[eval] Don't lock SegmentHolder for the entire duration of read operations

[Uzay-G]

Mirror of qdrant#8056 (MERGED) for Orpheus review evaluation.

Upstream: qdrant#8056

---

Original PR description:

Continuation of qdrant#8024

Benchmarks

Setup

Parallel updates + searches using the following commands:

# Upsert
bfb  -n 10M -d 512 --shards 3 --replication-factor 2 --on-disk-vectors true \
     --keywords 5000 --hnsw-m 0 --hnsw-payload-m 16 --tenants true -b 10 --timeout 60 --rps 100

# Search
bfb -n 230k -d 512 --skip-setup --search --keywords 5000 --rps 200 --measure-slow-requests true

For hardware, a 2 core 8GB cloud cluster with 1 node was used.

For the searches, a slightly modified version of BFB was used, mostly to measure slow requests in detail.
The changes can be found here: qdrant/bfb#97

Results

The results of 2-3 runs on each Qdrant@1.16.3 and this PR averaged.

PR

--- QPS (averaged) ---
Min qps:	192.970664440867
Avg qps:	199.982515579988
Median qps:	199.999541695647
Max qps:	201.976920003441

--- Slow requests (averaged) ---
Total: 976
Min slow request	time	0.100097543
Avg slow request	time	0.221581551442721
Median slow request	time	0.1821884285
p95 slow request	time	0.512773123
p99 slow request	time	0.578455576
Max slow request	time	0.6067236445

Qdrant@1.16.3

--- QPS (averaged) ---
Min qps:	194.190540742037
Avg qps:	199.985118634619
Median qps:	199.999534991853
Max qps:	204.55869924178

--- Slow requests (averaged) ---
Total: 1463
Min slow	request	time	0.100111962
Avg slow	request	time	0.314250290654919
Median slow	request	time	0.276687310333333
p95 slow	request	time	0.650480541
p99 slow	request	time	0.728994755
Max slow	request	time	0.792417519666667

Furthermore:

• On Dev, the upsert command almost always timeouted after ~11 minutes.
• On this PR, the timeout was around ~13-15 minutes.

These results show that this PR improves on slow requests during search

[orpheus-by-fulcrum-dev-uzay-g]

Review started.

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All checks passed.

Expected behavior

• SegmentHolder read lock is held only long enough to snapshot the segment list, not for the entire duration of read operations (search, retrieve, scroll, count, collection info, shard stats)
• Search results remain correct after the refactor — same results, same ordering
• Point retrieval by ID returns correct payloads and vectors
• Scroll, count, and collection info endpoints produce correct results
• Concurrent reads and writes do not deadlock, corrupt data, or produce significantly degraded latency
• Error handling (non-existent collections, wrong dimensions, bad JSON) is unaffected
• The non_appendable_then_appendable ordering invariant is preserved in all collected segment snapshots

What happens

• ✅ All read operations (search, retrieve, scroll, count, collection info) return correct results after the refactor
• ✅ Concurrent reads and writes: search latency stays low (avg 11ms, p99 20ms) under continuous upsert load, with no deadlocks or data corruption
• ✅ Data consistency verified: all 9400 points correctly stored and retrievable after concurrent read/write workload
• ✅ Error handling works correctly: proper error messages for non-existent collections, wrong vector dimensions, and malformed JSON
• ✅ Empty collection edge case: search returns empty results without errors

Detailed evidence

Setup

# Built Qdrant from the PR branch (eval/upstream-pr-8056)
export PATH="/home/agent/.cargo/bin:$PATH"
CARGO_PROFILE_RELEASE_CODEGEN_UNITS=16 CARGO_PROFILE_RELEASE_LTO=false cargo build --release -j 1
# Finished `release` profile [optimized] target(s) in 38m 43s

# Started Qdrant
/home/agent/qdrant/target/release/qdrant &
# Version: 1.16.4-dev, build: 34cf12f6

curl -s http://localhost:6333/healthz
# healthz check passed

Functional correctness: search, retrieve, scroll, count

# Created collection
curl -s -X PUT http://localhost:6333/collections/test_collection \
  -H 'Content-Type: application/json' \
  -d '{"vectors": {"size": 4, "distance": "Cosine"}}'
# {"result":true,"status":"ok","time":0.050764601}

# Upserted 10 points with payloads
curl -s -X PUT http://localhost:6333/collections/test_collection/points \
  -H 'Content-Type: application/json' \
  -d '{"points": [
    {"id": 1, "vector": [0.1, 0.2, 0.3, 0.4], "payload": {"city": "Berlin", "count": 10}},
    {"id": 5, "vector": [0.5, 0.6, 0.7, 0.8], "payload": {"city": "NYC", "count": 50}},
    ... (10 points total)
  ]}'
# {"result":{"operation_id":1,"status":"acknowledged"},"status":"ok","time":0.000721671}

# Search — returns correct nearest neighbors
curl -s -X POST http://localhost:6333/collections/test_collection/points/search \
  -H 'Content-Type: application/json' \
  -d '{"vector": [0.5, 0.6, 0.7, 0.8], "limit": 3, "with_payload": true, "with_vector": true}'
# Top result: point 5 (NYC) with score=1.0 (exact match), followed by 6 (Moscow, 0.9998) and 4 (Tokyo, 0.9995)

# Retrieve by ID — correct payloads and vectors
curl -s -X POST http://localhost:6333/collections/test_collection/points \
  -H 'Content-Type: application/json' \
  -d '{"ids": [1, 5, 10], "with_payload": true, "with_vector": true}'
# All 3 points returned with correct payloads: Berlin/10, NYC/50, Delhi/100

# Scroll — correct pagination
curl -s -X POST http://localhost:6333/collections/test_collection/points/scroll \
  -H 'Content-Type: application/json' \
  -d '{"limit": 5, "with_payload": true}'
# Returns first 5 points (1-5) with next_page_offset=6

# Count — correct total
curl -s -X POST http://localhost:6333/collections/test_collection/points/count \
  -H 'Content-Type: application/json' \
  -d '{"exact": true}'
# {"result":{"count":10},...}

# Collection info — correct segment/point counts
curl -s http://localhost:6333/collections/test_collection
# Status: green, points_count: 10, segments_count: 2

# Search with filter
curl -s -X POST http://localhost:6333/collections/test_collection/points/search \
  -H 'Content-Type: application/json' \
  -d '{"vector": [0.5, 0.6, 0.7, 0.8], "limit": 5, "filter": {"must": [{"key": "count", "range": {"gte": 30}}]}, "with_payload": true}'
# Returns 5 results, all with count >= 30, correctly filtered

# Count with filter
curl -s -X POST http://localhost:6333/collections/test_collection/points/count \
  -H 'Content-Type: application/json' \
  -d '{"filter": {"must": [{"key": "city", "match": {"value": "Paris"}}]}, "exact": true}'
# {"result":{"count":1},...}

Concurrent read/write contention test

# Created larger collection (128-dim, 2000 points, 4 segments)
curl -s -X PUT http://localhost:6333/collections/contention_test \
  -H 'Content-Type: application/json' \
  -d '{"vectors": {"size": 128, "distance": "Cosine"}, "shard_number": 1, "optimizers_config": {"default_segment_number": 4, "indexing_threshold": 100000}}'

# Bulk inserted 2000 points in batches of 200
for i in 0..9; do python3 gen_points.py 200 $((i*200+1)) | curl -s -X PUT .../points -d @-; done
# Count verified: {"result":{"count":2000},...}

# Baseline: 50 searches without concurrent writes
# avg=9.4ms, median=9ms, p95=12ms, p99=13ms, max=13ms

# Contention: 50 searches WITH continuous background upserts (100-point batches)
# avg=11.0ms, median=10ms, p95=16ms, p99=20ms, max=20ms
# ~7000 new points were upserted during the test

# After concurrent test:
# Total points: 9400 (2000 original + ~7400 from background writer)
# Status: green, segments: 4
# All original points still retrievable with correct payloads
# Search still returns correct results

Error handling

# Non-existent collection
curl -s -X POST http://localhost:6333/collections/nonexistent/points/search -d '...'
# {"status":{"error":"Not found: Collection `nonexistent` doesn't exist!"},...}

# Non-existent point ID
curl -s -X POST http://localhost:6333/collections/test_collection/points -d '{"ids": [99999], "with_payload": true}'
# {"result":[],"status":"ok",...}

# Wrong vector dimension
curl -s -X POST http://localhost:6333/collections/test_collection/points/search -d '{"vector": [0.1, 0.2], "limit": 3}'
# {"status":{"error":"Wrong input: Vector dimension error: expected dim: 4, got 2"},...}

# Empty collection search
curl -s -X POST http://localhost:6333/collections/empty_collection/points/search -d '...'
# {"result":[],"status":"ok",...}

Minor note

In lib/shard/src/segment_holder/read_points.rs:73, the timeout error message says "fill query context" but this method (read_points_locked) is called from retrieve_blocking, not from fill_query_context. This is cosmetic — the wrong error message would appear when a retrieve operation times out waiting for the segment holder lock.

Test suite

Could not run cargo test due to disk space constraints on the sandbox VM (9.9GB disk, ~1.1GB free after release build). The build itself compiled and linked successfully with no warnings.