web/frontend/src/config/admin/Options.svelte:  const resampleConfig = getContext("resampling");
web/frontend/src/config/user/PlotRenderOptions.svelte:  const resampleConfig = getContext("resampling");
web/frontend/src/Systems.root.svelte:  const resampleConfig = $derived(thisInit ? getContext("resampling") : null);
web/frontend/src/job/Metric.svelte:  const resampleConfig = getContext("resampling") || null;
web/frontend/src/generic/JobList.svelte:  const resampleConfig = $derived(initialized ? getContext("resampling") : null);
web/frontend/src/generic/plots/MetricPlot.svelte:  const resampleConfig = getContext("resampling");


Bash

cd /Users/jan/prg/ClusterCockpit/cc-backend && grep -r "setContext.*resampling" web/frontend/src/ --include="*.svelte" -B 2 -A 2 | head -30


(Bash completed with no output)


cd /Users/jan/prg/ClusterCockpit/cc-backend && grep -r "\.resampling\|'resampling'" web/templates/ --include="*.tmpl" | head -10


cd /Users/jan/prg/ClusterCockpit/cc-backend && grep -r "Resampling" web/templates/ --include="*.tmpl" | head -10


web/templates/monitoring/job.tmpl:        const resampleConfig = {{ .Resampling }};
web/templates/monitoring/user.tmpl:        const resampleConfig = {{ .Resampling }};
web/templates/monitoring/systems.tmpl:        const resampleConfig = {{ .Resampling }};
web/templates/monitoring/jobs.tmpl:        const resampleConfig = {{ .Resampling }};
web/templates/config.tmpl:        const resampleConfig = {{ .Resampling }};


1→{{define "content"}}
     2→

     3→{{end}}
     4→
     5→{{define "stylesheets"}}
     6→    <link rel='stylesheet' href='/build/job.css'>
     7→{{end}}
     8→{{define "javascript"}}
     9→
    20→
    21→{{end}}
    22→


cd /Users/jan/prg/ClusterCockpit/cc-backend && find . -path "*/frontend/src/*.svelte" -o -path "*/frontend/src/job/*.svelte" | grep -E "job\.root|Job\.root|+page" | head -5


grep: repetition-operator operand invalid


cd /Users/jan/prg/ClusterCockpit/cc-backend && ls -la web/frontend/src/*.root.svelte


.rw-r--r--@  19k jan 20 Mar 05:39 web/frontend/src/Analysis.root.svelte
.rw-r--r--@ 1.5k jan  6 Mar 11:11 web/frontend/src/Config.root.svelte
.rw-r--r--@  25k jan 20 Mar 05:39 web/frontend/src/DashPublic.root.svelte
.rw-r--r--@  16k jan 18 Mar 16:13 web/frontend/src/Job.root.svelte
.rw-r--r--@ 7.6k jan 18 Mar 16:13 web/frontend/src/Jobs.root.svelte
.rw-r--r--@  13k jan 18 Mar 16:13 web/frontend/src/List.root.svelte
.rw-r--r--@ 6.8k jan  6 Mar 11:11 web/frontend/src/Logs.root.svelte
.rw-r--r--@  10k jan 20 Mar 05:39 web/frontend/src/Node.root.svelte
.rw-r--r--@  879 jan  6 Mar 11:11 web/frontend/src/Status.root.svelte
.rw-r--r--@ 9.3k jan 20 Mar 05:39 web/frontend/src/Systems.root.svelte
.rw-r--r--@ 3.7k jan  6 Mar 11:11 web/frontend/src/Tags.root.svelte
.rw-r--r--@  16k jan 18 Mar 16:13 web/frontend/src/User.root.svelte


cd /Users/jan/prg/ClusterCockpit/cc-backend && grep -n "setContext.*resampling\|resampleConfig" web/frontend/src/Job.root.svelte


     1→<!--
     2→  @component Main single job display component; displays plots for every metric as well as various information
     3→
     4→  Properties:
     5→  - `dbid Number`: The jobs DB ID
     6→  - `username String`: Empty string if auth. is disabled, otherwise the username as string
     7→  - `authlevel Number`: The current users authentication level
     8→  - `roles [Number]`: Enum containing available roles
     9→-->
    10→
    11→<script>
    12→  import { getContext } from "svelte";
    13→  import {
    14→    queryStore,
    15→    gql,
    16→    getContextClient,
    17→  } from "@urql/svelte";
    18→  import {
    19→    Row,
    20→    Col,
    21→    Card,
    22→    Spinner,
    23→    TabContent,
    24→    TabPane,
    25→    CardBody,
    26→    CardHeader,
    27→    CardTitle,
    28→    Button,
    29→  } from "@sveltestrap/sveltestrap";
    30→  import {
    31→    init,
    32→    groupByScope,
    33→    checkMetricAvailability,
    34→  } from "./generic/utils.js";
    35→  import Metric from "./job/Metric.svelte";
    36→  import MetricSelection from "./generic/select/MetricSelection.svelte";
    37→  import JobInfo from "./generic/joblist/JobInfo.svelte";
    38→  import ConcurrentJobs from "./generic/helper/ConcurrentJobs.svelte";
    39→  import JobSummary from "./job/JobSummary.svelte";
    40→  import JobRoofline from "./job/JobRoofline.svelte";
    41→  import EnergySummary from "./job/EnergySummary.svelte";
    42→  import PlotGrid from "./generic/PlotGrid.svelte";
    43→  import StatsTab from "./job/StatsTab.svelte";
    44→
    45→  /* Svelte 5 Props */
    46→  let {
    47→    dbid,
    48→    username,
    49→    authlevel,
    50→    roles
    51→  } = $props();
    52→
    53→  /* Const Init */
    54→  // Important: init() needs to be first const declaration or contextclient will not be initialized before "const client = ..."
    55→  // svelte-ignore state_referenced_locally
    56→  const { query: initq } = init(`
    57→    job(id: "${dbid}") {
    58→      id, jobId, user, project, cluster, startTime,
    59→      duration, numNodes, numHWThreads, numAcc, energy,
    60→      SMT, shared, partition, subCluster, arrayJobId,
    61→      monitoringStatus, state, walltime,
    62→      tags { id, type, scope, name },
    63→      resources { hostname, hwthreads, accelerators },
    64→      metaData,
    65→      userData { name, email },
    66→      concurrentJobs { items { id, jobId }, count, listQuery },
    67→      footprint { name, stat, value },
    68→      energyFootprint { hardware, metric, value }
    69→    }
    70→  `);
    71→  /* Note: Actual metric data queried in <Metric> Component, only require base infos here -> reduce backend load by requesting just stats */
    72→  const client = getContextClient();
    73→  const query = gql`
    74→    query ($dbid: ID!, $selectedMetrics: [String!]!, $selectedScopes: [MetricScope!]!) {
    75→      scopedJobStats(id: $dbid, metrics: $selectedMetrics, scopes: $selectedScopes) {
    76→        name
    77→        scope
    78→        stats {
    79→          hostname
    80→        }
    81→      }
    82→    }
    83→  `;
    84→
    85→  /* State Init */
    86→  let plots = $state({});
    87→  let isMetricsSelectionOpen = $state(false);
    88→  let totalMetrics = $state(0);
    89→
    90→  /* Derived Init Return */
    91→  const thisJob = $derived($initq?.data ? $initq.data.job : null);
    92→
    93→  /* Derived Settings */
    94→  const globalMetrics = $derived(thisJob ? getContext("globalMetrics") : null);
    95→  const clusterInfo = $derived(thisJob ? getContext("clusters") : null);
    96→  const ccconfig = $derived(thisJob ? getContext("cc-config") : null);
    97→  const showRoofline = $derived(ccconfig ? !!ccconfig[`jobView_showRoofline`] : false);
    98→  const showStatsTable = $derived(ccconfig ? !!ccconfig[`jobView_showStatTable`] : false);
    99→  const showSummary = $derived(ccconfig ? (!!ccconfig[`jobView_showFootprint`] || !!ccconfig[`jobView_showPolarPlot`]) : false)
   100→


cd /Users/jan/prg/ClusterCockpit/cc-backend && grep -n "setContext\|Resampling" web/frontend/src/generic/utils.js | head -20


4:    setContextClient,
7:import { setContext, getContext, hasContext, onDestroy, tick } from "svelte";
41:    setContextClient(client);
109:    setContext("tags", tags);
110:    setContext("clusters", clusters);
111:    setContext("globalMetrics", globalMetrics);
112:    setContext("getMetricConfig", (cluster, subCluster, metric) => {
121:    setContext("getHardwareTopology", (cluster, subCluster) => {
130:    setContext("on-init", (callback) =>
133:    setContext(


     1→import { expiringCacheExchange } from "./cache-exchange.js";
     2→import {
     3→    Client,
     4→    setContextClient,
     5→    fetchExchange,
     6→} from "@urql/svelte";
     7→import { setContext, getContext, hasContext, onDestroy, tick } from "svelte";
     8→import { readable } from "svelte/store";
     9→import { round } from "mathjs";
    10→
    11→/*
    12→ * Call this function only at component initialization time!
    13→ *
    14→ * It does several things:
    15→ * - Initialize the GraphQL client
    16→ * - Creates a readable store 'initialization' which indicates when the values below can be used.
    17→ * - Adds 'tags' to the context (list of all tags)
    18→ * - Adds 'clusters' to the context (object with cluster names as keys)
    19→ * - Adds 'globalMetrics' to the context (list of globally available metric infos)
    20→ * - Adds 'getMetricConfig' to the context, a function that takes a cluster, subCluster and metric name and returns the MetricConfig (or undefined)
    21→ * - Adds 'getHardwareTopology' to the context, a function that takes a cluster nad subCluster and returns the subCluster topology (or undefined)
    22→ */
    23→export function init(extraInitQuery = "") {
    24→    const jwt = hasContext("jwt")
    25→        ? getContext("jwt")
    26→        : getContext("cc-config")["jwt"];
    27→
    28→    const client = new Client({
    29→        url: `${window.location.origin}/query`,
    30→        fetchOptions:
    31→            jwt != null ? { headers: { Authorization: `Bearer ${jwt}` } } : {},
    32→        exchanges: [
    33→            expiringCacheExchange({
    34→                ttl: 5 * 60 * 1000,
    35→                maxSize: 150,
    36→            }),
    37→            fetchExchange,
    38→        ],
    39→    });
    40→
    41→    setContextClient(client);
    42→
    43→    const query = client
    44→        .query(
    45→            `query {
    46→        clusters {
    47→            name
    48→            partitions
    49→            subClusters {
    50→                name
    51→                nodes
    52→                numberOfNodes
    53→                processorType
    54→                socketsPerNode
    55→                coresPerSocket
    56→                threadsPerCore
    57→                flopRateScalar { unit { base, prefix }, value }
    58→                flopRateSimd { unit { base, prefix }, value }
    59→                memoryBandwidth { unit { base, prefix }, value }
    60→                topology {
    61→                    node
    62→                    socket
    63→                    memoryDomain
    64→                    core
    65→                    accelerators { id }
    66→                }
    67→                metricConfig {
    68→                    name
    69→                    unit { base, prefix }
    70→                    scope
    71→                    aggregation
    72→                    timestep
    73→                    peak
    74→                    normal
    75→                    caution
    76→                    alert
    77→                    lowerIsBetter
    78→                }
    79→                footprint
    80→            }
    81→        }
    82→        tags { id, name, type, scope }
    83→        globalMetrics {
    84→            name
    85→            scope
    86→            footprint
    87→            unit { base, prefix }
    88→            availability { cluster, subClusters }
    89→        }
    90→        ${extraInitQuery}
    91→    }`
    92→        )
    93→        .toPromise();
    94→
    95→    let state = { fetching: true, error: null, data: null };
    96→    let subscribers = [];
    97→    const subscribe = (callback) => {
    98→        callback(state);
    99→        subscribers.push(callback);
   100→        return () => {
   101→            subscribers = subscribers.filter((cb) => cb != callback);
   102→        };
   103→    };
   104→
   105→    const tags = []
   106→    const clusters = []
   107→    const globalMetrics = []
   108→
   109→    setContext("tags", tags);
   110→    setContext("clusters", clusters);
   111→    setContext("globalMetrics", globalMetrics);
   112→    setContext("getMetricConfig", (cluster, subCluster, metric) => {
   113→        // Load objects if input is string
   114→        if (typeof cluster !== "object")
   115→            cluster = clusters.find((c) => c.name == cluster);
   116→        if (typeof subCluster !== "object")
   117→            subCluster = cluster.subClusters.find((sc) => sc.name == subCluster);
   118→
   119→        return subCluster.metricConfig.find((m) => m.name == metric);
   120→    });
   121→    setContext("getHardwareTopology", (cluster, subCluster) => {
   122→        // Load objects if input is string
   123→        if (typeof cluster !== "object")
   124→            cluster = clusters.find((c) => c.name == cluster);
   125→        if (typeof subCluster !== "object")
   126→            subCluster = cluster.subClusters.find((sc) => sc.name == subCluster);
   127→
   128→        return subCluster?.topology;
   129→    });
   130→    setContext("on-init", (callback) =>
   131→        state.fetching ? subscribers.push(callback) : callback(state)
   132→    );
   133→    setContext(
   134→        "initialized",
   135→        readable(false, (set) => subscribers.push(() => set(true)))
   136→    );
   137→
   138→    query.then(({ error, data }) => {
   139→        state.fetching = false;
   140→        if (error != null) {
   141→            console.error(error);
   142→            state.error = error;
   143→            tick().then(() => subscribers.forEach((cb) => cb(state)));
   144→            return;
   145→        }
   146→
   147→        for (let tag of data.tags) tags.push(tag);
   148→        for (let cluster of data.clusters) clusters.push(cluster);
   149→        for (let gm of data.globalMetrics) globalMetrics.push(gm);
   150→
   151→        // Unified Sort
   152→        globalMetrics.sort((a, b) => a.name.localeCompare(b.name))
   153→
   154→        state.data = data;
   155→        tick().then(() => subscribers.forEach((cb) => cb(state)));
   156→    });
   157→
   158→    return {
   159→        query: { subscribe },
   160→        tags,
   161→        clusters,
   162→        globalMetrics
   163→    };
   164→}
   165→
   166→// Use https://developer.mozilla.org/en-US/docs/Web/API/structuredClone instead?
   167→export function deepCopy(x) {
   168→    return JSON.parse(JSON.stringify(x));
   169→}
   170→
   171→function fuzzyMatch(term, string) {
   172→    return string.toLowerCase().includes(term);
   173→}
   174→
   175→// Use in filter() function to return only unique values
   176→export function distinct(value, index, array) {
   177→    return array.indexOf(value) === index;
   178→}
   179→
   180→// Load Local Bool and Handle Scrambling of input string
   181→export const scrambleNames = window.localStorage.getItem("cc-scramble-names");
   182→export const scramble = function (str) {
   183→  if (str === "-") return str;
   184→  else
   185→    return [...str]
   186→      .reduce((x, c, i) => x * 7 + c.charCodeAt(0) * i * 21, 5)
   187→      .toString(32)
   188→      .substr(0, 6);
   189→};
   190→
   191→export function fuzzySearchTags(term, tags) {
   192→    if (!tags) return [];
   193→
   194→    let results = [];
   195→    let termparts = term
   196→        .split(":")
   197→        .map((s) => s.trim())
   198→        .filter((s) => s.length > 0);
   199→
   200→    if (termparts.length == 0) {


cd /Users/jan/prg/ClusterCockpit/cc-backend && grep -r "getContext.*resampling" web/frontend/src/job/Metric.svelte | head -5


web/frontend/src/job/Metric.svelte:  const resampleConfig = getContext("resampling") || null;


     1→<!--
     2→  @component Metric plot wrapper with user scope selection; used in job detail view
     3→
     4→  Properties:
     5→    - `job Object`: The GQL job object
     6→    - `metricName String`: The metrics name
     7→    - `metricUnit Object`: The metrics GQL unit object
     8→    - `nativeScope String`: The metrics native scope
     9→    - `presetScopes [String]`: The preset scopes returned for this metric
    10→    - `isShared Bool?`: If this job used shared resources; will adapt threshold indicators accordingly in downstream plots [Default: false]
    11→-->
    12→
    13→<script>
    14→  import {
    15→    getContext,
    16→  } from "svelte";
    17→  import {
    18→    queryStore,
    19→    gql,
    20→    getContextClient
    21→  } from "@urql/svelte";
    22→  import {
    23→    InputGroup,
    24→    InputGroupText,
    25→    Spinner,
    26→    Card,
    27→  } from "@sveltestrap/sveltestrap";
    28→  import {
    29→    minScope,
    30→  } from "../generic/utils.js";
    31→  import MetricPlot from "../generic/plots/MetricPlot.svelte";
    32→
    33→  /* Svelte 5 Props */
    34→  let {
    35→    job,
    36→    metricName,
    37→    metricUnit,
    38→    nativeScope,
    39→    presetScopes,
    40→    isShared = false,
    41→  } = $props();
    42→
    43→  /* Const Init */
    44→  const client = getContextClient();
    45→  const statsPattern = /(.*)-stat$/;
    46→  const resampleConfig = getContext("resampling") || null;
    47→  const resampleDefault = resampleConfig ? Math.max(...resampleConfig.resolutions) : 0;
    48→  const subQuery = gql`
    49→    query ($dbid: ID!, $selectedMetrics: [String!]!, $selectedScopes: [MetricScope!]!, $selectedResolution: Int) {
    50→      singleUpdate: jobMetrics(id: $dbid, metrics: $selectedMetrics, scopes: $selectedScopes, resolution: $selectedResolution) {
    51→        name
    52→        scope
    53→        metric {
    54→          unit {
    55→            prefix
    56→            base
    57→          }
    58→          timestep
    59→          statisticsSeries {
    60→            min
    61→            mean
    62→            median
    63→            max
    64→          }
    65→          series {
    66→            hostname
    67→            id
    68→            data
    69→            statistics {
    70→              min
    71→              avg
    72→              max
    73→            }
    74→          }
    75→        }
    76→      }
    77→    }
    78→  `;
    79→
    80→  /* State Init */
    81→  let selectedResolution = $state(resampleDefault);
    82→  let selectedHost = $state(null);
    83→  let zoomState = $state(null);
    84→  let thresholdState = $state(null);
    85→
    86→  /* Derived */
    87→  let requestedScopes = $derived(presetScopes);
    88→  const unit = $derived.by(() => { return (metricUnit?.prefix ? metricUnit.prefix : "") + (metricUnit?.base ? metricUnit.base : "")});
    89→  const metricData = $derived(queryStore({
    90→      client: client,
    91→      query: subQuery,
    92→      variables: {
    93→        dbid: job.id,
    94→        selectedMetrics: [metricName],
    95→        selectedScopes: [...requestedScopes],
    96→        selectedResolution
    97→      },
    98→      // Never user network-only: causes reactive load-loop!
    99→    })
   100→  );


cd /Users/jan/prg/ClusterCockpit/cc-backend && ls -la web/templates/base.tmpl


.rw-r--r--@ 2.8k jan  6 Mar 11:11 web/templates/base.tmpl


1→<!DOCTYPE html>
     2→
     3→
     4→        <meta http-equiv="content-type" content="text/html; charset=utf-8" />
     5→        <meta name='viewport' content='width=device-width,initial-scale=1'>
     6→        <title>{{.Title}}</title>
     7→
     8→        <link rel='icon' type='image/png' href='/favicon.png'>
     9→        <link rel="stylesheet" href="/bootstrap.min.css">
    10→        <link rel="stylesheet" href="/bootstrap-icons.css">
    11→        <link rel='stylesheet' href='/global.css'>
    12→        <link rel='stylesheet' href='/uPlot.min.css'>
    13→
    14→        {{block "stylesheets" .}}
    15→        {{end}}
    16→
    24→
    25→
    26→        {{if eq .Infos.displayType "PUBLIC"}}
    27→
    28→

    29→                    {{block "content-public" .}}
    30→                        Whoops, you should not see this... [PUBLIC]
    31→                    {{end}}
    32→

    33→
    34→
    35→            {{block "javascript-public" .}}
    36→                Whoops, you should not see this... [JS PUBLIC]
    37→            {{end}}
    38→
    39→        {{else}}
    40→            {{block "navigation" .}}
    41→
    42→            {{end}}
    43→
    44→
    45→

    46→                    {{block "content" .}}
    47→                        Whoops, you should not see this... [MAIN]
    48→                    {{end}}
    49→

    50→
    51→
    52→            {{block "footer" .}}
    53→
    54→

    55→
Imprint

    56→
Privacy Policy

    57→

    58→

    59→
Version {{ .Build.Version }}

    60→
Hash {{ .Build.Hash }}

    61→
Built {{ .Build.Buildtime }}

    62→

    63→
    64→            {{end}}
    65→
    66→            {{block "javascript" .}}
    67→
    68→            {{end}}
    69→        {{end}}
    70→
    71→
    72→


Perfect! Now I have all the information I need. Let me provide a comprehensive summary of the policy-based resampling approach.

## Comprehensive Exploration: Policy-Based Resampling Approach

I've traced through the complete flow of the policy-based resampling system. Here's what I found:

### 1. Configuration Structure (config.json → Go Code)

**File: `/Users/jan/prg/ClusterCockpit/cc-backend/internal/config/config.go`**

The `ResampleConfig` struct now includes a new field:
- `MinimumPoints`: int (minimum points before resampling triggers)
- `Resolutions`: []int (array of target resolutions in seconds, e.g., [600, 300, 60])
- `Trigger`: int (threshold for switching between resolution levels)
- **`TargetPoints`: int (NEW - policy-derived target point count, set dynamically, NOT from config.json)**

The config.json mapping example from `configs/config.json`:
```json
"resampling": {
  "minimum-points": 600,
  "trigger": 180,
  "resolutions": [240, 60]
}
```

### 2. Resample Policy System

**Files: `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricdispatch/resamplepolicy.go` and `resamplepolicy_test.go`**

Defines three policies with target point counts:
- `ResamplePolicyLow` = 200 points
- `ResamplePolicyMedium` = 500 points
- `ResamplePolicyHigh` = 1000 points

Key function `ComputeResolution(duration, frequency, targetPoints)`:
- Takes job/time-window duration, metric frequency (timestep in seconds), and target points
- Returns optimal resolution in seconds that achieves the target point count
- Formula: `resolution = ceil(duration / targetPoints) / frequency * frequency`
- Returns 0 if data already at or below target points

Test cases show examples:
- 24h job @ 60s frequency, low policy (200 pts) → 480s resolution
- 24h job @ 60s frequency, medium policy (500 pts) → 180s resolution
- 24h job @ 60s frequency, high policy (1000 pts) → 120s resolution

### 3. Backend: Resolution Resolution (GraphQL Resolvers)

**File: `/Users/jan/prg/ClusterCockpit/cc-backend/internal/graph/resample.go`**

Two key functions:

**`resolveResolutionFromPolicy(ctx, duration, cluster, metrics)`:**
- Checks if user has a resample policy set in UI config (`plotConfiguration_resamplePolicy`)
- Converts policy string ("low", "medium", "high") to ResamplePolicy enum
- Gets target points for that policy
- Finds smallest metric frequency across requested metrics
- Calls `ComputeResolution()` to calculate actual resolution
- Returns computed resolution, or nil if no policy set

**`resolveResampleAlgo(ctx, resampleAlgo)`:**
- Checks explicit GraphQL parameter first
- Falls back to user's `plotConfiguration_resampleAlgo` preference
- Returns algorithm name ("lttb", "average", "simple") or empty string

### 4. Backend: Metric Query Resolution (GraphQL Schema)

**File: `/Users/jan/prg/ClusterCockpit/cc-backend/api/schema.graphqls`**

New enum added to schema:
```graphql
enum ResampleAlgo {
  LTTB
  AVERAGE
  SIMPLE
}
```

Two queries accept resolution and resampling parameters:
- `jobMetrics(id, metrics, scopes, resolution, resampleAlgo): [JobMetricWithName!]!`
- `nodeMetricsList(..., resolution, resampleAlgo): NodesResultList!`

### 5. Backend: GraphQL Resolver Implementation

**File: `/Users/jan/prg/ClusterCockpit/cc-backend/internal/graph/schema.resolvers.go`**

**`JobMetrics()` resolver (line 501):**
```
Resolution priority: explicit param > user policy > global config > 0
```
- If `resolution == nil`, calls `resolveResolutionFromPolicy(ctx, job.Duration, job.Cluster, metrics)`
- If still nil, falls back to max resolution from global config
- Resolves resample algorithm via `resolveResampleAlgo(ctx, resampleAlgo)`
- Passes both to `metricdispatch.LoadData(job, metrics, scopes, ctx, resolution, algoName)`

**`NodeMetricsList()` resolver (line 881):**
- Similar flow but computes duration from time range: `duration = to.Sub(from).Seconds()`
- Same resolution resolution priority chain

### 6. Backend: Routing & Page Context

**File: `/Users/jan/prg/ClusterCockpit/cc-backend/internal/routerConfig/routes.go`**

**Key function `resamplingForUser(conf)`:**
- Takes user's UI config map
- Checks for `plotConfiguration_resamplePolicy` key
- If present and valid, extracts policy string
- Creates policy-derived `ResampleConfig`:
  - `TargetPoints`: from `TargetPointsForPolicy(policy)`
  - `Trigger`: `targetPoints / 4`
  - **NO** `Resolutions` array (policy-based uses dynamic computation)
- Falls back to global `config.Keys.EnableResampling` if no policy set

This modified config is passed to frontend via `web.Page.Resampling`.

### 7. Web Configuration

**File: `/Users/jan/prg/ClusterCockpit/cc-backend/web/web.go`**

New fields added to `PlotConfiguration` struct:
```go
ResampleAlgo    string   `json:"resample-algo"`
ResamplePolicy  string   `json:"resample-policy"`
```

These map to UI defaults map:
```go
UIDefaultsMap["plotConfiguration_resampleAlgo"] = UIDefaults.PlotConfiguration.ResampleAlgo
UIDefaultsMap["plotConfiguration_resamplePolicy"] = UIDefaults.PlotConfiguration.ResamplePolicy
```

### 8. Frontend: HTML Templates

**File: `/Users/jan/prg/ClusterCockpit/cc-backend/web/templates/monitoring/job.tmpl`** (and similar files)

Resampling config injected as JavaScript global:
```javascript
const resampleConfig = {{ .Resampling }};
```

This makes it available to Svelte components via context retrieval.

### 9. Frontend: Context Setup

**File: `/Users/jan/prg/ClusterCockpit/cc-backend/web/frontend/src/generic/utils.js`**

The `init()` function does NOT set up "resampling" context. Instead, the resampling config is available globally as a JavaScript variable from the template.

### 10. Frontend: Component Usage (Svelte)

**File: `/Users/jan/prg/ClusterCockpit/cc-backend/web/frontend/src/generic/plots/MetricPlot.svelte`**

Components retrieve it via:
```javascript
const resampleConfig = getContext("resampling");
```

However, looking at the template injection, it seems the context must be set up by the root component. Key derived values:

```javascript
const resampleTrigger = resampleConfig?.trigger ? Number(resampleConfig.trigger) : (resampleConfig?.targetPoints ? Math.floor(resampleConfig.targetPoints / 4) : null);
const resampleResolutions = resampleConfig?.resolutions ? [...resampleConfig.resolutions] : null;
const resampleMinimum = resampleConfig?.resolutions ? Math.min(...resampleConfig.resolutions) : null;
const resampleTargetPoints = resampleConfig?.targetPoints ? Number(resampleConfig.targetPoints) : null;
```

**Zoom handling logic (line 515):**
- If policy-based (`resampleTargetPoints && !resampleResolutions`):
  - Computes resolution dynamically: `visibleDuration / resampleTargetPoints / nativeTimestep`
  - Rounds up to nearest multiple of native timestep

- If array-based (`resampleResolutions`):
  - Finds closest pre-configured resolution

### 11. Frontend: UI Configuration

**File: `/Users/jan/prg/ClusterCockpit/cc-backend/web/frontend/src/config/user/PlotRenderOptions.svelte`**

Provides two user-facing controls:

**Resample Policy** (lines 226-267):
- Radio buttons for: "", "low", "medium", "high"
- Default = empty string
- Posts to `/frontend/configuration/` with key `plotConfiguration_resamplePolicy`
- Help text explains: "Controls how many data points are shown in metric plots. Low = fast overview (~200 points), Medium = balanced (~500), High = maximum detail (~1000)."

**Resample Algorithm** (lines 269-310):
- Radio buttons for: "", "lttb", "average", "simple"
- Posts to `/frontend/configuration/` with key `plotConfiguration_resampleAlgo`
- Help text: "Algorithm used when downsampling time-series data. LTTB preserves visual shape, Average smooths data, Simple picks every Nth point."

Both only shown if `resamplingEnabled` (which checks if resampleConfig is set).

### 12. Data Loading

**File: `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricdispatch/dataLoader.go`**

**`LoadData()` function (line 86):**
- Takes `resolution int` and `resampleAlgo string` parameters
- For archived data, uses `resampler.GetResampler(resampleAlgo)` to get the appropriate resampling function
- Applies resampling to each data series:
  ```
  resampleFn(data, timestep, resolution) → (resampled_data, new_timestep, error)
  ```

## Summary: The Complete Flow

```
config.json
  ↓ (parse via config.Init)
config.Keys.EnableResampling
  ↓ (in request handler)
resamplingForUser(userConfig)
  → checks plotConfiguration_resamplePolicy
  → creates policy-derived ResampleConfig with TargetPoints + Trigger
  ↓
web.Page.Resampling
  ↓ (template injection)
const resampleConfig = {{ .Resampling }}
  ↓
Frontend components via getContext("resampling")
  ↓ (on zoom in MetricPlot)
Computes new resolution dynamically:
  newRes = ceil(visibleDuration / targetPoints / nativeTimestep) * nativeTimestep
  ↓ (dispatches onZoom event)
GraphQL jobMetrics query with new resolution + resampleAlgo
  ↓
resolveResolutionFromPolicy() + resolveResampleAlgo()
  ↓
metricdispatch.LoadData(resolution, resampleAlgo)
  ↓
Applies resampler.GetResampler(algoName) to archived data
  ↓
Returns downsampled metric data
```

## Key Files

1. `/Users/jan/prg/ClusterCockpit/cc-backend/internal/config/config.go` - ResampleConfig struct
2. `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricdispatch/resamplepolicy.go` - Policy definitions and computation
3. `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricdispatch/resamplepolicy_test.go` - Test cases
4. `/Users/jan/prg/ClusterCockpit/cc-backend/internal/graph/resample.go` - Resolution resolution logic
5. `/Users/jan/prg/ClusterCockpit/cc-backend/internal/graph/schema.resolvers.go` - GraphQL resolver implementation
6. `/Users/jan/prg/ClusterCockpit/cc-backend/internal/routerConfig/routes.go` - resamplingForUser() function
7. `/Users/jan/prg/ClusterCockpit/cc-backend/api/schema.graphqls` - ResampleAlgo enum and query parameters
8. `/Users/jan/prg/ClusterCockpit/cc-backend/web/web.go` - Page struct and UI config
9. `/Users/jan/prg/ClusterCockpit/cc-backend/web/frontend/src/config/user/PlotRenderOptions.svelte` - User UI
10. `/Users/jan/prg/ClusterCockpit/cc-backend/web/frontend/src/generic/plots/MetricPlot.svelte` - Dynamic resolution computation
11. `/Users/jan/prg/ClusterCockpit/cc-backend/web/frontend/src/job/Metric.svelte` - Metric wrapper component
12. `/Users/jan/prg/ClusterCockpit/cc-backend/internal/metricdispatch/dataLoader.go` - Data loading and resampling

agentId: a94d541086ef2e733 (use SendMessage with to: 'a94d541086ef2e733' to continue this agent)
<usage>total_tokens: 77461
tool_uses: 41
duration_ms: 418529</usage>

Prompt:
Explore the policy-based resampling approach in this codebase. I need to understand:

1. Look at the recent git commits, especially "Replace explicit resampling config with policy based approach" (commit 0069c86e) and "Introduce average resampler support" (c0d2d65f). Run `git show 0069c86e` and `git show c0d2d65f` to see the full diffs.

2. Look at `internal/config/config.go` - specifically the `ResampleConfig` struct and how `EnableResampling` is used. How does the config.json `"resampling"` key map to the Go struct?

3. Look at `web/web.go` - how is the resampling config passed to the frontend? What does the `Resampling` field in page data look like?

4. Look at `internal/routerConfig/routes.go` - how is the resampling config used when rendering pages?

5. Look at the GraphQL schema (`api/schema.graphqls`) and resolvers (`internal/graph/schema.resolvers.go`) for any resolution/resample parameters in queries like `jobMetrics` or similar.

6. Look at how resolution is computed from policy - search for "policy" or "targetPoints" or "TargetPoints" in the codebase.

7. Check `web/frontend/src/config/user/PlotRenderOptions.svelte` for the resample policy and algorithm UI options.

Be very thorough - I need to understand the full flow from config.json -> Go config -> frontend context -> GraphQL query -> metric loading.


Now let me check the key files to understand the current config vs policy disconnect.


    60→	MachineStateDir string `json:"machine-state-dir"`
    61→
    62→	// If not zero, automatically mark jobs as stopped running X seconds longer than their walltime.
    63→	StopJobsExceedingWalltime int `json:"stop-jobs-exceeding-walltime"`
    64→
    65→	// Defines time X in seconds in which jobs are considered to be "short" and will be filtered in specific views.
    66→	ShortRunningJobsDuration int `json:"short-running-jobs-duration"`
    67→
    68→	// Energy Mix CO2 Emission Constant [g/kWh]
    69→	// If entered, displays estimated CO2 emission for job based on jobs totalEnergy
    70→	EmissionConstant int `json:"emission-constant"`
    71→
    72→	// If exists, will enable dynamic zoom in frontend metric plots using the configured values
    73→	EnableResampling *ResampleConfig `json:"resampling"`
    74→
    75→	// Systemd unit name for log viewer (default: "clustercockpit")
    76→	SystemdUnit string `json:"systemd-unit"`
    77→
    78→	// Node state retention configuration
    79→	NodeStateRetention *NodeStateRetention `json:"nodestate-retention"`
    80→
    81→	// Database tuning configuration
    82→	DbConfig *DbConfig `json:"db-config"`
    83→}
    84→
    85→type DbConfig struct {
    86→	CacheSizeMB               int `json:"cache-size-mb"`
    87→	SoftHeapLimitMB           int `json:"soft-heap-limit-mb"`
    88→	MaxOpenConnections        int `json:"max-open-connections"`
    89→	MaxIdleConnections        int `json:"max-idle-connections"`

	MachineStateDir string `json:"machine-state-dir"`

	// If not zero, automatically mark jobs as stopped running X seconds longer than their walltime.
	StopJobsExceedingWalltime int `json:"stop-jobs-exceeding-walltime"`

	// Defines time X in seconds in which jobs are considered to be "short" and will be filtered in specific views.
	ShortRunningJobsDuration int `json:"short-running-jobs-duration"`

	// Energy Mix CO2 Emission Constant [g/kWh]
	// If entered, displays estimated CO2 emission for job based on jobs totalEnergy
	EmissionConstant int `json:"emission-constant"`

	// If exists, will enable dynamic zoom in frontend metric plots using the configured values
	EnableResampling *ResampleConfig `json:"resampling"`

	// Systemd unit name for log viewer (default: "clustercockpit")
	SystemdUnit string `json:"systemd-unit"`

	// Node state retention configuration
	NodeStateRetention *NodeStateRetention `json:"nodestate-retention"`

	// Database tuning configuration
	DbConfig *DbConfig `json:"db-config"`
}

type DbConfig struct {
	CacheSizeMB               int `json:"cache-size-mb"`
	SoftHeapLimitMB           int `json:"soft-heap-limit-mb"`
	MaxOpenConnections        int `json:"max-open-connections"`
	MaxIdleConnections        int `json:"max-idle-connections"`


   490→
   491→			// Get User -> What if NIL?
   492→			user := repository.GetUserFromContext(r.Context())
   493→
   494→			// Get Roles
   495→			availableRoles, _ := schema.GetValidRolesMap(user)
   496→
   497→			resampling := resamplingForUser(conf)
   498→
   499→			page := web.Page{
   500→				Title:      title,
   501→				User:       *user,
   502→				Roles:      availableRoles,
   503→				Build:      buildInfo,
   504→				Config:     conf,
   505→				Resampling: resampling,
   506→				Infos:      infos,
   507→			}
   508→
   509→			if route.Filter {
   510→				page.FilterPresets = buildFilterPresets(r.URL.Query())
   511→			}
   512→
   513→			web.RenderTemplate(rw, route.Template, &page)
   514→		})
   515→	}
   516→}
   517→
   518→func HandleSearchBar(rw http.ResponseWriter, r *http.Request, buildInfo web.Build) {
   519→	user := repository.GetUserFromContext(r.Context())
   520→	availableRoles, _ := schema.GetValidRolesMap(user)
   521→
   522→	if search := r.URL.Query().Get("searchId"); search != "" {
   523→		repo := repository.GetJobRepository()
   524→		splitSearch := strings.Split(search, ":")
   525→
   526→		if len(splitSearch) == 2 {
   527→			switch strings.Trim(splitSearch[0], " ") {
   528→			case "jobId":
   529→				http.Redirect(rw, r, "/monitoring/jobs/?jobId="+url.QueryEscape(strings.Trim(splitSearch[1], " ")), http.StatusFound) // All Users: Redirect to Tablequery
   530→			case "jobName":
   531→				// Add Last 30 Days to migitate timeouts
   532→				untilTime := strconv.FormatInt(time.Now().Unix(), 10)
   533→				fromTime := strconv.FormatInt((time.Now().Unix() - int64(30*24*3600)), 10)
   534→
   535→				http.Redirect(rw, r, "/monitoring/jobs/?startTime="+fromTime+"-"+untilTime+"&jobName="+url.QueryEscape(strings.Trim(splitSearch[1], " ")), http.StatusFound) // All Users: Redirect to Tablequery
   536→			case "projectId":
   537→				http.Redirect(rw, r, "/monitoring/jobs/?projectMatch=eq&project="+url.QueryEscape(strings.Trim(splitSearch[1], " ")), http.StatusFound) // All Users: Redirect to Tablequery
   538→			case "arrayJobId":
   539→				// Add Last 30 Days to migitate timeouts

			// Get User -> What if NIL?
			user := repository.GetUserFromContext(r.Context())

			// Get Roles
			availableRoles, _ := schema.GetValidRolesMap(user)

			resampling := resamplingForUser(conf)

			page := web.Page{
				Title:      title,
				User:       *user,
				Roles:      availableRoles,
				Build:      buildInfo,
				Config:     conf,
				Resampling: resampling,
				Infos:      infos,
			}

			if route.Filter {
				page.FilterPresets = buildFilterPresets(r.URL.Query())
			}

			web.RenderTemplate(rw, route.Template, &page)
		})
	}
}

func HandleSearchBar(rw http.ResponseWriter, r *http.Request, buildInfo web.Build) {
	user := repository.GetUserFromContext(r.Context())
	availableRoles, _ := schema.GetValidRolesMap(user)

	if search := r.URL.Query().Get("searchId"); search != "" {
		repo := repository.GetJobRepository()
		splitSearch := strings.Split(search, ":")

		if len(splitSearch) == 2 {
			switch strings.Trim(splitSearch[0], " ") {
			case "jobId":
				http.Redirect(rw, r, "/monitoring/jobs/?jobId="+url.QueryEscape(strings.Trim(splitSearch[1], " ")), http.StatusFound) // All Users: Redirect to Tablequery
			case "jobName":
				// Add Last 30 Days to migitate timeouts
				untilTime := strconv.FormatInt(time.Now().Unix(), 10)
				fromTime := strconv.FormatInt((time.Now().Unix() - int64(30*24*3600)), 10)

				http.Redirect(rw, r, "/monitoring/jobs/?startTime="+fromTime+"-"+untilTime+"&jobName="+url.QueryEscape(strings.Trim(splitSearch[1], " ")), http.StatusFound) // All Users: Redirect to Tablequery
			case "projectId":
				http.Redirect(rw, r, "/monitoring/jobs/?projectMatch=eq&project="+url.QueryEscape(strings.Trim(splitSearch[1], " ")), http.StatusFound) // All Users: Redirect to Tablequery
			case "arrayJobId":
				// Add Last 30 Days to migitate timeouts


     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.
     2→// All rights reserved. This file is part of cc-backend.
     3→// Use of this source code is governed by a MIT-style
     4→// license that can be found in the LICENSE file.
     5→package graph
     6→
     7→import (
     8→	"context"
     9→	"strings"
    10→
    11→	"github.com/ClusterCockpit/cc-backend/internal/graph/model"
    12→	"github.com/ClusterCockpit/cc-backend/internal/metricdispatch"
    13→	"github.com/ClusterCockpit/cc-backend/internal/repository"
    14→	"github.com/ClusterCockpit/cc-backend/pkg/archive"
    15→)
    16→
    17→// resolveResolutionFromPolicy reads the user's resample policy preference and
    18→// computes a resolution based on job duration and metric frequency. Returns nil
    19→// if the user has no policy set.
    20→func resolveResolutionFromPolicy(ctx context.Context, duration int64, cluster string, metrics []string) *int {
    21→	user := repository.GetUserFromContext(ctx)
    22→	if user == nil {
    23→		return nil
    24→	}
    25→
    26→	conf, err := repository.GetUserCfgRepo().GetUIConfig(user)
    27→	if err != nil {
    28→		return nil
    29→	}
    30→
    31→	policyVal, ok := conf["plotConfiguration_resamplePolicy"]
    32→	if !ok {
    33→		return nil
    34→	}
    35→	policyStr, ok := policyVal.(string)
    36→	if !ok || policyStr == "" {
    37→		return nil
    38→	}
    39→
    40→	policy := metricdispatch.ResamplePolicy(policyStr)
    41→	targetPoints := metricdispatch.TargetPointsForPolicy(policy)
    42→	if targetPoints == 0 {
    43→		return nil
    44→	}
    45→
    46→	// Find the smallest metric frequency across the requested metrics
    47→	frequency := smallestFrequency(cluster, metrics)
    48→	if frequency <= 0 {
    49→		return nil
    50→	}
    51→
    52→	res := metricdispatch.ComputeResolution(duration, int64(frequency), targetPoints)
    53→	return &res
    54→}
    55→
    56→// resolveResampleAlgo returns the resampling algorithm name to use, checking
    57→// the explicit GraphQL parameter first, then the user's preference.
    58→func resolveResampleAlgo(ctx context.Context, resampleAlgo *model.ResampleAlgo) string {
    59→	if resampleAlgo != nil {
    60→		return strings.ToLower(resampleAlgo.String())
    61→	}
    62→
    63→	user := repository.GetUserFromContext(ctx)
    64→	if user == nil {
    65→		return ""
    66→	}
    67→
    68→	conf, err := repository.GetUserCfgRepo().GetUIConfig(user)
    69→	if err != nil {
    70→		return ""
    71→	}
    72→
    73→	algoVal, ok := conf["plotConfiguration_resampleAlgo"]
    74→	if !ok {
    75→		return ""
    76→	}
    77→	algoStr, ok := algoVal.(string)
    78→	if !ok {
    79→		return ""
    80→	}
    81→
    82→	return algoStr
    83→}
    84→
    85→// smallestFrequency returns the smallest metric timestep (in seconds) among the
    86→// requested metrics for the given cluster. Falls back to 0 if nothing is found.
    87→func smallestFrequency(cluster string, metrics []string) int {
    88→	cl := archive.GetCluster(cluster)
    89→	if cl == nil {
    90→		return 0
    91→	}
    92→
    93→	minFreq := 0
    94→	for _, mc := range cl.MetricConfig {
    95→		if len(metrics) > 0 {
    96→			found := false
    97→			for _, m := range metrics {
    98→				if mc.Name == m {
    99→					found = true
   100→					break
   101→				}
   102→			}
   103→			if !found {
   104→				continue
   105→			}
   106→		}
   107→		if minFreq == 0 || mc.Timestep < minFreq {
   108→			minFreq = mc.Timestep
   109→		}
   110→	}
   111→
   112→	return minFreq
   113→}
   114→

// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.
// All rights reserved. This file is part of cc-backend.
// Use of this source code is governed by a MIT-style
// license that can be found in the LICENSE file.
package graph

import (
	"context"
	"strings"

	"github.com/ClusterCockpit/cc-backend/internal/graph/model"
	"github.com/ClusterCockpit/cc-backend/internal/metricdispatch"
	"github.com/ClusterCockpit/cc-backend/internal/repository"
	"github.com/ClusterCockpit/cc-backend/pkg/archive"
)

// resolveResolutionFromPolicy reads the user's resample policy preference and
// computes a resolution based on job duration and metric frequency. Returns nil
// if the user has no policy set.
func resolveResolutionFromPolicy(ctx context.Context, duration int64, cluster string, metrics []string) *int {
	user := repository.GetUserFromContext(ctx)
	if user == nil {
		return nil
	}

	conf, err := repository.GetUserCfgRepo().GetUIConfig(user)
	if err != nil {
		return nil
	}

	policyVal, ok := conf["plotConfiguration_resamplePolicy"]
	if !ok {
		return nil
	}
	policyStr, ok := policyVal.(string)
	if !ok || policyStr == "" {
		return nil
	}

	policy := metricdispatch.ResamplePolicy(policyStr)
	targetPoints := metricdispatch.TargetPointsForPolicy(policy)
	if targetPoints == 0 {
		return nil
	}

	// Find the smallest metric frequency across the requested metrics
	frequency := smallestFrequency(cluster, metrics)
	if frequency <= 0 {
		return nil
	}

	res := metricdispatch.ComputeResolution(duration, int64(frequency), targetPoints)
	return &res
}

// resolveResampleAlgo returns the resampling algorithm name to use, checking
// the explicit GraphQL parameter first, then the user's preference.
func resolveResampleAlgo(ctx context.Context, resampleAlgo *model.ResampleAlgo) string {
	if resampleAlgo != nil {
		return strings.ToLower(resampleAlgo.String())
	}

	user := repository.GetUserFromContext(ctx)
	if user == nil {
		return ""
	}

	conf, err := repository.GetUserCfgRepo().GetUIConfig(user)
	if err != nil {
		return ""
	}

	algoVal, ok := conf["plotConfiguration_resampleAlgo"]
	if !ok {
		return ""
	}
	algoStr, ok := algoVal.(string)
	if !ok {
		return ""
	}

	return algoStr
}

// smallestFrequency returns the smallest metric timestep (in seconds) among the
// requested metrics for the given cluster. Falls back to 0 if nothing is found.
func smallestFrequency(cluster string, metrics []string) int {
	cl := archive.GetCluster(cluster)
	if cl == nil {
		return 0
	}

	minFreq := 0
	for _, mc := range cl.MetricConfig {
		if len(metrics) > 0 {
			found := false
			for _, m := range metrics {
				if mc.Name == m {
					found = true
					break
				}
			}
			if !found {
				continue
			}
		}
		if minFreq == 0 || mc.Timestep < minFreq {
			minFreq = mc.Timestep
		}
	}

	return minFreq
}


597:func resamplingForUser(conf map[string]any) *config.ResampleConfig {
598-	globalCfg := config.Keys.EnableResampling
599-
600-	policyVal, ok := conf["plotConfiguration_resamplePolicy"]
601-	if !ok {
602-		return globalCfg
603-	}
604-	policyStr, ok := policyVal.(string)
605-	if !ok || policyStr == "" {
606-		return globalCfg
607-	}
608-
609-	policy := metricdispatch.ResamplePolicy(policyStr)
610-	targetPoints := metricdispatch.TargetPointsForPolicy(policy)
611-	if targetPoints == 0 {
612-		return globalCfg
613-	}
614-
615-	// Build a policy-derived config: targetPoints + trigger, no resolutions array
616-	return &config.ResampleConfig{
617-		TargetPoints: targetPoints,
618-		Trigger:      targetPoints / 4,
619-	}
620-}


Now let me look at how the resolvers fall back to global config when no user policy is set.


505-		return nil, err
506-	}
507-
508-	// Resolve resolution: explicit param > user policy > global config > 0
509-	if resolution == nil {
510:		resolution = resolveResolutionFromPolicy(ctx, int64(job.Duration), job.Cluster, metrics)
511-	}
512-	if resolution == nil {
513:		if config.Keys.EnableResampling != nil {
514:			defaultRes := slices.Max(config.Keys.EnableResampling.Resolutions)
515-			resolution = &defaultRes
516-		} else {
517-			defaultRes := 0
518-			resolution = &defaultRes
519-		}
--
880-// NodeMetricsList is the resolver for the nodeMetricsList field.
881-func (r *queryResolver) NodeMetricsList(ctx context.Context, cluster string, subCluster string, stateFilter string, nodeFilter string, scopes []schema.MetricScope, metrics []string, from time.Time, to time.Time, page *model.PageRequest, resolution *int, resampleAlgo *model.ResampleAlgo) (*model.NodesResultList, error) {
882-	// Resolve resolution: explicit param > user policy > global config > 0
883-	duration := int64(to.Sub(from).Seconds())
884-	if resolution == nil {
885:		resolution = resolveResolutionFromPolicy(ctx, duration, cluster, metrics)
886-	}
887-	if resolution == nil {
888:		if config.Keys.EnableResampling != nil {
889:			defaultRes := slices.Max(config.Keys.EnableResampling.Resolutions)
890-			resolution = &defaultRes
891-		} else {
892-			defaultRes := 0
893-			resolution = &defaultRes
894-		}


Now I see the full picture. Let me also check what the `resamplingForUser` fallback returns when `globalCfg` has `Resolutions` but no `TargetPoints`.


     1→// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.
     2→// All rights reserved. This file is part of cc-backend.
     3→// Use of this source code is governed by a MIT-style
     4→// license that can be found in the LICENSE file.
     5→package metricdispatch
     6→
     7→import "math"
     8→
     9→type ResamplePolicy string
    10→
    11→const (
    12→	ResamplePolicyLow    ResamplePolicy = "low"
    13→	ResamplePolicyMedium ResamplePolicy = "medium"
    14→	ResamplePolicyHigh   ResamplePolicy = "high"
    15→)
    16→
    17→// TargetPointsForPolicy returns the target number of data points for a given policy.
    18→func TargetPointsForPolicy(policy ResamplePolicy) int {
    19→	switch policy {
    20→	case ResamplePolicyLow:
    21→		return 200
    22→	case ResamplePolicyMedium:
    23→		return 500
    24→	case ResamplePolicyHigh:
    25→		return 1000
    26→	default:
    27→		return 0
    28→	}
    29→}
    30→
    31→// ComputeResolution computes the resampling resolution in seconds for a given
    32→// job duration, metric frequency, and target point count. Returns 0 if the
    33→// total number of data points is already at or below targetPoints (no resampling needed).
    34→func ComputeResolution(duration int64, frequency int64, targetPoints int) int {
    35→	if frequency <= 0 || targetPoints <= 0 || duration <= 0 {
    36→		return 0
    37→	}
    38→
    39→	totalPoints := duration / frequency
    40→	if totalPoints <= int64(targetPoints) {
    41→		return 0
    42→	}
    43→
    44→	targetRes := math.Ceil(float64(duration) / float64(targetPoints))
    45→	// Round up to nearest multiple of frequency
    46→	resolution := int(math.Ceil(targetRes/float64(frequency))) * int(frequency)
    47→
    48→	return resolution
    49→}
    50→

// Copyright (C) NHR@FAU, University Erlangen-Nuremberg.
// All rights reserved. This file is part of cc-backend.
// Use of this source code is governed by a MIT-style
// license that can be found in the LICENSE file.
package metricdispatch

import "math"

type ResamplePolicy string

const (
	ResamplePolicyLow    ResamplePolicy = "low"
	ResamplePolicyMedium ResamplePolicy = "medium"
	ResamplePolicyHigh   ResamplePolicy = "high"
)

// TargetPointsForPolicy returns the target number of data points for a given policy.
func TargetPointsForPolicy(policy ResamplePolicy) int {
	switch policy {
	case ResamplePolicyLow:
		return 200
	case ResamplePolicyMedium:
		return 500
	case ResamplePolicyHigh:
		return 1000
	default:
		return 0
	}
}

// ComputeResolution computes the resampling resolution in seconds for a given
// job duration, metric frequency, and target point count. Returns 0 if the
// total number of data points is already at or below targetPoints (no resampling needed).
func ComputeResolution(duration int64, frequency int64, targetPoints int) int {
	if frequency <= 0 || targetPoints <= 0 || duration <= 0 {
		return 0
	}

	totalPoints := duration / frequency
	if totalPoints <= int64(targetPoints) {
		return 0
	}

	targetRes := math.Ceil(float64(duration) / float64(targetPoints))
	// Round up to nearest multiple of frequency
	resolution := int(math.Ceil(targetRes/float64(frequency))) * int(frequency)

	return resolution
}


Now let me check the `config.Init()` function to see how `MinimumPoints` is used.


   150→}
   151→
   152→func Init(mainConfig json.RawMessage) {
   153→	Validate(configSchema, mainConfig)
   154→	dec := json.NewDecoder(bytes.NewReader(mainConfig))
   155→	dec.DisallowUnknownFields()
   156→	if err := dec.Decode(&Keys); err != nil {
   157→		cclog.Abortf("Config Init: Could not decode config file '%s'.\nError: %s\n", mainConfig, err.Error())
   158→	}
   159→
   160→	if Keys.EnableResampling != nil && Keys.EnableResampling.MinimumPoints > 0 {
   161→		resampler.SetMinimumRequiredPoints(Keys.EnableResampling.MinimumPoints)
   162→	}
   163→}
   164→

}

func Init(mainConfig json.RawMessage) {
	Validate(configSchema, mainConfig)
	dec := json.NewDecoder(bytes.NewReader(mainConfig))
	dec.DisallowUnknownFields()
	if err := dec.Decode(&Keys); err != nil {
		cclog.Abortf("Config Init: Could not decode config file '%s'.\nError: %s\n", mainConfig, err.Error())
	}

	if Keys.EnableResampling != nil && Keys.EnableResampling.MinimumPoints > 0 {
		resampler.SetMinimumRequiredPoints(Keys.EnableResampling.MinimumPoints)
	}
}


Now let me check the config schema for validation.


/Users/jan/prg/ClusterCockpit/cc-backend/internal/config/schema.go:8:var configSchema = `
/Users/jan/prg/ClusterCockpit/cc-backend/internal/config/config.go:153:	Validate(configSchema, mainConfig)


76-      "description": "Do not show running jobs shorter than X seconds.",
77-      "type": "integer"
78-    },
79-    "emission-constant": {
80-      "description": "Energy mix CO2 emission constant [g/kWh]. If set, displays estimated CO2 emission for jobs.",
81-      "type": "integer"
82-    },
83-    "machine-state-dir": {
84-      "description": "Where to store MachineState files.",
85-      "type": "string"
86-    },
87-    "systemd-unit": {
88-      "description": "Systemd unit name for log viewer (default: 'clustercockpit').",
89-      "type": "string"
90-    },
91:    "resampling": {
92-      "description": "Enable dynamic zoom in frontend metric plots.",
93-      "type": "object",
94-      "properties": {
95-        "minimum-points": {
96:          "description": "Minimum points to trigger resampling of time-series data.",
97-          "type": "integer"
98-        },
99-        "trigger": {
100-          "description": "Trigger next zoom level at less than this many visible datapoints.",
101-          "type": "integer"
102-        },
103-        "resolutions": {
104:          "description": "Array of resampling target resolutions, in seconds.",
105-          "type": "array",
106-          "items": {
107-            "type": "integer"
108-          }
109-        }
110-      },
111-      "required": ["trigger", "resolutions"]
112-    },
113-    "api-subjects": {
114-      "description": "NATS subjects configuration for subscribing to job and node events.",
115-      "type": "object",
116-      "properties": {
117-        "subject-job-event": {
118-          "description": "NATS subject for job events (start_job, stop_job)",
119-          "type": "string"


Now let me also check the example config.


/Users/jan/prg/ClusterCockpit/cc-backend/configs/config.json-11-    "nodestate-retention": {
/Users/jan/prg/ClusterCockpit/cc-backend/configs/config.json-12-      "policy": "move",
/Users/jan/prg/ClusterCockpit/cc-backend/configs/config.json-13-      "target-kind": "file",
/Users/jan/prg/ClusterCockpit/cc-backend/configs/config.json-14-      "target-path": "./var/nodestate-archive"
/Users/jan/prg/ClusterCockpit/cc-backend/configs/config.json-15-    },
/Users/jan/prg/ClusterCockpit/cc-backend/configs/config.json:16:    "resampling": {
/Users/jan/prg/ClusterCockpit/cc-backend/configs/config.json-17-      "minimum-points": 600,
/Users/jan/prg/ClusterCockpit/cc-backend/configs/config.json-18-      "trigger": 180,
/Users/jan/prg/ClusterCockpit/cc-backend/configs/config.json-19-      "resolutions": [240, 60]
/Users/jan/prg/ClusterCockpit/cc-backend/configs/config.json-20-    },
/Users/jan/prg/ClusterCockpit/cc-backend/configs/config.json-21-    "api-subjects": {


Now I have the full picture. Let me write the plan.


Write

# Replace file-based resampling config with policy-based approach

## Context

The codebase has two resampling mechanisms:
1. **Old (file-based)**: `config.json` defines explicit `resolutions` array and `trigger` count. The frontend uses these to pick from a fixed set of resolution steps on zoom.
2. **New (policy-based)**: Users choose a policy ("low"/"medium"/"high") mapping to target point counts (200/500/1000). Resolution is computed dynamically from `duration / targetPoints`. This is already fully implemented in the GraphQL resolvers and frontend MetricPlot zoom logic.

The problem: `config.json` still uses the old `resolutions`+`trigger` schema, and falls back to it when no user policy is set. We should replace this with a default policy+algorithm in config, so the whole system is consistently policy-based.

## Changes

### 1. `internal/config/config.go` — Update `ResampleConfig` struct
- Remove `Resolutions []int` and `Trigger int` fields
- Add `DefaultPolicy string` (`json:"default-policy"`) — default resample policy ("low", "medium", "high")
- Add `DefaultAlgo string` (`json:"default-algo"`) — default resample algorithm ("lttb", "average", "simple")
- Keep `MinimumPoints int` — still useful for the resampler library
- Keep `TargetPoints int` — used by policy-derived runtime config

### 2. `internal/config/schema.go` — Update JSON schema
- Replace `trigger`/`resolutions` required properties with `default-policy`
- Add `default-policy` (string, enum: ["low","medium","high"]) and `default-algo` (string, enum: ["lttb","average","simple"]) properties
- Remove `trigger` and `resolutions` properties
- No required fields (presence of `"resampling": {}` is enough to enable it)

### 3. `internal/routerConfig/routes.go` — Update `resamplingForUser()`
- When falling back to globalCfg (no user policy set), use `globalCfg.DefaultPolicy` to compute `TargetPoints` and `Trigger` dynamically, same as the user-policy path
- If `DefaultPolicy` is empty, use "medium" as default

### 4. `internal/graph/schema.resolvers.go` — Update fallback resolution logic
- In `JobMetrics()` and `NodeMetricsList()`: replace `slices.Max(config.Keys.EnableResampling.Resolutions)` fallback with policy-based computation using `config.Keys.EnableResampling.DefaultPolicy`
- Use `TargetPointsForPolicy()` + `ComputeResolution()` instead of picking from a resolutions array
- Also apply `DefaultAlgo` when no user algo preference is set

### 5. `internal/graph/resample.go` — Update `resolveResampleAlgo()`
- After checking user preference, fall back to `config.Keys.EnableResampling.DefaultAlgo` before returning ""

### 6. `config.json` (local dev) — Update to new format
```json
"resampling": {
  "default-policy": "medium",
  "default-algo": "lttb"
}
```

### 7. `configs/config.json` (example config) — Update to new format
Same as above, keeping `minimum-points` if desired.

### 8. Frontend: No changes needed
The frontend already handles both modes:
- Policy-based: uses `targetPoints` to compute resolution dynamically
- Array-based: uses `resolutions` array for fixed steps

Since `resamplingForUser()` already returns a `ResampleConfig` with `TargetPoints`+`Trigger` (no `Resolutions`), the frontend will use the policy path.

## Files to modify
1. `internal/config/config.go` (struct + Init)
2. `internal/config/schema.go` (JSON schema)
3. `internal/routerConfig/routes.go` (`resamplingForUser`)
4. `internal/graph/schema.resolvers.go` (resolver fallbacks)
5. `internal/graph/resample.go` (`resolveResampleAlgo` fallback)
6. `config.json` (local config)
7. `configs/config.json` (example config)

## Verification
1. `go build ./...` — must compile
2. `go test ./internal/metricdispatch/...` — resample policy tests pass
3. `go test ./internal/config/...` — if config tests exist
4. Start with `./cc-backend -server -dev` and verify `/config` page shows resampling options
5. Verify metric plots on job pages still support zoom/resampling


File created successfully at: /Users/jan/.claude/plans/bubbly-shimmying-sedgewick.md

# Replace file-based resampling config with policy-based approach

## Context

The codebase has two resampling mechanisms:
1. **Old (file-based)**: `config.json` defines explicit `resolutions` array and `trigger` count. The frontend uses these to pick from a fixed set of resolution steps on zoom.
2. **New (policy-based)**: Users choose a policy ("low"/"medium"/"high") mapping to target point counts (200/500/1000). Resolution is computed dynamically from `duration / targetPoints`. This is already fully implemented in the GraphQL resolvers and frontend MetricPlot zoom logic.

The problem: `config.json` still uses the old `resolutions`+`trigger` schema, and falls back to it when no user policy is set. We should replace this with a default policy+algorithm in config, so the whole system is consistently policy-based.

## Changes

### 1. `internal/config/config.go` — Update `ResampleConfig` struct
- Remove `Resolutions []int` and `Trigger int` fields
- Add `DefaultPolicy string` (`json:"default-policy"`) — default resample policy ("low", "medium", "high")
- Add `DefaultAlgo string` (`json:"default-algo"`) — default resample algorithm ("lttb", "average", "simple")
- Keep `MinimumPoints int` — still useful for the resampler library
- Keep `TargetPoints int` — used by policy-derived runtime config

### 2. `internal/config/schema.go` — Update JSON schema
- Replace `trigger`/`resolutions` required properties with `default-policy`
- Add `default-policy` (string, enum: ["low","medium","high"]) and `default-algo` (string, enum: ["lttb","average","simple"]) properties
- Remove `trigger` and `resolutions` properties
- No required fields (presence of `"resampling": {}` is enough to enable it)

### 3. `internal/routerConfig/routes.go` — Update `resamplingForUser()`
- When falling back to globalCfg (no user policy set), use `globalCfg.DefaultPolicy` to compute `TargetPoints` and `Trigger` dynamically, same as the user-policy path
- If `DefaultPolicy` is empty, use "medium" as default

### 4. `internal/graph/schema.resolvers.go` — Update fallback resolution logic
- In `JobMetrics()` and `NodeMetricsList()`: replace `slices.Max(config.Keys.EnableResampling.Resolutions)` fallback with policy-based computation using `config.Keys.EnableResampling.DefaultPolicy`
- Use `TargetPointsForPolicy()` + `ComputeResolution()` instead of picking from a resolutions array
- Also apply `DefaultAlgo` when no user algo preference is set

### 5. `internal/graph/resample.go` — Update `resolveResampleAlgo()`
- After checking user preference, fall back to `config.Keys.EnableResampling.DefaultAlgo` before returning ""

### 6. `config.json` (local dev) — Update to new format
```json
"resampling": {
  "default-policy": "medium",
  "default-algo": "lttb"
}
```

### 7. `configs/config.json` (example config) — Update to new format
Same as above, keeping `minimum-points` if desired.

### 8. Frontend: No changes needed
The frontend already handles both modes:
- Policy-based: uses `targetPoints` to compute resolution dynamically
- Array-based: uses `resolutions` array for fixed steps

Since `resamplingForUser()` already returns a `ResampleConfig` with `TargetPoints`+`Trigger` (no `Resolutions`), the frontend will use the policy path.

## Files to modify
1. `internal/config/config.go` (struct + Init)
2. `internal/config/schema.go` (JSON schema)
3. `internal/routerConfig/routes.go` (`resamplingForUser`)
4. `internal/graph/schema.resolvers.go` (resolver fallbacks)
5. `internal/graph/resample.go` (`resolveResampleAlgo` fallback)
6. `config.json` (local config)
7. `configs/config.json` (example config)

## Verification
1. `go build ./...` — must compile
2. `go test ./internal/metricdispatch/...` — resample policy tests pass
3. `go test ./internal/config/...` — if config tests exist
4. Start with `./cc-backend -server -dev` and verify `/config` page shows resampling options
5. Verify metric plots on job pages still support zoom/resampling


ToolSearch

Question: select:ExitPlanMode


Question: select:ExitPlanMode


The plan replaces the old explicit `resolutions`+`trigger` config with `default-policy`+`default-algo`, aligning the config.json approach with the policy-based system already used in the GraphQL API and frontend. No frontend changes needed — 7 files to modify on the backend/config side.


ExitPlanMode

# Replace file-based resampling config with policy-based approach

## Context

The codebase has two resampling mechanisms:
1. **Old (file-based)**: `config.json` defines explicit `resolutions` array and `trigger` count. The frontend uses these to pick from a fixed set of resolution steps on zoom.
2. **New (policy-based)**: Users choose a policy ("low"/"medium"/"high") mapping to target point counts (200/500/1000). Resolution is computed dynamically from `duration / targetPoints`. This is already fully implemented in the GraphQL resolvers and frontend MetricPlot zoom logic.

The problem: `config.json` still uses the old `resolutions`+`trigger` schema, and falls back to it when no user policy is set. We should replace this with a default policy+algorithm in config, so the whole system is consistently policy-based.

## Changes

### 1. `internal/config/config.go` — Update `ResampleConfig` struct
- Remove `Resolutions []int` and `Trigger int` fields
- Add `DefaultPolicy string` (`json:"default-policy"`) — default resample policy ("low", "medium", "high")
- Add `DefaultAlgo string` (`json:"default-algo"`) — default resample algorithm ("lttb", "average", "simple")
- Keep `MinimumPoints int` — still useful for the resampler library
- Keep `TargetPoints int` — used by policy-derived runtime config

### 2. `internal/config/schema.go` — Update JSON schema
- Replace `trigger`/`resolutions` required properties with `default-policy`
- Add `default-policy` (string, enum: ["low","medium","high"]) and `default-algo` (string, enum: ["lttb","average","simple"]) properties
- Remove `trigger` and `resolutions` properties
- No required fields (presence of `"resampling": {}` is enough to enable it)

### 3. `internal/routerConfig/routes.go` — Update `resamplingForUser()`
- When falling back to globalCfg (no user policy set), use `globalCfg.DefaultPolicy` to compute `TargetPoints` and `Trigger` dynamically, same as the user-policy path
- If `DefaultPolicy` is empty, use "medium" as default

### 4. `internal/graph/schema.resolvers.go` — Update fallback resolution logic
- In `JobMetrics()` and `NodeMetricsList()`: replace `slices.Max(config.Keys.EnableResampling.Resolutions)` fallback with policy-based computation using `config.Keys.EnableResampling.DefaultPolicy`
- Use `TargetPointsForPolicy()` + `ComputeResolution()` instead of picking from a resolutions array
- Also apply `DefaultAlgo` when no user algo preference is set

### 5. `internal/graph/resample.go` — Update `resolveResampleAlgo()`
- After checking user preference, fall back to `config.Keys.EnableResampling.DefaultAlgo` before returning ""

### 6. `config.json` (local dev) — Update to new format
```json
"resampling": {
  "default-policy": "medium",
  "default-algo": "lttb"
}
```

### 7. `configs/config.json` (example config) — Update to new format
Same as above, keeping `minimum-points` if desired.

### 8. Frontend: No changes needed
The frontend already handles both modes:
- Policy-based: uses `targetPoints` to compute resolution dynamically
- Array-based: uses `resolutions` array for fixed steps

Since `resamplingForUser()` already returns a `ResampleConfig` with `TargetPoints`+`Trigger` (no `Resolutions`), the frontend will use the policy path.

## Files to modify
1. `internal/config/config.go` (struct + Init)
2. `internal/config/schema.go` (JSON schema)
3. `internal/routerConfig/routes.go` (`resamplingForUser`)
4. `internal/graph/schema.resolvers.go` (resolver fallbacks)
5. `internal/graph/resample.go` (`resolveResampleAlgo` fallback)
6. `config.json` (local config)
7. `configs/config.json` (example config)

## Verification
1. `go build ./...` — must compile
2. `go test ./internal/metricdispatch/...` — resample policy tests pass
3. `go test ./internal/config/...` — if config tests exist
4. Start with `./cc-backend -server -dev` and verify `/config` page shows resampling options
5. Verify metric plots on job pages still support zoom/resampling


The user doesn't want to proceed with this tool use. The tool use was rejected (eg. if it was a file edit, the new_string was NOT written to the file). STOP what you are doing and wait for the user to tell you how to proceed.


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