## Motivation
The current primary-key vector index maintains one ANN segment per `(partition, bucket, level)`. When any file in a level changes due to compaction, the **entire level's segment must be fully rebuilt**, even if only a small subset of files was affected.
For a level with 10 files totaling 10M rows, replacing just 2 files (2M rows) forces a rebuild of all 10M rows. This creates:
- Excessive rebuild cost for DiskANN/HNSW (minutes for large levels)
- Low `canAccept()` success rate when compact frequency > build time
- Delayed index availability under `waitCompaction=false`
## Proposal
Introduce a **Sub-Shard** mechanism that splits a level's index into finer-grained segments, so that only affected shards are rebuilt when compaction changes files.
### Three Granularity Options
| Scope | Segment Covers | Rebuild Unit | Segments per Level (10 files) |
|-------|---------------|--------------|-------------------------------|
| **LEVEL** (current) | All files in level | Entire level | 1 |
| **FILE_GROUP** | 2-4 adjacent files by key range | Affected group only | 3-5 |
| **PER_FILE** | Single compact-output file | Single file only | 10 |
### Example: 10 files, compact replaces 2
| Metric | LEVEL (current) | FILE_GROUP | PER_FILE |
|--------|----------------|-----------|---------|
| Rebuild data volume | 10M rows | 3M rows | **2M rows** |
| IVF-Flat rebuild time | ~50s | ~15s | **~10s** |
| DiskANN rebuild time | ~300s | ~90s | **~60s** |
| Unchanged segments | 0 | 2 groups | 8 files |
| canAccept success rate | Low | Medium | **High** |
### Algorithm Compatibility
| Algorithm | Precision Impact (PER_FILE) | Recommendation |
|-----------|---------------------------|----------------|
| IVF-Flat / IVF-PQ | ≈0% (IVF is partition-based) | PER_FILE |
| DiskANN / HNSW | -3~8% (graph connectivity severed) | FILE_GROUP |
**IVF algorithms are naturally suited for sub-sharding** since they search each partition independently. Graph-based indexes (DiskANN/HNSW) lose cross-shard neighbor connectivity, so FILE_GROUP with larger shards (2-5M rows) is recommended for them.
## Design Overview
### Core Changes
1. **`ShardPartitioner`** (new): Partitions level files into shards based on configured scope
2. **`PrimaryKeyIndexLevels.pick()`**: Returns plans for individual shards instead of entire levels
3. **`PkVectorBucketIndexState`**: Validates segments per-shard (partial match) instead of exact full-level match
4. **`BucketedVectorIndexMaintainer`**: Supports multiple parallel `PendingBuild` tasks for different shards
5. **`PrimaryKeyVectorBucketSearch`**: Searches multiple sub-shard segments and merges results
6. **`PrimaryKeyIndexSourceMeta`**: Extended with `shardIndex` field (version 2, backward compatible)
### Configuration
```sql
'pk-vector.shard.scope' = 'per-file' -- level | file-group | per-file
'pk-vector.shard.target-row-count' = '5000000' -- file-group mode: target rows per group
'pk-vector.shard.max-files-per-group' = '4' -- file-group mode: max files per group
'pk-vector.shard.max-parallel-builds' = '3' -- max concurrent shard builds
Per-File mode, 10 segments in a level:
→ Parallel ANN search on 10 small segments (each ~1M rows, ~0.8ms)
→ Merge results → global topK
→ Total latency (parallel): ~1.8ms vs current 5ms (faster due to smaller graphs)
Search before asking
Motivation
Search Path
Backward Compatibility
PrimaryKeyIndexSourceMetaversion upgrade (V1 → V2), V1 still readablescope=levelpreserves existing behaviorscope=level→ next compact rebuilds full-level segmentPerformance Summary
canAcceptsuccessKey Code References
PrimaryKeyIndexLevels.java- current single-segment-per-level enforcementPkVectorBucketIndexState.java- current exact full-level match validationBucketedVectorIndexMaintainer.java- current single PendingBuild modelPrimaryKeyVectorBucketSearch.java- current single-segment search pathSolution
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Anything else?
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Are you willing to submit a PR?