Squashed 'vendor/ruvector/' content from commit b64c2172
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
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ruv
204
crates/ruvector-core/benches/batch_operations.rs
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204
crates/ruvector-core/benches/batch_operations.rs
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use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion};
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use ruvector_core::types::{DistanceMetric, SearchQuery};
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use ruvector_core::{DbOptions, VectorDB, VectorEntry};
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use tempfile::tempdir;
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fn bench_batch_insert(c: &mut Criterion) {
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let mut group = c.benchmark_group("batch_insert");
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for batch_size in [100, 1000, 10000].iter() {
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group.bench_with_input(
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BenchmarkId::from_parameter(batch_size),
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batch_size,
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|bench, &size| {
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bench.iter_batched(
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|| {
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// Setup: Create DB and vectors
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let dir = tempdir().unwrap();
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let mut options = DbOptions::default();
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options.storage_path =
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dir.path().join("bench.db").to_string_lossy().to_string();
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options.dimensions = 128;
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options.hnsw_config = None; // Use flat index for faster insertion
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let db = VectorDB::new(options).unwrap();
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let vectors: Vec<VectorEntry> = (0..size)
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.map(|i| VectorEntry {
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id: Some(format!("vec_{}", i)),
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vector: (0..128).map(|j| ((i + j) as f32) * 0.01).collect(),
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metadata: None,
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})
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.collect();
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(db, vectors, dir)
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},
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|(db, vectors, _dir)| {
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// Benchmark: Batch insert
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db.insert_batch(black_box(vectors)).unwrap()
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},
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criterion::BatchSize::LargeInput,
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);
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},
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);
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}
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group.finish();
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}
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fn bench_individual_insert_vs_batch(c: &mut Criterion) {
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let mut group = c.benchmark_group("individual_vs_batch_insert");
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let size = 1000;
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// Individual inserts
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group.bench_function("individual_1000", |bench| {
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bench.iter_batched(
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|| {
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let dir = tempdir().unwrap();
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let mut options = DbOptions::default();
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options.storage_path = dir.path().join("bench.db").to_string_lossy().to_string();
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options.dimensions = 64;
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options.hnsw_config = None;
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let db = VectorDB::new(options).unwrap();
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let vectors: Vec<VectorEntry> = (0..size)
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.map(|i| VectorEntry {
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id: Some(format!("vec_{}", i)),
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vector: vec![i as f32; 64],
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metadata: None,
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})
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.collect();
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(db, vectors, dir)
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},
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|(db, vectors, _dir)| {
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for vector in vectors {
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db.insert(black_box(vector)).unwrap();
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}
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},
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criterion::BatchSize::LargeInput,
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);
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});
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// Batch insert
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group.bench_function("batch_1000", |bench| {
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bench.iter_batched(
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|| {
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let dir = tempdir().unwrap();
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let mut options = DbOptions::default();
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options.storage_path = dir.path().join("bench.db").to_string_lossy().to_string();
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options.dimensions = 64;
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options.hnsw_config = None;
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let db = VectorDB::new(options).unwrap();
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let vectors: Vec<VectorEntry> = (0..size)
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.map(|i| VectorEntry {
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id: Some(format!("vec_{}", i)),
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vector: vec![i as f32; 64],
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metadata: None,
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})
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.collect();
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(db, vectors, dir)
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},
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|(db, vectors, _dir)| db.insert_batch(black_box(vectors)).unwrap(),
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criterion::BatchSize::LargeInput,
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);
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});
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group.finish();
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}
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fn bench_parallel_searches(c: &mut Criterion) {
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let dir = tempdir().unwrap();
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let mut options = DbOptions::default();
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options.storage_path = dir
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.path()
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.join("search_bench.db")
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.to_string_lossy()
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.to_string();
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options.dimensions = 128;
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options.distance_metric = DistanceMetric::Cosine;
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options.hnsw_config = None;
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let db = VectorDB::new(options).unwrap();
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// Insert test data
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let vectors: Vec<VectorEntry> = (0..1000)
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.map(|i| VectorEntry {
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id: Some(format!("vec_{}", i)),
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vector: (0..128).map(|j| ((i + j) as f32) * 0.01).collect(),
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metadata: None,
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})
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.collect();
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db.insert_batch(vectors).unwrap();
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// Benchmark multiple sequential searches
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c.bench_function("sequential_searches_100", |bench| {
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bench.iter(|| {
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for i in 0..100 {
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let query: Vec<f32> = (0..128).map(|j| ((i + j) as f32) * 0.01).collect();
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let _ = db
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.search(SearchQuery {
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vector: black_box(query),
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k: 10,
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filter: None,
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ef_search: None,
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})
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.unwrap();
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}
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});
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});
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}
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fn bench_batch_delete(c: &mut Criterion) {
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let mut group = c.benchmark_group("batch_delete");
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for size in [100, 1000].iter() {
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group.bench_with_input(BenchmarkId::from_parameter(size), size, |bench, &size| {
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bench.iter_batched(
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|| {
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// Setup: Create DB with vectors
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let dir = tempdir().unwrap();
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let mut options = DbOptions::default();
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options.storage_path =
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dir.path().join("bench.db").to_string_lossy().to_string();
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options.dimensions = 32;
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options.hnsw_config = None;
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let db = VectorDB::new(options).unwrap();
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let vectors: Vec<VectorEntry> = (0..size)
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.map(|i| VectorEntry {
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id: Some(format!("vec_{}", i)),
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vector: vec![i as f32; 32],
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metadata: None,
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})
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.collect();
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let ids = db.insert_batch(vectors).unwrap();
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(db, ids, dir)
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},
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|(db, ids, _dir)| {
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// Benchmark: Delete all
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for id in ids {
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db.delete(black_box(&id)).unwrap();
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}
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},
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criterion::BatchSize::LargeInput,
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);
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});
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}
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group.finish();
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}
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criterion_group!(
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benches,
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bench_batch_insert,
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bench_individual_insert_vs_batch,
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bench_parallel_searches,
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bench_batch_delete
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);
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criterion_main!(benches);
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