Implement the following plan:

# Fix: Memory Escalation in flattenCheckpointFile (68GB+)

## Context

Production gops shows `flattenCheckpointFile` allocating 68GB+ (74.89% of memory). The archiving pipeline accumulates ALL metric data from ALL hosts into a single `[]ParquetMetricRow` slice before writing to Parquet. For large HPC clusters this is catastrophic. Additionally, the `SortingWriterConfig` in the parquet writer buffers everything again internally.

## Root Cause

Two-layer unbounded accumulation:

1. **`archive.go:239-242`**: `allRows = append(allRows, r.rows...)` merges every host's rows into one giant slice
2. **`parquetArchive.go:108-116`**: `SortingWriterConfig` creates a sorting writer that buffers ALL rows until `Close()`
3. **`parquetArchive.go:199`**: `var rows []ParquetMetricRow` starts at zero capacity, grows via append doubling

Peak memory = (all hosts' rows) + (sorting writer copy) + (append overhead) = ~3x raw data size.

## Fix: Stream per-host to parquet writer

Instead of accumulating all rows, write each host's data as a separate row group.

### Step 1: Add streaming parquet writer (`parquetArchive.go`)

Replace `writeParquetArchive(filename, rows)` with a struct that supports incremental writes:

```go
type parquetArchiveWriter struct {
    writer *pq.GenericWriter[ParquetMetricRow]
    bw     *bufio.Writer
    f      *os.File
    count  int
}

func newParquetArchiveWriter(filename string) (*parquetArchiveWriter, error)
func (w *parquetArchiveWriter) WriteHostRows(rows []ParquetMetricRow) error  // Write + Flush (creates row group)
func (w *parquetArchiveWriter) Close() error
```

- **Remove `SortingWriterConfig`** - no global sort buffer
- Sort each host's rows in-place with `sort.Slice` before writing (cheap: single host data)
- Each `Flush()` creates a separate row group per host

### Step 2: Add row count estimation (`parquetArchive.go`)

```go
func estimateRowCount(cf *CheckpointFile) int
```

Pre-allocate `rows` slice in `archiveCheckpointsToParquet` to avoid append doubling per host.

### Step 3: Restructure `archiveCheckpoints` (`archive.go`)

Change from:
```
workers → channel → accumulate allRows → writeParquetArchive(allRows)
```

To:
```
open writer → workers → channel → for each host: sort rows, writer.WriteHostRows(rows) → close writer
```

- Only one host's rows in memory at a time
- Track `files`/`dir` for deletion separately (don't retain rows)
- Check `writer.count > 0` instead of `len(allRows) == 0`

### Step 4: Update test (`parquetArchive_test.go`)

- `TestParquetArchiveRoundtrip`: use new streaming writer API
- Keep `archiveCheckpointsToParquet` returning rows (it's per-host, manageable size)

## Files to Modify

- **`pkg/metricstore/parquetArchive.go`**: Add `parquetArchiveWriter`, `estimateRowCount`; remove `writeParquetArchive`; add `"sort"` import
- **`pkg/metricstore/archive.go`**: Restructure `archiveCheckpoints` to stream
- **`pkg/metricstore/parquetArchive_test.go`**: Update roundtrip test

## Memory Impact

- **Before**: All hosts in memory (~40GB for 256 nodes) + sorting buffer (~40GB) = 68GB+
- **After**: One host at a time (~16MB) + parquet page buffer (~1MB) = ~17MB peak

## Sorting Tradeoff

The output changes from one globally-sorted row group to N row groups (one per host), each internally sorted by (metric, timestamp). This is actually better for ClusterCockpit's per-host query patterns (enables row group skipping).

## Verification

```bash
go test -v ./pkg/metricstore/...
```

Also verify with `go vet ./pkg/metricstore/...` for correctness.


If you need specific details from before exiting plan mode (like exact code snippets, error messages, or content you generated), read the full transcript at: /Users/jan/.claude/projects/-Users-jan-prg-CC-cc-backend/71340843-de3d-4e83-9dcb-2fc130c50e0d.jsonl

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Are the any other cases with memory spikes using the Parquet Writer, e.g. in the nodestate retention?

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[Request interrupted by user for tool use]

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Compare the archive writer implementation with @~/tmp/cc-backend/pkg/metricstore/parquetArchive.go . Compare and explain differences.

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Add an Info logmessage in archiveCheckpoints that archving started and provide timing information how long it took.