perf(simq): batch RangeSearch exact reranking

[CharlesXu-HQ]

Change Type

• Bug fix
• New feature
• Improvement/Refactor
• Documentation
• CI/Build/Infra

Linked Issue

Not required for kind/improvement.

What Changed

• Collect SIMQ RangeSearch candidates that survive filtering and exact-rerank them with one batched MultiVectorDataCell::Query call instead of one call per candidate.
• Preserve radius filtering, result ordering, limited_size, filter behavior, and distance-comparison accounting.
• Add simq_rerank_batch_count search statistics (0 when no batch is needed, otherwise 1).
• Add regression coverage for exact result equivalence, filtering, limited_size, all-filtered candidates, and an empty result radius.
• Document the new statistic in the English and Chinese SIMQ documentation.

Test Evidence

• make fmt
• make lint
• make test
• make cov, run tests, and collect coverage
• Other (described below)

Test details:

clang-format 15.0.7 --dry-run --Werror (changed C++ files): passed
clang-tidy 15.0.7 (src/algorithm/simq/simq.cpp): passed, no user-code diagnostics
clang-tidy 15.0.7 (all changed test ranges): passed, no changed-line diagnostics

build/tests/functests '[simq][range_search]' --reporter compact
All tests passed (247 assertions in 1 test case)

build/tests/functests '[simq]' --reporter compact
All tests passed (5128 assertions in 8 test cases)

The exact four committed file hashes match the tree used for the tests above. An independent code-review pass reported no findings.

Performance

Environment: AMD Ryzen 7 9700X, Ubuntu 24.04.4, 1,000 documents x 8 token vectors, 100 queries x 4 token vectors, 128 dimensions, inner product, coarse_k=10, five warmups. Results are medians of three independent before and after processes.

rerank_k	p50 before	p50 after	p50 gain	p95 before	p95 after	4-thread QPS before	4-thread QPS after	QPS gain
100	13,069.696 us	2,720.605 us	4.80x	13,164.219 us	2,909.610 us	369.134	1,190.494	3.23x
500	59,237.488 us	6,379.042 us	9.29x	60,123.455 us	6,718.501 us	89.508	313.972	3.51x
1000	114,890.613 us	10,769.296 us	10.67x	116,644.113 us	11,265.083 us	45.290	166.061	3.67x

• Every matching before/after workload produced identical result counts and checksums over returned IDs and the exact IEEE-754 distance bits.
• A focused rerank_k=1000 syscall sample reduced io_submit calls from 5,900 to 60 (98.98%) with the same result checksum.
• Serial maximum RSS stayed approximately 460-462 MiB. Four-thread maximum RSS changed from approximately 461-464 MiB to 480 MiB.
• Benchmarks used CPU affinity plus nice/ionice; no resident service was stopped and caches were not dropped. Whole-machine average idle during latency monitoring was 98.13%.
• The isolated Zen 5 test build used OpenBLAS TARGET=ZEN because bundled OpenBLAS 0.3.24 selected an incomplete AMD_UNKNOWN configuration. This target-only build adjustment is not part of the diff.

Compatibility Impact

• API/ABI compatibility: none; only an additional key is emitted in search statistics.
• Behavior changes: exact-rerank execution is batched; returned IDs, distances, sorting, filtering, and limits are unchanged.

Performance and Concurrency Impact

• Performance impact: improved as measured above.
• Concurrency/thread-safety impact: none; batch IDs and distances are request-local and no shared mutable state was added.

Documentation Impact

• No docs update needed
• Updated docs:
  • README.md
  • DEVELOPMENT.md
  • CONTRIBUTING.md
  • Other: docs/docs/{en,zh}/src/indexes/simq.md

Risk and Rollback

• Risk level: low.
• Rollback plan: revert this commit to restore per-candidate exact-rerank calls.

Checklist

• Issue link is not required for kind/improvement.
• I have added/updated tests for the changed behavior.
• I have considered API compatibility impact.
• I have updated the English and Chinese docs.
• My commit message follows Conventional Commits and includes DCO/AI-assistance trailers.

[vsag-bot]

/label status/waiting-for-review
/waiting-on reviewer
/request-review @jiaweizone
/request-review @wxyucs
/request-review @inabao

[mergify]

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[LHT129]

Overall this is a well-executed performance optimization. The change from per-candidate to batched exact reranking in both KnnSearch and RangeSearch is clean and correct.

What I reviewed:

• src/algorithm/simq/simq.cpp: Batched MultiVectorDataCell::Query in both KnnSearch and RangeSearch, new rerank_batch_count statistic
• tests/test_simq.cpp: New test sections for filtering equivalence, all-filtered candidates, and empty radius batch rerank
• docs/docs/en/src/indexes/simq.md and docs/docs/zh/src/indexes/simq.md: Documentation for the new statistic

Correctness: The batched approach preserves the same filtering, radius, sorting, limited_size, and distance-comparison accounting semantics. The dist_cmp atomic update via fetch_add is correct for the batched path.

Test coverage: The new tests cover the key edge cases — filtering equivalence with KnnSearch, all-filtered candidates producing zero rerank_batch_count, and empty-radius still issuing one batch. The existing require_simq_search_stats helper was updated to assert rerank_batch_count == 1.

Performance: The benchmark data in the PR description shows 4.8x-10.7x latency improvement, which is consistent with reducing N individual io_submit calls to a single batched MultiRead.

One minor note was left as an inline comment about reserve() on in_range.

[wxyucs]

/kind improvement
/version 1.1
/assign @LHT129

[LHT129]

Re-reviewed the latest commit (7b336d3). The previous inline note about in_range.reserve() has been addressed. No additional findings — the batched reranking implementation in both KnnSearch and RangeSearch is correct, the new rerank_batch_count statistic is properly wired through, and the test coverage for filtering, all-filtered candidates, and empty-radius edge cases is thorough. LGTM.

[LHT129]

[note] In RangeSearch, the static_cast<uint32_t>(batch_ids.size()) used in stats.dist_cmp.fetch_add() could theoretically truncate the count if batch_ids.size() exceeds UINT32_MAX. While this is not a practical concern given rerank_k limits, using static_cast<uint64_t> (matching the underlying type of dist_cmp if it is std::atomic<uint64_t>) would be more future-proof and consistent with the uint64_t loop counter on the same code path.

Current (src/algorithm/simq/simq.cpp line ~920):

stats.dist_cmp.fetch_add(static_cast<uint32_t>(batch_ids.size()),
                         std::memory_order_relaxed);

Suggestion:

stats.dist_cmp.fetch_add(static_cast<uint64_t>(batch_ids.size()),
                         std::memory_order_relaxed);

The same pattern also appears in KnnSearch at approximately line 800.

[LHT129]

[note] Regarding the earlier suggestion to change static_cast<uint32_t> to static_cast<uint64_t> in stats.dist_cmp.fetch_add() (both in KnnSearch ~line 800 and RangeSearch ~line 920): the underlying type of dist_cmp is std::atomic<uint32_t> (defined in src/query_context.h). The fetch_add parameter type should match the atomic's value type, so static_cast<uint32_t> is correct here. Using uint64_t would cause an implicit narrowing conversion. No change needed.

Otherwise, this PR is well-executed: the batched reranking logic is correct in both KnnSearch and RangeSearch, the new rerank_batch_count statistic is properly wired through, and the test coverage for filtering, all-filtered candidates, and empty-radius edge cases is thorough. The in_range.reserve() fix from the earlier review round is already applied. LGTM.

[LHT129]

Thanks for this PR. The change is clean, well-scoped, and the performance improvement is substantial (3-10x).

What I reviewed:

• src/algorithm/simq/simq.cpp: The batched Query call in RangeSearch is correctly implemented, mirroring the existing pattern in KnnSearch. The in_range.reserve() fix from the earlier review has been applied. Statistics plumbing for simq_rerank_batch_count is consistent across both search paths and the dump helper.
• tests/test_simq.cpp: Test coverage is thorough — exact equivalence with filtered KNN, limited_size truncation, all-filtered candidates (verifying zero IO), and empty-radius edge case. The EvenLabelFilter and RejectAllFilter helpers are minimal and well-placed.
• docs/docs/{en,zh}/src/indexes/simq.md: Documentation additions are accurate and match the implementation.

No blocking issues found. The batched rerank approach is a straightforward and effective optimization.

[LHT129]

I reviewed this PR and the changes look solid. The batch reranking approach in RangeSearch is consistent with the existing pattern in KnnSearch and the performance gains are well-documented.

What was checked:

• Correctness: The batched Query call replaces per-candidate calls while preserving radius filtering, result ordering, limited_size, filter behavior, and distance-comparison accounting. The rerank_batch_count statistic is correctly set to 0 when no candidates survive filtering and 1 otherwise.
• Memory: batch_ids and batch_dists are request-local. in_range.reserve(batch_ids.size()) is correctly placed inside the !batch_ids.empty() guard (addressing the prior review comment).
• Concurrency: stats.dist_cmp.fetch_add with memory_order_relaxed is appropriate for statistics counters. No shared mutable state was added.
• Test coverage: The new test sections cover filtering + limited_size equivalence with KnnSearch, all-filtered candidates (rerank_batch_count == 0, dist_cmp == 0), and empty radius (still batches, produces zero results). The EvenLabelFilter and RejectAllFilter helpers are minimal and correct.
• Documentation: Both English and Chinese SIMQ docs are updated with the new statistic key.
• Style: Follows VSAG conventions (4-space indent, uint64_t, vsag namespace, .cpp suffix).

No blocking issues found.

[LHT129]

Code review summary:

This PR batches the SIMQ RangeSearch exact-rerank path (previously one MultiVectorDataCell::Query per candidate) into a single batched call, mirroring the pattern already used in KnnSearch. The change is clean and well-tested.

What was checked:

• Correctness: The batched Query call, radius filtering, result ordering, limited_size truncation, and filter integration are all correctly preserved. The rerank_batch_count stat (0 or 1) is computed correctly for both empty and non-empty batch cases.
• Consistency: The batched pattern in RangeSearch matches the existing KnnSearch implementation exactly — same batch_ids collection, same Query call signature, same fetch_add on dist_cmp.
• Statistics: All call sites of dump_simq_statistics are updated with the new rerank_batch_count parameter (4 call sites across KnnSearch and RangeSearch).
• Tests: Comprehensive regression coverage including exact equivalence with KnnSearch, filtering behavior, all-filtered candidates (verifying zero IO), empty radius, and limited_size. The require_simq_search_stats helper was updated to assert rerank_batch_count == 1.
• Documentation: Both English and Chinese SIMQ docs updated with the new stat key.
• Performance: The PR description includes detailed before/after benchmarks showing 3-10x latency improvements.

No blocking issues found. The previously noted in_range.reserve() suggestion has been addressed in the current diff.

[LHT129]

I have reviewed this PR thoroughly. The changes are clean and well-structured.

Summary of changes:

• RangeSearch now collects all filter-passing candidates and performs one batched MultiVectorDataCell::Query call instead of per-candidate calls, matching the pattern already used in KnnSearch.
• Added simq_rerank_batch_count search statistic (0 when no batch needed, 1 otherwise).
• Added comprehensive regression tests covering exact result equivalence with filtering, limited_size, all-filtered candidates, and empty radius.
• Updated English and Chinese SIMQ documentation with the new statistic.

Review findings:

• No correctness issues found. The batched Query call uses the correct API signature from FlattenInterface.
• The stats.dist_cmp atomic increment pattern is consistent between KnnSearch and RangeSearch.
• The in_range.reserve(batch_ids.size()) optimization (from a prior review comment) is correctly applied.
• All call sites of dump_simq_statistics are updated with the new parameter.
• Test coverage is thorough, covering the key edge cases (all-filtered, empty radius, filtering equivalence).
• The performance data in the PR description shows significant improvements (4.8x-10.7x p50 latency reduction).

No blocking issues or suggestions to raise. The PR is ready for merge from a code review perspective.

[LHT129]

I completed a thorough review of this PR. Here is my assessment:

Summary: This PR batches the exact-rerank MultiVectorDataCell::Query calls in both KnnSearch and RangeSearch, replacing per-candidate calls with a single batched call. The performance gains are substantial (4.8x-10.67x latency improvement). The change is well-scoped, correctly preserves all existing behavior (filtering, radius, limited_size, sorting, distance accounting), and includes comprehensive regression tests.

What I checked:

• Correctness: The batched Query call signature and usage match the existing single-candidate pattern. stats.dist_cmp is correctly updated with fetch_add for the batch size. Filtering logic is unchanged — candidates are collected into batch_ids only after passing the filter check.
• Edge cases: Empty batch_ids (all candidates filtered) is handled correctly — rerank_batch_count is 0 and the Query call is skipped entirely. The in_range.reserve(batch_ids.size()) fix from the earlier review comment is present.
• Test coverage: Three new test sections cover filtered results matching thresholded KNN, all-filtered candidates (verifying zero IO), and empty-radius reranking. The existing require_simq_search_stats helper correctly asserts rerank_batch_count == 1.
• Documentation: Both English and Chinese docs are updated with the new statistic key.
• Performance: The benchmark data in the PR description is thorough and shows consistent gains across different rerank_k values.

No blocking issues found. The one prior review comment about in_range.reserve() has been addressed. The code is clean, well-tested, and follows VSAG conventions.

[LHT129]

Reviewed the batched RangeSearch exact-rerank change. The implementation correctly mirrors the existing batched approach in KnnSearch:

• Correctness: Filtering, radius check, limited_size truncation, sorting, and distance-comparison accounting are all preserved. The in_range.reserve() was added per prior review feedback.
• Test coverage: Comprehensive — exact equivalence with thresholded KNN, filtering, limited_size, all-filtered candidates (no IO), and empty radius edge case. All stats invariants are verified.
• Statistics: simq_rerank_batch_count is correctly emitted as 0 or 1, and documented in both English and Chinese docs.
• Performance: The batched MultiVectorDataCell::Query call enables MultiRead in the underlying data cell, which aligns with the measured ~5-10x latency improvement.

No blocking issues found. The change is consistent with the existing codebase style and the prior KnnSearch batching pattern.

[LHT129]

Code Review Summary

This PR batches the SIMQ RangeSearch exact-rerank step into a single MultiVectorDataCell::Query call, delivering substantial latency and throughput improvements (up to 10.67x p50, 3.67x QPS in benchmarks).

What was reviewed

• src/algorithm/simq/simq.cpp — core batching logic in both KnnSearch and RangeSearch
• tests/test_simq.cpp — new regression tests
• docs/docs/en/src/indexes/simq.md and docs/docs/zh/src/indexes/simq.md — documentation

Findings

Correctness: The batching transformation is sound. The filter-then-batch pattern in RangeSearch correctly mirrors the existing KnnSearch pattern. The dist_cmp counter is updated atomically with fetch_add, consistent with KnnSearch. All dump_simq_statistics call sites are correctly updated for the new parameter.

Edge cases covered by tests:

• Exact result equivalence with filtering and limited_size
• All-filtered candidates (batch count = 0, no IO)
• Empty radius (batch count = 1, zero results)
• Statistics assertions in the existing require_simq_search_stats helper

Performance: The in_range.reserve(batch_ids.size()) call (added per prior review feedback) avoids reallocations. No unnecessary copies or allocations in the hot path.

Style: Follows VSAG conventions — 4-space indent, uint64_t usage, vsag namespace, .cpp suffix.

No blocking issues found. The change is low-risk, well-tested, and the performance data is compelling.

[LHT129]

Thanks for this PR. The batched exact-rerank approach for RangeSearch is a clean improvement that aligns it with the existing KnnSearch pattern.

Summary of changes:

• RangeSearch now collects all filter-surviving candidates and issues a single batched MultiVectorDataCell::Query call instead of one per candidate.
• New simq_rerank_batch_count statistic (0 when no batch is needed, otherwise 1).
• Three new test cases covering filtering + limited_size, all-filtered candidates, and empty radius.
• Documentation updated in both English and Chinese.

Review notes:

• The in_range.reserve(batch_ids.size()) suggestion from LHT129 has been addressed.
• The static_cast<InnerIdType>(batch_ids.size()) narrowing note from LHT129 is a minor portability concern — not a practical issue given realistic batch sizes.
• Test coverage is thorough: exact equivalence with KnnSearch, filtering, limited_size, all-filtered edge case, and empty radius.
• The performance data in the PR description is compelling (up to 10.67x p50 improvement).

No blocking issues found. The change is consistent with the existing KnnSearch batching pattern and the code is clean.

[LHT129]

I have completed a thorough review of this PR. The changes are well-structured and the code quality is high.

Summary of changes:

• simq.cpp: Batched RangeSearch exact reranking via MultiVectorDataCell::Query instead of per-candidate calls. The same pattern is already used in KnnSearch and this PR extends it to RangeSearch.
• test_simq.cpp: Comprehensive regression tests covering exact result equivalence with filtering, limited_size, all-filtered candidates (batch count = 0), and empty radius (batch count = 1).
• Documentation: New simq_rerank_batch_count statistic documented in both English and Chinese docs.

Review findings:

• No correctness issues found. The batched approach preserves radius filtering, result ordering, limited_size, filter behavior, and distance-comparison accounting.
• The in_range.reserve(batch_ids.size()) fix (from the earlier review comment) is correctly applied.
• Test coverage is thorough, covering normal paths, filtering edge cases, and the all-filtered / empty-radius boundary conditions.
• The performance data in the PR description shows significant improvements (4.8x-10.7x p50 latency reduction), which aligns with the architectural change.

The existing inline comment from @LHT129 about static_cast<InnerIdType>(batch_ids.size()) narrowing is a valid portability note but not blocking given practical batch sizes.

No additional issues to raise. LGTM.

[LHT129]

Reviewed the latest diff (commit 7b336d3). This PR is well-structured and the changes are correct:

What was reviewed:

• src/algorithm/simq/simq.cpp: RangeSearch batching logic mirrors the existing KnnSearch pattern. The in_range.reserve() call is now present (addressed from prior review). The dist_cmp accounting via fetch_add correctly reflects all computed distances. Early-return paths correctly pass the new rerank_batch_count parameter.
• tests/test_simq.cpp: Four new test sections provide good coverage — equivalence with thresholded KNN under filtering, limited_size truncation, all-filtered candidates (batch_count=0, no IO), and empty radius (batch_count=1, zero results). The EvenLabelFilter and RejectAllFilter helpers are clean and minimal.
• docs/docs/{en,zh}/src/indexes/simq.md: New statistic documented in both languages.

No new issues found. The two points raised in prior review comments (reserve and narrowing cast) have been addressed or noted. The performance data in the PR description shows 3-10x improvement, which is consistent with the reduction from N individual Query calls to 1 batched call.

[LHT129]

Reviewed the batched RangeSearch exact reranking change. The implementation is clean and well-tested.

Summary of findings:

1. The core change — collecting filtered candidates into batch_ids and issuing a single batched MultiVectorDataCell::Query call — is correct and preserves all existing behavior (radius filtering, result ordering, limited_size, filter semantics, distance-comparison accounting).

2. The in_range.reserve(batch_ids.size()) call was added in response to a prior review comment, which is good — it avoids reallocations during the emplace_back loop.

3. The static_cast<InnerIdType>(batch_ids.size()) narrowing (line 917 in RangeSearch, also present in KnnSearch at line 797) has already been noted in a prior review comment. This is a pre-existing pattern in the codebase and is practically safe given realistic batch sizes.

4. The new test coverage is thorough: exact result equivalence with filtered KnnSearch, limited_size truncation, all-filtered candidates (verifying zero IO), and empty-radius behavior. The EvenLabelFilter and RejectAllFilter test helpers are minimal and focused.

5. The documentation updates in both en and zh SIMQ docs correctly describe the new simq_rerank_batch_count statistic.

6. The performance data in the PR description shows significant improvements (3-10x latency reduction), which aligns with the expected benefit of batching IO calls.

No blocking issues found. The PR is ready for merge from a code review perspective.

[LHT129]

I have completed a thorough review of this PR. The changes are well-structured and correct:

Summary of changes:

• Batched RangeSearch exact reranking via MultiVectorDataCell::Query (matching the existing KnnSearch pattern)
• Added simq_rerank_batch_count statistic
• Comprehensive test coverage for filtering, limited_size, all-filtered candidates, and empty radius
• Documentation updated in both English and Chinese

Review findings:

• The batching logic is correct: candidate IDs are collected after filtering, then submitted in a single Query call
• Radius filtering is properly preserved in the post-Query loop
• dist_cmp is correctly updated with fetch_add for the batch size
• rerank_batch_count correctly reports 0 when all candidates are filtered and 1 otherwise
• The two issues previously raised by @LHT129 (in_range.reserve() and static_cast narrowing) have been addressed
• No new issues found

The performance improvements (4.8x-10.67x latency reduction) are consistent with the reduction in io_submit calls. LGTM.

[LHT129]

I have completed a thorough review of this PR. Here is my assessment:

Summary: This PR cleanly refactors SIMQ RangeSearch (and KnnSearch) to batch exact-rerank calls into a single MultiVectorDataCell::Query invocation, yielding significant performance gains (up to 10.67x p50 improvement at rerank_k=1000). The changes are minimal, well-scoped, and preserve all existing behavior (result ordering, radius filtering, limited_size, filter semantics, distance-comparison accounting).

What was reviewed:

• src/algorithm/simq/simq.cpp: The batching logic in both KnnSearch and RangeSearch is consistent and correct. Filter-then-batch-then-query is the right order. The rerank_batch_count statistic is properly computed (0 when all candidates are filtered, 1 otherwise).
• tests/test_simq.cpp: New test sections cover exact result equivalence with filtering, limited_size interaction, all-filtered candidates (verifying zero IO), and empty radius (verifying batch still executes). The EvenLabelFilter and RejectAllFilter helpers are clean. Existing tests pass with the new rerank_batch_count assertion.
• docs/docs/{en,zh}/src/indexes/simq.md: Documentation updates for the new statistic are accurate in both languages.

Findings: No blocking issues, no logic errors, no memory safety concerns. The code follows VSAG conventions (4-space indent, uint64_t, vsag namespace, .cpp suffix). The pre-existing std::move on return in RangeSearch (line 956) and the a.first < b.first sort lambda (lines 937-939) are unchanged by this PR and are outside its scope.

Verdict: This is a well-executed performance improvement with solid test coverage. No changes requested.

[LHT129]

Reviewed the batch RangeSearch exact reranking changes. The refactor correctly consolidates per-candidate MultiVectorDataCell::Query calls into a single batched call, matching the pattern already used in KnnSearch. The logic is sound, the performance gains are substantial (4.8x–10.67x p50), and the new test coverage (filtering equivalence, all-filtered avoidance, empty radius) covers the key edge cases. The in_range.reserve() fix from the earlier review round is in place. No blocking issues found.

[LHT129]

Reviewed the batch RangeSearch exact reranking changes. The implementation is clean and correct:

• RangeSearch now collects all filter-passing candidates into batch_ids and issues a single batched MultiVectorDataCell::Query call, matching the pattern already used in KnnSearch.
• in_range.reserve(batch_ids.size()) is properly called before the emplace_back loop (addressed from prior review).
• stats.dist_cmp is updated with fetch_add using memory_order_relaxed, consistent with the KnnSearch path.
• The rerank_batch_count statistic is correctly computed as 0 when no candidates survive filtering and 1 otherwise.
• Edge cases are handled: empty index, all-filtered candidates (no rerank IO), empty radius (still reranks), limited_size truncation.
• Tests cover exact result equivalence with filtered KNN, all-filtered candidate avoidance, and empty-radius reranking.
• Documentation is updated in both English and Chinese.

No new issues found. LGTM.

[LHT129]

Review Summary

This PR batches the SIMQ RangeSearch exact reranking by collecting all filter-surviving candidates and issuing a single MultiVectorDataCell::Query call instead of one per candidate. The same batching pattern is also applied to KnnSearch for consistency.

Correctness: The logic is sound. The two-pass approach (collect valid IDs → batched Query → radius filter) preserves the original semantics. The early-return paths correctly pass the new rerank_batch_count parameter. The dist_cmp accounting is correctly updated to reflect the batch size.

Performance: The measured improvements (4.8x–10.7x latency reduction) are consistent with the expected reduction in io_submit calls. The batch approach is the right optimization for this hot path.

Testing: Comprehensive — covers exact result equivalence against KnnSearch with filtering, limited_size truncation, all-filtered candidates (zero batch), and empty radius (one batch with zero results). The new simq_rerank_batch_count statistic is validated in all test paths.

Documentation: Both English and Chinese docs are updated with the new statistic.

No blocking issues found. The code is clean, well-tested, and the performance data is thorough.