\"encoding/json\"\n    60→\t\"fmt\"\n    61→\t\"net/http\"\n    62→\t\"strings\"\n    63→\t\"time\"\n    64→\n    65→\t\"github.com/ClusterCockpit/cc-backend/pkg/archive\"\n    66→\t\"github.com/ClusterCockpit/cc-backend/pkg/metricstore\"\n    67→\tcclog \"github.com/ClusterCockpit/cc-lib/v2/ccLogger\"\n    68→\t\"github.com/ClusterCockpit/cc-lib/v2/schema\"\n    69→)\n    70→\n    71→// CCMetricStore is the HTTP client for communicating with cc-metric-store.\n    72→// It manages connection details, authentication, and provides methods for querying metrics.\n    73→type CCMetricStore struct {\n    74→\tclient        http.Client                 // HTTP client with 10-second timeout\n    75→\tjwt           string                      // JWT Bearer token for authentication\n    76→\turl           string                      // Base URL of cc-metric-store instance\n    77→\tqueryEndpoint string                      // Full URL to query API endpoint\n    78→\ttopologyCache map[string]*schema.Topology // cluster -> topology cache\n    79→}\n    80→\n    81→// APIQueryRequest represents a request to the cc-metric-store query API.\n    82→// It supports both explicit queries and \"for-all-nodes\" bulk queries.\n    83→type APIQueryRequest struct {\n    84→\tCluster     string     `json:\"cluster\"`       // Target cluster name\n    85→\tQueries     []APIQuery `json:\"queries\"`       // Explicit list of metric queries\n    86→\tForAllNodes []string   `json:\"for-all-nodes\"` // Metrics to query for all nodes\n    87→\tFrom        int64      `json:\"from\"`          // Start time (Unix timestamp)\n    88→\tTo          int64      `json:\"to\"`            // End time (Unix timestamp)\n    89→\tWithStats   bool       `json:\"with-stats\"`    // Include min/avg/max statistics\n    90→\tWithData    bool       `json:\"with-data\"`     // Include time series data points\n    91→}\n    92→\n    93→// APIQuery specifies a single metric query with optional scope filtering.\n    94→// Type and TypeIds define the hardware scope (core, socket, accelerator, etc.).\n    95→type APIQuery struct {\n    96→\tType       *string  `json:\"type,omitempty\"`        // Scope type (e.g., \"core\", \"socket\")\n    97→\tSubType    *string  `json:\"subtype,omitempty\"`     // Sub-scope type (reserved for future use)\n    98→\tMetric     string   `json:\"metric\"`                // Metric name\n    99→\tHostname   string   `json:\"host\"`                  // Target hostname\n   100→\tResolution int      `json:\"resolution\"`            // Data resolution in seconds (0 = native)\n   101→\tTypeIds    []string `json:\"type-ids,omitempty\"`    // IDs for the scope type (e.g., core IDs)\n   102→\tSubTypeIds []string `json:\"subtype-ids,omitempty\"` // IDs for sub-scope (reserved)\n   103→\tAggregate  bool     `json:\"aggreg\"`                // Aggregate across TypeIds\n   104→}\n   105→\n   106→// APIQueryResponse contains the results from a cc-metric-store query.\n   107→// Results align with the Queries slice by index.\n   108→type APIQueryResponse struct {\n   109→\tQueries []APIQuery        `json:\"queries,omitempty\"` // Echoed queries (for bulk requests)\n   110→\tResults [][]APIMetricData `json:\"results\"`           // Result data, indexed by query\n   111→}\n   112→\n   113→// APIMetricData represents time series data and statistics for a single metric series.\n   114→// Error is set if this particular series failed to load.\n   115→type APIMetricData struct {\n   116→\tError      *string        `json:\"error\"`      // Error message if query failed\n   117→\tData       []schema.Float `json:\"data\"`       // Time series data points\n   118→\tFrom       int64          `json:\"from\"`       // Actual start time of data\n   119→\tTo         int64          `json:\"to\"`         // Actual end time of data\n   120→\tResolution int            `json:\"resolution\"` // Actual resolution of data in seconds\n   121→\tAvg        schema.Float   `json:\"avg\"`        // Average value across time range\n   122→\tMin        schema.Float   `json:\"min\"`        // Minimum value in time range\n   123→\tMax        schema.Float   `json:\"max\"`        // Maximum value in time range\n   124→}\n   125→\n   126→// NewCCMetricStore creates and initializes a new (external) CCMetricStore client.\n   127→// The url parameter should include the protocol and port (e.g., \"http://localhost:8080\").\n   128→// The token parameter is a JWT used for Bearer authentication; pass empty string if auth is disabled.\n   129→func NewCCMetricStore(url string, token string) *CCMetricStore {\n   130→\treturn &CCMetricStore{\n   131→\t\turl:           url,\n   132→\t\tqueryEndpoint: fmt.Sprintf(\"%s/api/query\", url),\n   133→\t\tjwt:           token,\n   134→\t\tclient: http.Client{\n   135→\t\t\tTimeout: 10 * time.Second,\n   136→\t\t},\n   137→\t\ttopologyCache: make(map[string]*schema.Topology),\n   138→\t}\n   139→}\n   140→\n   141→// doRequest executes an HTTP POST request to the cc-metric-store query API.\n   142→// It handles JSON encoding/decoding, authentication, and API versioning.\n   143→// The request body is automatically closed to prevent resource leaks.\n   144→func (ccms *CCMetricStore) doRequest(\n   145→\tctx context.Context,\n   146→\tbody *APIQueryRequest,\n   147→) (*APIQueryResponse, error) {\n   148→\tbuf := &bytes.Buffer{}\n   149→\tif err := json.NewEncoder(buf).Encode(body); err != nil {\n   150→\t\tcclog.Errorf(\"Error while encoding request body: %s\", err.Error())\n   151→\t\treturn nil, err\n   152→\t}\n   153→\n   154→\treq, err := http.NewRequestWithContext(ctx, http.MethodGet, ccms.queryEndpoint, buf)\n   155→\tif err != nil {\n   156→\t\tcclog.Errorf(\"Error while building request body: %s\", err.Error())\n   157→\t\treturn nil, err\n   158→\t}\n   159→\tif ccms.jwt != \"\" {\n   160→\t\treq.Header.Add(\"Authorization\", fmt.Sprintf(\"Bearer %s\", ccms.jwt))\n   161→\t}\n   162→\n   163→\t// versioning the cc-metric-store query API.\n   164→\t// v2 = data with resampling\n   165→\t// v1 = data without resampling\n   166→\tq := req.URL.Query()\n   167→\tq.Add(\"version\", \"v2\")\n   168→\treq.URL.RawQuery = q.Encode()\n   169→\n   170→\tres, err := ccms.client.Do(req)\n   171→\tif err != nil {\n   172→\t\tcclog.Errorf(\"Error while performing request: %s\", err.Error())\n   173→\t\treturn nil, err\n   174→\t}\n   175→\tdefer res.Body.Close()\n   176→\n   177→\tif res.StatusCode != http.StatusOK {\n   178→\t\treturn nil, fmt.Errorf(\"'%s': HTTP Status: %s\", ccms.queryEndpoint, res.Status)\n   179→\t}\n   180→\n   181→\tvar resBody APIQueryResponse\n   182→\tif err := json.NewDecoder(bufio.NewReader(res.Body)).Decode(&resBody); err != nil {\n   183→\t\tcclog.Errorf(\"Error while decoding result body: %s\", err.Error())\n   184→\t\treturn nil, err\n   185→\t}\n   186→\n   187→\treturn &resBody, nil\n   188→}\n   189→\n   190→// getTopology returns the topology for a given cluster and subcluster, caching it if not already present\n   191→func (ccms *CCMetricStore) getTopology(cluster, subCluster string) (*schema.Topology, error) {\n   192→\tcacheKey := fmt.Sprintf(\"%s:%s\", cluster, subCluster)\n   193→\tif topology, ok := ccms.topologyCache[cacheKey]; ok {\n   194→\t\treturn topology, nil\n   195→\t}\n   196→\n   197→\tsubcluster, err := archive.GetSubCluster(cluster, subCluster)\n   198→\tif err != nil {\n   199→\t\treturn nil, err\n   200→\t}\n   201→\n   202→\tccms.topologyCache[cacheKey] = &subcluster.Topology\n   203→\treturn &subcluster.Topology, nil\n   204→}\n   205→\n   206→// getTopologyByNode returns the topology for a given cluster and node, caching it if not already present\n   207→func (ccms *CCMetricStore) getTopologyByNode(cluster, node string) (*schema.Topology, error) {\n   208→\tsubCluster, err := archive.GetSubClusterByNode(cluster, node)\n   209→\tif err != nil {\n   210→\t\treturn nil, err\n   211→\t}\n   212→\n   213→\treturn ccms.getTopology(cluster, subCluster)\n   214→}\n   215→\n   216→// LoadData retrieves time series data and statistics for the specified job and metrics.\n   217→// It queries data for the job's time range and resources, handling scope transformations automatically.\n   218→//\n   219→// Parameters:\n   220→//   - job: Job metadata including cluster, time range, and allocated resources\n   221→//   - metrics: List of metric names to retrieve\n   222→//   - scopes: Requested metric scopes (node, socket, core, etc.)\n   223→//   - ctx: Context for cancellation and timeouts\n   224→//   - resolution: Data resolution in seconds (0 for native resolution)\n   225→//\n   226→// Returns JobData organized as: metric -> scope -> series list.\n   227→// Supports partial errors: returns available data even if some queries fail.\n   228→func (ccms *CCMetricStore) LoadData(\n   229→\tjob *schema.Job,\n   230→\tmetrics []string,\n   231→\tscopes []schema.MetricScope,\n   232→\tctx context.Context,\n   233→\tresolution int,\n   234→) (schema.JobData, error) {\n   235→\tqueries, assignedScope, err := ccms.buildQueries(job, metrics, scopes, resolution)\n   236→\tif err != nil {\n   237→\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n   238→\t\treturn nil, err\n   239→\t}\n   240→\n   241→\t// Verify assignment is correct - log any inconsistencies for debugging\n   242→\tif len(queries) != len(assignedScope) {\n   243→\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildQueries: %d vs %d\",\n   244→\t\t\tlen(queries), len(assignedScope))\n   245→\t}\n   246→\n   247→\treq := APIQueryRequest{\n   248→\t\tCluster:   job.Cluster,\n   249→\t\tFrom:      job.StartTime,\n   250→\t\tTo:        job.StartTime + int64(job.Duration),\n   251→\t\tQueries:   queries,\n   252→\t\tWithStats: true,\n   253→\t\tWithData:  true,\n   254→\t}\n   255→\n   256→\tresBody, err := ccms.doRequest(ctx, &req)\n   257→\tif err != nil {\n   258→\t\tcclog.Errorf(\"Error while performing request for job %d: %s\", job.JobID, err.Error())\n   259→\t\treturn nil, err\n   260→\t}\n   261→\n   262→\tvar errors []string\n   263→\tjobData := make(schema.JobData)\n   264→\n   265→\t// Add safety check for potential index out of range errors\n   266→\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n   267→\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n   268→\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n   269→\t\tif len(resBody.Results) > len(req.Queries) {\n   270→\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n   271→\t\t}\n   272→\t\tif len(assignedScope) > len(req.Queries) {\n   273→\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n   274→\t\t}\n   275→\t}\n   276→\n   277→\tfor i, row := range resBody.Results {\n   278→\t\t// Safety check to prevent index out of range errors\n   279→\t\tif i >= len(req.Queries) || i >= len(assignedScope) {\n   280→\t\t\tcclog.Warnf(\"Index out of range prevented: i=%d, queries=%d, assignedScope=%d\",\n   281→\t\t\t\ti, len(req.Queries), len(assignedScope))\n   282→\t\t\tcontinue\n   283→\t\t}\n   284→\n   285→\t\tquery := req.Queries[i]\n   286→\t\tmetric := query.Metric\n   287→\t\tscope := assignedScope[i]\n   288→\t\tmc := archive.GetMetricConfig(job.Cluster, metric)\n   289→\n   290→\t\tif mc == nil {\n   291→\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, job.Cluster)\n   292→\t\t\tcontinue\n   293→\t\t}\n   294→\n   295→\t\tif _, ok := jobData[metric]; !ok {\n   296→\t\t\tjobData[metric] = make(map[schema.MetricScope]*schema.JobMetric)\n   297→\t\t}\n   298→\n   299→\t\tres := mc.Timestep\n   300→\t\tif len(row) > 0 {\n   301→\t\t\tres = row[0].Resolution\n   302→\t\t}\n   303→\n   304→\t\tjobMetric, ok := jobData[metric][scope]\n   305→\t\tif !ok {\n   306→\t\t\tjobMetric = &schema.JobMetric{\n   307→\t\t\t\tUnit:     mc.Unit,\n   308→\t\t\t\tTimestep: res,\n   309→\t\t\t\tSeries:   make([]schema.Series, 0),\n   310→\t\t\t}\n   311→\t\t\tjobData[metric][scope] = jobMetric\n   312→\t\t}\n   313→\n   314→\t\tfor ndx, res := range row {\n   315→\t\t\tif res.Error != nil {\n   316→\t\t\t\t/* Build list for \"partial errors\", if any */\n   317→\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n   318→\t\t\t\tcontinue\n   319→\t\t\t}\n   320→\n   321→\t\t\tid := (*string)(nil)\n   322→\t\t\tif query.Type != nil {\n   323→\t\t\t\t// Check if ndx is within the bounds of TypeIds slice\n   324→\t\t\t\tif ndx < len(query.TypeIds) {\n   325→\t\t\t\t\tid = new(string)\n   326→\t\t\t\t\t*id = query.TypeIds[ndx]\n   327→\t\t\t\t} else {\n   328→\t\t\t\t\t// Log the error but continue processing\n   329→\t\t\t\t\tcclog.Warnf(\"TypeIds index out of range: %d with length %d for metric %s on host %s\",\n   330→\t\t\t\t\t\tndx, len(query.TypeIds), query.Metric, query.Hostname)\n   331→\t\t\t\t}\n   332→\t\t\t}\n   333→\n   334→\t\t\tsanitizeStats(&res.Avg, &res.Min, &res.Max)\n   335→\n   336→\t\t\tjobMetric.Series = append(jobMetric.Series, schema.Series{\n   337→\t\t\t\tHostname: query.Hostname,\n   338→\t\t\t\tID:       id,\n   339→\t\t\t\tStatistics: schema.MetricStatistics{\n   340→\t\t\t\t\tAvg: float64(res.Avg),\n   341→\t\t\t\t\tMin: float64(res.Min),\n   342→\t\t\t\t\tMax: float64(res.Max),\n   343→\t\t\t\t},\n   344→\t\t\t\tData: res.Data,\n   345→\t\t\t})\n   346→\t\t}\n   347→\n   348→\t\t// So that one can later check len(jobData):\n   349→\t\tif len(jobMetric.Series) == 0 {\n   350→\t\t\tdelete(jobData[metric], scope)\n   351→\t\t\tif len(jobData[metric]) == 0 {\n   352→\t\t\t\tdelete(jobData, metric)\n   353→\t\t\t}\n   354→\t\t}\n   355→\t}\n   356→\n   357→\tif len(errors) != 0 {\n   358→\t\t/* Returns list for \"partial errors\" */\n   359→\t\treturn jobData, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n   360→\t}\n   361→\treturn jobData, nil\n   362→}\n   363→\n   364→// LoadStats retrieves min/avg/max statistics for job metrics at node scope.\n   365→// This is faster than LoadData when only statistical summaries are needed (no time series data).\n   366→//\n   367→// Returns statistics organized as: metric -> hostname -> statistics.\n   368→func (ccms *CCMetricStore) LoadStats(\n   369→\tjob *schema.Job,\n   370→\tmetrics []string,\n   371→\tctx context.Context,\n   372→) (map[string]map[string]schema.MetricStatistics, error) {\n   373→\tqueries, _, err := ccms.buildQueries(job, metrics, []schema.MetricScope{schema.MetricScopeNode}, 0) // #166 Add scope shere for analysis view accelerator normalization?\n   374→\tif err != nil {\n   375→\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v: %s\", job.JobID, metrics, err.Error())\n   376→\t\treturn nil, err\n   377→\t}\n   378→\n   379→\treq := APIQueryRequest{\n   380→\t\tCluster:   job.Cluster,\n   381→\t\tFrom:      job.StartTime,\n   382→\t\tTo:        job.StartTime + int64(job.Duration),\n   383→\t\tQueries:   queries,\n   384→\t\tWithStats: true,\n   385→\t\tWithData:  false,\n   386→\t}\n   387→\n   388→\tresBody, err := ccms.doRequest(ctx, &req)\n   389→\tif err != nil {\n   390→\t\tcclog.Errorf(\"Error while performing request for job %d: %s\", job.JobID, err.Error())\n   391→\t\treturn nil, err\n   392→\t}\n   393→\n   394→\tstats := make(map[string]map[string]schema.MetricStatistics, len(metrics))\n   395→\tfor i, res := range resBody.Results {\n   396→\t\tif len(res) == 0 {\n   397→\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n   398→\t\t\tcontinue\n   399→\t\t}\n   400→\t\tquery := req.Queries[i]\n   401→\t\tmetric := query.Metric\n   402→\t\tdata := res[0]\n   403→\t\tif data.Error != nil {\n   404→\t\t\tcclog.Warnf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *data.Error)\n   405→\t\t\tcontinue\n   406→\t\t}\n   407→\n   408→\t\tmetricdata, ok := stats[metric]\n   409→\t\tif !ok {\n   410→\t\t\tmetricdata = make(map[string]schema.MetricStatistics, job.NumNodes)\n   411→\t\t\tstats[metric] = metricdata\n   412→\t\t}\n   413→\n   414→\t\tif hasNaNStats(data.Avg, data.Min, data.Max) {\n   415→\t\t\tcclog.Warnf(\"fetching %s for node %s failed: one of avg/min/max is NaN\", metric, query.Hostname)\n   416→\t\t\tcontinue\n   417→\t\t}\n   418→\n   419→\t\tmetricdata[query.Hostname] = schema.MetricStatistics{\n   420→\t\t\tAvg: float64(data.Avg),\n   421→\t\t\tMin: float64(data.Min),\n   422→\t\t\tMax: float64(data.Max),\n   423→\t\t}\n   424→\t}\n   425→\n   426→\treturn stats, nil\n   427→}\n   428→\n   429→// LoadScopedStats retrieves statistics for job metrics across multiple scopes.\n   430→// Used for the Job-View Statistics Table to display per-scope breakdowns.\n   431→//\n   432→// Returns statistics organized as: metric -> scope -> list of scoped statistics.\n   433→// Each scoped statistic includes hostname, hardware ID (if applicable), and min/avg/max values.\n   434→func (ccms *CCMetricStore) LoadScopedStats(\n   435→\tjob *schema.Job,\n   436→\tmetrics []string,\n   437→\tscopes []schema.MetricScope,\n   438→\tctx context.Context,\n   439→) (schema.ScopedJobStats, error) {\n   440→\tqueries, assignedScope, err := ccms.buildQueries(job, metrics, scopes, 0)\n   441→\tif err != nil {\n   442→\t\tcclog.Errorf(\"Error while building queries for jobId %d, Metrics %v, Scopes %v: %s\", job.JobID, metrics, scopes, err.Error())\n   443→\t\treturn nil, err\n   444→\t}\n   445→\n   446→\treq := APIQueryRequest{\n   447→\t\tCluster:   job.Cluster,\n   448→\t\tFrom:      job.StartTime,\n   449→\t\tTo:        job.StartTime + int64(job.Duration),\n   450→\t\tQueries:   queries,\n   451→\t\tWithStats: true,\n   452→\t\tWithData:  false,\n   453→\t}\n   454→\n   455→\tresBody, err := ccms.doRequest(ctx, &req)\n   456→\tif err != nil {\n   457→\t\tcclog.Errorf(\"Error while performing request for job %d: %s\", job.JobID, err.Error())\n   458→\t\treturn nil, err\n   459→\t}\n   460→\n   461→\tvar errors []string\n   462→\tscopedJobStats := make(schema.ScopedJobStats)\n   463→\n   464→\tfor i, row := range resBody.Results {\n   465→\t\tquery := req.Queries[i]\n   466→\t\tmetric := query.Metric\n   467→\t\tscope := assignedScope[i]\n   468→\n   469→\t\tif _, ok := scopedJobStats[metric]; !ok {\n   470→\t\t\tscopedJobStats[metric] = make(map[schema.MetricScope][]*schema.ScopedStats)\n   471→\t\t}\n   472→\n   473→\t\tif _, ok := scopedJobStats[metric][scope]; !ok {\n   474→\t\t\tscopedJobStats[metric][scope] = make([]*schema.ScopedStats, 0)\n   475→\t\t}\n   476→\n   477→\t\tfor ndx, res := range row {\n   478→\t\t\tif res.Error != nil {\n   479→\t\t\t\t/* Build list for \"partial errors\", if any */\n   480→\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n   481→\t\t\t\tcontinue\n   482→\t\t\t}\n   483→\n   484→\t\t\tid := (*string)(nil)\n   485→\t\t\tif query.Type != nil {\n   486→\t\t\t\t// Check if ndx is within the bounds of TypeIds slice\n   487→\t\t\t\tif ndx < len(query.TypeIds) {\n   488→\t\t\t\t\tid = new(string)\n   489→\t\t\t\t\t*id = query.TypeIds[ndx]\n   490→\t\t\t\t} else {\n   491→\t\t\t\t\t// Log the error but continue processing\n   492→\t\t\t\t\tcclog.Warnf(\"TypeIds index out of range: %d with length %d for metric %s on host %s\",\n   493→\t\t\t\t\t\tndx, len(query.TypeIds), query.Metric, query.Hostname)\n   494→\t\t\t\t}\n   495→\t\t\t}\n   496→\n   497→\t\t\tsanitizeStats(&res.Avg, &res.Min, &res.Max)\n   498→\n   499→\t\t\tscopedJobStats[metric][scope] = append(scopedJobStats[metric][scope], &schema.ScopedStats{\n   500→\t\t\t\tHostname: query.Hostname,\n   501→\t\t\t\tID:       id,\n   502→\t\t\t\tData: &schema.MetricStatistics{\n   503→\t\t\t\t\tAvg: float64(res.Avg),\n   504→\t\t\t\t\tMin: float64(res.Min),\n   505→\t\t\t\t\tMax: float64(res.Max),\n   506→\t\t\t\t},\n   507→\t\t\t})\n   508→\t\t}\n   509→\n   510→\t\t// So that one can later check len(scopedJobStats[metric][scope]): Remove from map if empty\n   511→\t\tif len(scopedJobStats[metric][scope]) == 0 {\n   512→\t\t\tdelete(scopedJobStats[metric], scope)\n   513→\t\t\tif len(scopedJobStats[metric]) == 0 {\n   514→\t\t\t\tdelete(scopedJobStats, metric)\n   515→\t\t\t}\n   516→\t\t}\n   517→\t}\n   518→\n   519→\tif len(errors) != 0 {\n   520→\t\t/* Returns list for \"partial errors\" */\n   521→\t\treturn scopedJobStats, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n   522→\t}\n   523→\treturn scopedJobStats, nil\n   524→}\n   525→\n   526→// LoadNodeData retrieves current metric data for specified nodes in a cluster.\n   527→// Used for the Systems-View Node-Overview to display real-time node status.\n   528→//\n   529→// If nodes is nil, queries all metrics for all nodes in the cluster (bulk query).\n   530→// Returns data organized as: hostname -> metric -> list of JobMetric (with time series and stats).\n   531→func (ccms *CCMetricStore) LoadNodeData(\n   532→\tcluster string,\n   533→\tmetrics, nodes []string,\n   534→\tscopes []schema.MetricScope,\n   535→\tfrom, to time.Time,\n   536→\tctx context.Context,\n   537→) (map[string]map[string][]*schema.JobMetric, error) {\n   538→\treq := APIQueryRequest{\n   539→\t\tCluster:   cluster,\n   540→\t\tFrom:      from.Unix(),\n   541→\t\tTo:        to.Unix(),\n   542→\t\tWithStats: true,\n   543→\t\tWithData:  true,\n   544→\t}\n   545→\n   546→\tif nodes == nil {\n   547→\t\treq.ForAllNodes = append(req.ForAllNodes, metrics...)\n   548→\t} else {\n   549→\t\tfor _, node := range nodes {\n   550→\t\t\tfor _, metric := range metrics {\n   551→\t\t\t\treq.Queries = append(req.Queries, APIQuery{\n   552→\t\t\t\t\tHostname:   node,\n   553→\t\t\t\t\tMetric:     metric,\n   554→\t\t\t\t\tResolution: 0, // Default for Node Queries: Will return metric $Timestep Resolution\n   555→\t\t\t\t})\n   556→\t\t\t}\n   557→\t\t}\n   558→\t}\n   559→\n   560→\tresBody, err := ccms.doRequest(ctx, &req)\n   561→\tif err != nil {\n   562→\t\tcclog.Errorf(\"Error while performing request for cluster %s: %s\", cluster, err.Error())\n   563→\t\treturn nil, err\n   564→\t}\n   565→\n   566→\tvar errors []string\n   567→\tdata := make(map[string]map[string][]*schema.JobMetric)\n   568→\tfor i, res := range resBody.Results {\n   569→\t\tif len(res) == 0 {\n   570→\t\t\t// No Data Found For Metric, Logged in FetchData to Warn\n   571→\t\t\tcontinue\n   572→\t\t}\n   573→\n   574→\t\tvar query APIQuery\n   575→\t\tif resBody.Queries != nil {\n   576→\t\t\tquery = resBody.Queries[i]\n   577→\t\t} else {\n   578→\t\t\tquery = req.Queries[i]\n   579→\t\t}\n   580→\n   581→\t\tmetric := query.Metric\n   582→\t\tqdata := res[0]\n   583→\t\tif qdata.Error != nil {\n   584→\t\t\terrors = append(errors, fmt.Sprintf(\"fetching %s for node %s failed: %s\", metric, query.Hostname, *qdata.Error))\n   585→\t\t}\n   586→\n   587→\t\tsanitizeStats(&qdata.Avg, &qdata.Min, &qdata.Max)\n   588→\n   589→\t\thostdata, ok := data[query.Hostname]\n   590→\t\tif !ok {\n   591→\t\t\thostdata = make(map[string][]*schema.JobMetric)\n   592→\t\t\tdata[query.Hostname] = hostdata\n   593→\t\t}\n   594→\n   595→\t\tmc := archive.GetMetricConfig(cluster, metric)\n   596→\t\thostdata[metric] = append(hostdata[metric], &schema.JobMetric{\n   597→\t\t\tUnit:     mc.Unit,\n   598→\t\t\tTimestep: mc.Timestep,\n   599→\t\t\tSeries: []schema.Series{\n   600→\t\t\t\t{\n   601→\t\t\t\t\tHostname: query.Hostname,\n   602→\t\t\t\t\tData:     qdata.Data,\n   603→\t\t\t\t\tStatistics: schema.MetricStatistics{\n   604→\t\t\t\t\t\tAvg: float64(qdata.Avg),\n   605→\t\t\t\t\t\tMin: float64(qdata.Min),\n   606→\t\t\t\t\t\tMax: float64(qdata.Max),\n   607→\t\t\t\t\t},\n   608→\t\t\t\t},\n   609→\t\t\t},\n   610→\t\t})\n   611→\t}\n   612→\n   613→\tif len(errors) != 0 {\n   614→\t\t/* Returns list of \"partial errors\" */\n   615→\t\treturn data, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n   616→\t}\n   617→\n   618→\treturn data, nil\n   619→}\n   620→\n   621→// LoadNodeListData retrieves paginated node metrics for the Systems-View Node-List.\n   622→//\n   623→// Supports filtering by subcluster and node name pattern. The nodeFilter performs\n   624→// substring matching on hostnames.\n   625→//\n   626→// Returns:\n   627→//   - Node data organized as: hostname -> JobData (metric -> scope -> series)\n   628→//   - Total node count (before pagination)\n   629→//   - HasNextPage flag indicating if more pages are available\n   630→//   - Error (may be partial error with some data returned)\n   631→func (ccms *CCMetricStore) LoadNodeListData(\n   632→\tcluster, subCluster string,\n   633→\tnodes []string,\n   634→\tmetrics []string,\n   635→\tscopes []schema.MetricScope,\n   636→\tresolution int,\n   637→\tfrom, to time.Time,\n   638→\tctx context.Context,\n   639→) (map[string]schema.JobData, error) {\n   640→\tqueries, assignedScope, err := ccms.buildNodeQueries(cluster, subCluster, nodes, metrics, scopes, resolution)\n   641→\tif err != nil {\n   642→\t\tcclog.Errorf(\"Error while building node queries for Cluster %s, SubCluster %s, Metrics %v, Scopes %v: %s\", cluster, subCluster, metrics, scopes, err.Error())\n   643→\t\treturn nil, err\n   644→\t}\n   645→\n   646→\t// Verify assignment is correct - log any inconsistencies for debugging\n   647→\tif len(queries) != len(assignedScope) {\n   648→\t\tcclog.Errorf(\"Critical error: queries and assignedScope have different lengths after buildNodeQueries: %d vs %d\",\n   649→\t\t\tlen(queries), len(assignedScope))\n   650→\t}\n   651→\n   652→\treq := APIQueryRequest{\n   653→\t\tCluster:   cluster,\n   654→\t\tQueries:   queries,\n   655→\t\tFrom:      from.Unix(),\n   656→\t\tTo:        to.Unix(),\n   657→\t\tWithStats: true,\n   658→\t\tWithData:  true,\n   659→\t}\n   660→\n   661→\tresBody, err := ccms.doRequest(ctx, &req)\n   662→\tif err != nil {\n   663→\t\tcclog.Errorf(\"Error while performing request for cluster %s: %s\", cluster, err.Error())\n   664→\t\treturn nil, err\n   665→\t}\n   666→\n   667→\tvar errors []string\n   668→\tdata := make(map[string]schema.JobData)\n   669→\n   670→\t// Add safety check for index out of range issues\n   671→\tif len(resBody.Results) != len(req.Queries) || len(assignedScope) != len(req.Queries) {\n   672→\t\tcclog.Warnf(\"Mismatch in query results count: queries=%d, results=%d, assignedScope=%d\",\n   673→\t\t\tlen(req.Queries), len(resBody.Results), len(assignedScope))\n   674→\t\tif len(resBody.Results) > len(req.Queries) {\n   675→\t\t\tresBody.Results = resBody.Results[:len(req.Queries)]\n   676→\t\t}\n   677→\t\tif len(assignedScope) > len(req.Queries) {\n   678→\t\t\tassignedScope = assignedScope[:len(req.Queries)]\n   679→\t\t}\n   680→\t}\n   681→\n   682→\tfor i, row := range resBody.Results {\n   683→\t\t// Safety check to prevent index out of range errors\n   684→\t\tif i >= len(req.Queries) || i >= len(assignedScope) {\n   685→\t\t\tcclog.Warnf(\"Index out of range prevented: i=%d, queries=%d, assignedScope=%d\",\n   686→\t\t\t\ti, len(req.Queries), len(assignedScope))\n   687→\t\t\tcontinue\n   688→\t\t}\n   689→\n   690→\t\tvar query APIQuery\n   691→\t\tif resBody.Queries != nil {\n   692→\t\t\tif i < len(resBody.Queries) {\n   693→\t\t\t\tquery = resBody.Queries[i]\n   694→\t\t\t} else {\n   695→\t\t\t\tcclog.Warnf(\"Index out of range prevented for resBody.Queries: i=%d, len=%d\",\n   696→\t\t\t\t\ti, len(resBody.Queries))\n   697→\t\t\t\tcontinue\n   698→\t\t\t}\n   699→\t\t} else {\n   700→\t\t\tquery = req.Queries[i]\n   701→\t\t}\n   702→\n   703→\t\tmetric := query.Metric\n   704→\t\tscope := assignedScope[i]\n   705→\t\tmc := archive.GetMetricConfig(cluster, metric)\n   706→\t\tif mc == nil {\n   707→\t\t\tcclog.Warnf(\"Metric config not found for %s on cluster %s\", metric, cluster)\n   708→\t\t\tcontinue\n   709→\t\t}\n   710→\n   711→\t\tres := mc.Timestep\n   712→\t\tif len(row) > 0 {\n   713→\t\t\tres = row[0].Resolution\n   714→\t\t}\n   715→\n   716→\t\t// Init Nested Map Data Structures If Not Found\n   717→\t\thostData, ok := data[query.Hostname]\n   718→\t\tif !ok {\n   719→\t\t\thostData = make(schema.JobData)\n   720→\t\t\tdata[query.Hostname] = hostData\n   721→\t\t}\n   722→\n   723→\t\tmetricData, ok := hostData[metric]\n   724→\t\tif !ok {\n   725→\t\t\tmetricData = make(map[schema.MetricScope]*schema.JobMetric)\n   726→\t\t\tdata[query.Hostname][metric] = metricData\n   727→\t\t}\n   728→\n   729→\t\tscopeData, ok := metricData[scope]\n   730→\t\tif !ok {\n   731→\t\t\tscopeData = &schema.JobMetric{\n   732→\t\t\t\tUnit:     mc.Unit,\n   733→\t\t\t\tTimestep: res,\n   734→\t\t\t\tSeries:   make([]schema.Series, 0),\n   735→\t\t\t}\n   736→\t\t\tdata[query.Hostname][metric][scope] = scopeData\n   737→\t\t}\n   738→\n   739→\t\tfor ndx, res := range row {\n   740→\t\t\tif res.Error != nil {\n   741→\t\t\t\t/* Build list for \"partial errors\", if any */\n   742→\t\t\t\terrors = append(errors, fmt.Sprintf(\"failed to fetch '%s' from host '%s': %s\", query.Metric, query.Hostname, *res.Error))\n   743→\t\t\t\tcontinue\n   744→\t\t\t}\n   745→\n   746→\t\t\tid := (*string)(nil)\n   747→\t\t\tif query.Type != nil {\n   748→\t\t\t\t// Check if ndx is within the bounds of TypeIds slice\n   749→\t\t\t\tif ndx < len(query.TypeIds) {\n   750→\t\t\t\t\tid = new(string)\n   751→\t\t\t\t\t*id = query.TypeIds[ndx]\n   752→\t\t\t\t} else {\n   753→\t\t\t\t\t// Log the error but continue processing\n   754→\t\t\t\t\tcclog.Warnf(\"TypeIds index out of range: %d with length %d for metric %s on host %s\",\n   755→\t\t\t\t\t\tndx, len(query.TypeIds), query.Metric, query.Hostname)\n   756→\t\t\t\t}\n   757→\t\t\t}\n   758→\n   759→\t\t\tsanitizeStats(&res.Avg, &res.Min, &res.Max)\n   760→\n   761→\t\t\tscopeData.Series = append(scopeData.Series, schema.Series{\n   762→\t\t\t\tHostname: query.Hostname,\n   763→\t\t\t\tID:       id,\n   764→\t\t\t\tStatistics: schema.MetricStatistics{\n   765→\t\t\t\t\tAvg: float64(res.Avg),\n   766→\t\t\t\t\tMin: float64(res.Min),\n   767→\t\t\t\t\tMax: float64(res.Max),\n   768→\t\t\t\t},\n   769→\t\t\t\tData: res.Data,\n   770→\t\t\t})\n   771→\t\t}\n   772→\t}\n   773→\n   774→\tif len(errors) != 0 {\n   775→\t\t/* Returns list of \"partial errors\" */\n   776→\t\treturn data, fmt.Errorf(\"METRICDATA/EXTERNAL-CCMS > Errors: %s\", strings.Join(errors, \", \"))\n   777→\t}\n   778→\n   779→\treturn data, nil\n   780→}\n   781→\n   782→// HealthCheck queries the external cc-metric-store's health check endpoint.\n   783→// It sends a HealthCheckReq as the request body to /api/healthcheck and\n   784→// returns the per-node health check results.\n   785→func (ccms *CCMetricStore) HealthCheck(cluster string,\n   786→\tnodes []string, metrics []string,\n   787→) (map[string]metricstore.HealthCheckResult, error) {\n   788→\treq := metricstore.HealthCheckReq{\n   789→\t\tCluster:     cluster,\n   790→\t\tNodes:       nodes,\n   791→\t\tMetricNames: metrics,\n   792→\t}\n   793→\n   794→\tbuf := &bytes.Buffer{}\n   795→\tif err := json.NewEncoder(buf).Encode(req); err != nil {\n   796→\t\tcclog.Errorf(\"Error while encoding health check request body: %s\", err.Error())\n   797→\t\treturn nil, err\n   798→\t}\n   799→\n   800→\tendpoint := fmt.Sprintf(\"%s/api/healthcheck\", ccms.url)\n   801→\thttpReq, err := http.NewRequest(http.MethodGet, endpoint, buf)\n   802→\tif err != nil {\n   803→\t\tcclog.Errorf(\"Error while building health check request: %s\", err.Error())\n   804→\t\treturn nil, err\n   805→\t}\n   806→\tif ccms.jwt != \"\" {\n   807→\t\thttpReq.Header.Add(\"Authorization\", fmt.Sprintf(\"Bearer %s\", ccms.jwt))\n   808→\t}\n   809→\n   810→\tres, err := ccms.client.Do(httpReq)\n   811→\tif err != nil {\n   812→\t\tcclog.Errorf(\"Error while performing health check request: %s\", err.Error())\n   813→\t\treturn nil, err\n   814→\t}\n   815→\tdefer res.Body.Close()\n   816→\n   817→\tif res.StatusCode != http.StatusOK {\n   818→\t\treturn nil, fmt.Errorf(\"'%s': HTTP Status: %s\", endpoint, res.Status)\n   819→\t}\n   820→\n   821→\tvar results map[string]metricstore.HealthCheckResult\n   822→\tif err := json.NewDecoder(bufio.NewReader(res.Body)).Decode(&results); err != nil {\n   823→\t\tcclog.Errorf(\"Error while decoding health check response: %s\", err.Error())\n   824→\t\treturn nil, err\n   825→\t}\n   826→\n   827→\treturn results, nil\n   828→}\n   829→\n   830→// sanitizeStats replaces NaN values in statistics with 0 to enable JSON marshaling.\n   831→// Regular float64 values cannot be JSONed when NaN.\n   832→func sanitizeStats(avg, min, max *schema.Float) {\n   833→\tif avg.IsNaN() || min.IsNaN() || max.IsNaN() {\n   834→\t\t*avg = schema.Float(0)\n   835→\t\t*min = schema.Float(0)\n   836→\t\t*max = schema.Float(0)\n   837→\t}\n   838→}\n   839→\n   840→// hasNaNStats returns true if any of the statistics contain NaN values.\n   841→func hasNaNStats(avg, min, max schema.Float) bool {\n   842→\treturn avg.IsNaN() || min.IsNaN() || max.IsNaN()\n   843→}\n   844→"}]},"uuid":"0ec0ab51-9d51-4f90-bedf-f81798dd7c52","timestamp":"2026-03-04T15:25:36.584Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"aeb62a7c5fea57dd2"},"toolUseID":"agent_msg_017PhdenzLz5rCHUntWZksNg","parentToolUseID":"toolu_01TXANL4MzLEvewR2e42CBCn","uuid":"39c5b280-4006-4423-958a-dd2363d5ea4e","timestamp":"2026-03-04T15:25:36.809Z"}
{"parentUuid":"39c5b280-4006-4423-958a-dd2363d5ea4e","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-04T15:25:37.124Z","message":{"model":"claude-opus-4-6","id":"msg_01GwQki2Y7PDwGmTTaoXNCuj","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Hfs8ou4SAb2HW5LsgNnchB","name":"Grep","input":{"pattern":"type APIQuery struct","path":"/Users/jan/prg/ClusterCockpit/cc-backend","output_mode":"content","-A":15},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":16028,"cache_read_input_tokens":28884,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":16028},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYiGnSPhvAvQHGNxosw9r","uuid":"1f959ab9-0d83-48f6-9c5a-8589d2221c38"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"aeb62a7c5fea57dd2"},"toolUseID":"agent_msg_017PhdenzLz5rCHUntWZksNg","parentToolUseID":"toolu_01TXANL4MzLEvewR2e42CBCn","uuid":"f09371ae-bb5b-4baa-86f2-8d784c889c90","timestamp":"2026-03-04T15:25:37.124Z"}
{"parentUuid":"f09371ae-bb5b-4baa-86f2-8d784c889c90","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Hfs8ou4SAb2HW5LsgNnchB","type":"tool_result","content":"pkg/metricstore/api.go:83:type APIQuery struct {\npkg/metricstore/api.go-84-\tType        *string      `json:\"type,omitempty\"`\npkg/metricstore/api.go-85-\tSubType     *string      `json:\"subtype,omitempty\"`\npkg/metricstore/api.go-86-\tMetric      string       `json:\"metric\"`\npkg/metricstore/api.go-87-\tHostname    string       `json:\"host\"`\npkg/metricstore/api.go-88-\tResolution  int64        `json:\"resolution\"`\npkg/metricstore/api.go-89-\tTypeIds     []string     `json:\"type-ids,omitempty\"`\npkg/metricstore/api.go-90-\tSubTypeIds  []string     `json:\"subtype-ids,omitempty\"`\npkg/metricstore/api.go-91-\tScaleFactor schema.Float `json:\"scale-by,omitempty\"`\npkg/metricstore/api.go-92-\tAggregate   bool         `json:\"aggreg\"`\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/api.go-93-}\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/api.go-94-\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/api.go-95-// AddStats computes and populates the Avg, Min, and Max fields from the Data array.\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/api.go-96-//\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/api.go-97-// NaN values in the data are ignored during computation. If all values are NaN,\n/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/api.go-98-// the statistics fields will be set to NaN.\n--\ninternal/metricstoreclient/cc-metric-store.go:95:type APIQuery struct {\ninternal/metricstoreclient/cc-metric-store.go-96-\tType       *string  `json:\"type,omitempty\"`        // Scope type (e.g., \"core\", \"socket\")\ninternal/metricstoreclient/cc-metric-store.go-97-\tSubType    *string  `json:\"subtype,omitempty\"`     // Sub-scope type (reserved for future use)\ninternal/metricstoreclient/cc-metric-store.go-98-\tMetric     string   `json:\"metric\"`                // Metric name\ninternal/metricstoreclient/cc-metric-store.go-99-\tHostname   string   `json:\"host\"`                  // Target hostname\ninternal/metricstoreclient/cc-metric-store.go-100-\tResolution int      `json:\"resolution\"`            // Data resolution in seconds (0 = native)\ninternal/metricstoreclient/cc-metric-store.go-101-\tTypeIds    []string `json:\"type-ids,omitempty\"`    // IDs for the scope type (e.g., core IDs)\ninternal/metricstoreclient/cc-metric-store.go-102-\tSubTypeIds []string `json:\"subtype-ids,omitempty\"` // IDs for sub-scope (reserved)\ninternal/metricstoreclient/cc-metric-store.go-103-\tAggregate  bool     `json:\"aggreg\"`                // Aggregate across TypeIds\n/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go-104-}\n/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go-105-\n/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go-106-// APIQueryResponse contains the results from a cc-metric-store query.\n/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go-107-// Results align with the Queries slice by index.\n/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go-108-type APIQueryResponse struct {\ninternal/metricstoreclient/cc-metric-store.go-109-\tQueries []APIQuery        `json:\"queries,omitempty\"` // Echoed queries (for bulk requests)\ninternal/metricstoreclient/cc-metric-store.go-110-\tResults [][]APIMetricData `json:\"results\"`           // Result data, indexed by query"}]},"uuid":"975e82a7-8e92-467f-b3ee-c618f28dcfb3","timestamp":"2026-03-04T15:25:37.438Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"aeb62a7c5fea57dd2"},"toolUseID":"agent_msg_017PhdenzLz5rCHUntWZksNg","parentToolUseID":"toolu_01TXANL4MzLEvewR2e42CBCn","uuid":"31c2f4cf-431e-4bc5-a739-4e5d11e941b0","timestamp":"2026-03-04T15:25:37.439Z"}
{"parentUuid":"31c2f4cf-431e-4bc5-a739-4e5d11e941b0","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"assistant","timestamp":"2026-03-04T15:25:41.823Z","message":{"model":"claude-opus-4-6","id":"msg_01KKuUdwh94AgHfw3HBcunjY","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01P7hoK9UtCHzteXaL6d5xGS","name":"Read","input":{"file_path":"/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/api.go","offset":60,"limit":80},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":13272,"cache_read_input_tokens":44912,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":13272},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"requestId":"req_011CYiGnpx2Lwi2w7vtcb4GM","uuid":"99130693-419f-48d1-8693-094a019f86f5"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"aeb62a7c5fea57dd2"},"toolUseID":"agent_msg_017PhdenzLz5rCHUntWZksNg","parentToolUseID":"toolu_01TXANL4MzLEvewR2e42CBCn","uuid":"91c4aadc-ede6-4f61-99c5-c16062529d57","timestamp":"2026-03-04T15:25:41.823Z"}
{"parentUuid":"91c4aadc-ede6-4f61-99c5-c16062529d57","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01P7hoK9UtCHzteXaL6d5xGS","type":"tool_result","content":"    60→\tWithData    bool       `json:\"with-data\"`\n    61→\tWithPadding bool       `json:\"with-padding\"`\n    62→}\n    63→\n    64→// APIQueryResponse represents the response to an APIQueryRequest.\n    65→//\n    66→// Results is a 2D array where each outer element corresponds to a query,\n    67→// and each inner element corresponds to a selector within that query\n    68→// (e.g., multiple CPUs or cores).\n    69→type APIQueryResponse struct {\n    70→\tQueries []APIQuery        `json:\"queries,omitempty\"`\n    71→\tResults [][]APIMetricData `json:\"results\"`\n    72→}\n    73→\n    74→// APIQuery represents a single metric query with optional hierarchical selectors.\n    75→//\n    76→// The hierarchical selection works as follows:\n    77→//   - Hostname: The node to query\n    78→//   - Type + TypeIds: First level of hierarchy (e.g., \"cpu\" + [\"0\", \"1\", \"2\"])\n    79→//   - SubType + SubTypeIds: Second level of hierarchy (e.g., \"core\" + [\"0\", \"1\"])\n    80→//\n    81→// If Aggregate is true, data from multiple type/subtype IDs will be aggregated according\n    82→// to the metric's aggregation strategy. Otherwise, separate results are returned for each combination.\n    83→type APIQuery struct {\n    84→\tType        *string      `json:\"type,omitempty\"`\n    85→\tSubType     *string      `json:\"subtype,omitempty\"`\n    86→\tMetric      string       `json:\"metric\"`\n    87→\tHostname    string       `json:\"host\"`\n    88→\tResolution  int64        `json:\"resolution\"`\n    89→\tTypeIds     []string     `json:\"type-ids,omitempty\"`\n    90→\tSubTypeIds  []string     `json:\"subtype-ids,omitempty\"`\n    91→\tScaleFactor schema.Float `json:\"scale-by,omitempty\"`\n    92→\tAggregate   bool         `json:\"aggreg\"`\n    93→}\n    94→\n    95→// AddStats computes and populates the Avg, Min, and Max fields from the Data array.\n    96→//\n    97→// NaN values in the data are ignored during computation. If all values are NaN,\n    98→// the statistics fields will be set to NaN.\n    99→//\n   100→// TODO: Optimize this, just like the stats endpoint!\n   101→func (data *APIMetricData) AddStats() {\n   102→\tn := 0\n   103→\tsum, min, max := 0.0, math.MaxFloat64, -math.MaxFloat64\n   104→\tfor _, x := range data.Data {\n   105→\t\tif x.IsNaN() {\n   106→\t\t\tcontinue\n   107→\t\t}\n   108→\n   109→\t\tn += 1\n   110→\t\tsum += float64(x)\n   111→\t\tmin = math.Min(min, float64(x))\n   112→\t\tmax = math.Max(max, float64(x))\n   113→\t}\n   114→\n   115→\tif n > 0 {\n   116→\t\tavg := sum / float64(n)\n   117→\t\tdata.Avg = schema.Float(avg)\n   118→\t\tdata.Min = schema.Float(min)\n   119→\t\tdata.Max = schema.Float(max)\n   120→\t} else {\n   121→\t\tdata.Avg, data.Min, data.Max = schema.NaN, schema.NaN, schema.NaN\n   122→\t}\n   123→}\n   124→\n   125→// ScaleBy multiplies all data points and statistics by the given factor.\n   126→//\n   127→// This is commonly used for unit conversion (e.g., bytes to gigabytes).\n   128→// Scaling by 0 or 1 is a no-op for performance reasons.\n   129→func (data *APIMetricData) ScaleBy(f schema.Float) {\n   130→\tif f == 0 || f == 1 {\n   131→\t\treturn\n   132→\t}\n   133→\n   134→\tdata.Avg *= f\n   135→\tdata.Min *= f\n   136→\tdata.Max *= f\n   137→\tfor i := 0; i < len(data.Data); i++ {\n   138→\t\tdata.Data[i] *= f\n   139→\t}"}]},"uuid":"1fcd3505-a3f1-49f0-bae6-391f562d58bb","timestamp":"2026-03-04T15:25:41.845Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"aeb62a7c5fea57dd2"},"toolUseID":"agent_msg_017PhdenzLz5rCHUntWZksNg","parentToolUseID":"toolu_01TXANL4MzLEvewR2e42CBCn","uuid":"39942974-8cc0-4b39-be69-c337188287fc","timestamp":"2026-03-04T15:25:42.704Z"}
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{"parentUuid":"18031879-2136-42c5-be30-bef380d3b313","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"progress","data":{"message":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XGHeBiLn7HWes5zR9DFJUs","type":"tool_result","content":"/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store-queries.go-449-\tif nativeScope == schema.MetricScopeMemoryDomain && scope == schema.MetricScopeSocket {\ninternal/metricstoreclient/cc-metric-store-queries.go-450-\t\tmemDomains, _ := topology.GetMemoryDomainsFromHWThreads(hwthreads)\ninternal/metricstoreclient/cc-metric-store-queries.go:451:\t\tsocketToDomains, err := topology.GetMemoryDomainsBySocket(memDomains)\n/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store-queries.go-452-\t\tif err != nil {\ninternal/metricstoreclient/cc-metric-store-queries.go-453-\t\t\tcclog.Errorf(\"Error mapping memory domains to sockets, return unchanged: %v\", err)\n/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store-queries.go-454-\t\t\t// Rare Error Case -> Still Continue -> Return Empty Slices\n/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store-queries.go-455-\t\t\treturn queries, scopes\n/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store-queries.go-456-\t\t}"}]},"uuid":"53bf8322-aaf1-4204-be46-38028792dc32","timestamp":"2026-03-04T15:25:47.675Z"},"normalizedMessages":[],"type":"agent_progress","prompt":"","agentId":"aeb62a7c5fea57dd2"},"toolUseID":"agent_msg_017PhdenzLz5rCHUntWZksNg","parentToolUseID":"toolu_01TXANL4MzLEvewR2e42CBCn","uuid":"2c8ed185-f75e-4bd2-b7ee-2f3e0e28391c","timestamp":"2026-03-04T15:25:47.676Z"}
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{"parentUuid":"ff830631-3e76-4c5a-9a43-d0ae540820df","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TXANL4MzLEvewR2e42CBCn","type":"tool_result","content":[{"type":"text","text":"Now I have a complete picture. Let me design the plan.\n\n---\n\n## Implementation Plan: Consolidate Query-Building Logic\n\n### Summary of Differences Found\n\nAfter thorough analysis, here are all the concrete differences between the two implementations:\n\n**Structural differences:**\n1. `buildScopeQueries()` -- exists as a standalone function in `metricstoreclient`, inline in `metricstore`\n2. Scope dedup: `map[schema.MetricScope]bool` (metricstore) vs labeled-loop + `[]schema.MetricScope` (metricstoreclient)\n3. Topology access: inline `archive.GetSubCluster()` (metricstore) vs `getTopology()`/`getTopologyByNode()` with caching (metricstoreclient)\n\n**Functional differences:**\n4. **MemoryDomain -> Socket** transformation: present in `metricstoreclient/buildScopeQueries()` (lines 449-472), **missing** from `metricstore/buildQueries()` and `metricstore/buildNodeQueries()`\n5. `sanitizeStats` signature: `(data *APIMetricData)` in metricstore vs `(avg, min, max *schema.Float)` in metricstoreclient. The metricstoreclient version zeros ALL three if ANY is NaN; the metricstore version zeros each independently.\n\n**Type differences (not shareable without unification):**\n6. `APIQuery.Resolution`: `int64` (metricstore) vs `int` (metricstoreclient)\n7. `APIMetricData.Resolution`: `int64` vs `int`\n8. `APIMetricData.Data`: `schema.FloatArray` vs `[]schema.Float`\n9. `APIQuery.ScaleFactor`: present in metricstore, absent in metricstoreclient\n\n### Design Decisions\n\nGiven the constraints (separate `APIQuery` types, different resolution types, different data fetching), the plan focuses on what **can** be shared without large-scale type unification:\n\n**Share: `buildScopeQueries` logic** -- This is the highest-value target. It is pure scope-transformation logic that depends only on `schema.Topology`, `schema.MetricScope`, and string/int slices. It does NOT depend on `APIQuery` at all internally -- it only constructs the output fields.\n\n**Approach: Generic scope-query builder returning intermediate structs.** Create a shared package-level function that returns a simple intermediate type (not `APIQuery`), which each package then converts to its own `APIQuery`. This avoids coupling to either `APIQuery` type.\n\n**Alternative considered and rejected:** Making `buildScopeQueries` return `metricstoreclient.APIQuery` and importing it from `metricstore` -- this creates a wrong dependency direction (`pkg/` importing `internal/`). The reverse direction (moving to `pkg/`) is possible but `metricstoreclient` is an `internal/` package.\n\n**Best location for shared code:** A new file in `pkg/metricstore/` (e.g., `pkg/metricstore/scopequery.go`), since `metricstoreclient` already imports `pkg/metricstore`. Both packages can access code there.\n\n### Step-by-Step Plan\n\n#### Step 1: Define shared intermediate type and function in `pkg/metricstore/scopequery.go`\n\nCreate a new file `pkg/metricstore/scopequery.go` containing:\n\n```go\npackage metricstore\n\nimport \"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\n// ScopeQueryResult holds the fields needed to construct an APIQuery for a single scope query.\n// This is a package-independent intermediate that both metricstore and metricstoreclient\n// can convert to their respective APIQuery types.\ntype ScopeQueryResult struct {\n    Metric    string\n    Hostname  string\n    Aggregate bool\n    Type      *string            // nil for node-scope queries\n    TypeIds   []string\n    Scope     schema.MetricScope // The assigned (actual) scope\n}\n\n// BuildScopeQueries generates scope query descriptors for a given scope transformation.\n// It returns (results, nil) on success, (empty, nil) for expected exceptions,\n// and (nil, error) for unhandled cases.\nfunc BuildScopeQueries(\n    nativeScope, requestedScope schema.MetricScope,\n    metric, hostname string,\n    topology *schema.Topology,\n    hwthreads []int,\n    accelerators []string,\n) ([]ScopeQueryResult, error) {\n    // ... (port from metricstoreclient/buildScopeQueries, \n    //      omitting Resolution since callers set it)\n}\n```\n\nKey design points:\n- `Resolution` is omitted from `ScopeQueryResult` because it differs in type (`int` vs `int64`) and callers always know it.\n- The function is exported so both packages can use it.\n- Move the scope string constants (`hwthreadString`, etc.) here as well -- both packages have identical copies.\n- Move `intToStringSlice` here -- both packages have copies (the metricstore version is slightly more optimized with buffer reuse, use that).\n- Include the **MemoryDomain -> Socket** transformation from metricstoreclient.\n\n#### Step 2: Update `pkg/metricstore/query.go`\n\nRefactor `buildQueries()` and `buildNodeQueries()`:\n- Replace the inline scope-transformation if-chain (lines 317-534 in `buildQueries`, lines 1099-1314 in `buildNodeQueries`) with a call to `BuildScopeQueries()`.\n- Convert each `ScopeQueryResult` to the local `APIQuery` by adding `Resolution: resolution`.\n- Handle the return value convention: empty slice = expected exception (continue), nil = error.\n- Remove the duplicated scope string constants and `intToStringSlice` (now in `scopequery.go`).\n- Switch dedup from `map[schema.MetricScope]bool` to the labeled-loop + slice pattern used in metricstoreclient (more consistent, avoids allocation for the typical 1-3 scopes).\n\nThis change also **adds the missing MemoryDomain -> Socket transformation** to the internal store.\n\n#### Step 3: Update `internal/metricstoreclient/cc-metric-store-queries.go`\n\nRefactor `buildQueries()` and `buildNodeQueries()`:\n- Replace call to local `buildScopeQueries()` with `metricstore.BuildScopeQueries()`.\n- Convert each `ScopeQueryResult` to the local `APIQuery` by adding `Resolution: resolution`.\n- Delete the local `buildScopeQueries()` function.\n- Delete the local scope string constants and `intToStringSlice` (now in `pkg/metricstore/scopequery.go`).\n\n#### Step 4: Synchronize `sanitizeStats`\n\nTwo options:\n\n**Option A (recommended -- minimal change):** Align the metricstore `sanitizeStats` to match the metricstoreclient behavior (zero ALL if ANY is NaN). This is the safer behavior since partial NaN stats are likely corrupt. Change `pkg/metricstore/query.go` `sanitizeStats` to:\n```go\nfunc sanitizeStats(data *APIMetricData) {\n    if data.Avg.IsNaN() || data.Min.IsNaN() || data.Max.IsNaN() {\n        data.Avg = schema.Float(0)\n        data.Min = schema.Float(0)\n        data.Max = schema.Float(0)\n    }\n}\n```\n\nDo NOT share the function itself since the signatures differ due to different `APIMetricData` types. Just synchronize the logic.\n\n**Option B:** Extract a shared `SanitizeStats(avg, min, max *schema.Float)` into `pkg/metricstore/scopequery.go` and have both call it. The metricstore version would wrap it:\n```go\nfunc sanitizeStats(data *APIMetricData) {\n    metricstore.SanitizeStats(&data.Avg, &data.Min, &data.Max)\n}\n```\nThis is cleaner but adds an extra function call. Low cost, reasonable.\n\nI recommend **Option B** for consistency.\n\n#### Step 5: Add `hasNaNStats` to shared code (optional)\n\nMove `hasNaNStats` to `pkg/metricstore/scopequery.go` as an exported function. Currently only used in `metricstoreclient/LoadStats`, but `metricstore/LoadStats` has the same check inline (line 602). Both can use the shared version.\n\n#### Step 6: Add tests for `BuildScopeQueries`\n\nCreate `pkg/metricstore/scopequery_test.go` with table-driven tests covering all scope transformation cases, including the MemoryDomain -> Socket case. This is currently untested in both packages.\n\n### What NOT to share (and why)\n\n- **`buildQueries` / `buildNodeQueries` themselves**: These differ in topology access patterns (caching vs inline), empty-resource checks, and are methods on different receiver types. The shared `BuildScopeQueries` extracts the core logic while letting each package handle its own orchestration.\n- **`APIQuery` / `APIMetricData` types**: Different `Resolution` types (`int` vs `int64`), different `Data` types (`schema.FloatArray` vs `[]schema.Float`), and `ScaleFactor` only in internal. Unifying would require touching too much code across both packages for marginal benefit.\n- **Load* functions**: They share the same result-processing pattern but depend on package-specific types (`APIQuery`, `APIMetricData`) and fetching mechanisms (`FetchData` vs `doRequest`). Extracting generic result processors would require generics or interfaces that add complexity without proportional benefit.\n\n### Migration sequence\n\n1. Create `pkg/metricstore/scopequery.go` with `ScopeQueryResult`, `BuildScopeQueries`, `IntToStringSlice`, `SanitizeStats`, and scope string constants.\n2. Add `pkg/metricstore/scopequery_test.go`.\n3. Update `pkg/metricstore/query.go` to use the shared functions. Run `go test ./pkg/metricstore/...`.\n4. Update `internal/metricstoreclient/cc-metric-store-queries.go` to use the shared functions. Run `go test ./internal/metricstoreclient/...` (currently no tests, but compile check).\n5. Run full test suite: `make test`.\n\n### Risks and mitigations\n\n- **MemoryDomain -> Socket addition to internal store**: This is a functional change. The internal store previously returned an error for this case. The metricstoreclient handles it correctly. Adding it is a bug fix, not a regression.\n- **sanitizeStats behavior change**: Changing from \"zero each independently\" to \"zero all if any is NaN\" could change output for edge cases where only one stat is NaN. This is arguably more correct (partial NaN stats are suspect). Low risk.\n- **Import cycle risk**: None. `internal/metricstoreclient` already imports `pkg/metricstore`. The shared code goes into `pkg/metricstore`, which does not import `internal/metricstoreclient`.\n\n### Critical Files for Implementation\n- `/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/query.go` - Contains the inline scope transformation to replace with shared BuildScopeQueries calls; also buildQueries and buildNodeQueries to refactor\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store-queries.go` - Contains the reference buildScopeQueries implementation to extract; buildQueries and buildNodeQueries to refactor\n- `/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/api.go` - Defines the internal APIQuery/APIMetricData types; context for type differences\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go` - Defines external APIQuery/APIMetricData types and Load* functions with result processing to synchronize\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricdispatch/metricdata.go` - The MetricDataRepository interface both packages implement; must not be broken"},{"type":"text","text":"agentId: aeb62a7c5fea57dd2 (for resuming to continue this agent's work if needed)\n<usage>total_tokens: 65135\ntool_uses: 15\nduration_ms: 102215</usage>"}]}]},"uuid":"f87f9792-0acc-4409-b718-c7eb078f209d","timestamp":"2026-03-04T15:26:58.686Z","toolUseResult":{"status":"completed","prompt":"I need to design a plan to synchronize and consolidate the query-building logic between two packages that implement the same MetricDataRepository interface:\n\n## Context\n\n1. **`pkg/metricstore/query.go`** (InternalMetricStore) — in-memory metric store\n2. **`internal/metricstoreclient/cc-metric-store-queries.go`** (CCMetricStore) — HTTP client to external cc-metric-store\n\nBoth implement the `MetricDataRepository` interface from `internal/metricdispatch/metricdata.go`.\n\n## Key Differences Found\n\n### buildQueries differences:\n- **pkg/metricstore**: Inline scope transformation (long chain of if-statements in the inner loop). Uses `map[schema.MetricScope]bool` for dedup. Resolution is `int64`.\n- **metricstoreclient**: Extracted `buildScopeQueries()` helper function. Uses labeled loop + `[]schema.MetricScope` slice for dedup. Resolution is `int`. Has topology caching via `getTopology()`.\n\n### buildScopeQueries (only in metricstoreclient):\n- Clean standalone function handling all scope transformations\n- Returns empty slices for expected exceptions, nil for unhandled cases\n- Includes **MemoryDomain → Socket** transformation (MISSING from pkg/metricstore)\n\n### buildNodeQueries differences:\n- **pkg/metricstore**: Gets topology inline per node. Uses `map[schema.MetricScope]bool` dedup. Has inline scope transformation.\n- **metricstoreclient**: Uses `getTopology()`/`getTopologyByNode()` with caching. Uses labeled loop dedup. Delegates to `buildScopeQueries()`.\n\n### APIQuery type differences:\n- **pkg/metricstore**: `Resolution int64`, has `ScaleFactor schema.Float`\n- **metricstoreclient**: `Resolution int`, no ScaleFactor\n\n### sanitizeStats differences:\n- **pkg/metricstore**: `sanitizeStats(data *APIMetricData)` — zeros each independently\n- **metricstoreclient**: `sanitizeStats(avg, min, max *schema.Float)` — zeros all if any is NaN. Also has `hasNaNStats()` helper.\n\n### LoadData/LoadStats/LoadScopedStats/LoadNodeData/LoadNodeListData:\n- Nearly identical result-processing logic\n- Different data fetching: `FetchData(req)` vs `ccms.doRequest(ctx, &req)`  \n- Internal store has `TestLoadDataCallback` test hook\n- External client has topology caching\n\n### Missing from pkg/metricstore:\n- **MemoryDomain → Socket** scope transformation (present in metricstoreclient)\n\n### Missing from metricstoreclient:\n- Nothing significant missing from metricstoreclient\n\n## Consolidation Approach\n\nThe user said metricstoreclient is the cleaner implementation. I agree — `buildScopeQueries()` as a standalone function is much cleaner than the inline if-chain.\n\nDesign a plan to:\n1. Extract `buildScopeQueries()` as a shared helper (possibly in a shared package or as a package-level function in metricstore)\n2. Synchronize the scope transformations (add MemoryDomain → Socket to internal)\n3. Evaluate if result-processing logic can be shared\n4. Consider type differences (int vs int64 Resolution)\n\nKey constraints:\n- The two packages have separate APIQuery types (different Resolution types, internal has ScaleFactor)\n- The two packages use different data fetching mechanisms\n- Must not break the MetricDataRepository interface contract\n- Keep changes minimal and focused\n\nProvide a detailed implementation plan.","agentId":"aeb62a7c5fea57dd2","content":[{"type":"text","text":"Now I have a complete picture. Let me design the plan.\n\n---\n\n## Implementation Plan: Consolidate Query-Building Logic\n\n### Summary of Differences Found\n\nAfter thorough analysis, here are all the concrete differences between the two implementations:\n\n**Structural differences:**\n1. `buildScopeQueries()` -- exists as a standalone function in `metricstoreclient`, inline in `metricstore`\n2. Scope dedup: `map[schema.MetricScope]bool` (metricstore) vs labeled-loop + `[]schema.MetricScope` (metricstoreclient)\n3. Topology access: inline `archive.GetSubCluster()` (metricstore) vs `getTopology()`/`getTopologyByNode()` with caching (metricstoreclient)\n\n**Functional differences:**\n4. **MemoryDomain -> Socket** transformation: present in `metricstoreclient/buildScopeQueries()` (lines 449-472), **missing** from `metricstore/buildQueries()` and `metricstore/buildNodeQueries()`\n5. `sanitizeStats` signature: `(data *APIMetricData)` in metricstore vs `(avg, min, max *schema.Float)` in metricstoreclient. The metricstoreclient version zeros ALL three if ANY is NaN; the metricstore version zeros each independently.\n\n**Type differences (not shareable without unification):**\n6. `APIQuery.Resolution`: `int64` (metricstore) vs `int` (metricstoreclient)\n7. `APIMetricData.Resolution`: `int64` vs `int`\n8. `APIMetricData.Data`: `schema.FloatArray` vs `[]schema.Float`\n9. `APIQuery.ScaleFactor`: present in metricstore, absent in metricstoreclient\n\n### Design Decisions\n\nGiven the constraints (separate `APIQuery` types, different resolution types, different data fetching), the plan focuses on what **can** be shared without large-scale type unification:\n\n**Share: `buildScopeQueries` logic** -- This is the highest-value target. It is pure scope-transformation logic that depends only on `schema.Topology`, `schema.MetricScope`, and string/int slices. It does NOT depend on `APIQuery` at all internally -- it only constructs the output fields.\n\n**Approach: Generic scope-query builder returning intermediate structs.** Create a shared package-level function that returns a simple intermediate type (not `APIQuery`), which each package then converts to its own `APIQuery`. This avoids coupling to either `APIQuery` type.\n\n**Alternative considered and rejected:** Making `buildScopeQueries` return `metricstoreclient.APIQuery` and importing it from `metricstore` -- this creates a wrong dependency direction (`pkg/` importing `internal/`). The reverse direction (moving to `pkg/`) is possible but `metricstoreclient` is an `internal/` package.\n\n**Best location for shared code:** A new file in `pkg/metricstore/` (e.g., `pkg/metricstore/scopequery.go`), since `metricstoreclient` already imports `pkg/metricstore`. Both packages can access code there.\n\n### Step-by-Step Plan\n\n#### Step 1: Define shared intermediate type and function in `pkg/metricstore/scopequery.go`\n\nCreate a new file `pkg/metricstore/scopequery.go` containing:\n\n```go\npackage metricstore\n\nimport \"github.com/ClusterCockpit/cc-lib/v2/schema\"\n\n// ScopeQueryResult holds the fields needed to construct an APIQuery for a single scope query.\n// This is a package-independent intermediate that both metricstore and metricstoreclient\n// can convert to their respective APIQuery types.\ntype ScopeQueryResult struct {\n    Metric    string\n    Hostname  string\n    Aggregate bool\n    Type      *string            // nil for node-scope queries\n    TypeIds   []string\n    Scope     schema.MetricScope // The assigned (actual) scope\n}\n\n// BuildScopeQueries generates scope query descriptors for a given scope transformation.\n// It returns (results, nil) on success, (empty, nil) for expected exceptions,\n// and (nil, error) for unhandled cases.\nfunc BuildScopeQueries(\n    nativeScope, requestedScope schema.MetricScope,\n    metric, hostname string,\n    topology *schema.Topology,\n    hwthreads []int,\n    accelerators []string,\n) ([]ScopeQueryResult, error) {\n    // ... (port from metricstoreclient/buildScopeQueries, \n    //      omitting Resolution since callers set it)\n}\n```\n\nKey design points:\n- `Resolution` is omitted from `ScopeQueryResult` because it differs in type (`int` vs `int64`) and callers always know it.\n- The function is exported so both packages can use it.\n- Move the scope string constants (`hwthreadString`, etc.) here as well -- both packages have identical copies.\n- Move `intToStringSlice` here -- both packages have copies (the metricstore version is slightly more optimized with buffer reuse, use that).\n- Include the **MemoryDomain -> Socket** transformation from metricstoreclient.\n\n#### Step 2: Update `pkg/metricstore/query.go`\n\nRefactor `buildQueries()` and `buildNodeQueries()`:\n- Replace the inline scope-transformation if-chain (lines 317-534 in `buildQueries`, lines 1099-1314 in `buildNodeQueries`) with a call to `BuildScopeQueries()`.\n- Convert each `ScopeQueryResult` to the local `APIQuery` by adding `Resolution: resolution`.\n- Handle the return value convention: empty slice = expected exception (continue), nil = error.\n- Remove the duplicated scope string constants and `intToStringSlice` (now in `scopequery.go`).\n- Switch dedup from `map[schema.MetricScope]bool` to the labeled-loop + slice pattern used in metricstoreclient (more consistent, avoids allocation for the typical 1-3 scopes).\n\nThis change also **adds the missing MemoryDomain -> Socket transformation** to the internal store.\n\n#### Step 3: Update `internal/metricstoreclient/cc-metric-store-queries.go`\n\nRefactor `buildQueries()` and `buildNodeQueries()`:\n- Replace call to local `buildScopeQueries()` with `metricstore.BuildScopeQueries()`.\n- Convert each `ScopeQueryResult` to the local `APIQuery` by adding `Resolution: resolution`.\n- Delete the local `buildScopeQueries()` function.\n- Delete the local scope string constants and `intToStringSlice` (now in `pkg/metricstore/scopequery.go`).\n\n#### Step 4: Synchronize `sanitizeStats`\n\nTwo options:\n\n**Option A (recommended -- minimal change):** Align the metricstore `sanitizeStats` to match the metricstoreclient behavior (zero ALL if ANY is NaN). This is the safer behavior since partial NaN stats are likely corrupt. Change `pkg/metricstore/query.go` `sanitizeStats` to:\n```go\nfunc sanitizeStats(data *APIMetricData) {\n    if data.Avg.IsNaN() || data.Min.IsNaN() || data.Max.IsNaN() {\n        data.Avg = schema.Float(0)\n        data.Min = schema.Float(0)\n        data.Max = schema.Float(0)\n    }\n}\n```\n\nDo NOT share the function itself since the signatures differ due to different `APIMetricData` types. Just synchronize the logic.\n\n**Option B:** Extract a shared `SanitizeStats(avg, min, max *schema.Float)` into `pkg/metricstore/scopequery.go` and have both call it. The metricstore version would wrap it:\n```go\nfunc sanitizeStats(data *APIMetricData) {\n    metricstore.SanitizeStats(&data.Avg, &data.Min, &data.Max)\n}\n```\nThis is cleaner but adds an extra function call. Low cost, reasonable.\n\nI recommend **Option B** for consistency.\n\n#### Step 5: Add `hasNaNStats` to shared code (optional)\n\nMove `hasNaNStats` to `pkg/metricstore/scopequery.go` as an exported function. Currently only used in `metricstoreclient/LoadStats`, but `metricstore/LoadStats` has the same check inline (line 602). Both can use the shared version.\n\n#### Step 6: Add tests for `BuildScopeQueries`\n\nCreate `pkg/metricstore/scopequery_test.go` with table-driven tests covering all scope transformation cases, including the MemoryDomain -> Socket case. This is currently untested in both packages.\n\n### What NOT to share (and why)\n\n- **`buildQueries` / `buildNodeQueries` themselves**: These differ in topology access patterns (caching vs inline), empty-resource checks, and are methods on different receiver types. The shared `BuildScopeQueries` extracts the core logic while letting each package handle its own orchestration.\n- **`APIQuery` / `APIMetricData` types**: Different `Resolution` types (`int` vs `int64`), different `Data` types (`schema.FloatArray` vs `[]schema.Float`), and `ScaleFactor` only in internal. Unifying would require touching too much code across both packages for marginal benefit.\n- **Load* functions**: They share the same result-processing pattern but depend on package-specific types (`APIQuery`, `APIMetricData`) and fetching mechanisms (`FetchData` vs `doRequest`). Extracting generic result processors would require generics or interfaces that add complexity without proportional benefit.\n\n### Migration sequence\n\n1. Create `pkg/metricstore/scopequery.go` with `ScopeQueryResult`, `BuildScopeQueries`, `IntToStringSlice`, `SanitizeStats`, and scope string constants.\n2. Add `pkg/metricstore/scopequery_test.go`.\n3. Update `pkg/metricstore/query.go` to use the shared functions. Run `go test ./pkg/metricstore/...`.\n4. Update `internal/metricstoreclient/cc-metric-store-queries.go` to use the shared functions. Run `go test ./internal/metricstoreclient/...` (currently no tests, but compile check).\n5. Run full test suite: `make test`.\n\n### Risks and mitigations\n\n- **MemoryDomain -> Socket addition to internal store**: This is a functional change. The internal store previously returned an error for this case. The metricstoreclient handles it correctly. Adding it is a bug fix, not a regression.\n- **sanitizeStats behavior change**: Changing from \"zero each independently\" to \"zero all if any is NaN\" could change output for edge cases where only one stat is NaN. This is arguably more correct (partial NaN stats are suspect). Low risk.\n- **Import cycle risk**: None. `internal/metricstoreclient` already imports `pkg/metricstore`. The shared code goes into `pkg/metricstore`, which does not import `internal/metricstoreclient`.\n\n### Critical Files for Implementation\n- `/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/query.go` - Contains the inline scope transformation to replace with shared BuildScopeQueries calls; also buildQueries and buildNodeQueries to refactor\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store-queries.go` - Contains the reference buildScopeQueries implementation to extract; buildQueries and buildNodeQueries to refactor\n- `/Users/jan/prg/ClusterCockpit/cc-backend/pkg/metricstore/api.go` - Defines the internal APIQuery/APIMetricData types; context for type differences\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricstoreclient/cc-metric-store.go` - Defines external APIQuery/APIMetricData types and Load* functions with result processing to synchronize\n- `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricdispatch/metricdata.go` - The MetricDataRepository interface both packages implement; must not be broken"}],"totalDurationMs":102215,"totalTokens":65135,"totalToolUseCount":15,"usage":{"input_tokens":1,"cache_creation_input_tokens":919,"cache_read_input_tokens":61210,"output_tokens":3005,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":919,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"sourceToolAssistantUUID":"ff830631-3e76-4c5a-9a43-d0ae540820df"}
{"parentUuid":"f87f9792-0acc-4409-b718-c7eb078f209d","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","message":{"model":"claude-opus-4-6","id":"msg_01YLjMEvaoXL9SrqMHicbYqj","type":"message","role":"assistant","content":[{"type":"text","text":"\n\nNow let me write the final plan."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":4166,"cache_read_input_tokens":85789,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":4166},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"}},"requestId":"req_011CYiGtrFaRNc8doYxZZcxa","type":"assistant","uuid":"9b7ea6f4-8555-44c9-bc2c-d3bcde1efaf4","timestamp":"2026-03-04T15:27:02.321Z"}
{"parentUuid":"9b7ea6f4-8555-44c9-bc2c-d3bcde1efaf4","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","message":{"model":"claude-opus-4-6","id":"msg_01YLjMEvaoXL9SrqMHicbYqj","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XP2qtCAUXbCvMSDxcTbeYx","name":"Write","input":{"file_path":"/Users/jan/.claude/plans/curious-dancing-nebula.md","content":"# Consolidate buildQueries / Scope Transformation Logic\n\n## Context\n\n`pkg/metricstore/query.go` (InternalMetricStore) and `internal/metricstoreclient/cc-metric-store-queries.go` (CCMetricStore) both implement the `MetricDataRepository` interface. They contain nearly identical scope-transformation logic for building metric queries, but the code has diverged:\n\n- **metricstoreclient** has the cleaner design: a standalone `buildScopeQueries()` function handling all transformations\n- **metricstore** has the same logic inlined as a long if-chain inside `buildQueries()` and duplicated in `buildNodeQueries()`\n- **metricstore is missing** the `MemoryDomain → Socket` transformation that metricstoreclient has\n- Both packages duplicate scope string constants, `intToStringSlice`, and `sanitizeStats`\n\nGoal: Extract shared scope-transformation logic, synchronize behavior, and eliminate duplication.\n\n## Approach\n\nExtract `BuildScopeQueries` as a shared exported function in `pkg/metricstore/` (since metricstoreclient already imports it). Return a package-independent intermediate type (`ScopeQueryResult`) that each package converts to its own `APIQuery` — this avoids coupling the two different `APIQuery` types (they differ in `Resolution` type: `int64` vs `int`, and internal has `ScaleFactor`).\n\n## Steps\n\n### 1. Create `pkg/metricstore/scopequery.go`\n\nNew file containing:\n\n- **`ScopeQueryResult`** struct — intermediate type with fields: `Metric`, `Hostname`, `Aggregate`, `Type *string`, `TypeIds []string`, `Scope schema.MetricScope` (no Resolution — callers set it)\n- **`BuildScopeQueries()`** — exported function ported from `metricstoreclient/buildScopeQueries()` (lines 257-517), returning `([]ScopeQueryResult, error)`. Returns empty slice for expected exceptions, error for unhandled cases\n- **Scope string constants** — `HWThreadString`, `CoreString`, `MemoryDomainString`, `SocketString`, `AcceleratorString` (exported, move from both packages)\n- **`IntToStringSlice()`** — exported, using the optimized buffer-reuse version from metricstore\n- **`SanitizeStats(avg, min, max *schema.Float)`** — exported, using the metricstoreclient behavior (zero ALL if ANY is NaN)\n\n### 2. Update `pkg/metricstore/query.go`\n\n- Replace inline if-chain in `buildQueries()` (lines 317-534) with call to `BuildScopeQueries()`, converting each `ScopeQueryResult` → local `APIQuery` (adding `Resolution`)\n- Replace inline if-chain in `buildNodeQueries()` (lines 1099-1316) with same pattern\n- Delete local scope string constants, `intToStringSlice`, local `sanitizeStats`\n- Update `sanitizeStats` calls in `LoadData`/`LoadScopedStats`/`LoadNodeData`/`LoadNodeListData` to use `SanitizeStats(&res.Avg, &res.Min, &res.Max)`\n- Switch scope dedup from `map[schema.MetricScope]bool` to labeled-loop + slice (matches metricstoreclient, avoids allocation for typical 1-3 scopes)\n- This **adds the missing MemoryDomain → Socket transformation** to the internal store\n\n### 3. Update `internal/metricstoreclient/cc-metric-store-queries.go`\n\n- Replace call to local `buildScopeQueries()` with `metricstore.BuildScopeQueries()`, converting each `ScopeQueryResult` → local `APIQuery` (adding `Resolution`)\n- Delete local `buildScopeQueries()` function\n- Delete local scope string constants and `intToStringSlice`\n- Update `sanitizeStats` in `cc-metric-store.go` to call `metricstore.SanitizeStats()`\n\n### 4. Add tests: `pkg/metricstore/scopequery_test.go`\n\nTable-driven tests for `BuildScopeQueries` covering all transformation cases:\n- Same-scope (HWThread→HWThread, Core→Core, Socket→Socket, Node→Node, etc.)\n- Aggregation (HWThread→Core, HWThread→Socket, HWThread→Node, Core→Socket, Core→Node, etc.)\n- Accelerator cases (Accelerator→Accelerator, Accelerator→Node)\n- MemoryDomain cases including MemoryDomain→Socket\n- Unhandled case returns error\n\n## Files Modified\n\n| File | Action |\n|------|--------|\n| `pkg/metricstore/scopequery.go` | **New** — shared types and functions |\n| `pkg/metricstore/scopequery_test.go` | **New** — tests for BuildScopeQueries |\n| `pkg/metricstore/query.go` | Refactor buildQueries/buildNodeQueries to use BuildScopeQueries; remove duplicated helpers |\n| `internal/metricstoreclient/cc-metric-store-queries.go` | Replace local buildScopeQueries with shared; remove duplicated helpers |\n| `internal/metricstoreclient/cc-metric-store.go` | Update sanitizeStats to use shared SanitizeStats |\n\n## What is NOT shared (intentionally)\n\n- **`APIQuery`/`APIMetricData` types** — different Resolution types, Data types, ScaleFactor field\n- **`buildQueries`/`buildNodeQueries` themselves** — different topology access (caching vs inline), different receiver types\n- **Load* result-processing functions** — depend on package-specific types and fetch mechanisms\n\n## Verification\n\n1. `go build ./...` — compile check\n2. `go test ./pkg/metricstore/...` — existing + new scopequery tests\n3. `go test ./internal/metricstoreclient/...` — compile check\n4. `make test` — full test suite\n"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":3,"cache_creation_input_tokens":4166,"cache_read_input_tokens":85789,"output_tokens":1578,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":4166,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYiGtrFaRNc8doYxZZcxa","type":"assistant","uuid":"71da99f4-a3e2-4486-a763-088198b6fbd3","timestamp":"2026-03-04T15:27:25.319Z"}
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{"parentUuid":"71da99f4-a3e2-4486-a763-088198b6fbd3","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XP2qtCAUXbCvMSDxcTbeYx","type":"tool_result","content":"File created successfully at: /Users/jan/.claude/plans/curious-dancing-nebula.md"}]},"uuid":"f90b43e1-5816-4489-b38b-5998dc83445b","timestamp":"2026-03-04T15:27:25.793Z","toolUseResult":{"type":"create","filePath":"/Users/jan/.claude/plans/curious-dancing-nebula.md","content":"# Consolidate buildQueries / Scope Transformation Logic\n\n## Context\n\n`pkg/metricstore/query.go` (InternalMetricStore) and `internal/metricstoreclient/cc-metric-store-queries.go` (CCMetricStore) both implement the `MetricDataRepository` interface. They contain nearly identical scope-transformation logic for building metric queries, but the code has diverged:\n\n- **metricstoreclient** has the cleaner design: a standalone `buildScopeQueries()` function handling all transformations\n- **metricstore** has the same logic inlined as a long if-chain inside `buildQueries()` and duplicated in `buildNodeQueries()`\n- **metricstore is missing** the `MemoryDomain → Socket` transformation that metricstoreclient has\n- Both packages duplicate scope string constants, `intToStringSlice`, and `sanitizeStats`\n\nGoal: Extract shared scope-transformation logic, synchronize behavior, and eliminate duplication.\n\n## Approach\n\nExtract `BuildScopeQueries` as a shared exported function in `pkg/metricstore/` (since metricstoreclient already imports it). Return a package-independent intermediate type (`ScopeQueryResult`) that each package converts to its own `APIQuery` — this avoids coupling the two different `APIQuery` types (they differ in `Resolution` type: `int64` vs `int`, and internal has `ScaleFactor`).\n\n## Steps\n\n### 1. Create `pkg/metricstore/scopequery.go`\n\nNew file containing:\n\n- **`ScopeQueryResult`** struct — intermediate type with fields: `Metric`, `Hostname`, `Aggregate`, `Type *string`, `TypeIds []string`, `Scope schema.MetricScope` (no Resolution — callers set it)\n- **`BuildScopeQueries()`** — exported function ported from `metricstoreclient/buildScopeQueries()` (lines 257-517), returning `([]ScopeQueryResult, error)`. Returns empty slice for expected exceptions, error for unhandled cases\n- **Scope string constants** — `HWThreadString`, `CoreString`, `MemoryDomainString`, `SocketString`, `AcceleratorString` (exported, move from both packages)\n- **`IntToStringSlice()`** — exported, using the optimized buffer-reuse version from metricstore\n- **`SanitizeStats(avg, min, max *schema.Float)`** — exported, using the metricstoreclient behavior (zero ALL if ANY is NaN)\n\n### 2. Update `pkg/metricstore/query.go`\n\n- Replace inline if-chain in `buildQueries()` (lines 317-534) with call to `BuildScopeQueries()`, converting each `ScopeQueryResult` → local `APIQuery` (adding `Resolution`)\n- Replace inline if-chain in `buildNodeQueries()` (lines 1099-1316) with same pattern\n- Delete local scope string constants, `intToStringSlice`, local `sanitizeStats`\n- Update `sanitizeStats` calls in `LoadData`/`LoadScopedStats`/`LoadNodeData`/`LoadNodeListData` to use `SanitizeStats(&res.Avg, &res.Min, &res.Max)`\n- Switch scope dedup from `map[schema.MetricScope]bool` to labeled-loop + slice (matches metricstoreclient, avoids allocation for typical 1-3 scopes)\n- This **adds the missing MemoryDomain → Socket transformation** to the internal store\n\n### 3. Update `internal/metricstoreclient/cc-metric-store-queries.go`\n\n- Replace call to local `buildScopeQueries()` with `metricstore.BuildScopeQueries()`, converting each `ScopeQueryResult` → local `APIQuery` (adding `Resolution`)\n- Delete local `buildScopeQueries()` function\n- Delete local scope string constants and `intToStringSlice`\n- Update `sanitizeStats` in `cc-metric-store.go` to call `metricstore.SanitizeStats()`\n\n### 4. Add tests: `pkg/metricstore/scopequery_test.go`\n\nTable-driven tests for `BuildScopeQueries` covering all transformation cases:\n- Same-scope (HWThread→HWThread, Core→Core, Socket→Socket, Node→Node, etc.)\n- Aggregation (HWThread→Core, HWThread→Socket, HWThread→Node, Core→Socket, Core→Node, etc.)\n- Accelerator cases (Accelerator→Accelerator, Accelerator→Node)\n- MemoryDomain cases including MemoryDomain→Socket\n- Unhandled case returns error\n\n## Files Modified\n\n| File | Action |\n|------|--------|\n| `pkg/metricstore/scopequery.go` | **New** — shared types and functions |\n| `pkg/metricstore/scopequery_test.go` | **New** — tests for BuildScopeQueries |\n| `pkg/metricstore/query.go` | Refactor buildQueries/buildNodeQueries to use BuildScopeQueries; remove duplicated helpers |\n| `internal/metricstoreclient/cc-metric-store-queries.go` | Replace local buildScopeQueries with shared; remove duplicated helpers |\n| `internal/metricstoreclient/cc-metric-store.go` | Update sanitizeStats to use shared SanitizeStats |\n\n## What is NOT shared (intentionally)\n\n- **`APIQuery`/`APIMetricData` types** — different Resolution types, Data types, ScaleFactor field\n- **`buildQueries`/`buildNodeQueries` themselves** — different topology access (caching vs inline), different receiver types\n- **Load* result-processing functions** — depend on package-specific types and fetch mechanisms\n\n## Verification\n\n1. `go build ./...` — compile check\n2. `go test ./pkg/metricstore/...` — existing + new scopequery tests\n3. `go test ./internal/metricstoreclient/...` — compile check\n4. `make test` — full test suite\n","structuredPatch":[],"originalFile":null},"sourceToolAssistantUUID":"71da99f4-a3e2-4486-a763-088198b6fbd3"}
{"parentUuid":"f90b43e1-5816-4489-b38b-5998dc83445b","isSidechain":false,"userType":"external","cwd":"/Users/jan/prg/ClusterCockpit/cc-backend","sessionId":"eec1fc87-d8f6-42f6-b296-d7a2f79af002","version":"2.1.66","gitBranch":"dev","slug":"curious-dancing-nebula","message":{"model":"claude-opus-4-6","id":"msg_01HTxBEbrs8YfsKQbbPhC8MQ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0125wZZDgwtMLqPthmrBZUG7","name":"ExitPlanMode","input":{"plan":"# Consolidate buildQueries / Scope Transformation Logic\n\n## Context\n\n`pkg/metricstore/query.go` (InternalMetricStore) and `internal/metricstoreclient/cc-metric-store-queries.go` (CCMetricStore) both implement the `MetricDataRepository` interface. They contain nearly identical scope-transformation logic for building metric queries, but the code has diverged:\n\n- **metricstoreclient** has the cleaner design: a standalone `buildScopeQueries()` function handling all transformations\n- **metricstore** has the same logic inlined as a long if-chain inside `buildQueries()` and duplicated in `buildNodeQueries()`\n- **metricstore is missing** the `MemoryDomain → Socket` transformation that metricstoreclient has\n- Both packages duplicate scope string constants, `intToStringSlice`, and `sanitizeStats`\n\nGoal: Extract shared scope-transformation logic, synchronize behavior, and eliminate duplication.\n\n## Approach\n\nExtract `BuildScopeQueries` as a shared exported function in `pkg/metricstore/` (since metricstoreclient already imports it). Return a package-independent intermediate type (`ScopeQueryResult`) that each package converts to its own `APIQuery` — this avoids coupling the two different `APIQuery` types (they differ in `Resolution` type: `int64` vs `int`, and internal has `ScaleFactor`).\n\n## Steps\n\n### 1. Create `pkg/metricstore/scopequery.go`\n\nNew file containing:\n\n- **`ScopeQueryResult`** struct — intermediate type with fields: `Metric`, `Hostname`, `Aggregate`, `Type *string`, `TypeIds []string`, `Scope schema.MetricScope` (no Resolution — callers set it)\n- **`BuildScopeQueries()`** — exported function ported from `metricstoreclient/buildScopeQueries()` (lines 257-517), returning `([]ScopeQueryResult, error)`. Returns empty slice for expected exceptions, error for unhandled cases\n- **Scope string constants** — `HWThreadString`, `CoreString`, `MemoryDomainString`, `SocketString`, `AcceleratorString` (exported, move from both packages)\n- **`IntToStringSlice()`** — exported, using the optimized buffer-reuse version from metricstore\n- **`SanitizeStats(avg, min, max *schema.Float)`** — exported, using the metricstoreclient behavior (zero ALL if ANY is NaN)\n\n### 2. Update `pkg/metricstore/query.go`\n\n- Replace inline if-chain in `buildQueries()` (lines 317-534) with call to `BuildScopeQueries()`, converting each `ScopeQueryResult` → local `APIQuery` (adding `Resolution`)\n- Replace inline if-chain in `buildNodeQueries()` (lines 1099-1316) with same pattern\n- Delete local scope string constants, `intToStringSlice`, local `sanitizeStats`\n- Update `sanitizeStats` calls in `LoadData`/`LoadScopedStats`/`LoadNodeData`/`LoadNodeListData` to use `SanitizeStats(&res.Avg, &res.Min, &res.Max)`\n- Switch scope dedup from `map[schema.MetricScope]bool` to labeled-loop + slice (matches metricstoreclient, avoids allocation for typical 1-3 scopes)\n- This **adds the missing MemoryDomain → Socket transformation** to the internal store\n\n### 3. Update `internal/metricstoreclient/cc-metric-store-queries.go`\n\n- Replace call to local `buildScopeQueries()` with `metricstore.BuildScopeQueries()`, converting each `ScopeQueryResult` → local `APIQuery` (adding `Resolution`)\n- Delete local `buildScopeQueries()` function\n- Delete local scope string constants and `intToStringSlice`\n- Update `sanitizeStats` in `cc-metric-store.go` to call `metricstore.SanitizeStats()`\n\n### 4. Add tests: `pkg/metricstore/scopequery_test.go`\n\nTable-driven tests for `BuildScopeQueries` covering all transformation cases:\n- Same-scope (HWThread→HWThread, Core→Core, Socket→Socket, Node→Node, etc.)\n- Aggregation (HWThread→Core, HWThread→Socket, HWThread→Node, Core→Socket, Core→Node, etc.)\n- Accelerator cases (Accelerator→Accelerator, Accelerator→Node)\n- MemoryDomain cases including MemoryDomain→Socket\n- Unhandled case returns error\n\n## Files Modified\n\n| File | Action |\n|------|--------|\n| `pkg/metricstore/scopequery.go` | **New** — shared types and functions |\n| `pkg/metricstore/scopequery_test.go` | **New** — tests for BuildScopeQueries |\n| `pkg/metricstore/query.go` | Refactor buildQueries/buildNodeQueries to use BuildScopeQueries; remove duplicated helpers |\n| `internal/metricstoreclient/cc-metric-store-queries.go` | Replace local buildScopeQueries with shared; remove duplicated helpers |\n| `internal/metricstoreclient/cc-metric-store.go` | Update sanitizeStats to use shared SanitizeStats |\n\n## What is NOT shared (intentionally)\n\n- **`APIQuery`/`APIMetricData` types** — different Resolution types, Data types, ScaleFactor field\n- **`buildQueries`/`buildNodeQueries` themselves** — different topology access (caching vs inline), different receiver types\n- **Load* result-processing functions** — depend on package-specific types and fetch mechanisms\n\n## Verification\n\n1. `go build ./...` — compile check\n2. `go test ./pkg/metricstore/...` — existing + new scopequery tests\n3. `go test ./internal/metricstoreclient/...` — compile check\n4. `make test` — full test suite\n"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1705,"cache_read_input_tokens":89955,"output_tokens":38,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":1705,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CYiGvpHozAszC5Q8XpAQF","type":"assistant","uuid":"1835697e-dd3c-497f-9c3e-2a48bc029c65","timestamp":"2026-03-04T15:27:28.208Z"}
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