perf(storage): resolve KNN rowids in one query instead of one per hit - #86
perf(storage): resolve KNN rowids in one query instead of one per hit#86citron07r wants to merge 4 commits into
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Included review availability: Your plan provides up to 10 included reviews per hour; 9 remain after this review. 📝 WalkthroughWalkthroughVector search now batches rowid-to-chunk-ID resolution, preserves KNN distance ordering, avoids caching statements with dynamic limits, and validates large lookups and missing mappings. ChangesVector search optimization
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: ⚪ Minimal · up to This change batches vector-result row ID lookups while preserving result ordering and avoids caching SQL that varies per call; no actionable merge-blocking risk remains beyond normal checks and review. Possibly related PRs
Suggested reviewers: 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
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Inline comments:
In `@crates/vera-core/src/storage/vector.rs`:
- Around line 273-297: Update chunk_ids_for_rowids to return vera-core’s
module-specific thiserror error type instead of anyhow::Result, and add the
corresponding typed database-error variant/conversions. Convert that typed error
to anyhow only at the outer storage boundary where this helper is called,
preserving the existing lookup behavior.
- Around line 278-291: Update chunk_ids_for_rowids around the chunk_id_map
lookup to avoid exceeding SQLite’s configured host-parameter limit when hits
contains up to MAX_KNN_K row IDs. Batch the rowid queries using a safe
parameter-sized chunk and combine the mapped results, preserving the existing
rowid-to-chunk_id behavior.
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crates/vera-core/src/storage/vector.rs
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1 issue found across 1 file
Prompt for AI agents (unresolved issues)
Check if these issues are valid — if so, understand the root cause of each and fix them. If appropriate, use sub-agents to investigate and fix each issue separately.
<file name="crates/vera-core/src/storage/vector.rs">
<violation number="1" location="crates/vera-core/src/storage/vector.rs:278">
P2: Batch the `chunk_id_map` lookup or cap each `IN` list to SQLite’s host-parameter limit; `hits.len()` can reach 4096 and make searches fail on builds or configurations with a lower limit.</violation>
</file>
Reply with feedback, questions, or to request a fix.
Fix all with cubic | Re-trigger cubic
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| Ok(results) | ||
| let placeholders = std::iter::repeat_n("?", hits.len()) |
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P2: Batch the chunk_id_map lookup or cap each IN list to SQLite’s host-parameter limit; hits.len() can reach 4096 and make searches fail on builds or configurations with a lower limit.
Prompt for AI agents
Check if this issue is valid — if so, understand the root cause and fix it. At crates/vera-core/src/storage/vector.rs, line 278:
<comment>Batch the `chunk_id_map` lookup or cap each `IN` list to SQLite’s host-parameter limit; `hits.len()` can reach 4096 and make searches fail on builds or configurations with a lower limit.</comment>
<file context>
@@ -236,29 +242,59 @@ impl VectorStore {
}
- Ok(results)
+ let placeholders = std::iter::repeat_n("?", hits.len())
+ .collect::<Vec<_>>()
+ .join(",");
</file context>
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Four findings. One was a real defect in my test and is fixed in dfc536e; the other three I am declining, with reasons. Equidistant fixture made the ordering assertion a tie-break (valid, fixed). This was the important one and it was my mistake.
Typed Introducing one typed error here would make it the only typed-error surface in the module and force callers to handle two error styles from the same type. Converting the module is a real change with a decision behind it — the Cap the The caveat is real though, and it is the one thing in this thread I had not considered: The minor at line 291 arrived with only its category header and no finding text, so there is nothing for me to evaluate. Happy to look if it gets re-posted. 773 |
`search` mapped each KNN hit's rowid back to a chunk_id with its own `Connection::query_row`, which re-prepares every call, once per candidate — up to MAX_KNN_K (4096) on a filtered natural-language query. Measured against this repository's own vectors.db: n=100 per-row 0.19ms batch 0.07ms n=1000 per-row 1.63ms batch 0.44ms n=4096 per-row 6.64ms batch 1.86ms Those were taken through Python's sqlite3, which caches statements internally, so they understate the real gap. The vec0 KNN query is deliberately untouched — joining chunk_id_map into it would risk losing the KNN optimization — so the mapping is a separate batched lookup afterwards. Both statements now use `prepare` rather than `prepare_cached`. Their text varies (with `limit`, and with the number of ids), so each distinct shape was its own cache key: they could never hit, and they evicted the statements that do have stable text, since rusqlite's cache is a 16-entry LRU shared by the whole connection. Batching means the results come back as a HashMap and must be re-projected into distance order. `search_pairs_each_chunk_id_with_its_own_distance` pins that: it inserts so rowid order is the reverse of distance order and asserts the chunk_id/distance pairing, not just that distances ascend. Confirmed it fails on a SQL-order zip (returns far/mid/near instead of near/mid/far) while the pre-existing `nearest_neighbor_self_query` passes either way. Search output on a real index is identical to the released binary.
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Actionable comments posted: 1
🤖 Prompt for all review comments with AI agents
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Inline comments:
In `@crates/vera-core/src/storage/vector.rs`:
- Around line 282-306: Change chunk_ids_for_rowids to accept a slice of i64
rowids and update its placeholder count and parameter binding to use those
values directly. In search, pass the extracted rowid list to this helper, and
update the test inputs to use rowids without dummy distance values.
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crates/vera-core/src/storage/vector.rs
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The helper took `&[(i64, f64)]` but only ever read the rowid, so its signature overstated what it needed and cfd2aa9's boundary test had to invent `0.0` distances to call it. It now takes `&[i64]`. The caller projects the rowids once before the call, which is a single allocation bounded by MAX_KNN_K.
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Valid, fixed in e003eff. Rebased onto cfd2aa9 rather than over it.
The caller projects the rowids once before the call. That is one allocation bounded by 796 |
2 participants
Problem
Two things in
VectorStore::search, both on the hot search path.One uncached query per KNN hit. After the KNN query returns, each hit's rowid was mapped back to a
chunk_idwith a separateConnection::query_rowinside the result loop.query_rowon aConnectionre-prepares the statement every call, and the loop runs once per candidate — up toMAX_KNN_K(4096) on a filtered natural-language query.LIMITinterpolated into aprepare_cachedstatement. The SQL text varied withlimit, so every distinct limit was a distinct cache key. rusqlite's statement cache is an LRU with a default capacity of 16 (STATEMENT_CACHE_DEFAULT_CAPACITY), and Vera never raises it — so these entries could never hit, and they evicted the statements that do have stable text on the same connection.Change
One batched
SELECT rowid, chunk_id FROM chunk_id_map WHERE rowid IN (...)after the KNN query, then re-project.The vec0 KNN query itself is deliberately left alone. Joining
chunk_id_mapinto it would risk losing the KNN optimization, which would cost far more than this saves.Both statements switch from
prepare_cachedtoprepare, since both have text that varies per call. sqlite-vec does not accept a bound parameter for the KNNLIMIT, so the interpolation stays — it just stops occupying a cache slot it can never reuse.Measurement
Against this repository's own
vectors.db, per-row lookups versus one batch:~4.8 ms per search at the ceiling. Worth being explicit that these were taken through Python's sqlite3, which caches prepared statements internally — so they understate the real gap, because the production path re-prepared on every call.
End to end on a real index, search output is byte-identical to the released binary.
The part that needed a real test
Batching means the mapping comes back as a
HashMap, so the results have to be re-projected into the KNN distance order. That is the same shape as the bug in #73, so it gets the same treatment.search_pairs_each_chunk_id_with_its_own_distanceinserts three vectors so that rowid order is the exact reverse of distance order, then asserts the chunk_id/distance pairing — not merely that distances ascend, which holds for any implementation emitting one result per hit in hit order.Verified it catches the regression rather than assuming: zipping the hits against SQL order returns
["far", "mid", "near"]instead of["near", "mid", "far"], and the test fails. The pre-existingnearest_neighbor_self_querypasses either way, which is why the new one was needed.773
vera-coretests, 97vera-cli,cargo fmt --checkclean, clippy unchanged at the pre-existing warnings.Fixes #85
Summary by cubic
Resolve KNN rowids in one batched query and stop caching SQL that varies per call. Old behavior: one uncached row lookup per hit and
prepare_cachedwith interpolatedLIMIT; new behavior: a singleIN (...)lookup andprepare, cutting lookup time from 6.64 ms to 1.86 ms at 4096 hits with identical results.chunk_id_mapto preservesqlite-vecoptimization.LIMIT(cannot bind withsqlite-vec) and useprepareto avoid pollutingrusqlite’s 16-entry cache.chunk_idmapping.chunk_ids_for_rowidsnow takes&[i64]; the caller projects rowids once (bounded by MAX_KNN_K).Written for commit e003eff. Summary will update on new commits.
Summary by CodeRabbit