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use std::sync::atomic::AtomicBool;
use common::types::PointOffsetType;
use criterion::{criterion_group, criterion_main, BatchSize, Criterion};
use dataset::Dataset;
use indicatif::{ProgressBar, ProgressDrawTarget, ProgressStyle};
use itertools::Itertools as _;
use rand::rngs::StdRng;
use rand::SeedableRng;
use segment::fixtures::sparse_fixtures::fixture_sparse_index_from_iter;
use segment::index::sparse_index::sparse_index_config::{SparseIndexConfig, SparseIndexType};
use segment::index::sparse_index::sparse_vector_index::{
SparseVectorIndex, SparseVectorIndexOpenArgs,
};
use segment::index::{PayloadIndex, VectorIndex};
use segment::types::PayloadSchemaType::Keyword;
use segment::types::{Condition, FieldCondition, Filter, Payload};
use serde_json::json;
use sparse::common::sparse_vector::SparseVector;
use sparse::common::sparse_vector_fixture::{random_positive_sparse_vector, random_sparse_vector};
use sparse::index::inverted_index::inverted_index_compressed_mmap::InvertedIndexCompressedMmap;
use sparse::index::inverted_index::inverted_index_ram::InvertedIndexRam;
use sparse::index::loaders::Csr;
use tempfile::Builder;
#[cfg(not(target_os = "windows"))]
mod prof;
const NUM_VECTORS: usize = 50_000;
const MAX_SPARSE_DIM: usize = 30_000;
const NUM_QUERIES: usize = 2048;
const TOP: usize = 10;
const FULL_SCAN_THRESHOLD: usize = 1; // low value to trigger index usage by default
fn sparse_vector_index_search_benchmark(c: &mut Criterion) {
let mut rnd = StdRng::seed_from_u64(0);
let query_vectors = (0..NUM_QUERIES)
// Positive values to test pruning.
.map(|_| random_positive_sparse_vector(&mut rnd, MAX_SPARSE_DIM))
.collect::<Vec<_>>();
let mut rnd = StdRng::seed_from_u64(42);
let random_vectors = (0..NUM_VECTORS).map(|_| random_sparse_vector(&mut rnd, MAX_SPARSE_DIM));
sparse_vector_index_search_benchmark_impl(c, "random-50k", random_vectors, &query_vectors);
let dataset_vectors = Csr::open(Dataset::NeurIps2023_1M.download().unwrap()).unwrap();
let query_vectors = Csr::open(Dataset::NeurIps2023Queries.download().unwrap())
.unwrap()
.iter()
.map(|v| v.unwrap())
.collect_vec();
sparse_vector_index_search_benchmark_impl(
c,
"neurips2023-1M",
dataset_vectors.iter().map(|v| v.unwrap()),
&query_vectors,
);
}
fn sparse_vector_index_search_benchmark_impl(
c: &mut Criterion,
group: &str,
vectors: impl ExactSizeIterator<Item = SparseVector>,
query_vectors: &[SparseVector],
) {
let mut group = c.benchmark_group(format!("sparse_vector_index_search/{group}"));
group.sample_size(10);
let vectors_len = vectors.len();
let stopped = AtomicBool::new(false);
let data_dir = Builder::new().prefix("data_dir").tempdir().unwrap();
let sparse_vector_index = fixture_sparse_index_from_iter::<InvertedIndexRam>(
data_dir.path(),
progress("Indexing (1/2)", vectors_len).wrap_iter(vectors),
FULL_SCAN_THRESHOLD,
SparseIndexType::MutableRam,
)
.unwrap();
// adding payload on field
let field_name = "field";
let field_value = "important value";
let payload: Payload = json!({
field_name: field_value,
})
.into();
// all points have the same payload
let mut payload_index = sparse_vector_index.payload_index().borrow_mut();
for idx in 0..NUM_VECTORS {
payload_index
.set_payload(idx as PointOffsetType, &payload, &None)
.unwrap();
}
drop(payload_index);
let mut query_vector_it = query_vectors.iter().cycle();
// mmap inverted index
let mmap_index_dir = Builder::new().prefix("mmap_index_dir").tempdir().unwrap();
let sparse_index_config =
SparseIndexConfig::new(Some(FULL_SCAN_THRESHOLD), SparseIndexType::Mmap, None);
let pb = progress("Indexing (2/2)", vectors_len);
let sparse_vector_index_mmap: SparseVectorIndex<InvertedIndexCompressedMmap<f32>> =
SparseVectorIndex::open(SparseVectorIndexOpenArgs {
config: sparse_index_config,
id_tracker: sparse_vector_index.id_tracker().clone(),
vector_storage: sparse_vector_index.vector_storage().clone(),
payload_index: sparse_vector_index.payload_index().clone(),
path: mmap_index_dir.path(),
stopped: &stopped,
tick_progress: || pb.inc(1),
})
.unwrap();
pb.finish_and_clear();
assert_eq!(sparse_vector_index_mmap.indexed_vector_count(), vectors_len);
// intent: bench `search` without filter on mmap inverted index
group.bench_function("mmap-inverted-index-search", |b| {
b.iter_batched(
|| query_vector_it.next().unwrap().clone().into(),
|vec| {
let results = sparse_vector_index_mmap
.search(&[&vec], None, TOP, None, &Default::default())
.unwrap();
assert_eq!(results[0].len(), TOP);
},
BatchSize::SmallInput,
)
});
// intent: bench `search` without filter
group.bench_function("inverted-index-search", |b| {
b.iter_batched(
|| query_vector_it.next().unwrap().clone().into(),
|vec| {
let results = sparse_vector_index
.search(&[&vec], None, TOP, None, &Default::default())
.unwrap();
assert_eq!(results[0].len(), TOP);
},
BatchSize::SmallInput,
)
});
// filter by field
let filter = Filter::new_must(Condition::Field(FieldCondition::new_match(
field_name.parse().unwrap(),
field_value.to_owned().into(),
)));
// intent: bench plain search when the filtered payload key is not indexed
if vectors_len < 100_000 {
group.bench_function("inverted-index-filtered-plain", |b| {
b.iter_batched(
|| query_vector_it.next().unwrap(),
|vec| {
let mut prefiltered_points = None;
let results = sparse_vector_index
.search_plain(
vec,
&filter,
TOP,
&mut prefiltered_points,
&Default::default(),
)
.unwrap();
assert_eq!(results.len(), TOP);
},
BatchSize::SmallInput,
)
});
}
let mut payload_index = sparse_vector_index.payload_index().borrow_mut();
// create payload field index
payload_index
.set_indexed(&field_name.parse().unwrap(), Keyword)
.unwrap();
drop(payload_index);
// intent: bench `search` when the filtered payload key is indexed
group.bench_function("inverted-index-filtered-payload-index", |b| {
b.iter_batched(
|| query_vector_it.next().unwrap().clone().into(),
|vec| {
let results = sparse_vector_index
.search(&[&vec], Some(&filter), TOP, None, &Default::default())
.unwrap();
assert_eq!(results[0].len(), TOP);
},
BatchSize::SmallInput,
);
});
// intent: bench plain search when the filtered payload key is indexed
if vectors_len < 100_000 {
group.bench_function("plain-filtered-payload-index", |b| {
b.iter_batched(
|| query_vector_it.next().unwrap(),
|vec| {
let mut prefiltered_points = None;
let results = sparse_vector_index
.search_plain(
vec,
&filter,
TOP,
&mut prefiltered_points,
&Default::default(),
)
.unwrap();
assert_eq!(results.len(), TOP);
},
BatchSize::SmallInput,
)
});
}
group.finish();
}
fn progress(name: &str, len: usize) -> ProgressBar {
let pb =
ProgressBar::with_draw_target(Some(len as u64), ProgressDrawTarget::stderr_with_hz(12));
pb.set_style(
ProgressStyle::default_bar()
.template("{msg} {wide_bar} {pos}/{len} (eta:{eta})")
.unwrap(),
);
pb.set_message(name.to_owned());
pb
}
#[cfg(not(target_os = "windows"))]
criterion_group! {
name = benches;
config = Criterion::default().with_profiler(prof::FlamegraphProfiler::new(100));
targets = sparse_vector_index_search_benchmark
}
#[cfg(target_os = "windows")]
criterion_group! {
name = benches;
config = Criterion::default();
targets = sparse_vector_index_search_benchmark,
}
criterion_main!(benches);
|