Files
qdrant/lib/segment/tests/integration/byte_storage_quantization_test.rs
qdrant-cloud-bot 0e37dfd290 ci(windows): skip IO-heavy tests that aren't OS-specific (#9188)
* ci(windows): skip IO-heavy tests that aren't OS-specific

On the Windows CI runner, several tests are 3-25x slower than on Ubuntu
purely due to slow filesystem IO. These tests exercise platform-agnostic
logic (optimizer, snapshot, WAL recovery, dedup, deferred points) and
are fully covered by the Linux and macOS jobs.

Mark them with `#[cfg_attr(target_os = "windows", ignore = "...")]` so:
- Windows CI skips them and finishes faster.
- They're still listed and runnable via `cargo test -- --ignored`
  on Windows for local debugging.

Based on JUnit timings from CI run 26462785436 (PR #8827), this should
save ~5 minutes wall-clock on the Windows job, taking it closer to the
~13min Ubuntu and ~9min macOS jobs (currently 20m24s).

Tests affected:
- lib/wal: check_wal, check_last_index, check_clear, check_reopen,
  check_truncate, check_prefix_truncate, test_prefix_truncate_parametric
- lib/edge/optimize: full tests module
- lib/segment deferred-point tests: read_operations,
  dense_segment_combinations, sparse, facets
- lib/collection: snapshot_test, points_dedup, wal_recovery,
  collection_test::test_ordered_read_api, snapshot_recovery_test

Co-authored-by: Cursor <cursoragent@cursor.com>

* revert(ci/windows): keep WAL and WAL-recovery tests on Windows

Reviewer correctly pointed out that WAL is mmap-backed and has
substantial Windows-specific code paths:

- Different segment allocation (fs4 vs rustix::ftruncate)
- Windows-specific delete_windows() with mmap-drop + retry loop
- Windows-specific sync_all() because directory fsync is unavailable
- Windows-specific lock proxy file (directories aren't lockable)

So those tests genuinely need Windows coverage. Reverted skips for:
- lib/wal/src/lib.rs: all check_* tests and test_prefix_truncate_parametric
- lib/collection/src/tests/wal_recovery_test.rs: all three tests

Still skipped on Windows (no OS-specific code in their production paths):
- lib/edge/optimize.rs (no cfg(windows) in source)
- lib/segment deferred-point tests (segment/ has no cfg(windows))
- lib/collection snapshot/dedup tests (collection/ has no cfg(windows))
- lib/collection integration snapshot_recovery + ordered_read_api

Co-authored-by: Cursor <cursoragent@cursor.com>

* revert(ci/windows): keep collection integration and snapshot_test

Per reviewer request, keep running these on Windows:
- lib/collection/tests/integration/* (snapshot_recovery_test,
  collection_test::test_ordered_read_api)
- lib/collection/src/tests/snapshot_test.rs

These exercise higher-level collection/snapshot behavior that benefits
from cross-platform validation.

Remaining Windows skips (production code has no cfg(windows) branches):
- lib/edge/src/optimize.rs: 14 optimizer tests
- lib/segment/src/segment/tests/mod.rs: 4 deferred-point tests
- lib/collection/src/tests/points_dedup.rs: 2 dedup tests

Co-authored-by: Cursor <cursoragent@cursor.com>

* ci(windows): also skip HNSW/quantization integration tests

Per reviewer, also skip these segment integration test modules on Windows:
- hnsw_quantized_search_test::* (25 tests)
- multivector_filtrable_hnsw_test::* (rstest cases)
- multivector_quantization_test::* (rstest cases)
- byte_storage_quantization_test::* (rstest cases)
- payload_index_test::test_struct_payload_index_nested_fields

These exercise pure HNSW/quantization correctness on top of standard
segment IO that is already covered by tests we keep running on Windows.

Adds ~930s of sequential time to the Windows skip list, bringing the
expected wall-clock saving from ~3 min to ~8-10 min.

Co-authored-by: Cursor <cursoragent@cursor.com>

---------

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-06-03 14:49:45 +02:00

425 lines
13 KiB
Rust

use std::collections::{BTreeSet, HashMap};
use std::sync::Arc;
use std::sync::atomic::AtomicBool;
use atomic_refcell::AtomicRefCell;
use common::budget::ResourcePermit;
use common::flags::FeatureFlags;
use common::progress_tracker::ProgressTracker;
use common::types::ScoredPointOffset;
use ordered_float::OrderedFloat;
use rand::prelude::StdRng;
use rand::{Rng, RngExt, SeedableRng};
use rstest::rstest;
use segment::data_types::vectors::{
DEFAULT_VECTOR_NAME, DenseVector, QueryVector, only_default_vector,
};
use segment::entry::entry_point::SegmentEntry;
use segment::fixtures::payload_fixtures::{random_dense_byte_vector, random_int_payload};
use segment::fixtures::query_fixtures::QueryVariant;
use segment::index::hnsw_index::hnsw::{HNSWIndex, HnswIndexOpenArgs};
use segment::index::{PayloadIndex, VectorIndexRead};
use segment::segment_constructor::build_segment;
use segment::types::{
BinaryQuantizationConfig, CompressionRatio, Condition, Distance, FieldCondition, Filter,
HnswConfig, HnswGlobalConfig, Indexes, PayloadSchemaType, ProductQuantizationConfig,
QuantizationSearchParams, Range, ScalarQuantizationConfig, SearchParams, SegmentConfig,
SeqNumberType, VectorDataConfig, VectorStorageDatatype, VectorStorageType,
};
use segment::vector_storage::VectorStorageEnum;
use segment::vector_storage::quantized::quantized_vectors::{
QuantizedVectors, QuantizedVectorsStorageType,
};
use tempfile::Builder;
enum QuantizationVariant {
Scalar,
PQ,
Binary,
}
fn random_vector<R>(rnd_gen: &mut R, dim: usize, data_type: VectorStorageDatatype) -> DenseVector
where
R: Rng + ?Sized,
{
match data_type {
VectorStorageDatatype::Float32 => unreachable!(),
VectorStorageDatatype::Float16 => {
let mut vector = segment::fixtures::payload_fixtures::random_vector(rnd_gen, dim);
vector.iter_mut().for_each(|x| *x -= 0.5);
vector
}
VectorStorageDatatype::Uint8 => random_dense_byte_vector(rnd_gen, dim),
}
}
fn random_query<R: Rng + ?Sized>(
variant: &QueryVariant,
rng: &mut R,
dim: usize,
data_type: VectorStorageDatatype,
) -> QueryVector {
segment::fixtures::query_fixtures::random_query(variant, rng, |rng| {
random_vector(rng, dim, data_type).into()
})
}
fn sames_count(a: &[Vec<ScoredPointOffset>], b: &[Vec<ScoredPointOffset>]) -> usize {
a[0].iter()
.map(|x| x.idx)
.collect::<BTreeSet<_>>()
.intersection(&b[0].iter().map(|x| x.idx).collect())
.count()
}
#[cfg_attr(target_os = "windows", ignore = "slow on Windows, not OS-specific")]
#[rstest]
#[case::nearest_binary_dot(
QueryVariant::Nearest,
VectorStorageDatatype::Float16,
QuantizationVariant::Binary,
Distance::Dot,
128, // dim
32, // ef
10., // min_acc out of 100
)]
#[case::nearest_binary_dot(
QueryVariant::Nearest,
VectorStorageDatatype::Uint8,
QuantizationVariant::Binary,
Distance::Dot,
128, // dim
32, // ef
5., // min_acc out of 100
)]
#[case::discover_binary_dot(
QueryVariant::Discover,
VectorStorageDatatype::Uint8,
QuantizationVariant::Binary,
Distance::Dot,
128, // dim
128, // ef
1., // min_acc out of 100
)]
#[case::recobestscore_binary_dot(
QueryVariant::RecoBestScore,
VectorStorageDatatype::Uint8,
QuantizationVariant::Binary,
Distance::Dot,
128, // dim
64, // ef
1., // min_acc out of 100
)]
#[case::recosumscores_binary_dot(
QueryVariant::RecoSumScores,
VectorStorageDatatype::Uint8,
QuantizationVariant::Binary,
Distance::Dot,
128, // dim
64, // ef
1., // min_acc out of 100
)]
#[case::nearest_binary_cosine(
QueryVariant::Nearest,
VectorStorageDatatype::Uint8,
QuantizationVariant::Binary,
Distance::Cosine,
128, // dim
32, // ef
25., // min_acc out of 100
)]
#[case::discover_binary_cosine(
QueryVariant::Discover,
VectorStorageDatatype::Uint8,
QuantizationVariant::Binary,
Distance::Cosine,
128, // dim
128, // ef
15., // min_acc out of 100
)]
#[case::recobestscore_binary_cosine(
QueryVariant::RecoBestScore,
VectorStorageDatatype::Uint8,
QuantizationVariant::Binary,
Distance::Cosine,
128, // dim
64, // ef
15., // min_acc out of 100
)]
#[case::recosumscores_binary_cosine(
QueryVariant::RecoSumScores,
VectorStorageDatatype::Uint8,
QuantizationVariant::Binary,
Distance::Cosine,
128, // dim
64, // ef
15., // min_acc out of 100
)]
#[case::nearest_scalar_dot(
QueryVariant::Nearest,
VectorStorageDatatype::Float16,
QuantizationVariant::Scalar,
Distance::Dot,
32, // dim
32, // ef
80., // min_acc out of 100
)]
#[case::nearest_scalar_dot(
QueryVariant::Nearest,
VectorStorageDatatype::Uint8,
QuantizationVariant::Scalar,
Distance::Dot,
32, // dim
32, // ef
80., // min_acc out of 100
)]
#[case::nearest_scalar_cosine(
QueryVariant::Nearest,
VectorStorageDatatype::Uint8,
QuantizationVariant::Scalar,
Distance::Cosine,
32, // dim
32, // ef
80., // min_acc out of 100
)]
#[case::nearest_pq_dot(
QueryVariant::Nearest,
VectorStorageDatatype::Uint8,
QuantizationVariant::PQ,
Distance::Dot,
16, // dim
32, // ef
70., // min_acc out of 100
)]
fn test_byte_storage_binary_quantization_hnsw(
#[case] query_variant: QueryVariant,
#[case] storage_data_type: VectorStorageDatatype,
#[case] quantization_variant: QuantizationVariant,
#[case] distance: Distance,
#[case] dim: usize,
#[case] ef: usize,
#[case] min_acc: f64, // out of 100
) {
use common::counter::hardware_counter::HardwareCounterCell;
use segment::json_path::JsonPath;
use segment::payload_json;
use segment::segment_constructor::VectorIndexBuildArgs;
let stopped = AtomicBool::new(false);
let m = 8;
let num_vectors: u64 = 5_000;
let ef_construct = 16;
let full_scan_threshold = 16; // KB
let num_payload_values = 2;
let mut rng = StdRng::seed_from_u64(42);
let dir_byte = Builder::new().prefix("segment_dir_byte").tempdir().unwrap();
let quantized_data_path = dir_byte.path();
let hnsw_dir_byte = Builder::new().prefix("hnsw_dir_byte").tempdir().unwrap();
let config_byte = SegmentConfig {
vector_data: HashMap::from([(
DEFAULT_VECTOR_NAME.to_owned(),
VectorDataConfig {
size: dim,
distance,
storage_type: VectorStorageType::default(),
index: Indexes::Plain {},
quantization_config: None,
multivector_config: None,
datatype: Some(storage_data_type),
},
)]),
sparse_vector_data: Default::default(),
payload_storage_type: Default::default(),
};
let int_key = "int";
let mut segment_byte = build_segment(dir_byte.path(), &config_byte, None, true).unwrap();
// check that `segment_byte` uses byte or half storage
{
let borrowed_storage = segment_byte.vector_data[DEFAULT_VECTOR_NAME]
.vector_storage
.borrow();
let raw_storage: &VectorStorageEnum = &borrowed_storage;
assert!(matches!(
raw_storage,
&VectorStorageEnum::DenseAppendableMemmapByte(_)
| &VectorStorageEnum::DenseAppendableMemmapHalf(_),
));
}
let hw_counter = HardwareCounterCell::new();
for n in 0..num_vectors {
let idx = n.into();
let vector = random_vector(&mut rng, dim, storage_data_type);
let int_payload = random_int_payload(&mut rng, num_payload_values..=num_payload_values);
let payload = payload_json! {int_key: int_payload};
segment_byte
.upsert_point(
n as SeqNumberType,
idx,
only_default_vector(&vector),
&hw_counter,
)
.unwrap();
segment_byte
.set_full_payload(n as SeqNumberType, idx, &payload, &hw_counter)
.unwrap();
}
segment_byte
.payload_index
.borrow_mut()
.set_indexed(
&JsonPath::new(int_key),
PayloadSchemaType::Integer,
&hw_counter,
)
.unwrap();
let quantization_config = match quantization_variant {
QuantizationVariant::Scalar => ScalarQuantizationConfig {
r#type: Default::default(),
quantile: None,
always_ram: None,
}
.into(),
QuantizationVariant::PQ => ProductQuantizationConfig {
compression: CompressionRatio::X8,
always_ram: None,
}
.into(),
QuantizationVariant::Binary => BinaryQuantizationConfig {
always_ram: None,
encoding: None,
query_encoding: None,
}
.into(),
};
segment_byte
.vector_data
.values_mut()
.for_each(|vector_storage| {
let quantized_vectors = QuantizedVectors::create(
&vector_storage.vector_storage.borrow(),
&quantization_config,
QuantizedVectorsStorageType::Immutable,
quantized_data_path,
4,
&stopped,
)
.unwrap();
vector_storage.quantized_vectors =
Arc::new(AtomicRefCell::new(Some(quantized_vectors)));
});
let hnsw_config = HnswConfig {
m,
ef_construct,
full_scan_threshold,
max_indexing_threads: 2,
on_disk: Some(false),
payload_m: None,
inline_storage: None,
};
let permit_cpu_count = 1; // single-threaded for deterministic build
let permit = Arc::new(ResourcePermit::dummy(permit_cpu_count as u32));
let hnsw_index_byte = HNSWIndex::build(
HnswIndexOpenArgs {
path: hnsw_dir_byte.path(),
id_tracker: segment_byte.id_tracker.clone(),
vector_storage: segment_byte.vector_data[DEFAULT_VECTOR_NAME]
.vector_storage
.clone(),
quantized_vectors: segment_byte.vector_data[DEFAULT_VECTOR_NAME]
.quantized_vectors
.clone(),
payload_index: segment_byte.payload_index.clone(),
hnsw_config,
},
VectorIndexBuildArgs {
permit,
old_indices: &[],
gpu_device: None,
rng: &mut rng,
stopped: &stopped,
hnsw_global_config: &HnswGlobalConfig::default(),
feature_flags: FeatureFlags::default(),
progress: ProgressTracker::new_for_test(),
},
)
.unwrap();
let top = 5;
let mut sames = 0;
let attempts = 100;
for _ in 0..attempts {
let query = random_query(&query_variant, &mut rng, dim, storage_data_type);
let range_size = 40;
let left_range = rng.random_range(0..400);
let right_range = left_range + range_size;
let filter = Filter::new_must(Condition::Field(FieldCondition::new_range(
JsonPath::new(int_key),
Range {
lt: None,
gt: None,
gte: Some(OrderedFloat(f64::from(left_range))),
lte: Some(OrderedFloat(f64::from(right_range))),
},
)));
let filter_query = Some(&filter);
let index_result_byte = hnsw_index_byte
.search(
&[&query],
filter_query,
top,
Some(&SearchParams {
hnsw_ef: Some(ef),
quantization: Some(QuantizationSearchParams {
oversampling: Some(2.0),
..Default::default()
}),
..Default::default()
}),
&Default::default(),
)
.unwrap();
let plain_result_byte = hnsw_index_byte
.search(
&[&query],
filter_query,
top,
Some(&SearchParams {
hnsw_ef: Some(ef),
quantization: Some(QuantizationSearchParams {
ignore: true,
..Default::default()
}),
exact: true,
..Default::default()
}),
&Default::default(),
)
.unwrap();
sames += sames_count(&plain_result_byte, &index_result_byte);
}
let acc = 100.0 * sames as f64 / (attempts * top) as f64;
println!("sames = {sames}, attempts = {attempts}, top = {top}, acc = {acc}");
assert!(acc > min_acc);
}