From b1185292e60f4709b7bf20e6304a14ee1e07f182 Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sun, 10 May 2026 01:33:21 -0700 Subject: [PATCH 01/37] refactor(mem_wal): redesign FTS mem index for single-writer multi-reader Replace the SkipMap<(token, row_position)> postings layout with per-term ArcSwap slices and an ArcSwap-published per-batch visibility watermark. Readers grab a snapshot, walk per-term chunks filtered by chunk.batch_position < snapshot.visible_count, and score with snapshot-coupled BM25 stats. Writer publishes the snapshot only after every term chunk for a batch is linked, so readers never observe a partial document or BM25 stats out of sync with the postings. Also: tokenize-time tokenizer ownership moves out of a shared Mutex into a tokenizer pool plus a writer-dedicated slot, so search calls do not serialize against the writer; add Utf8View to the supported text types; add memory_usage() for size-based flush triggers; reuse lance-index InvertedIndex's TokenSet/DocSet/PostingListBuilder for the flush path. --- rust/lance/Cargo.toml | 1 + rust/lance/src/dataset/mem_wal/index/fts.rs | 2579 +++++++++---------- 2 files changed, 1251 insertions(+), 1329 deletions(-) diff --git a/rust/lance/Cargo.toml b/rust/lance/Cargo.toml index 83d0f5f989f..e1e5a3bc823 100644 --- a/rust/lance/Cargo.toml +++ b/rust/lance/Cargo.toml @@ -68,6 +68,7 @@ rayon.workspace = true futures.workspace = true uuid.workspace = true arrow.workspace = true +arc-swap.workspace = true # TODO: use datafusion sub-modules to reduce build size? datafusion.workspace = true datafusion-functions.workspace = true diff --git a/rust/lance/src/dataset/mem_wal/index/fts.rs b/rust/lance/src/dataset/mem_wal/index/fts.rs index e928790aa64..5b924617bd6 100644 --- a/rust/lance/src/dataset/mem_wal/index/fts.rs +++ b/rust/lance/src/dataset/mem_wal/index/fts.rs @@ -1,36 +1,45 @@ // SPDX-License-Identifier: Apache-2.0 // SPDX-FileCopyrightText: Copyright The Lance Authors -//! In-memory Full-Text Search (FTS) index. +//! In-memory Full-Text Search (FTS) index with single-writer / multi-reader +//! semantics. //! -//! Provides inverted index for text search using crossbeam-skiplist. -//! Uses the same tokenization as Lance's InvertedIndex for consistency. +//! # Concurrency model //! -//! ## Current Features -//! - BM25 scoring algorithm for relevance ranking -//! - Automatic result ordering by score (descending) -//! - Single-column term queries -//! - Phrase queries with slop support +//! - **One writer** (`insert` / `insert_with_batch_position`) at a time per +//! index. Callers are responsible for that invariant; this is consistent +//! with `IndexStore`'s usage from `ShardWriter`. +//! - **Many readers** (`search*`, `expand_fuzzy`, `to_index_builder_reversed`) +//! in parallel with the writer. Reads are lock-free aside from a brief +//! tokenizer-pool checkout. +//! - **Per-batch monotonic visibility**: a reader either sees every row of +//! batch `b` or none of them. A reader never sees a batch numbered above +//! the published `visible_count`. BM25 statistics observed by a reader +//! (`doc_count`, `total_tokens`, per-term `df`) are mutually consistent +//! with the postings the reader walks. //! -//! ## Pending Features (TODO) -//! - Multi-column search: Search across multiple columns simultaneously -//! - Boolean queries: MUST/SHOULD/MUST_NOT for complex query logic -//! - Fuzzy matching: Typo tolerance with configurable edit distance -//! - Boost queries: Positive/negative boosting for relevance tuning -//! - WAND factor: Performance/recall tradeoff control -//! - Per-term/column boost: Fine-grained relevance weighting +//! Visibility is published atomically by replacing a single `Snapshot` value +//! via `ArcSwap`. The writer first installs all term chunks into the +//! per-term `ArcSwap` slots, then atomically swaps in a new +//! `Snapshot` whose `visible_count` covers the new batch. Readers load the +//! `Snapshot` first and filter every term chunk by `batch_position < +//! snapshot.visible_count`. //! -//! **Note**: FTS index flush to persistent storage is NOT YET IMPLEMENTED. -//! The in-memory index works for real-time queries on MemTable data, -//! but is skipped during MemTable flush. +//! # On-disk format +//! +//! At flush time we hand off to `lance_index::scalar::inverted::builder::InnerBuilder` +//! via `to_index_builder_reversed`. The on-disk format is unchanged from +//! Lance's existing inverted index. -use std::collections::HashMap; +use std::collections::{HashMap, HashSet}; +use std::sync::Arc; use std::sync::Mutex; use std::sync::atomic::{AtomicUsize, Ordering}; -use arrow_array::RecordBatch; +use arc_swap::ArcSwap; +use arrow_array::{Array, LargeStringArray, RecordBatch, StringArray, StringViewArray}; +use arrow_schema::DataType; use crossbeam_skiplist::SkipMap; -use datafusion::common::ScalarValue; use lance_core::{Error, Result}; use lance_index::scalar::InvertedIndexParams; use lance_index::scalar::inverted::tokenizer::document_tokenizer::LanceTokenizer; @@ -38,18 +47,11 @@ use lance_tokenizer::TokenStream; use super::RowPosition; -/// Composite key for FTS index. -/// -/// By combining (token, row_position), each entry is unique. -#[derive(Clone, Debug, PartialEq, Eq, PartialOrd, Ord)] -pub struct FtsKey { - /// The indexed token (lowercase). - pub token: String, - /// Row position (makes the key unique for tokens appearing in multiple docs). - pub row_position: RowPosition, -} +// ============================================================================ +// Public types preserved from previous API +// ============================================================================ -/// In-memory FTS (Full-Text Search) index entry (returned from search). +/// In-memory FTS index entry returned from search. #[derive(Debug, Clone)] pub struct FtsEntry { /// Row position in MemTable. @@ -59,9 +61,6 @@ pub struct FtsEntry { } /// Full-text search query expression for composable queries. -/// -/// Supports simple term matches, phrase queries, fuzzy matching, and Boolean -/// combinations with MUST/SHOULD/MUST_NOT logic. #[derive(Debug, Clone)] pub enum FtsQueryExpr { /// Simple term match query. @@ -85,9 +84,9 @@ pub enum FtsQueryExpr { /// The search query string. query: String, /// Maximum edit distance (Levenshtein distance). - /// None means auto-fuzziness based on token length. + /// `None` means auto-fuzziness based on token length. fuzziness: Option, - /// Maximum number of terms to expand to (default 50). + /// Maximum number of terms to expand to. max_expansions: usize, /// Boost factor applied to the score (default 1.0). boost: f32, @@ -101,18 +100,14 @@ pub enum FtsQueryExpr { /// No MUST_NOT clause may match (excludes documents). must_not: Vec, }, - /// Boosting query with positive and optional negative components. - /// - /// Documents matching the positive query are returned. - /// If a negative query is provided, documents matching both positive - /// and negative have their scores reduced by `negative_boost`. + /// Boosting query with a positive and an optional negative component. Boost { - /// The primary query (documents must match this). + /// Documents must match this query. positive: Box, - /// Optional query to demote matching documents. + /// Optional query whose matches are demoted. negative: Option>, - /// Boost factor for documents matching negative query (typically < 1.0). - /// Score becomes: original_score * negative_boost for docs matching negative. + /// Multiplier applied to documents matching `negative` (typically + /// `< 1.0` to demote). negative_boost: f32, }, } @@ -124,23 +119,10 @@ pub const DEFAULT_MAX_EXPANSIONS: usize = 50; pub const DEFAULT_WAND_FACTOR: f32 = 1.0; /// Search options for controlling performance/recall tradeoffs. -/// -/// The WAND (Weak AND) factor allows trading recall for performance: -/// - `wand_factor = 1.0`: Full recall (default), all matching documents returned -/// - `wand_factor < 1.0`: Faster but may miss some results. Documents with -/// scores below `top_k_score * wand_factor` are pruned. -/// -/// # Example -/// ```ignore -/// let options = SearchOptions::default() -/// .with_limit(10) -/// .with_wand_factor(0.5); -/// let results = index.search_with_options(&query, options); -/// ``` #[derive(Debug, Clone)] pub struct SearchOptions { /// WAND factor for early termination (0.0 to 1.0). - /// 1.0 = full recall, <1.0 = faster but may miss low-scoring results. + /// 1.0 = full recall (default). pub wand_factor: f32, /// Maximum number of results to return. None means unlimited. pub limit: Option, @@ -156,16 +138,11 @@ impl Default for SearchOptions { } impl SearchOptions { - /// Create new SearchOptions with default values. pub fn new() -> Self { Self::default() } /// Set the WAND factor for early termination. - /// - /// - 1.0 = full recall (default) - /// - 0.5 = prune documents scoring below 50% of the current k-th best score - /// - 0.0 = only return the absolute best match pub fn with_wand_factor(mut self, wand_factor: f32) -> Self { self.wand_factor = wand_factor.clamp(0.0, 1.0); self @@ -179,7 +156,6 @@ impl SearchOptions { } impl FtsQueryExpr { - /// Create a simple match query. pub fn match_query(query: impl Into) -> Self { Self::Match { query: query.into(), @@ -187,7 +163,6 @@ impl FtsQueryExpr { } } - /// Create a phrase query with exact matching (slop=0). pub fn phrase(query: impl Into) -> Self { Self::Phrase { query: query.into(), @@ -196,7 +171,6 @@ impl FtsQueryExpr { } } - /// Create a phrase query with specified slop. pub fn phrase_with_slop(query: impl Into, slop: u32) -> Self { Self::Phrase { query: query.into(), @@ -205,22 +179,15 @@ impl FtsQueryExpr { } } - /// Create a fuzzy match query with auto-fuzziness. - /// - /// Auto-fuzziness is calculated based on token length: - /// - 0-2 chars: 0 (exact match) - /// - 3-5 chars: 1 - /// - 6+ chars: 2 pub fn fuzzy(query: impl Into) -> Self { Self::Fuzzy { query: query.into(), - fuzziness: None, // auto + fuzziness: None, max_expansions: DEFAULT_MAX_EXPANSIONS, boost: 1.0, } } - /// Create a fuzzy match query with specified edit distance. pub fn fuzzy_with_distance(query: impl Into, fuzziness: u32) -> Self { Self::Fuzzy { query: query.into(), @@ -230,7 +197,6 @@ impl FtsQueryExpr { } } - /// Create a fuzzy match query with specified edit distance and max expansions. pub fn fuzzy_with_options( query: impl Into, fuzziness: Option, @@ -244,14 +210,10 @@ impl FtsQueryExpr { } } - /// Create a Boolean query. pub fn boolean() -> BooleanQueryBuilder { BooleanQueryBuilder::new() } - /// Create a boosting query with only a positive component. - /// - /// This is equivalent to just running the positive query. pub fn boosting(positive: Self) -> Self { Self::Boost { positive: Box::new(positive), @@ -260,17 +222,6 @@ impl FtsQueryExpr { } } - /// Create a boosting query with positive and negative components. - /// - /// Documents matching the positive query are returned. - /// Documents matching both positive and negative have their scores - /// multiplied by `negative_boost` (typically < 1.0 to demote). - /// - /// # Arguments - /// - /// * `positive` - The primary query (documents must match this) - /// * `negative` - Query to demote matching documents - /// * `negative_boost` - Multiplier for documents matching negative (e.g., 0.5) pub fn boosting_with_negative(positive: Self, negative: Self, negative_boost: f32) -> Self { Self::Boost { positive: Box::new(positive), @@ -279,7 +230,6 @@ impl FtsQueryExpr { } } - /// Apply a boost factor to this query. pub fn with_boost(self, boost: f32) -> Self { match self { Self::Match { query, .. } => Self::Match { query, boost }, @@ -295,42 +245,15 @@ impl FtsQueryExpr { max_expansions, boost, }, - Self::Boolean { - must, - should, - must_not, - } => { - // For Boolean queries, boost is not directly applied - // (would need to apply to sub-queries) - Self::Boolean { - must, - should, - must_not, - } - } - Self::Boost { - positive, - negative, - negative_boost, - } => { - // For Boost queries, we wrap the positive in a boosted match - // This is a bit unusual - typically you'd boost individual sub-queries - Self::Boost { - positive, - negative, - negative_boost, - } - } + // Boolean and Boost don't carry a top-level boost field today. + // Preserved as-is to keep behavior identical to the previous impl. + other @ (Self::Boolean { .. } | Self::Boost { .. }) => other, } } } -/// Calculate auto-fuzziness based on token length. -/// -/// This follows the same algorithm as Lance's existing InvertedIndex: -/// - 0-2 chars: 0 (exact match only) -/// - 3-5 chars: 1 edit allowed -/// - 6+ chars: 2 edits allowed +/// Auto-fuzziness based on token length: +/// 0–2 chars → 0, 3–5 chars → 1, 6+ chars → 2. pub fn auto_fuzziness(token: &str) -> u32 { match token.chars().count() { 0..=2 => 0, @@ -339,17 +262,13 @@ pub fn auto_fuzziness(token: &str) -> u32 { } } -/// Calculate Levenshtein distance between two strings. -/// -/// Returns the minimum number of single-character edits (insertions, -/// deletions, or substitutions) required to transform one string into another. +/// Levenshtein distance using two-row dynamic programming. pub fn levenshtein_distance(a: &str, b: &str) -> u32 { let a_chars: Vec = a.chars().collect(); let b_chars: Vec = b.chars().collect(); let m = a_chars.len(); let n = b_chars.len(); - // Handle edge cases if m == 0 { return n as u32; } @@ -357,21 +276,17 @@ pub fn levenshtein_distance(a: &str, b: &str) -> u32 { return m as u32; } - // Use two rows instead of full matrix for space efficiency let mut prev_row: Vec = (0..=n as u32).collect(); let mut curr_row: Vec = vec![0; n + 1]; for (i, a_char) in a_chars.iter().enumerate() { curr_row[0] = (i + 1) as u32; - for (j, b_char) in b_chars.iter().enumerate() { let cost = if a_char == b_char { 0 } else { 1 }; - - curr_row[j + 1] = (prev_row[j + 1] + 1) // deletion - .min(curr_row[j] + 1) // insertion - .min(prev_row[j] + cost); // substitution + curr_row[j + 1] = (prev_row[j + 1] + 1) + .min(curr_row[j] + 1) + .min(prev_row[j] + cost); } - std::mem::swap(&mut prev_row, &mut curr_row); } @@ -387,30 +302,25 @@ pub struct BooleanQueryBuilder { } impl BooleanQueryBuilder { - /// Create a new Boolean query builder. pub fn new() -> Self { Self::default() } - /// Add a MUST clause (document must match). pub fn must(mut self, query: FtsQueryExpr) -> Self { self.must.push(query); self } - /// Add a SHOULD clause (document should match, adds to score). pub fn should(mut self, query: FtsQueryExpr) -> Self { self.should.push(query); self } - /// Add a MUST_NOT clause (document must not match). pub fn must_not(mut self, query: FtsQueryExpr) -> Self { self.must_not.push(query); self } - /// Build the Boolean query. pub fn build(self) -> FtsQueryExpr { FtsQueryExpr::Boolean { must: self.must, @@ -420,51 +330,292 @@ impl BooleanQueryBuilder { } } -/// Posting value stored in the inverted index. -/// Contains term frequency and positions for phrase query support. -#[derive(Clone, Debug)] -pub struct PostingValue { - /// Term frequency in the document. - pub frequency: u32, - /// Token positions within the document (0-indexed). - /// Used for phrase matching. - pub positions: Vec, +// ============================================================================ +// Internal types +// ============================================================================ + +/// Compressed sparse row (CSR) layout for per-document positions. +/// +/// `offsets[i]..offsets[i+1]` is the slice of `data` belonging to the i-th +/// document in this `TermChunk`. `offsets.len() == row_positions.len() + 1`. +#[derive(Debug)] +struct Positions { + offsets: Vec, + data: Vec, +} + +impl Positions { + fn empty() -> Self { + Self { + offsets: vec![0], + data: Vec::new(), + } + } + + fn push_doc(&mut self, positions: &[u32]) { + self.data.extend_from_slice(positions); + self.offsets.push(self.data.len() as u32); + } + + fn doc_positions(&self, doc_idx: usize) -> &[u32] { + let start = self.offsets[doc_idx] as usize; + let end = self.offsets[doc_idx + 1] as usize; + &self.data[start..end] + } + + fn memory_size(&self) -> usize { + self.offsets.capacity() * std::mem::size_of::() + + self.data.capacity() * std::mem::size_of::() + } +} + +/// Postings produced by a single batch insert for a single term. +/// +/// SoA layout for cache locality and trivial handoff to +/// `PostingListBuilder`. `row_positions` is sorted ascending. +#[derive(Debug)] +struct TermChunk { + batch_position: usize, + row_positions: Vec, + frequencies: Vec, + /// Present iff `params.has_positions()` was true at construction time. + /// Independent of that, in-memory phrase queries always work when + /// positions were tracked at insert time. + positions: Option, +} + +impl TermChunk { + fn doc_count(&self) -> usize { + self.row_positions.len() + } + + fn memory_size(&self) -> usize { + let base = std::mem::size_of::() + + self.row_positions.capacity() * std::mem::size_of::() + + self.frequencies.capacity() * std::mem::size_of::(); + base + self.positions.as_ref().map_or(0, Positions::memory_size) + } +} + +/// Append-only list of `TermChunk`s for a single term, replaced atomically +/// via `ArcSwap`. +#[derive(Debug, Default)] +struct TermSlice { + chunks: Vec>, +} + +impl TermSlice { + fn empty() -> Arc { + Arc::new(Self { chunks: Vec::new() }) + } + + /// Returns a fresh `Arc` containing all current chunks plus the + /// new one. The previous slice is left unchanged so any reader holding + /// it continues to see a consistent state. + fn with_chunk_appended(&self, chunk: Arc) -> Arc { + let mut chunks = Vec::with_capacity(self.chunks.len() + 1); + chunks.extend(self.chunks.iter().cloned()); + chunks.push(chunk); + Arc::new(Self { chunks }) + } + + fn memory_size(&self) -> usize { + std::mem::size_of::() + + self.chunks.capacity() * std::mem::size_of::>() + + self.chunks.iter().map(|c| c.memory_size()).sum::() + } +} + +/// Per-batch row metadata. +#[derive(Debug)] +struct BatchMeta { + batch_position: usize, + row_offset: u64, + /// `doc_lengths[i]` is the token count of the row at `row_offset + i`. + doc_lengths: Vec, + rows: u32, +} + +impl BatchMeta { + fn dl(&self, row_position: u64) -> Option { + if row_position < self.row_offset { + return None; + } + let idx = (row_position - self.row_offset) as usize; + self.doc_lengths.get(idx).copied() + } + + fn memory_size(&self) -> usize { + std::mem::size_of::() + self.doc_lengths.capacity() * std::mem::size_of::() + } +} + +/// Atomic snapshot of the visible state. Replaced via `ArcSwap` after each +/// batch is fully linked. +#[derive(Debug)] +struct Snapshot { + /// Number of batches visible to readers. `0` means empty index. + visible_count: usize, + /// Visible-batch metadata. `batches.len() >= visible_count`; the writer + /// may have already appended a still-invisible batch to this list, but + /// readers walk only `batches[0..visible_count]`. In the publication + /// order described in the module docs, readers never observe + /// `visible_count` exceeding the actual length. + batches: Arc<[Arc]>, + /// `Σ batches[i].rows` for `i < visible_count`. + cumulative_doc_count: u64, + /// `Σ batches[i].doc_lengths.iter().sum()` for `i < visible_count`. + cumulative_total_tokens: u64, +} + +impl Snapshot { + fn empty() -> Arc { + Arc::new(Self { + visible_count: 0, + batches: Arc::from(Vec::>::new().into_boxed_slice()), + cumulative_doc_count: 0, + cumulative_total_tokens: 0, + }) + } + + fn batch_for(&self, batch_position: usize) -> Option<&Arc> { + // Visible batches are densely numbered starting at 0 in the order + // they were inserted. Use direct index when possible. + self.batches + .get(batch_position) + .filter(|m| m.batch_position == batch_position) + .or_else(|| { + self.batches[..self.visible_count] + .iter() + .find(|m| m.batch_position == batch_position) + }) + } +} + +/// Bounded pool of reader tokenizers with a writer-dedicated slot. +/// +/// `LanceTokenizer::token_stream_for_*` takes `&mut self`, so each concurrent +/// caller needs its own tokenizer instance. Builds are cheap for English but +/// can load dictionaries for CJK tokenizers, so we amortize via the pool. +struct TokenizerPool { + template: Box, + free: Mutex>>, + cap: usize, +} + +impl std::fmt::Debug for TokenizerPool { + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + f.debug_struct("TokenizerPool") + .field("cap", &self.cap) + .finish() + } +} + +impl TokenizerPool { + fn new(params: &InvertedIndexParams, cap: usize) -> Result { + let template = params.build()?; + Ok(Self { + template, + free: Mutex::new(Vec::new()), + cap: cap.max(1), + }) + } + + /// Acquire a tokenizer. Pops from the free list, otherwise clones the + /// template. + fn acquire(&self) -> Box { + if let Some(t) = self.free.lock().ok().and_then(|mut g| g.pop()) { + return t; + } + self.template.box_clone() + } + + /// Return a tokenizer to the pool, dropping it if the pool is at cap. + fn release(&self, tokenizer: Box) { + if let Ok(mut g) = self.free.lock() + && g.len() < self.cap + { + g.push(tokenizer); + } + } +} + +/// RAII guard that returns the tokenizer to the pool on drop. +struct PooledTokenizer<'a> { + pool: &'a TokenizerPool, + inner: Option>, +} + +impl<'a> PooledTokenizer<'a> { + fn new(pool: &'a TokenizerPool) -> Self { + Self { + pool, + inner: Some(pool.acquire()), + } + } + + fn get_mut(&mut self) -> &mut dyn LanceTokenizer { + self.inner.as_mut().expect("tokenizer in scope").as_mut() + } +} + +impl<'a> Drop for PooledTokenizer<'a> { + fn drop(&mut self) { + if let Some(t) = self.inner.take() { + self.pool.release(t); + } + } } -/// In-memory FTS index for full-text search. +// ============================================================================ +// FtsMemIndex +// ============================================================================ + +/// In-memory full-text search index. See module docs for the concurrency +/// model and visibility contract. pub struct FtsMemIndex { - /// Field ID this index is built on. field_id: i32, - /// Column name (for Arrow batch lookups). column_name: String, - /// Inverted index: (token, row_position) -> (frequency, positions). - postings: SkipMap, - /// Total document count. - doc_count: AtomicUsize, - /// Tokenizer for text processing (same as Lance's InvertedIndex). - tokenizer: Mutex>, - /// The parameters used to create the tokenizer (for flush). params: InvertedIndexParams, - /// Document lengths: row_position -> token count (for BM25). - doc_lengths: SkipMap, - /// Total token count across all documents (for computing avgdl). - total_tokens: AtomicUsize, - /// Document frequency: term -> number of documents containing the term. - doc_freq: SkipMap, + + tokenizer_pool: Arc, + /// Writer-only tokenizer slot. Held under a Mutex purely so `insert` + /// can take `&self`. Single-writer assumption means this is uncontested. + writer_tokenizer: Mutex>, + + /// Per-term posting slices. `Arc` interns the term so a single + /// allocation backs every chunk that mentions it. + terms: SkipMap, ArcSwap>, + + /// Atomically-swapped visibility snapshot. + snapshot: ArcSwap, + + /// Strictly-monotonic batch position counter, used by `insert` (the + /// no-explicit-position variant) to assign sequential ids. Reads of + /// this counter are not part of the visibility contract — callers + /// passing explicit positions to `insert_with_batch_position` must keep + /// these monotonic themselves. + next_batch_position: AtomicUsize, } impl std::fmt::Debug for FtsMemIndex { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + let snap = self.snapshot.load(); f.debug_struct("FtsMemIndex") .field("field_id", &self.field_id) .field("column_name", &self.column_name) - .field("doc_count", &self.doc_count) + .field("visible_doc_count", &snap.cumulative_doc_count) + .field("visible_batch_count", &snap.visible_count) .field("params", &self.params) .finish() } } impl FtsMemIndex { + /// Default reader-tokenizer pool capacity. Small but enough to absorb + /// short bursts of concurrent search calls without thrashing. + const DEFAULT_TOKENIZER_POOL_CAP: usize = 8; + /// Create a new FTS index for the given field with default parameters. pub fn new(field_id: i32, column_name: String) -> Self { Self::with_params(field_id, column_name, InvertedIndexParams::default()) @@ -472,547 +623,439 @@ impl FtsMemIndex { /// Create a new FTS index with custom tokenizer parameters. pub fn with_params(field_id: i32, column_name: String, params: InvertedIndexParams) -> Self { - let tokenizer = params.build().expect("Failed to build tokenizer"); + let pool = TokenizerPool::new(¶ms, Self::DEFAULT_TOKENIZER_POOL_CAP) + .expect("Failed to build tokenizer"); + let writer_tokenizer = pool.template.box_clone(); Self { field_id, column_name, - postings: SkipMap::new(), - doc_count: AtomicUsize::new(0), - tokenizer: Mutex::new(tokenizer), params, - doc_lengths: SkipMap::new(), - total_tokens: AtomicUsize::new(0), - doc_freq: SkipMap::new(), + tokenizer_pool: Arc::new(pool), + writer_tokenizer: Mutex::new(writer_tokenizer), + terms: SkipMap::new(), + snapshot: ArcSwap::from(Snapshot::empty()), + next_batch_position: AtomicUsize::new(0), } } - /// Get the field ID this index is built on. pub fn field_id(&self) -> i32 { self.field_id } - /// Get the inverted index parameters. + pub fn column_name(&self) -> &str { + &self.column_name + } + pub fn params(&self) -> &InvertedIndexParams { &self.params } - /// Insert documents from a batch into the index. + /// Number of visible documents. + pub fn doc_count(&self) -> usize { + self.snapshot.load().cumulative_doc_count as usize + } + + /// Whether there are any visible documents. + pub fn is_empty(&self) -> bool { + self.snapshot.load().visible_count == 0 + } + + /// Total number of (term, doc) postings currently stored. + /// + /// Counts every chunk regardless of visibility — the writer's invariant + /// is that chunks become visible together with the snapshot bump, so + /// in steady state this equals the number of visible postings. + pub fn entry_count(&self) -> usize { + self.terms + .iter() + .map(|e| { + let slice = e.value().load(); + slice.chunks.iter().map(|c| c.doc_count()).sum::() + }) + .sum() + } + + /// Estimated bytes of heap memory held by this index. + pub fn memory_usage(&self) -> usize { + let mut total = std::mem::size_of::(); + for entry in self.terms.iter() { + // ~32 bytes for SkipMap node overhead plus the interned term itself. + let term: &Arc = entry.key(); + total += std::mem::size_of::>() + term.len() + 32; + let slice = entry.value().load(); + total += slice.memory_size(); + } + let snap = self.snapshot.load(); + total += snap.batches.iter().map(|b| b.memory_size()).sum::(); + total + } + + // ------------------------------------------------------------------ + // Insert + // ------------------------------------------------------------------ + + /// Insert a batch using a sequentially-derived batch position. pub fn insert(&self, batch: &RecordBatch, row_offset: u64) -> Result<()> { - let col_idx = batch + let batch_position = self.next_batch_position.fetch_add(1, Ordering::Relaxed); + self.insert_with_batch_position(batch, row_offset, batch_position) + } + + /// Insert a batch with an explicit batch position. The position is part + /// of the visibility contract — see module docs. + pub fn insert_with_batch_position( + &self, + batch: &RecordBatch, + row_offset: u64, + batch_position: usize, + ) -> Result<()> { + // Keep next_batch_position ahead of any explicit caller-supplied + // position so a later `insert` (no-position) doesn't collide. + let _ = self + .next_batch_position + .fetch_max(batch_position.saturating_add(1), Ordering::Relaxed); + + let Some(col_idx) = batch .schema() .column_with_name(&self.column_name) - .map(|(idx, _)| idx); - - if col_idx.is_none() { - return Ok(()); - } - - let column = batch.column(col_idx.unwrap()); - - for row_idx in 0..batch.num_rows() { - let value = ScalarValue::try_from_array(column.as_ref(), row_idx)?; - let row_position = row_offset + row_idx as u64; - - if let ScalarValue::Utf8(Some(text)) | ScalarValue::LargeUtf8(Some(text)) = value { - // Use the tokenizer (same as InvertedIndex) - // Track both frequency and positions for each term - let mut term_data: HashMap)> = HashMap::new(); - { - let mut tokenizer = self.tokenizer.lock().unwrap(); - let mut token_stream = tokenizer.token_stream_for_doc(&text); - let mut position: u32 = 0; - while let Some(token) = token_stream.next() { - let entry = term_data.entry(token.text.clone()).or_default(); - entry.0 += 1; // frequency - entry.1.push(position); // position - position += 1; - } - } + .map(|(idx, _)| idx) + else { + // Column missing: nothing to index, but advance the snapshot so + // the caller's position counter stays in sync. + return self.publish_empty_batch(batch.num_rows() as u32, row_offset, batch_position); + }; - // Calculate document length (total token count in this doc) - let doc_length: u32 = term_data.values().map(|(freq, _)| freq).sum(); - self.doc_lengths.insert(row_position, doc_length); - self.total_tokens - .fetch_add(doc_length as usize, Ordering::Relaxed); + let column = batch.column(col_idx); + let texts = extract_texts(column.as_ref())?; + debug_assert_eq!(texts.len(), batch.num_rows()); + + let mut tok_guard = self + .writer_tokenizer + .lock() + .expect("writer tokenizer poisoned — single-writer invariant violated"); + let tokenizer: &mut dyn LanceTokenizer = tok_guard.as_mut(); + + // Per-term builders (frequency + per-doc positions, indexed by + // local doc index in this batch). + let mut term_builders: HashMap, BatchTermBuilder> = HashMap::new(); + let mut doc_lengths: Vec = Vec::with_capacity(batch.num_rows()); + let mut total_tokens: u64 = 0; + + for (local_doc_idx, text_opt) in texts.iter().enumerate() { + // Track each doc's position even for null/missing rows so the + // dense `doc_lengths` array stays aligned with `row_offset + i`. + let mut doc_token_count: u32 = 0; + // term -> (frequency, positions). Keyed by `String` so we only + // pay the `Arc` allocation once per unique term per doc, + // when transferring to `term_builders` below. + let mut per_doc: HashMap)> = HashMap::new(); + + if let Some(text) = text_opt { + let mut stream = tokenizer.token_stream_for_doc(text); + let mut position: u32 = 0; + while let Some(tok) = stream.next() { + let entry = per_doc + .entry(tok.text.clone()) + .or_insert_with(|| (0, Vec::new())); + entry.0 += 1; + entry.1.push(position); + position += 1; + doc_token_count += 1; + } + } - for (token, (freq, positions)) in term_data { - // Update document frequency for this term - if let Some(entry) = self.doc_freq.get(&token) { - entry.value().fetch_add(1, Ordering::Relaxed); + doc_lengths.push(doc_token_count); + total_tokens += doc_token_count as u64; + + let row_position = row_offset + local_doc_idx as u64; + for (term, (freq, positions)) in per_doc { + // Reuse an interned `Arc` if we've already seen the + // term in this batch. The first occurrence pays the + // allocation; subsequent occurrences clone the Arc. + let term_arc: Arc = + if let Some((existing, _)) = term_builders.get_key_value(term.as_str()) { + Arc::clone(existing) } else { - self.doc_freq.insert(token.clone(), AtomicUsize::new(1)); - } - - let key = FtsKey { - token, - row_position, + Arc::::from(term.as_str()) }; - self.postings.insert( - key, - PostingValue { - frequency: freq, - positions, - }, - ); - } + let builder = term_builders + .entry(term_arc) + .or_insert_with(BatchTermBuilder::new); + builder.push_doc(row_position, freq, positions); } + } - self.doc_count.fetch_add(1, Ordering::Relaxed); + // Drop the tokenizer guard before any work that could be slow so we + // don't hold it across allocations. + drop(tok_guard); + + // Install per-term chunks first so any chunk a reader can possibly + // see (via a later snapshot store) is fully linked. + for (term, builder) in term_builders { + let chunk = builder.build(batch_position); + let entry = self + .terms + .get_or_insert_with(term, TermSlice::empty_arc_swap); + let cur = entry.value().load(); + entry.value().store(cur.with_chunk_appended(chunk)); } + let new_meta = Arc::new(BatchMeta { + batch_position, + row_offset, + doc_lengths, + rows: batch.num_rows() as u32, + }); + + self.publish_batch(new_meta, total_tokens); Ok(()) } - /// Search for documents containing a term. - /// - /// The term is tokenized using the same tokenizer as the index. - /// Returns all matching documents with their BM25 scores. - pub fn search(&self, term: &str) -> Vec { - // Tokenize the search term using token_stream_for_search - let tokens: Vec = { - let mut tokenizer = self.tokenizer.lock().unwrap(); - let mut token_stream = tokenizer.token_stream_for_search(term); - let mut tokens = Vec::new(); - while let Some(token) = token_stream.next() { - tokens.push(token.text.clone()); - } - tokens - }; - - // BM25 parameters - const K1: f32 = 1.2; - const B: f32 = 0.75; - - let n = self.doc_count.load(Ordering::Relaxed) as f32; - let total_tokens = self.total_tokens.load(Ordering::Relaxed) as f32; - let avgdl = if n > 0.0 { total_tokens / n } else { 1.0 }; - - // Collect term frequencies per document for all query tokens - // Map: row_position -> Vec<(term_freq, doc_freq_for_term)> - let mut doc_term_info: HashMap> = HashMap::new(); - - for token in &tokens { - // Get document frequency for this term - let df = self - .doc_freq - .get(token) - .map(|e| e.value().load(Ordering::Relaxed)) - .unwrap_or(0); + /// Publish a snapshot for an "empty" batch (e.g. column missing) so the + /// visibility counters keep up with the writer's batch_position stream. + fn publish_empty_batch(&self, rows: u32, row_offset: u64, batch_position: usize) -> Result<()> { + let meta = Arc::new(BatchMeta { + batch_position, + row_offset, + doc_lengths: vec![0; rows as usize], + rows, + }); + self.publish_batch(meta, 0); + Ok(()) + } - if df == 0 { - continue; - } + fn publish_batch(&self, new_meta: Arc, batch_total_tokens: u64) { + let cur = self.snapshot.load(); + let new_rows = new_meta.rows as u64; + let mut batches: Vec> = Vec::with_capacity(cur.batches.len() + 1); + batches.extend(cur.batches.iter().cloned()); + batches.push(new_meta); + let new_snap = Snapshot { + visible_count: cur.visible_count + 1, + batches: Arc::from(batches.into_boxed_slice()), + cumulative_doc_count: cur.cumulative_doc_count + new_rows, + cumulative_total_tokens: cur.cumulative_total_tokens + batch_total_tokens, + }; + self.snapshot.store(Arc::new(new_snap)); + } - let start = FtsKey { - token: token.clone(), - row_position: 0, - }; - let end = FtsKey { - token: token.clone(), - row_position: u64::MAX, - }; + // ------------------------------------------------------------------ + // Read path + // ------------------------------------------------------------------ - for entry in self.postings.range(start..=end) { - doc_term_info - .entry(entry.key().row_position) - .or_default() - .push((entry.value().frequency, df)); - } + /// Search for documents containing a term. + /// + /// The term is tokenized using the configured tokenizer. Returns all + /// matching documents with BM25 scores. Result order is unspecified; + /// use `search_with_options` for sorted/limited output. + pub fn search(&self, term: &str) -> Vec { + let snap = self.snapshot.load_full(); + if snap.visible_count == 0 { + return Vec::new(); } - // Compute BM25 score for each document - doc_term_info - .into_iter() - .map(|(row_position, term_infos)| { - let dl = self - .doc_lengths - .get(&row_position) - .map(|e| *e.value() as f32) - .unwrap_or(1.0); - - let mut score: f32 = 0.0; - for (tf, df) in term_infos { - // IDF = log((N - n + 0.5) / (n + 0.5) + 1) - let df_f = df as f32; - let idf = ((n - df_f + 0.5) / (df_f + 0.5) + 1.0).ln(); - - // BM25 term score = IDF * (tf * (k1 + 1)) / (tf + k1 * (1 - b + b * (dl / avgdl))) - let tf_f = tf as f32; - let numerator = tf_f * (K1 + 1.0); - let denominator = tf_f + K1 * (1.0 - B + B * (dl / avgdl)); - score += idf * (numerator / denominator); - } + let tokens = self.tokenize_for_search(term); - FtsEntry { - row_position, - score, - } - }) - .collect() + score_terms(&snap, &self.terms, &tokens) } - /// Search for documents containing an exact phrase. - /// - /// The phrase is tokenized and documents must contain all tokens - /// in the correct order (within the specified slop distance). - /// - /// # Arguments - /// * `phrase` - The phrase to search for - /// * `slop` - Maximum allowed distance between consecutive tokens. - /// 0 means exact phrase match (tokens must be adjacent). - /// 1 allows one intervening token, etc. - /// - /// Returns matching documents with BM25 scores. + /// Search for documents containing an exact phrase, optionally allowing + /// `slop` intervening tokens between consecutive query tokens. pub fn search_phrase(&self, phrase: &str, slop: u32) -> Vec { - // Tokenize the phrase - let tokens: Vec = { - let mut tokenizer = self.tokenizer.lock().unwrap(); - let mut token_stream = tokenizer.token_stream_for_search(phrase); - let mut tokens = Vec::new(); - while let Some(token) = token_stream.next() { - tokens.push(token.text.clone()); - } - tokens - }; + let snap = self.snapshot.load_full(); + if snap.visible_count == 0 { + return Vec::new(); + } + let tokens = self.tokenize_for_search(phrase); if tokens.is_empty() { - return vec![]; + return Vec::new(); } - - // Single token phrase is just a regular search if tokens.len() == 1 { - return self.search(phrase); + // Same shortcut as the previous implementation: a single-token + // phrase reduces to a regular term search. + return score_terms(&snap, &self.terms, &tokens); } - // BM25 parameters - const K1: f32 = 1.2; - const B: f32 = 0.75; - - let n = self.doc_count.load(Ordering::Relaxed) as f32; - let total_tokens = self.total_tokens.load(Ordering::Relaxed) as f32; - let avgdl = if n > 0.0 { total_tokens / n } else { 1.0 }; - - // Collect posting lists for each token - // Map: token_index -> Map - let mut token_postings: Vec> = Vec::new(); - - for token in &tokens { - let start = FtsKey { - token: token.clone(), - row_position: 0, - }; - let end = FtsKey { - token: token.clone(), - row_position: u64::MAX, - }; - - let mut postings_for_token: HashMap = HashMap::new(); - for entry in self.postings.range(start..=end) { - postings_for_token.insert(entry.key().row_position, entry.value().clone()); + // Gather visible chunks per token. + let mut per_token_chunks: Vec>> = Vec::with_capacity(tokens.len()); + for tok in &tokens { + match self.terms.get(tok.as_str()) { + Some(entry) => { + let slice = entry.value().load_full(); + let visible: Vec> = slice + .chunks + .iter() + .filter(|c| c.batch_position < snap.visible_count) + .cloned() + .collect(); + if visible.is_empty() { + return Vec::new(); + } + per_token_chunks.push(visible); + } + None => return Vec::new(), } - token_postings.push(postings_for_token); } - // Find documents that contain ALL tokens - let first_token_docs: Vec = token_postings[0].keys().copied().collect(); - - let mut matching_docs: Vec = Vec::new(); + // Per-term `df` for IDF. + let dfs: Vec = per_token_chunks + .iter() + .map(|chunks| chunks.iter().map(|c| c.doc_count() as u32).sum::()) + .collect(); - for row_position in first_token_docs { - // Check if this document contains all tokens - let all_tokens_present = token_postings - .iter() - .all(|tp| tp.contains_key(&row_position)); - if !all_tokens_present { - continue; - } + let n = snap.cumulative_doc_count as f32; + let avgdl = if n > 0.0 { + snap.cumulative_total_tokens as f32 / n + } else { + 1.0 + }; - // Check if the phrase matches (positions are in order within slop) - if self.check_phrase_positions(&token_postings, row_position, slop) { - // Calculate BM25 score - let dl = self - .doc_lengths - .get(&row_position) - .map(|e| *e.value() as f32) - .unwrap_or(1.0); - - let mut score: f32 = 0.0; - for (token_idx, token) in tokens.iter().enumerate() { - let df = self - .doc_freq - .get(token) - .map(|e| e.value().load(Ordering::Relaxed)) - .unwrap_or(1) as f32; - let tf = token_postings[token_idx] - .get(&row_position) - .map(|p| p.frequency as f32) - .unwrap_or(1.0); - - // IDF = log((N - n + 0.5) / (n + 0.5) + 1) - let idf = ((n - df + 0.5) / (df + 0.5) + 1.0).ln(); - - // BM25 term score - let numerator = tf * (K1 + 1.0); - let denominator = tf + K1 * (1.0 - B + B * (dl / avgdl)); - score += idf * (numerator / denominator); + // Find candidate documents: rows that appear in every token's + // posting set. Start with the smallest token's docs to bound work. + let smallest_idx = (0..per_token_chunks.len()) + .min_by_key(|&i| { + per_token_chunks[i] + .iter() + .map(|c| c.doc_count()) + .sum::() + }) + .unwrap(); + let candidate_chunks = &per_token_chunks[smallest_idx]; + + let mut results = Vec::new(); + for chunk in candidate_chunks { + for (doc_idx, &row_position) in chunk.row_positions.iter().enumerate() { + let pos = chunk + .positions + .as_ref() + .map(|p| p.doc_positions(doc_idx).to_vec()); + let Some(pos) = pos else { continue }; + let mut all_positions: Vec> = vec![Vec::new(); tokens.len()]; + all_positions[smallest_idx] = pos; + let mut all_present = true; + let mut frequencies = vec![0u32; tokens.len()]; + frequencies[smallest_idx] = chunk.frequencies[doc_idx]; + for (ti, chunks) in per_token_chunks.iter().enumerate() { + if ti == smallest_idx { + continue; + } + match find_doc_in_chunks(chunks, row_position) { + Some((c, doc_idx_other)) => { + frequencies[ti] = c.frequencies[doc_idx_other]; + all_positions[ti] = c + .positions + .as_ref() + .map(|p| p.doc_positions(doc_idx_other).to_vec()) + .unwrap_or_default(); + } + None => { + all_present = false; + break; + } + } + } + if !all_present { + continue; + } + if !phrase_matches(&all_positions, slop) { + continue; } - matching_docs.push(FtsEntry { + let dl = lookup_dl(&snap, row_position).unwrap_or(1) as f32; + let mut score = 0.0f32; + for (ti, _tok) in tokens.iter().enumerate() { + let tf = frequencies[ti] as f32; + let df = dfs[ti] as f32; + score += bm25_term_score(tf, df.max(1.0), n, dl, avgdl); + } + results.push(FtsEntry { row_position, score, }); } } - matching_docs - } - - /// Check if phrase positions match within the given slop. - /// - /// Uses relative position algorithm: for each token, compute - /// `relative_pos = doc_position - query_position`. If all tokens - /// have the same relative position (within slop), the phrase matches. - fn check_phrase_positions( - &self, - token_postings: &[HashMap], - row_position: RowPosition, - slop: u32, - ) -> bool { - // Get positions for each token in this document - let mut all_positions: Vec<&Vec> = Vec::new(); - for tp in token_postings { - if let Some(posting) = tp.get(&row_position) { - all_positions.push(&posting.positions); - } else { - return false; - } - } - - // For each position of the first token, check if we can form a phrase - for &first_pos in all_positions[0] { - if Self::check_phrase_from_position(&all_positions, first_pos, slop) { - return true; - } - } - - false - } - - /// Check if a phrase can be formed starting from a given position of the first token. - fn check_phrase_from_position(all_positions: &[&Vec], first_pos: u32, slop: u32) -> bool { - let mut expected_pos = first_pos; - - for positions in all_positions.iter().skip(1) { - // Find a position for this token that's within slop of expected - // For slop=0, next token must be at expected_pos+1 (adjacent) - // For slop=1, next token can be at expected_pos+1 or expected_pos+2 - let min_pos = expected_pos.saturating_add(1); - let max_pos = expected_pos.saturating_add(1 + slop); - - // Find the actual position used (smallest valid one) - if let Some(&actual_pos) = positions - .iter() - .filter(|&&pos| pos >= min_pos && pos <= max_pos) - .min() - { - expected_pos = actual_pos; - } else { - return false; - } - } - - true - } - - /// Get the number of entries in the index. - /// Note: This counts (token, row_position) pairs, not unique tokens. - pub fn entry_count(&self) -> usize { - self.postings.len() - } - - /// Get the document count. - pub fn doc_count(&self) -> usize { - self.doc_count.load(Ordering::Relaxed) - } - - /// Check if the index is empty. - pub fn is_empty(&self) -> bool { - self.doc_count.load(Ordering::Relaxed) == 0 - } - - /// Get the column name. - pub fn column_name(&self) -> &str { - &self.column_name + results } /// Expand a term to fuzzy matches within the specified edit distance. - /// - /// Returns a list of (matching_term, edit_distance) tuples, sorted by - /// edit distance (closest matches first), limited to max_expansions. + /// Returns `(matched_term, distance)` pairs sorted by distance, capped + /// at `max_expansions`. pub fn expand_fuzzy( &self, term: &str, max_distance: u32, max_expansions: usize, ) -> Vec<(String, u32)> { + let snap = self.snapshot.load(); let mut matches: Vec<(String, u32)> = Vec::new(); - // If max_distance is 0, only exact matches if max_distance == 0 { - if self.doc_freq.get(term).is_some() { + if let Some(entry) = self.terms.get(term) + && has_visible_chunk(&entry.value().load(), snap.visible_count) + { matches.push((term.to_string(), 0)); } return matches; } - // Iterate through all tokens in doc_freq - for entry in self.doc_freq.iter() { - let indexed_term = entry.key(); - let distance = levenshtein_distance(term, indexed_term); - - if distance <= max_distance { - matches.push((indexed_term.clone(), distance)); + for entry in self.terms.iter() { + let key: &Arc = entry.key(); + if !has_visible_chunk(&entry.value().load(), snap.visible_count) { + continue; + } + let dist = levenshtein_distance(term, key); + if dist <= max_distance { + matches.push((key.to_string(), dist)); } } - - // Sort by distance (prefer closer matches) matches.sort_by_key(|(_, d)| *d); - - // Limit to max_expansions matches.truncate(max_expansions); - matches } - /// Search for documents using fuzzy matching. - /// - /// Each query token is expanded to fuzzy matches within the edit distance, - /// then searched. Results from all expansions are combined. + /// Search for documents using fuzzy matching on each query token. pub fn search_fuzzy( &self, query: &str, fuzziness: Option, max_expansions: usize, ) -> Vec { - // Tokenize the query - let tokens: Vec = { - let mut tokenizer = self.tokenizer.lock().unwrap(); - let mut token_stream = tokenizer.token_stream_for_search(query); - let mut tokens = Vec::new(); - while let Some(token) = token_stream.next() { - tokens.push(token.text.clone()); - } - tokens - }; + let snap = self.snapshot.load_full(); + if snap.visible_count == 0 { + return Vec::new(); + } + let tokens = self.tokenize_for_search(query); if tokens.is_empty() { - return vec![]; + return Vec::new(); } - // BM25 parameters - const K1: f32 = 1.2; - const B: f32 = 0.75; - - let n = self.doc_count.load(Ordering::Relaxed) as f32; - let total_tokens = self.total_tokens.load(Ordering::Relaxed) as f32; - let avgdl = if n > 0.0 { total_tokens / n } else { 1.0 }; - - // Collect term frequencies per document for all expanded tokens - // Map: row_position -> Vec<(term_freq, doc_freq_for_term)> - let mut doc_term_info: HashMap> = HashMap::new(); - - for token in &tokens { - // Determine fuzziness for this token - let max_distance = fuzziness.unwrap_or_else(|| auto_fuzziness(token)); - - // Expand to fuzzy matches - let expanded = self.expand_fuzzy(token, max_distance, max_expansions); - - for (matched_term, _distance) in expanded { - // Get document frequency for this term - let df = self - .doc_freq - .get(&matched_term) - .map(|e| e.value().load(Ordering::Relaxed)) - .unwrap_or(0); - - if df == 0 { - continue; - } - - let start = FtsKey { - token: matched_term.clone(), - row_position: 0, - }; - let end = FtsKey { - token: matched_term, - row_position: u64::MAX, - }; - - for entry in self.postings.range(start..=end) { - doc_term_info - .entry(entry.key().row_position) - .or_default() - .push((entry.value().frequency, df)); - } + let mut expanded: Vec = Vec::new(); + for tok in &tokens { + let max_dist = fuzziness.unwrap_or_else(|| auto_fuzziness(tok)); + for (matched, _) in self.expand_fuzzy(tok, max_dist, max_expansions) { + expanded.push(matched); } } + if expanded.is_empty() { + return Vec::new(); + } - // Compute BM25 score for each document - doc_term_info - .into_iter() - .map(|(row_position, term_infos)| { - let dl = self - .doc_lengths - .get(&row_position) - .map(|e| *e.value() as f32) - .unwrap_or(1.0); - - let mut score: f32 = 0.0; - for (tf, df) in term_infos { - // IDF = log((N - n + 0.5) / (n + 0.5) + 1) - let df_f = df as f32; - let idf = ((n - df_f + 0.5) / (df_f + 0.5) + 1.0).ln(); - - // BM25 term score - let tf_f = tf as f32; - let numerator = tf_f * (K1 + 1.0); - let denominator = tf_f + K1 * (1.0 - B + B * (dl / avgdl)); - score += idf * (numerator / denominator); - } - - FtsEntry { - row_position, - score, - } - }) - .collect() + score_terms(&snap, &self.terms, &expanded) } /// Execute a query expression and return matching documents with scores. - /// - /// This is the main entry point for executing complex queries including - /// match, phrase, fuzzy, and Boolean queries. - /// - /// For performance optimization with limits, use `search_with_options()` instead. pub fn search_query(&self, query: &FtsQueryExpr) -> Vec { match query { FtsQueryExpr::Match { query, boost } => { let mut results = self.search(query); - if *boost != 1.0 { - for entry in &mut results { - entry.score *= boost; - } - } + apply_boost(&mut results, *boost); results } FtsQueryExpr::Phrase { query, slop, boost } => { let mut results = self.search_phrase(query, *slop); - if *boost != 1.0 { - for entry in &mut results { - entry.score *= boost; - } - } + apply_boost(&mut results, *boost); results } FtsQueryExpr::Fuzzy { @@ -1022,11 +1065,7 @@ impl FtsMemIndex { boost, } => { let mut results = self.search_fuzzy(query, *fuzziness, *max_expansions); - if *boost != 1.0 { - for entry in &mut results { - entry.score *= boost; - } - } + apply_boost(&mut results, *boost); results } FtsQueryExpr::Boolean { @@ -1042,131 +1081,73 @@ impl FtsMemIndex { } } - /// Execute a query with options for performance/recall tradeoffs. - /// - /// This method extends `search_query()` with: - /// - **WAND factor**: Early termination based on score threshold. - /// With `wand_factor < 1.0`, documents scoring below - /// `threshold = top_k_score * wand_factor` are pruned after scoring. - /// - **Limit**: Maximum number of results to return (top-k by score). - /// - /// Results are always sorted by score in descending order. - /// - /// # Arguments - /// * `query` - The query expression to execute - /// * `options` - Search options including wand_factor and limit - /// - /// # Example - /// ```ignore - /// let options = SearchOptions::default() - /// .with_limit(10) - /// .with_wand_factor(0.8); - /// let results = index.search_with_options(&query, options); - /// ``` + /// Execute a query with options (sort + WAND prune + limit). pub fn search_with_options( &self, query: &FtsQueryExpr, options: SearchOptions, ) -> Vec { - // Execute the query to get all results let mut results = self.search_query(query); - - // Sort by score descending results.sort_by(|a, b| { b.score .partial_cmp(&a.score) .unwrap_or(std::cmp::Ordering::Equal) }); - - // Apply WAND factor pruning if wand_factor < 1.0 and we have a limit if options.wand_factor < 1.0 { if let Some(limit) = options.limit { if results.len() > limit { - // Get the k-th best score (at position limit-1) let top_k_score = results[limit - 1].score; let threshold = top_k_score * options.wand_factor; - - // Keep results scoring above the threshold, plus all results up to limit - // This ensures we don't accidentally prune results that would be in top-k - results.retain(|e| e.score >= threshold); - } - } else { - // No limit but wand_factor < 1.0: prune relative to max score - if let Some(max_entry) = results.first() { - let threshold = max_entry.score * options.wand_factor; results.retain(|e| e.score >= threshold); } + } else if let Some(max_entry) = results.first() { + let threshold = max_entry.score * options.wand_factor; + results.retain(|e| e.score >= threshold); } } - - // Apply limit if let Some(limit) = options.limit { results.truncate(limit); } - results } - /// Execute a boosting query. - /// - /// Returns documents matching the positive query. Documents that also - /// match the negative query have their scores multiplied by `negative_boost`. fn search_boost( &self, positive: &FtsQueryExpr, negative: Option<&FtsQueryExpr>, negative_boost: f32, ) -> Vec { - // Execute positive query to get base results let mut results = self.search_query(positive); - - // If no negative query, just return positive results - let Some(neg_query) = negative else { + let Some(neg) = negative else { return results; }; - - // Execute negative query - let negative_results = self.search_query(neg_query); - - // Build a set of row positions that match the negative query - let negative_positions: std::collections::HashSet = - negative_results.iter().map(|e| e.row_position).collect(); - - // Apply negative boost to documents matching both queries + let negative_results = self.search_query(neg); + let negative_set: HashSet = negative_results + .into_iter() + .map(|e| e.row_position) + .collect(); for entry in &mut results { - if negative_positions.contains(&entry.row_position) { + if negative_set.contains(&entry.row_position) { entry.score *= negative_boost; } } - results } - /// Execute a Boolean query with MUST/SHOULD/MUST_NOT logic. - /// - /// - MUST: All clauses must match (intersection). Scores are summed. - /// - SHOULD: At least one clause should match (union). Scores are added. - /// - MUST_NOT: No clause may match (exclusion). - /// - /// If only SHOULD clauses are present, at least one must match. - /// If MUST clauses are present, SHOULD clauses just add to the score. fn search_boolean( &self, must: &[FtsQueryExpr], should: &[FtsQueryExpr], must_not: &[FtsQueryExpr], ) -> Vec { - // Collect MUST_NOT results for exclusion - let excluded: std::collections::HashSet = must_not + let excluded: HashSet = must_not .iter() .flat_map(|q| self.search_query(q)) .map(|e| e.row_position) .collect(); - // Start with MUST clauses (intersection) let mut result_map: HashMap = if must.is_empty() { - // No MUST clauses: start with all SHOULD results - let mut map = HashMap::new(); + let mut map: HashMap = HashMap::new(); for q in should { for entry in self.search_query(q) { *map.entry(entry.row_position).or_default() += entry.score; @@ -1174,29 +1155,22 @@ impl FtsMemIndex { } map } else { - // Execute first MUST clause let first_results = self.search_query(&must[0]); let mut map: HashMap = first_results .into_iter() .map(|e| (e.row_position, e.score)) .collect(); - - // Intersect with remaining MUST clauses for q in must.iter().skip(1) { let results = self.search_query(q); let result_set: HashMap = results .into_iter() .map(|e| (e.row_position, e.score)) .collect(); - - // Keep only documents in both sets, sum scores map = map .into_iter() .filter_map(|(pos, score)| result_set.get(&pos).map(|s| (pos, score + s))) .collect(); } - - // Add SHOULD clause scores (don't require match since MUST already filters) for q in should { for entry in self.search_query(q) { if let Some(score) = map.get_mut(&entry.row_position) { @@ -1204,16 +1178,13 @@ impl FtsMemIndex { } } } - map }; - // Filter out MUST_NOT results for pos in &excluded { result_map.remove(pos); } - // Convert to FtsEntry list result_map .into_iter() .map(|(row_position, score)| FtsEntry { @@ -1223,18 +1194,27 @@ impl FtsMemIndex { .collect() } - /// Export the in-memory FTS index to an `InnerBuilder` for direct flush. - /// - /// This creates an `InnerBuilder` containing all the index data with - /// reversed row positions for efficient LSM scan. The builder can then - /// be written directly to disk without re-tokenizing the documents. - /// - /// # Arguments - /// * `partition_id` - Partition ID for the index files - /// * `total_rows` - Total number of rows in the MemTable (for position reversal) + fn tokenize_for_search(&self, text: &str) -> Vec { + let mut tok = PooledTokenizer::new(&self.tokenizer_pool); + let mut stream = tok.get_mut().token_stream_for_search(text); + let mut out = Vec::new(); + while let Some(t) = stream.next() { + out.push(t.text.clone()); + } + out + } + + // ------------------------------------------------------------------ + // Flush to Lance inverted index format + // ------------------------------------------------------------------ + + /// Export the in-memory FTS index to an `InnerBuilder` ready to be + /// written to disk. /// - /// # Returns - /// An `InnerBuilder` ready to be written to disk + /// `total_rows` is the total number of rows in the MemTable being + /// flushed; row positions are reversed (`reversed = total_rows - pos - + /// 1`) to match the LSM-friendly newest-first flush order used by the + /// rest of MemWAL. pub fn to_index_builder_reversed( &self, partition_id: u64, @@ -1243,120 +1223,344 @@ impl FtsMemIndex { use lance_index::scalar::inverted::builder::{InnerBuilder, PositionRecorder}; use lance_index::scalar::inverted::{DocSet, PostingListBuilder, TokenSet}; - if self.is_empty() { + let snap = self.snapshot.load_full(); + let with_position = self.params.has_positions(); + + if snap.visible_count == 0 { return Ok(InnerBuilder::new( partition_id, - self.params.has_positions(), + with_position, Default::default(), )); } let total_rows_u64 = total_rows as u64; - let with_position = self.params.has_positions(); - - // Step 1: Build DocSet with reversed row positions - // Collect (original_pos, num_tokens) -> (reversed_pos, num_tokens) - let mut doc_entries: Vec<(u64, u32)> = self - .doc_lengths - .iter() - .map(|e| { - let original_pos = *e.key(); - let reversed_pos = total_rows_u64 - original_pos - 1; - (reversed_pos, *e.value()) - }) - .collect(); - // Sort by reversed position so doc_id assignment matches flushed data order - doc_entries.sort_by_key(|(pos, _)| *pos); + // Step 1: collect (reversed_pos, num_tokens) for every visible row. + // We walk visible BatchMetas in order so the reverse-sort below has + // a small, dense input. + let mut entries: Vec<(u64, u32)> = Vec::new(); + for batch in snap.batches.iter().take(snap.visible_count) { + for i in 0..batch.rows as usize { + let original_pos = batch.row_offset + i as u64; + if original_pos >= total_rows_u64 { + return Err(Error::io(format!( + "FTS flush: row position {} >= total_rows {}", + original_pos, total_rows + ))); + } + let reversed = total_rows_u64 - original_pos - 1; + entries.push((reversed, batch.doc_lengths[i])); + } + } + entries.sort_by_key(|(rev, _)| *rev); - // Build DocSet and create mapping from reversed_pos -> doc_id + // Step 2: assign doc_ids in ascending reversed-pos order. let mut docs = DocSet::default(); - let mut reversed_pos_to_doc_id: HashMap = - HashMap::with_capacity(doc_entries.len()); - for (idx, (reversed_pos, num_tokens)) in doc_entries.into_iter().enumerate() { - docs.append(reversed_pos, num_tokens); - reversed_pos_to_doc_id.insert(reversed_pos, idx as u32); + let mut original_to_doc_id: HashMap = HashMap::with_capacity(entries.len()); + for (rev, num_tokens) in &entries { + let doc_id = docs.append(*rev, *num_tokens); + // Recover the original position from the reversed position so + // we can map per-term postings without a second pass. + let original = total_rows_u64 - rev - 1; + original_to_doc_id.insert(original, doc_id); } - // Step 2: Build TokenSet and group postings by token + // Step 3: walk terms in alphabetic (skip-list) order and emit + // posting lists into the builder. let mut tokens = TokenSet::default(); - let mut token_postings: HashMap> = HashMap::new(); - - for entry in self.postings.iter() { - let token = entry.key().token.clone(); - let original_pos = entry.key().row_position; - let reversed_pos = total_rows_u64 - original_pos - 1; - let doc_id = *reversed_pos_to_doc_id.get(&reversed_pos).ok_or_else(|| { - Error::io(format!( - "FTS index internal error: doc_id not found for reversed position {} (original: {}, total_rows: {})", - reversed_pos, original_pos, total_rows - )) - })?; - - token_postings - .entry(token) - .or_default() - .push((doc_id, entry.value().clone())); - } - - // Assign token IDs in sorted order for FST format - let mut sorted_tokens: Vec<_> = token_postings.keys().cloned().collect(); - sorted_tokens.sort(); - for token in &sorted_tokens { - tokens.add(token.clone()); - } - - // Step 3: Build posting lists - let mut posting_lists: Vec = (0..tokens.len()) - .map(|_| PostingListBuilder::new(with_position)) - .collect(); + let mut posting_lists: Vec = Vec::new(); + for entry in self.terms.iter() { + let term: &Arc = entry.key(); + let slice = entry.value().load(); + + // Collect per-doc postings filtered by visibility. + let mut docs_for_term: Vec<(u32, u32, Option>)> = Vec::new(); + for chunk in &slice.chunks { + if chunk.batch_position >= snap.visible_count { + continue; + } + for (i, row_position) in chunk.row_positions.iter().enumerate() { + let Some(&doc_id) = original_to_doc_id.get(row_position) else { + // Should not happen: a chunk's batch_position is + // visible only if its meta is visible, and the meta + // contributed every row above. + continue; + }; + let pos = if with_position { + chunk + .positions + .as_ref() + .map(|p| p.doc_positions(i).to_vec()) + } else { + None + }; + docs_for_term.push((doc_id, chunk.frequencies[i], pos)); + } + } + if docs_for_term.is_empty() { + continue; + } + docs_for_term.sort_by_key(|(doc_id, _, _)| *doc_id); + + let token_id = tokens.add(term.to_string()) as usize; + // PostingListBuilder vector is indexed by token_id. New token + // ids are assigned monotonically by `add`, so we just push. + debug_assert_eq!(token_id, posting_lists.len()); + posting_lists.push(PostingListBuilder::new(with_position)); + + let plb = &mut posting_lists[token_id]; + for (doc_id, freq, pos) in docs_for_term { + let recorder = if with_position { + PositionRecorder::Position(pos.unwrap_or_default().into()) + } else { + PositionRecorder::Count(freq) + }; + plb.add(doc_id, recorder); + } + } + + let mut builder = InnerBuilder::new(partition_id, with_position, Default::default()); + builder.set_tokens(tokens); + builder.set_docs(docs); + builder.set_posting_lists(posting_lists); + Ok(builder) + } +} + +// ============================================================================ +// Internal helpers +// ============================================================================ + +impl TermSlice { + fn empty_arc_swap() -> ArcSwap { + ArcSwap::from(Self::empty()) + } +} + +/// Helper used during insert to accumulate a single batch's contribution to +/// one term. +struct BatchTermBuilder { + row_positions: Vec, + frequencies: Vec, + positions: Vec>, +} + +impl BatchTermBuilder { + fn new() -> Self { + Self { + row_positions: Vec::new(), + frequencies: Vec::new(), + positions: Vec::new(), + } + } + + fn push_doc(&mut self, row_position: u64, frequency: u32, positions: Vec) { + self.row_positions.push(row_position); + self.frequencies.push(frequency); + self.positions.push(positions); + } + + /// Always materializes the per-doc positions: in-memory phrase queries + /// rely on them regardless of `params.has_positions()`. The flush path + /// consults `params.has_positions()` and emits a `Count` recorder + /// instead of `Position` when positions should not be persisted. + fn build(self, batch_position: usize) -> Arc { + let mut p = Positions::empty(); + for doc in &self.positions { + p.push_doc(doc); + } + Arc::new(TermChunk { + batch_position, + row_positions: self.row_positions, + frequencies: self.frequencies, + positions: Some(p), + }) + } +} + +/// Borrowed text for a row, or `None` for null/missing. +type TextOpt<'a> = Option<&'a str>; + +fn extract_texts(column: &dyn Array) -> Result>> { + match column.data_type() { + DataType::Utf8 => { + let array = column + .as_any() + .downcast_ref::() + .expect("Utf8 array"); + Ok((0..array.len()) + .map(|i| (!array.is_null(i)).then(|| array.value(i))) + .collect()) + } + DataType::LargeUtf8 => { + let array = column + .as_any() + .downcast_ref::() + .expect("LargeUtf8 array"); + Ok((0..array.len()) + .map(|i| (!array.is_null(i)).then(|| array.value(i))) + .collect()) + } + DataType::Utf8View => { + let array = column + .as_any() + .downcast_ref::() + .expect("Utf8View array"); + Ok((0..array.len()) + .map(|i| (!array.is_null(i)).then(|| array.value(i))) + .collect()) + } + other => Err(Error::invalid_input(format!( + "FTS index only supports Utf8, LargeUtf8, and Utf8View columns; got {other:?}" + ))), + } +} + +fn has_visible_chunk(slice: &TermSlice, visible_count: usize) -> bool { + slice + .chunks + .iter() + .any(|c| c.batch_position < visible_count) +} + +fn lookup_dl(snap: &Snapshot, row_position: u64) -> Option { + snap.batches[..snap.visible_count] + .iter() + .find_map(|b| b.dl(row_position)) +} + +fn find_doc_in_chunks( + chunks: &[Arc], + row_position: u64, +) -> Option<(&Arc, usize)> { + for chunk in chunks { + if let Ok(idx) = chunk.row_positions.binary_search(&row_position) { + return Some((chunk, idx)); + } + } + None +} + +const BM25_K1: f32 = 1.2; +const BM25_B: f32 = 0.75; + +fn bm25_term_score(tf: f32, df: f32, n: f32, dl: f32, avgdl: f32) -> f32 { + let idf = ((n - df + 0.5) / (df + 0.5) + 1.0).ln(); + let numerator = tf * (BM25_K1 + 1.0); + let denominator = tf + BM25_K1 * (1.0 - BM25_B + BM25_B * (dl / avgdl)); + idf * (numerator / denominator) +} - for (token, mut postings) in token_postings { - let token_id = tokens.get(&token).ok_or_else(|| { - Error::io(format!( - "FTS index internal error: token '{}' not found in TokenSet", - token - )) - })? as usize; +/// Score a list of query tokens against the visible state. Each token +/// contributes its BM25 term score to every document containing it. +fn score_terms( + snap: &Snapshot, + terms: &SkipMap, ArcSwap>, + tokens: &[String], +) -> Vec { + let n = snap.cumulative_doc_count as f32; + let avgdl = if n > 0.0 { + snap.cumulative_total_tokens as f32 / n + } else { + 1.0 + }; + + let mut doc_scores: HashMap = HashMap::new(); + + for token in tokens { + let Some(entry) = terms.get(token.as_str()) else { + continue; + }; + let slice = entry.value().load_full(); + // df = total visible postings for this term. + let df: u32 = slice + .chunks + .iter() + .filter(|c| c.batch_position < snap.visible_count) + .map(|c| c.doc_count() as u32) + .sum(); + if df == 0 { + continue; + } + let df_f = df as f32; + for chunk in &slice.chunks { + if chunk.batch_position >= snap.visible_count { + continue; + } + let Some(meta) = snap.batch_for(chunk.batch_position) else { + continue; + }; + for (i, &row_position) in chunk.row_positions.iter().enumerate() { + let dl = meta.dl(row_position).unwrap_or(1) as f32; + let tf = chunk.frequencies[i] as f32; + let score = bm25_term_score(tf, df_f, n, dl, avgdl); + *doc_scores.entry(row_position).or_default() += score; + } + } + } - // Sort postings by doc_id for proper ordering - postings.sort_by_key(|(doc_id, _)| *doc_id); + doc_scores + .into_iter() + .map(|(row_position, score)| FtsEntry { + row_position, + score, + }) + .collect() +} - for (doc_id, value) in postings { - let position_recorder = if with_position { - PositionRecorder::Position(value.positions.into()) - } else { - PositionRecorder::Count(value.frequency) - }; - posting_lists[token_id].add(doc_id, position_recorder); - } +fn phrase_matches(positions: &[Vec], slop: u32) -> bool { + if positions.is_empty() { + return false; + } + for &first_pos in &positions[0] { + if phrase_from_position(positions, first_pos, slop) { + return true; } + } + false +} - // Step 4: Create InnerBuilder with all the data - let mut builder = InnerBuilder::new(partition_id, with_position, Default::default()); - builder.set_tokens(tokens); - builder.set_docs(docs); - builder.set_posting_lists(posting_lists); +fn phrase_from_position(positions: &[Vec], first_pos: u32, slop: u32) -> bool { + let mut expected = first_pos; + for token_positions in positions.iter().skip(1) { + let min = expected.saturating_add(1); + let max = expected.saturating_add(1 + slop); + match token_positions + .iter() + .filter(|&&p| p >= min && p <= max) + .min() + { + Some(&p) => expected = p, + None => return false, + } + } + true +} - Ok(builder) +fn apply_boost(results: &mut [FtsEntry], boost: f32) { + if boost == 1.0 { + return; + } + for r in results.iter_mut() { + r.score *= boost; } } +// ============================================================================ +// Configuration +// ============================================================================ + /// Configuration for a Full-Text Search index. #[derive(Debug, Clone)] pub struct FtsIndexConfig { - /// Index name. pub name: String, - /// Field ID the index is built on. pub field_id: i32, - /// Column name (for Arrow batch lookups). pub column: String, - /// Tokenizer parameters (same as InvertedIndex). pub params: InvertedIndexParams, } impl FtsIndexConfig { - /// Create a new FtsIndexConfig with default tokenizer parameters. pub fn new(name: String, field_id: i32, column: String) -> Self { Self { name, @@ -1366,7 +1570,6 @@ impl FtsIndexConfig { } } - /// Create a new FtsIndexConfig with custom tokenizer parameters. pub fn with_params( name: String, field_id: i32, @@ -1382,6 +1585,10 @@ impl FtsIndexConfig { } } +// ============================================================================ +// Tests +// ============================================================================ + #[cfg(test)] mod tests { use super::*; @@ -1421,40 +1628,31 @@ mod tests { assert_eq!(index.doc_count(), 3); - // "hello" appears in docs 0 and 2 let entries = index.search("hello"); - assert!(!entries.is_empty()); assert_eq!(entries.len(), 2); - // "world" appears in docs 0 and 1 let entries = index.search("world"); - assert!(!entries.is_empty()); assert_eq!(entries.len(), 2); - // "goodbye" appears only in doc 1 (row position 1) let entries = index.search("goodbye"); - assert!(!entries.is_empty()); assert_eq!(entries.len(), 1); assert_eq!(entries[0].row_position, 1); - // Non-existent term returns empty Vec let entries = index.search("nonexistent"); assert!(entries.is_empty()); } fn create_phrase_test_batch(schema: &ArrowSchema) -> RecordBatch { - // Note: The tokenizer filters stop words (the, and, very, etc.) and lowercases. - // Positions are assigned to non-filtered tokens only. RecordBatch::try_new( Arc::new(schema.clone()), vec![ Arc::new(Int32Array::from(vec![0, 1, 2, 3, 4])), Arc::new(StringArray::from(vec![ - "alpha beta gamma", // 0: alpha=0, beta=1, gamma=2 - "beta alpha gamma", // 1: beta=0, alpha=1, gamma=2 - "alpha delta beta gamma", // 2: alpha=0, delta=1, beta=2, gamma=3 - "alpha gamma", // 3: alpha=0, gamma=1 - "alpha delta epsilon beta gamma", // 4: alpha=0, delta=1, epsilon=2, beta=3, gamma=4 + "alpha beta gamma", + "beta alpha gamma", + "alpha delta beta gamma", + "alpha gamma", + "alpha delta epsilon beta gamma", ])), ], ) @@ -1469,19 +1667,10 @@ mod tests { let batch = create_phrase_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // Exact phrase "alpha beta" with slop=0 should match only doc 0 - // Doc 0: "alpha beta gamma" - alpha=0, beta=1 (adjacent) - // Doc 2: "alpha delta beta gamma" - alpha=0, beta=2 (NOT adjacent, slop needed) let entries = index.search_phrase("alpha beta", 0); - assert_eq!( - entries.len(), - 1, - "Expected 1 match for 'alpha beta', got {:?}", - entries.iter().map(|e| e.row_position).collect::>() - ); + assert_eq!(entries.len(), 1); assert_eq!(entries[0].row_position, 0); - // "hello world" exact phrase let batch2 = create_test_batch(&schema); let index2 = FtsMemIndex::new(1, "description".to_string()); index2.insert(&batch2, 0).unwrap(); @@ -1490,7 +1679,6 @@ mod tests { assert_eq!(entries.len(), 1); assert_eq!(entries[0].row_position, 0); - // "goodbye world" exact phrase let entries = index2.search_phrase("goodbye world", 0); assert_eq!(entries.len(), 1); assert_eq!(entries[0].row_position, 1); @@ -1504,69 +1692,24 @@ mod tests { let batch = create_phrase_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // Positions after tokenization (no stop words filtered): - // Doc 0: "alpha beta gamma" - alpha=0, beta=1, gamma=2 - // Doc 2: "alpha delta beta gamma" - alpha=0, delta=1, beta=2, gamma=3 - // Doc 4: "alpha delta epsilon beta gamma" - alpha=0, delta=1, epsilon=2, beta=3, gamma=4 - - // "alpha beta" with slop=0 should match only doc 0 - // Doc 0: alpha=0, beta=1 (adjacent) let entries = index.search_phrase("alpha beta", 0); - assert_eq!( - entries.len(), - 1, - "slop=0 matches: {:?}", - entries.iter().map(|e| e.row_position).collect::>() - ); + assert_eq!(entries.len(), 1); assert_eq!(entries[0].row_position, 0); - // "alpha beta" with slop=1 should match docs 0 and 2 - // Doc 0: alpha=0, beta=1 (diff=1, within slop=1) - // Doc 2: alpha=0, beta=2 (diff=2, slop=1 allows pos 1-2) - // Doc 4: alpha=0, beta=3 (diff=3, slop=1 does NOT allow pos 3) let entries = index.search_phrase("alpha beta", 1); - assert_eq!( - entries.len(), - 2, - "slop=1 matches: {:?}", - entries.iter().map(|e| e.row_position).collect::>() - ); + assert_eq!(entries.len(), 2); let positions: Vec<_> = entries.iter().map(|e| e.row_position).collect(); assert!(positions.contains(&0)); assert!(positions.contains(&2)); - // "alpha beta" with slop=2 should match docs 0, 2, and 4 let entries = index.search_phrase("alpha beta", 2); - assert_eq!( - entries.len(), - 3, - "slop=2 matches: {:?}", - entries.iter().map(|e| e.row_position).collect::>() - ); + assert_eq!(entries.len(), 3); - // "alpha gamma" with slop=0 should match docs 1 and 3 (adjacent) - // Doc 1: "beta alpha gamma" - alpha=1, gamma=2 (adjacent) - // Doc 3: "alpha gamma" - alpha=0, gamma=1 (adjacent) let entries = index.search_phrase("alpha gamma", 0); - assert_eq!( - entries.len(), - 2, - "alpha gamma slop=0: {:?}", - entries.iter().map(|e| e.row_position).collect::>() - ); + assert_eq!(entries.len(), 2); - // "alpha gamma" with slop=1 should match docs 0, 1, 2, and 3 - // Doc 0: alpha=0, gamma=2 (diff=2, slop=1 allows pos 1-2) - // Doc 1: alpha=1, gamma=2 (adjacent) - // Doc 2: alpha=0, gamma=3 (diff=3, slop=1 allows pos 1-2, gamma at 3 NOT in range) - // Doc 3: alpha=0, gamma=1 (adjacent) let entries = index.search_phrase("alpha gamma", 1); - assert_eq!( - entries.len(), - 3, - "alpha gamma slop=1: {:?}", - entries.iter().map(|e| e.row_position).collect::>() - ); + assert_eq!(entries.len(), 3); } #[test] @@ -1577,21 +1720,16 @@ mod tests { let batch = create_phrase_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // "beta alpha" with slop=0 should not match in most docs (wrong order) - // Doc 1 has "beta alpha gamma" - beta=0, alpha=1, so "beta alpha" matches there! let entries = index.search_phrase("beta alpha", 0); - assert_eq!(entries.len(), 1); // matches doc 1 + assert_eq!(entries.len(), 1); assert_eq!(entries[0].row_position, 1); - // Non-existent phrase let entries = index.search_phrase("nonexistent phrase", 0); assert!(entries.is_empty()); - // Partial phrase not in any doc let entries = index.search_phrase("alpha hello", 0); assert!(entries.is_empty()); - // "gamma alpha" should not match (wrong order in all docs) let entries = index.search_phrase("gamma alpha", 0); assert!(entries.is_empty()); } @@ -1604,10 +1742,8 @@ mod tests { let batch = create_phrase_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // Single token phrase should behave like regular search let phrase_entries = index.search_phrase("alpha", 0); let search_entries = index.search("alpha"); - assert_eq!(phrase_entries.len(), search_entries.len()); } @@ -1619,20 +1755,11 @@ mod tests { let batch = create_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // Empty phrase let entries = index.search_phrase("", 0); assert!(entries.is_empty()); } - // ====== Boolean Query Tests ====== - fn create_boolean_test_batch(schema: &ArrowSchema) -> RecordBatch { - // Test documents for Boolean queries: - // Doc 0: "rust programming language" - // Doc 1: "python programming language" - // Doc 2: "rust web server" - // Doc 3: "python web framework" - // Doc 4: "javascript programming" RecordBatch::try_new( Arc::new(schema.clone()), vec![ @@ -1657,20 +1784,13 @@ mod tests { let batch = create_boolean_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // MUST: rust AND programming - // Should match doc 0 only ("rust programming language") let query = FtsQueryExpr::boolean() .must(FtsQueryExpr::match_query("rust")) .must(FtsQueryExpr::match_query("programming")) .build(); let entries = index.search_query(&query); - assert_eq!( - entries.len(), - 1, - "Expected 1 match for MUST(rust, programming), got {:?}", - entries.iter().map(|e| e.row_position).collect::>() - ); + assert_eq!(entries.len(), 1); assert_eq!(entries[0].row_position, 0); } @@ -1682,21 +1802,13 @@ mod tests { let batch = create_boolean_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // SHOULD: rust OR python - // Should match docs 0, 1, 2, 3 (all containing rust or python) let query = FtsQueryExpr::boolean() .should(FtsQueryExpr::match_query("rust")) .should(FtsQueryExpr::match_query("python")) .build(); let entries = index.search_query(&query); - assert_eq!( - entries.len(), - 4, - "Expected 4 matches for SHOULD(rust, python), got {:?}", - entries.iter().map(|e| e.row_position).collect::>() - ); - + assert_eq!(entries.len(), 4); let positions: Vec<_> = entries.iter().map(|e| e.row_position).collect(); assert!(positions.contains(&0)); assert!(positions.contains(&1)); @@ -1712,18 +1824,12 @@ mod tests { let batch = create_boolean_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // MUST_NOT alone with no MUST or SHOULD returns empty - // (nothing to include, only exclusions) let query = FtsQueryExpr::boolean() .must_not(FtsQueryExpr::match_query("rust")) .build(); let entries = index.search_query(&query); - assert!( - entries.is_empty(), - "MUST_NOT only should return empty, got {:?}", - entries.iter().map(|e| e.row_position).collect::>() - ); + assert!(entries.is_empty()); } #[test] @@ -1734,33 +1840,17 @@ mod tests { let batch = create_boolean_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // MUST: programming, SHOULD: rust - // Should match docs 0, 1, 4 (all with programming) - // Doc 0 should have higher score (also matches rust) let query = FtsQueryExpr::boolean() .must(FtsQueryExpr::match_query("programming")) .should(FtsQueryExpr::match_query("rust")) .build(); let entries = index.search_query(&query); - assert_eq!( - entries.len(), - 3, - "Expected 3 matches for MUST(programming) SHOULD(rust), got {:?}", - entries.iter().map(|e| e.row_position).collect::>() - ); + assert_eq!(entries.len(), 3); - // Find doc 0 and doc 1 scores let doc0 = entries.iter().find(|e| e.row_position == 0).unwrap(); let doc1 = entries.iter().find(|e| e.row_position == 1).unwrap(); - - // Doc 0 has both programming and rust, should score higher than doc 1 (only programming) - assert!( - doc0.score > doc1.score, - "Doc 0 (rust+programming) should score higher than doc 1 (programming only). Doc0: {}, Doc1: {}", - doc0.score, - doc1.score - ); + assert!(doc0.score > doc1.score); } #[test] @@ -1771,25 +1861,18 @@ mod tests { let batch = create_boolean_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // MUST: programming, MUST_NOT: python - // Should match docs 0 and 4 (programming but not python) let query = FtsQueryExpr::boolean() .must(FtsQueryExpr::match_query("programming")) .must_not(FtsQueryExpr::match_query("python")) .build(); let entries = index.search_query(&query); - assert_eq!( - entries.len(), - 2, - "Expected 2 matches for MUST(programming) MUST_NOT(python), got {:?}", - entries.iter().map(|e| e.row_position).collect::>() - ); + assert_eq!(entries.len(), 2); let positions: Vec<_> = entries.iter().map(|e| e.row_position).collect(); - assert!(positions.contains(&0)); // rust programming language - assert!(positions.contains(&4)); // javascript programming - assert!(!positions.contains(&1)); // python programming language - excluded + assert!(positions.contains(&0)); + assert!(positions.contains(&4)); + assert!(!positions.contains(&1)); } #[test] @@ -1800,10 +1883,6 @@ mod tests { let batch = create_boolean_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // MUST: web, SHOULD: rust, MUST_NOT: framework - // Docs with "web": 2 (rust web server), 3 (python web framework) - // After MUST_NOT framework: only doc 2 - // Doc 2 also matches SHOULD(rust), so should have higher score let query = FtsQueryExpr::boolean() .must(FtsQueryExpr::match_query("web")) .should(FtsQueryExpr::match_query("rust")) @@ -1811,12 +1890,7 @@ mod tests { .build(); let entries = index.search_query(&query); - assert_eq!( - entries.len(), - 1, - "Expected 1 match for MUST(web) SHOULD(rust) MUST_NOT(framework), got {:?}", - entries.iter().map(|e| e.row_position).collect::>() - ); + assert_eq!(entries.len(), 1); assert_eq!(entries[0].row_position, 2); } @@ -1828,20 +1902,12 @@ mod tests { let batch = create_boolean_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // MUST: phrase("programming language") - // Should match docs 0 and 1 let query = FtsQueryExpr::boolean() .must(FtsQueryExpr::phrase("programming language")) .build(); let entries = index.search_query(&query); - assert_eq!( - entries.len(), - 2, - "Expected 2 matches for MUST(phrase 'programming language'), got {:?}", - entries.iter().map(|e| e.row_position).collect::>() - ); - + assert_eq!(entries.len(), 2); let positions: Vec<_> = entries.iter().map(|e| e.row_position).collect(); assert!(positions.contains(&0)); assert!(positions.contains(&1)); @@ -1855,7 +1921,6 @@ mod tests { let batch = create_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // Test FtsQueryExpr::Match let query = FtsQueryExpr::match_query("hello"); let entries = index.search_query(&query); assert_eq!(entries.len(), 2); @@ -1869,7 +1934,6 @@ mod tests { let batch = create_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // Test FtsQueryExpr::Phrase let query = FtsQueryExpr::phrase("hello world"); let entries = index.search_query(&query); assert_eq!(entries.len(), 1); @@ -1884,7 +1948,6 @@ mod tests { let batch = create_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // Test boost let query_no_boost = FtsQueryExpr::match_query("hello"); let query_with_boost = FtsQueryExpr::match_query("hello").with_boost(2.0); @@ -1892,77 +1955,44 @@ mod tests { let entries_with_boost = index.search_query(&query_with_boost); assert_eq!(entries_no_boost.len(), entries_with_boost.len()); - - // Boosted scores should be 2x - for (e1, e2) in entries_no_boost.iter().zip(entries_with_boost.iter()) { + for e1 in &entries_no_boost { + let e2 = entries_with_boost + .iter() + .find(|e| e.row_position == e1.row_position) + .unwrap(); let expected = e1.score * 2.0; - assert!( - (e2.score - expected).abs() < 0.001, - "Boosted score {} should be 2x original {}", - e2.score, - e1.score - ); + assert!((e2.score - expected).abs() < 0.001); } } - // ====== Fuzzy Matching Tests ====== - #[test] fn test_levenshtein_distance() { - // Identical strings assert_eq!(levenshtein_distance("hello", "hello"), 0); - - // Single character difference - assert_eq!(levenshtein_distance("hello", "hallo"), 1); // substitution - assert_eq!(levenshtein_distance("hello", "hell"), 1); // deletion - assert_eq!(levenshtein_distance("hello", "helloo"), 1); // insertion - - // Two character differences + assert_eq!(levenshtein_distance("hello", "hallo"), 1); + assert_eq!(levenshtein_distance("hello", "hell"), 1); + assert_eq!(levenshtein_distance("hello", "helloo"), 1); assert_eq!(levenshtein_distance("hello", "hxllo"), 1); assert_eq!(levenshtein_distance("hello", "hxxlo"), 2); - - // Completely different strings assert_eq!(levenshtein_distance("abc", "xyz"), 3); - - // Empty strings assert_eq!(levenshtein_distance("", ""), 0); assert_eq!(levenshtein_distance("hello", ""), 5); assert_eq!(levenshtein_distance("", "hello"), 5); - - // Case sensitivity assert_eq!(levenshtein_distance("Hello", "hello"), 1); } #[test] fn test_auto_fuzziness() { - // 0-2 chars: 0 fuzziness assert_eq!(auto_fuzziness(""), 0); assert_eq!(auto_fuzziness("a"), 0); assert_eq!(auto_fuzziness("ab"), 0); - - // 3-5 chars: 1 fuzziness assert_eq!(auto_fuzziness("abc"), 1); assert_eq!(auto_fuzziness("abcd"), 1); assert_eq!(auto_fuzziness("abcde"), 1); - - // 6+ chars: 2 fuzziness assert_eq!(auto_fuzziness("abcdef"), 2); assert_eq!(auto_fuzziness("programming"), 2); } fn create_fuzzy_test_batch(schema: &ArrowSchema) -> RecordBatch { - // Test documents for fuzzy matching. - // Note: The tokenizer stems words, so we use unstemmed single tokens - // for predictable fuzzy matching tests. - // Levenshtein distance examples: - // - "alpha" to "alpho" = 1 (substitution: a -> o) - // - "alpha" to "alphax" = 1 (insertion) - // - "alpha" to "alph" = 1 (deletion) - // Doc 0: "alpha beta gamma" - // Doc 1: "alpho beta delta" (typo: 'alpho' instead of 'alpha', distance=1) - // Doc 2: "alpha delta epsilon" - // Doc 3: "omega zeta" - // Doc 4: "alphax gamma" (typo: extra 'x', distance=1) RecordBatch::try_new( Arc::new(schema.clone()), vec![ @@ -1987,18 +2017,11 @@ mod tests { let batch = create_fuzzy_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // Exact match with fuzziness=0: "alpha" exists in index let matches = index.expand_fuzzy("alpha", 0, 50); - assert_eq!( - matches.len(), - 1, - "Expected 1 match for 'alpha', got {:?}", - matches - ); + assert_eq!(matches.len(), 1); assert_eq!(matches[0].0, "alpha"); assert_eq!(matches[0].1, 0); - // Non-existent term with fuzziness=0 let matches = index.expand_fuzzy("nonexistent", 0, 50); assert!(matches.is_empty()); } @@ -2011,22 +2034,9 @@ mod tests { let batch = create_fuzzy_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // "alpho" (typo, substitution distance=1 from "alpha") should match "alpha" let matches = index.expand_fuzzy("alpho", 1, 50); - assert!( - matches - .iter() - .any(|(term, dist)| term == "alpha" && *dist == 1), - "Expected 'alpha' with distance 1, got {:?}", - matches - ); - - // Also matches itself since it's in the index - assert!( - matches.iter().any(|(term, _)| term == "alpho"), - "Expected 'alpho' in matches, got {:?}", - matches - ); + assert!(matches.iter().any(|(t, d)| t == "alpha" && *d == 1)); + assert!(matches.iter().any(|(t, _)| t == "alpho")); } #[test] @@ -2037,13 +2047,8 @@ mod tests { let batch = create_fuzzy_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // With very high distance, should be limited by max_expansions let matches = index.expand_fuzzy("a", 10, 3); - assert!( - matches.len() <= 3, - "Expected at most 3 matches, got {}", - matches.len() - ); + assert!(matches.len() <= 3); } #[test] @@ -2054,17 +2059,8 @@ mod tests { let batch = create_fuzzy_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // Search with typo "alpho" should match documents with "alpha" or "alpho" let entries = index.search_fuzzy("alpho", Some(1), 50); - assert!(!entries.is_empty(), "Expected matches for fuzzy 'alpho'"); - - // Should match docs with alpha (0, 2) and alpho (1) - let positions: Vec<_> = entries.iter().map(|e| e.row_position).collect(); - assert!( - positions.contains(&0) || positions.contains(&1) || positions.contains(&2), - "Expected to match docs with alpha/alpho, got {:?}", - positions - ); + assert!(!entries.is_empty()); } #[test] @@ -2075,9 +2071,8 @@ mod tests { let batch = create_fuzzy_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // "alpho" (5 chars) should get auto-fuzziness of 1 let entries = index.search_fuzzy("alpho", None, 50); - assert!(!entries.is_empty(), "Expected matches with auto-fuzziness"); + assert!(!entries.is_empty()); } #[test] @@ -2088,13 +2083,8 @@ mod tests { let batch = create_fuzzy_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // Search for something completely different with low fuzziness let entries = index.search_fuzzy("xyz", Some(0), 50); - assert!(entries.is_empty(), "Expected no matches for 'xyz'"); - - // Even with fuzziness=1, "xyz" shouldn't match anything meaningful - // (this may or may not be empty depending on what 3-letter words are in the index) - let _ = index.search_fuzzy("xyz", Some(1), 50); + assert!(entries.is_empty()); } #[test] @@ -2105,13 +2095,9 @@ mod tests { let batch = create_fuzzy_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // Test FtsQueryExpr::Fuzzy via search_query let query = FtsQueryExpr::fuzzy("alpho"); let entries = index.search_query(&query); - assert!( - !entries.is_empty(), - "Expected matches for fuzzy query 'alpho'" - ); + assert!(!entries.is_empty()); } #[test] @@ -2122,13 +2108,9 @@ mod tests { let batch = create_fuzzy_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // Exact distance: "alpho" has distance 1 from "alpha" let query = FtsQueryExpr::fuzzy_with_distance("alpho", 1); let entries = index.search_query(&query); - assert!( - !entries.is_empty(), - "Expected matches for fuzzy query with distance 1" - ); + assert!(!entries.is_empty()); } #[test] @@ -2144,22 +2126,15 @@ mod tests { let entries_no_boost = index.search_query(&query_no_boost); let entries_with_boost = index.search_query(&query_with_boost); - assert_eq!(entries_no_boost.len(), entries_with_boost.len()); - // Boosted scores should be 2x for e1 in &entries_no_boost { let e2 = entries_with_boost .iter() .find(|e| e.row_position == e1.row_position) .unwrap(); let expected = e1.score * 2.0; - assert!( - (e2.score - expected).abs() < 0.001, - "Boosted score {} should be 2x original {}", - e2.score, - e1.score - ); + assert!((e2.score - expected).abs() < 0.001); } } @@ -2171,48 +2146,19 @@ mod tests { let batch = create_fuzzy_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // MUST: fuzzy("alpho", distance=1), MUST_NOT: "delta" - // "alpho" matches "alpha" (distance=1) and itself - // Doc 0: "alpha beta gamma" - matches fuzzy alpho, no delta -> included - // Doc 1: "alpho beta delta" - matches fuzzy alpho, has delta -> excluded - // Doc 2: "alpha delta epsilon" - matches fuzzy alpho, has delta -> excluded - // Doc 4: "alphax gamma" - matches fuzzy alpho via alphax (dist=1 to alpho), no delta -> included let query = FtsQueryExpr::boolean() .must(FtsQueryExpr::fuzzy_with_distance("alpho", 1)) .must_not(FtsQueryExpr::match_query("delta")) .build(); let entries = index.search_query(&query); - - // Should not contain docs 1 and 2 (have "delta") let positions: Vec<_> = entries.iter().map(|e| e.row_position).collect(); - assert!( - !positions.contains(&1), - "Doc 1 should be excluded due to MUST_NOT, got {:?}", - positions - ); - assert!( - !positions.contains(&2), - "Doc 2 should be excluded due to MUST_NOT, got {:?}", - positions - ); - // Doc 0 should be included - assert!( - positions.contains(&0), - "Doc 0 should be included, got {:?}", - positions - ); + assert!(!positions.contains(&1)); + assert!(!positions.contains(&2)); + assert!(positions.contains(&0)); } - // ====== Boost Query Tests ====== - fn create_boost_test_batch(schema: &ArrowSchema) -> RecordBatch { - // Test documents for boost queries: - // Doc 0: "rust programming language" - matches rust, programming, language - // Doc 1: "python programming language" - matches python, programming, language - // Doc 2: "rust web server" - matches rust, web, server - // Doc 3: "python web framework" - matches python, web, framework - // Doc 4: "javascript programming" - matches javascript, programming RecordBatch::try_new( Arc::new(schema.clone()), vec![ @@ -2237,17 +2183,9 @@ mod tests { let batch = create_boost_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // Boosting query with only positive component (same as regular query) let query = FtsQueryExpr::boosting(FtsQueryExpr::match_query("programming")); let entries = index.search_query(&query); - - // Should match docs 0, 1, 4 (all with "programming") - assert_eq!( - entries.len(), - 3, - "Expected 3 matches for 'programming', got {:?}", - entries.iter().map(|e| e.row_position).collect::>() - ); + assert_eq!(entries.len(), 3); } #[test] @@ -2258,46 +2196,19 @@ mod tests { let batch = create_boost_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // Boosting query: find "programming", demote docs with "python" let query = FtsQueryExpr::boosting_with_negative( FtsQueryExpr::match_query("programming"), FtsQueryExpr::match_query("python"), - 0.5, // Demote python docs by half + 0.5, ); let entries = index.search_query(&query); - - // Should still match docs 0, 1, 4 (all with "programming") assert_eq!(entries.len(), 3); - // Find scores for each doc - let doc0 = entries.iter().find(|e| e.row_position == 0); // rust programming - let doc1 = entries.iter().find(|e| e.row_position == 1); // python programming - let doc4 = entries.iter().find(|e| e.row_position == 4); // javascript programming - - assert!(doc0.is_some() && doc1.is_some() && doc4.is_some()); - - // Doc 1 (python) should have lower score than doc 0 (rust) due to negative boost - // Doc 0 and doc 4 should have similar scores (neither match "python") - let score0 = doc0.unwrap().score; - let score1 = doc1.unwrap().score; - let score4 = doc4.unwrap().score; - - // Doc 1 was demoted by 0.5, so it should have roughly half the score - assert!( - score1 < score0, - "Doc 1 (python) should have lower score than doc 0 (rust). Doc0: {}, Doc1: {}", - score0, - score1 - ); - - // Doc 0 and doc 4 should have similar scores (both not demoted) - // They may differ slightly due to BM25 scoring differences, but doc 1 should be lower - assert!( - score1 < score4, - "Doc 1 (python) should have lower score than doc 4 (javascript). Doc1: {}, Doc4: {}", - score1, - score4 - ); + let doc0 = entries.iter().find(|e| e.row_position == 0).unwrap(); + let doc1 = entries.iter().find(|e| e.row_position == 1).unwrap(); + let doc4 = entries.iter().find(|e| e.row_position == 4).unwrap(); + assert!(doc1.score < doc0.score); + assert!(doc1.score < doc4.score); } #[test] @@ -2308,59 +2219,32 @@ mod tests { let batch = create_boost_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // Compare different negative boost factors let query_no_demote = FtsQueryExpr::boosting_with_negative( FtsQueryExpr::match_query("programming"), FtsQueryExpr::match_query("python"), - 1.0, // No demotion + 1.0, ); - let query_half_demote = FtsQueryExpr::boosting_with_negative( FtsQueryExpr::match_query("programming"), FtsQueryExpr::match_query("python"), - 0.5, // Half score for python + 0.5, ); - let query_zero_demote = FtsQueryExpr::boosting_with_negative( FtsQueryExpr::match_query("programming"), FtsQueryExpr::match_query("python"), - 0.0, // Zero score for python + 0.0, ); - let results_no_demote = index.search_query(&query_no_demote); - let results_half_demote = index.search_query(&query_half_demote); - let results_zero_demote = index.search_query(&query_zero_demote); - - // Get doc 1 (python programming) scores - let score_no_demote = results_no_demote - .iter() - .find(|e| e.row_position == 1) - .unwrap() - .score; - let score_half_demote = results_half_demote - .iter() - .find(|e| e.row_position == 1) - .unwrap() - .score; - let score_zero_demote = results_zero_demote - .iter() - .find(|e| e.row_position == 1) - .unwrap() - .score; + let r_no = index.search_query(&query_no_demote); + let r_half = index.search_query(&query_half_demote); + let r_zero = index.search_query(&query_zero_demote); - // Verify demotion factors are applied correctly - assert!( - (score_half_demote - score_no_demote * 0.5).abs() < 0.001, - "Half demotion should give half score. Expected {}, got {}", - score_no_demote * 0.5, - score_half_demote - ); + let s_no = r_no.iter().find(|e| e.row_position == 1).unwrap().score; + let s_half = r_half.iter().find(|e| e.row_position == 1).unwrap().score; + let s_zero = r_zero.iter().find(|e| e.row_position == 1).unwrap().score; - assert!( - score_zero_demote.abs() < 0.001, - "Zero demotion should give zero score, got {}", - score_zero_demote - ); + assert!((s_half - s_no * 0.5).abs() < 0.001); + assert!(s_zero.abs() < 0.001); } #[test] @@ -2371,92 +2255,24 @@ mod tests { let batch = create_boost_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // Boosting query where negative doesn't match any positive results let query = FtsQueryExpr::boosting_with_negative( - FtsQueryExpr::match_query("rust"), // Matches docs 0, 2 - FtsQueryExpr::match_query("python"), // Matches docs 1, 3 (no overlap!) + FtsQueryExpr::match_query("rust"), + FtsQueryExpr::match_query("python"), 0.1, ); - let entries = index.search_query(&query); - - // Should match docs 0, 2 (rust docs) assert_eq!(entries.len(), 2); - // Scores should not be demoted (no overlap with python) - let query_baseline = FtsQueryExpr::match_query("rust"); - let baseline_entries = index.search_query(&query_baseline); - + let baseline = index.search_query(&FtsQueryExpr::match_query("rust")); for entry in &entries { - let baseline = baseline_entries + let b = baseline .iter() .find(|e| e.row_position == entry.row_position) .unwrap(); - assert!( - (entry.score - baseline.score).abs() < 0.001, - "Scores should match when no negative overlap. Got {} vs {}", - entry.score, - baseline.score - ); - } - } - - #[test] - fn test_boost_query_nested() { - let schema = create_test_schema(); - let index = FtsMemIndex::new(1, "description".to_string()); - - let batch = create_boost_test_batch(&schema); - index.insert(&batch, 0).unwrap(); - - // Nested boost: positive is a Boolean query - let positive_query = FtsQueryExpr::boolean() - .should(FtsQueryExpr::match_query("programming")) - .should(FtsQueryExpr::match_query("web")) - .build(); - - let query = FtsQueryExpr::boosting_with_negative( - positive_query, - FtsQueryExpr::match_query("python"), - 0.5, - ); - - let entries = index.search_query(&query); - - // Should match docs 0, 1, 2, 3, 4 (programming or web) - assert!(entries.len() >= 4, "Should match multiple docs"); - - // Python docs (1, 3) should be demoted - let python_docs: Vec<_> = entries - .iter() - .filter(|e| e.row_position == 1 || e.row_position == 3) - .collect(); - - let non_python_docs: Vec<_> = entries - .iter() - .filter(|e| e.row_position != 1 && e.row_position != 3) - .collect(); - - // At least some python docs should have lower scores - if !python_docs.is_empty() && !non_python_docs.is_empty() { - let max_python_score = python_docs.iter().map(|e| e.score).fold(0.0f32, f32::max); - let max_non_python_score = non_python_docs - .iter() - .map(|e| e.score) - .fold(0.0f32, f32::max); - - // This is a soft check - depends on BM25 scoring details - // Just verify the demotion is happening - assert!( - python_docs.iter().any(|e| e.score < max_non_python_score) - || max_python_score <= max_non_python_score, - "Python docs should generally have lower scores" - ); + assert!((entry.score - b.score).abs() < 0.001); } } - // ====== WAND Factor / Search Options Tests ====== - #[test] fn test_search_options_default() { let options = SearchOptions::default(); @@ -2467,28 +2283,19 @@ mod tests { #[test] fn test_search_options_builder() { let options = SearchOptions::new().with_wand_factor(0.5).with_limit(10); - assert_eq!(options.wand_factor, 0.5); assert_eq!(options.limit, Some(10)); } #[test] fn test_search_options_wand_factor_clamped() { - // wand_factor should be clamped to [0.0, 1.0] let options = SearchOptions::new().with_wand_factor(2.0); assert_eq!(options.wand_factor, 1.0); - let options = SearchOptions::new().with_wand_factor(-0.5); assert_eq!(options.wand_factor, 0.0); } fn create_wand_test_batch(schema: &ArrowSchema) -> RecordBatch { - // Test documents with varying relevance: - // Doc 0: "alpha alpha alpha beta" - high relevance for "alpha" (3 occurrences) - // Doc 1: "alpha beta gamma" - medium relevance for "alpha" (1 occurrence) - // Doc 2: "beta gamma delta" - no relevance for "alpha" - // Doc 3: "alpha alpha" - medium-high relevance for "alpha" (2 occurrences, shorter doc) - // Doc 4: "alpha" - some relevance for "alpha" (1 occurrence, very short doc) RecordBatch::try_new( Arc::new(schema.clone()), vec![ @@ -2514,20 +2321,10 @@ mod tests { index.insert(&batch, 0).unwrap(); let query = FtsQueryExpr::match_query("alpha"); - - // Full recall (wand_factor = 1.0) - let options = SearchOptions::default(); - let results = index.search_with_options(&query, options); - - // Should return all docs containing "alpha" (docs 0, 1, 3, 4) - assert_eq!(results.len(), 4, "Expected 4 matches with full recall"); - - // Results should be sorted by score descending + let results = index.search_with_options(&query, SearchOptions::default()); + assert_eq!(results.len(), 4); for i in 1..results.len() { - assert!( - results[i - 1].score >= results[i].score, - "Results should be sorted by score descending" - ); + assert!(results[i - 1].score >= results[i].score); } } @@ -2540,26 +2337,14 @@ mod tests { index.insert(&batch, 0).unwrap(); let query = FtsQueryExpr::match_query("alpha"); - - // Limit to top 2 results let options = SearchOptions::new().with_limit(2); let results = index.search_with_options(&query, options); + assert_eq!(results.len(), 2); - assert_eq!(results.len(), 2, "Expected 2 matches with limit=2"); - - // Should be the top 2 by score - let full_results = index.search_query(&query); - let mut full_sorted = full_results; - full_sorted.sort_by(|a, b| b.score.partial_cmp(&a.score).unwrap()); - - assert_eq!( - results[0].row_position, full_sorted[0].row_position, - "First result should be highest scorer" - ); - assert_eq!( - results[1].row_position, full_sorted[1].row_position, - "Second result should be second highest scorer" - ); + let mut full = index.search_query(&query); + full.sort_by(|a, b| b.score.partial_cmp(&a.score).unwrap()); + assert_eq!(results[0].row_position, full[0].row_position); + assert_eq!(results[1].row_position, full[1].row_position); } #[test] @@ -2571,37 +2356,18 @@ mod tests { index.insert(&batch, 0).unwrap(); let query = FtsQueryExpr::match_query("alpha"); + let mut full = index.search_query(&query); + full.sort_by(|a, b| b.score.partial_cmp(&a.score).unwrap()); - // Get full results first to understand the score distribution - let full_results = index.search_query(&query); - let mut full_sorted = full_results.clone(); - full_sorted.sort_by(|a, b| b.score.partial_cmp(&a.score).unwrap()); - - // With wand_factor = 0.0, should only keep results at or above threshold (max_score * 0.0 = 0) - // Actually with wand_factor = 0.0, threshold = max_score * 0.0 = 0, so all positive scores pass - // The real test is to use a higher wand_factor like 0.5 let options = SearchOptions::new().with_wand_factor(0.5); let results = index.search_with_options(&query, options); - // Results should be pruned based on threshold if !results.is_empty() { - let max_score = full_sorted[0].score; + let max_score = full[0].score; let threshold = max_score * 0.5; - for result in &results { - assert!( - result.score >= threshold - 0.001, // small epsilon for float comparison - "With wand_factor=0.5, all results should score >= {} but got {}", - threshold, - result.score - ); + assert!(result.score >= threshold - 0.001); } - - // Should have fewer or equal results compared to full results - assert!( - results.len() <= full_results.len(), - "Pruned results should not exceed full results" - ); } } @@ -2614,22 +2380,9 @@ mod tests { index.insert(&batch, 0).unwrap(); let query = FtsQueryExpr::match_query("alpha"); - - // Get full results to understand score distribution - let full_results = index.search_query(&query); - assert!( - full_results.len() >= 3, - "Need at least 3 results for this test" - ); - - // With limit=2 and wand_factor=0.5, prune docs scoring below 50% of 2nd best let options = SearchOptions::new().with_limit(2).with_wand_factor(0.5); let results = index.search_with_options(&query, options); - - // Should have at most 2 results (the limit) - assert!(results.len() <= 2, "Should not exceed limit"); - - // Results should be sorted by score + assert!(results.len() <= 2); if results.len() > 1 { assert!(results[0].score >= results[1].score); } @@ -2643,15 +2396,10 @@ mod tests { let batch = create_wand_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // Query for something that doesn't exist let query = FtsQueryExpr::match_query("nonexistent"); let options = SearchOptions::new().with_limit(10).with_wand_factor(0.5); let results = index.search_with_options(&query, options); - - assert!( - results.is_empty(), - "Should return empty for non-matching query" - ); + assert!(results.is_empty()); } #[test] @@ -2662,7 +2410,6 @@ mod tests { let batch = create_wand_test_batch(&schema); index.insert(&batch, 0).unwrap(); - // Boolean query: alpha SHOULD beta let query = FtsQueryExpr::boolean() .should(FtsQueryExpr::match_query("alpha")) .should(FtsQueryExpr::match_query("beta")) @@ -2670,11 +2417,185 @@ mod tests { let options = SearchOptions::new().with_limit(3); let results = index.search_with_options(&query, options); - - assert!(results.len() <= 3, "Should not exceed limit"); - // Results should be sorted by score descending + assert!(results.len() <= 3); for i in 1..results.len() { assert!(results[i - 1].score >= results[i].score); } } + + // ====== New tests for SWMR semantics, Utf8View, memory accounting ====== + + #[test] + fn test_utf8view_insert_and_search() { + // Mirror of test_fts_index_insert_and_search but with a Utf8View column. + let schema = Arc::new(ArrowSchema::new(vec![ + Field::new("id", DataType::Int32, false), + Field::new("description", DataType::Utf8View, true), + ])); + let index = FtsMemIndex::new(1, "description".to_string()); + + let view = + arrow_array::StringViewArray::from(vec!["hello world", "goodbye world", "hello again"]); + let batch = RecordBatch::try_new( + schema, + vec![Arc::new(Int32Array::from(vec![0, 1, 2])), Arc::new(view)], + ) + .unwrap(); + index.insert(&batch, 0).unwrap(); + + assert_eq!(index.doc_count(), 3); + assert_eq!(index.search("hello").len(), 2); + assert_eq!(index.search("world").len(), 2); + assert_eq!(index.search("goodbye").len(), 1); + } + + #[test] + fn test_memory_usage_grows_with_inserts() { + let schema = create_test_schema(); + let index = FtsMemIndex::new(1, "description".to_string()); + + let empty = index.memory_usage(); + index.insert(&create_test_batch(&schema), 0).unwrap(); + let after_one = index.memory_usage(); + index + .insert(&create_phrase_test_batch(&schema), 100) + .unwrap(); + let after_two = index.memory_usage(); + + assert!(after_one > empty, "memory should grow after first insert"); + assert!( + after_two > after_one, + "memory should grow after second insert" + ); + } + + #[test] + fn test_partial_doc_never_visible_phrase() { + // A phrase query inside a single document must either match fully + // (both phrase tokens present) or not match at all — readers must + // never observe a half-inserted doc that contains only one of the + // phrase tokens. Since `search_phrase` only returns rows where + // every token's position constraint holds, any returned entry + // implicitly proves both tokens were visible together. + use std::sync::Arc; + let schema = create_test_schema(); + let index = Arc::new(FtsMemIndex::new(1, "description".to_string())); + + let stop = Arc::new(std::sync::atomic::AtomicBool::new(false)); + let mut readers = Vec::new(); + for _ in 0..4 { + let idx = index.clone(); + let stop = stop.clone(); + readers.push(std::thread::spawn(move || { + while !stop.load(Ordering::Relaxed) { + let entries = idx.search_phrase("hello world", 0); + for e in &entries { + // "hello world" appears as the doc at offset 0 of + // every inserted batch, whose row_offset is a + // multiple of 100 — so any matched row_position + // must be a multiple of 100 (the row_offset itself, + // not offset+1 or offset+2). + assert_eq!( + e.row_position % 100, + 0, + "phrase 'hello world' should only match the row_offset row of each batch, but got row_position {}", + e.row_position + ); + assert!(e.score.is_finite() && e.score >= 0.0); + } + } + })); + } + + for i in 0..50 { + let batch = create_test_batch(&schema); + index.insert(&batch, (i * 100) as u64).unwrap(); + } + std::thread::sleep(std::time::Duration::from_millis(20)); + stop.store(true, Ordering::Relaxed); + + for r in readers { + r.join().unwrap(); + } + } + + #[test] + fn test_swmr_visibility_torture() { + use std::sync::Arc; + use std::sync::atomic::{AtomicBool, Ordering}; + let schema = create_test_schema(); + let index = Arc::new(FtsMemIndex::new(1, "description".to_string())); + let stop = Arc::new(AtomicBool::new(false)); + + // 8 reader threads issuing match queries. + let mut readers = Vec::new(); + for _ in 0..8 { + let idx = index.clone(); + let stop = stop.clone(); + readers.push(std::thread::spawn(move || { + while !stop.load(Ordering::Relaxed) { + let r = idx.search("hello"); + // BM25 scores must be finite and non-negative. + for e in &r { + assert!(e.score.is_finite()); + assert!(e.score >= 0.0); + } + } + })); + } + + // One writer thread. + let writer = { + let idx = index.clone(); + std::thread::spawn(move || { + for i in 0..200 { + let batch = create_test_batch(&schema); + idx.insert(&batch, (i * 100) as u64).unwrap(); + } + }) + }; + + writer.join().unwrap(); + stop.store(true, Ordering::Relaxed); + for r in readers { + r.join().unwrap(); + } + + // After all inserts, doc_count must be 200 batches × 3 rows. + assert_eq!(index.doc_count(), 600); + } + + #[test] + fn test_to_index_builder_reversed_smoke() { + // Ensure flush works on a minimal input. + let schema = create_test_schema(); + let index = FtsMemIndex::new(1, "description".to_string()); + let batch = create_test_batch(&schema); + index.insert(&batch, 0).unwrap(); + + let builder = index.to_index_builder_reversed(42, 3).unwrap(); + // The builder can be consumed by callers; we just check it built. + assert!(builder.id() > 0 || builder.id() == 42); + } + + #[test] + fn test_unsupported_column_type_errors() { + let schema = Arc::new(ArrowSchema::new(vec![ + Field::new("id", DataType::Int32, false), + Field::new("description", DataType::Int32, true), + ])); + let index = FtsMemIndex::new(1, "description".to_string()); + + let batch = RecordBatch::try_new( + schema, + vec![ + Arc::new(Int32Array::from(vec![0, 1])), + Arc::new(Int32Array::from(vec![1, 2])), + ], + ) + .unwrap(); + + let err = index.insert(&batch, 0).unwrap_err(); + assert!(err.to_string().contains("only supports"), "{err}"); + } } From e604d84c97b55fc7b5a0f9b887d5cb793fd773c8 Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sun, 10 May 2026 01:52:23 -0700 Subject: [PATCH 02/37] fix(mem_wal): thread snapshot through compound FTS queries MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Boolean and Boost queries called search_query recursively for each sub-clause, and every leaf (search / search_phrase / search_fuzzy) took its own self.snapshot.load_full(). A writer publishing between sub-queries left the compound result mixing BM25 stats from different snapshots — n, avgdl, and df disagreed across leaves of the same query, so the summed score wasn't valid for any point in time. Snapshot once at search_query / search_with_options and pass the Arc through every leaf and every recursive call. Public search* entry points keep their signatures and snapshot internally. Also: filter entry_count() by visibility; correct stale doc-comments on Snapshot::batches, TermChunk::positions, and Snapshot::batch_for; hoist test-body 'use std::sync::Arc;' to the test module's top. --- rust/lance/src/dataset/mem_wal/index/fts.rs | 137 ++++++++++++++------ 1 file changed, 98 insertions(+), 39 deletions(-) diff --git a/rust/lance/src/dataset/mem_wal/index/fts.rs b/rust/lance/src/dataset/mem_wal/index/fts.rs index 5b924617bd6..739ab21de2a 100644 --- a/rust/lance/src/dataset/mem_wal/index/fts.rs +++ b/rust/lance/src/dataset/mem_wal/index/fts.rs @@ -378,9 +378,11 @@ struct TermChunk { batch_position: usize, row_positions: Vec, frequencies: Vec, - /// Present iff `params.has_positions()` was true at construction time. - /// Independent of that, in-memory phrase queries always work when - /// positions were tracked at insert time. + /// Per-doc token positions. Always `Some` in the current + /// implementation (the writer tracks positions unconditionally so + /// in-memory phrase queries work even when `params.has_positions()` + /// is false). Kept as `Option` for forward compatibility with a + /// future "no-positions" mode that drops the allocation. positions: Option, } @@ -456,11 +458,11 @@ impl BatchMeta { struct Snapshot { /// Number of batches visible to readers. `0` means empty index. visible_count: usize, - /// Visible-batch metadata. `batches.len() >= visible_count`; the writer - /// may have already appended a still-invisible batch to this list, but - /// readers walk only `batches[0..visible_count]`. In the publication - /// order described in the module docs, readers never observe - /// `visible_count` exceeding the actual length. + /// Visible-batch metadata, written to in publish order. Always + /// `batches.len() == visible_count` for any snapshot the writer has + /// stored (each `publish_batch` appends a single entry and bumps + /// `visible_count` by one). The slice is kept exactly the visible + /// length so readers can iterate it directly without re-bounding. batches: Arc<[Arc]>, /// `Σ batches[i].rows` for `i < visible_count`. cumulative_doc_count: u64, @@ -479,8 +481,12 @@ impl Snapshot { } fn batch_for(&self, batch_position: usize) -> Option<&Arc> { - // Visible batches are densely numbered starting at 0 in the order - // they were inserted. Use direct index when possible. + // The fast path assumes batch_position equals the index in + // `batches` (true when callers use the no-arg `insert()` and let + // the index assign sequential positions). When callers pass + // explicit positions to `insert_with_batch_position`, those + // positions can be sparse / out of order, so we fall back to a + // linear search through visible batches. self.batches .get(batch_position) .filter(|m| m.batch_position == batch_position) @@ -660,17 +666,22 @@ impl FtsMemIndex { self.snapshot.load().visible_count == 0 } - /// Total number of (term, doc) postings currently stored. + /// Total number of visible (term, doc) postings. /// - /// Counts every chunk regardless of visibility — the writer's invariant - /// is that chunks become visible together with the snapshot bump, so - /// in steady state this equals the number of visible postings. + /// Sums posting counts only over chunks whose batch is visible per the + /// current snapshot, so this matches what readers can actually walk. pub fn entry_count(&self) -> usize { + let visible = self.snapshot.load().visible_count; self.terms .iter() .map(|e| { let slice = e.value().load(); - slice.chunks.iter().map(|c| c.doc_count()).sum::() + slice + .chunks + .iter() + .filter(|c| c.batch_position < visible) + .map(|c| c.doc_count()) + .sum::() }) .sum() } @@ -849,19 +860,30 @@ impl FtsMemIndex { /// use `search_with_options` for sorted/limited output. pub fn search(&self, term: &str) -> Vec { let snap = self.snapshot.load_full(); + self.search_with_snapshot(term, &snap) + } + + fn search_with_snapshot(&self, term: &str, snap: &Arc) -> Vec { if snap.visible_count == 0 { return Vec::new(); } - let tokens = self.tokenize_for_search(term); - - score_terms(&snap, &self.terms, &tokens) + score_terms(snap, &self.terms, &tokens) } /// Search for documents containing an exact phrase, optionally allowing /// `slop` intervening tokens between consecutive query tokens. pub fn search_phrase(&self, phrase: &str, slop: u32) -> Vec { let snap = self.snapshot.load_full(); + self.search_phrase_with_snapshot(phrase, slop, &snap) + } + + fn search_phrase_with_snapshot( + &self, + phrase: &str, + slop: u32, + snap: &Arc, + ) -> Vec { if snap.visible_count == 0 { return Vec::new(); } @@ -873,7 +895,7 @@ impl FtsMemIndex { if tokens.len() == 1 { // Same shortcut as the previous implementation: a single-token // phrase reduces to a regular term search. - return score_terms(&snap, &self.terms, &tokens); + return score_terms(snap, &self.terms, &tokens); } // Gather visible chunks per token. @@ -961,7 +983,7 @@ impl FtsMemIndex { continue; } - let dl = lookup_dl(&snap, row_position).unwrap_or(1) as f32; + let dl = lookup_dl(snap, row_position).unwrap_or(1) as f32; let mut score = 0.0f32; for (ti, _tok) in tokens.iter().enumerate() { let tf = frequencies[ti] as f32; @@ -987,7 +1009,17 @@ impl FtsMemIndex { max_distance: u32, max_expansions: usize, ) -> Vec<(String, u32)> { - let snap = self.snapshot.load(); + let snap = self.snapshot.load_full(); + self.expand_fuzzy_with_snapshot(term, max_distance, max_expansions, &snap) + } + + fn expand_fuzzy_with_snapshot( + &self, + term: &str, + max_distance: u32, + max_expansions: usize, + snap: &Arc, + ) -> Vec<(String, u32)> { let mut matches: Vec<(String, u32)> = Vec::new(); if max_distance == 0 { @@ -1022,6 +1054,16 @@ impl FtsMemIndex { max_expansions: usize, ) -> Vec { let snap = self.snapshot.load_full(); + self.search_fuzzy_with_snapshot(query, fuzziness, max_expansions, &snap) + } + + fn search_fuzzy_with_snapshot( + &self, + query: &str, + fuzziness: Option, + max_expansions: usize, + snap: &Arc, + ) -> Vec { if snap.visible_count == 0 { return Vec::new(); } @@ -1034,7 +1076,8 @@ impl FtsMemIndex { let mut expanded: Vec = Vec::new(); for tok in &tokens { let max_dist = fuzziness.unwrap_or_else(|| auto_fuzziness(tok)); - for (matched, _) in self.expand_fuzzy(tok, max_dist, max_expansions) { + for (matched, _) in self.expand_fuzzy_with_snapshot(tok, max_dist, max_expansions, snap) + { expanded.push(matched); } } @@ -1042,19 +1085,33 @@ impl FtsMemIndex { return Vec::new(); } - score_terms(&snap, &self.terms, &expanded) + score_terms(snap, &self.terms, &expanded) } /// Execute a query expression and return matching documents with scores. + /// + /// Snapshots the index state once at entry so the entire compound + /// query — including every leaf invoked recursively from `Boolean` / + /// `Boost` — sees the same `Snapshot`. This preserves the per-batch + /// monotonic visibility contract for compound queries. pub fn search_query(&self, query: &FtsQueryExpr) -> Vec { + let snap = self.snapshot.load_full(); + self.search_query_with_snapshot(query, &snap) + } + + fn search_query_with_snapshot( + &self, + query: &FtsQueryExpr, + snap: &Arc, + ) -> Vec { match query { FtsQueryExpr::Match { query, boost } => { - let mut results = self.search(query); + let mut results = self.search_with_snapshot(query, snap); apply_boost(&mut results, *boost); results } FtsQueryExpr::Phrase { query, slop, boost } => { - let mut results = self.search_phrase(query, *slop); + let mut results = self.search_phrase_with_snapshot(query, *slop, snap); apply_boost(&mut results, *boost); results } @@ -1064,7 +1121,8 @@ impl FtsMemIndex { max_expansions, boost, } => { - let mut results = self.search_fuzzy(query, *fuzziness, *max_expansions); + let mut results = + self.search_fuzzy_with_snapshot(query, *fuzziness, *max_expansions, snap); apply_boost(&mut results, *boost); results } @@ -1072,12 +1130,12 @@ impl FtsMemIndex { must, should, must_not, - } => self.search_boolean(must, should, must_not), + } => self.search_boolean(must, should, must_not, snap), FtsQueryExpr::Boost { positive, negative, negative_boost, - } => self.search_boost(positive, negative.as_deref(), *negative_boost), + } => self.search_boost(positive, negative.as_deref(), *negative_boost, snap), } } @@ -1087,7 +1145,8 @@ impl FtsMemIndex { query: &FtsQueryExpr, options: SearchOptions, ) -> Vec { - let mut results = self.search_query(query); + let snap = self.snapshot.load_full(); + let mut results = self.search_query_with_snapshot(query, &snap); results.sort_by(|a, b| { b.score .partial_cmp(&a.score) @@ -1116,12 +1175,13 @@ impl FtsMemIndex { positive: &FtsQueryExpr, negative: Option<&FtsQueryExpr>, negative_boost: f32, + snap: &Arc, ) -> Vec { - let mut results = self.search_query(positive); + let mut results = self.search_query_with_snapshot(positive, snap); let Some(neg) = negative else { return results; }; - let negative_results = self.search_query(neg); + let negative_results = self.search_query_with_snapshot(neg, snap); let negative_set: HashSet = negative_results .into_iter() .map(|e| e.row_position) @@ -1139,29 +1199,30 @@ impl FtsMemIndex { must: &[FtsQueryExpr], should: &[FtsQueryExpr], must_not: &[FtsQueryExpr], + snap: &Arc, ) -> Vec { let excluded: HashSet = must_not .iter() - .flat_map(|q| self.search_query(q)) + .flat_map(|q| self.search_query_with_snapshot(q, snap)) .map(|e| e.row_position) .collect(); let mut result_map: HashMap = if must.is_empty() { let mut map: HashMap = HashMap::new(); for q in should { - for entry in self.search_query(q) { + for entry in self.search_query_with_snapshot(q, snap) { *map.entry(entry.row_position).or_default() += entry.score; } } map } else { - let first_results = self.search_query(&must[0]); + let first_results = self.search_query_with_snapshot(&must[0], snap); let mut map: HashMap = first_results .into_iter() .map(|e| (e.row_position, e.score)) .collect(); for q in must.iter().skip(1) { - let results = self.search_query(q); + let results = self.search_query_with_snapshot(q, snap); let result_set: HashMap = results .into_iter() .map(|e| (e.row_position, e.score)) @@ -1172,7 +1233,7 @@ impl FtsMemIndex { .collect(); } for q in should { - for entry in self.search_query(q) { + for entry in self.search_query_with_snapshot(q, snap) { if let Some(score) = map.get_mut(&entry.row_position) { *score += entry.score; } @@ -1595,6 +1656,7 @@ mod tests { use arrow_array::{Int32Array, StringArray}; use arrow_schema::{DataType, Field, Schema as ArrowSchema}; use std::sync::Arc; + use std::sync::atomic::AtomicBool; fn create_test_schema() -> Arc { Arc::new(ArrowSchema::new(vec![ @@ -2477,7 +2539,6 @@ mod tests { // phrase tokens. Since `search_phrase` only returns rows where // every token's position constraint holds, any returned entry // implicitly proves both tokens were visible together. - use std::sync::Arc; let schema = create_test_schema(); let index = Arc::new(FtsMemIndex::new(1, "description".to_string())); @@ -2521,8 +2582,6 @@ mod tests { #[test] fn test_swmr_visibility_torture() { - use std::sync::Arc; - use std::sync::atomic::{AtomicBool, Ordering}; let schema = create_test_schema(); let index = Arc::new(FtsMemIndex::new(1, "description".to_string())); let stop = Arc::new(AtomicBool::new(false)); From 967813094b623d795c47e8b63788248f29f6805c Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sun, 10 May 2026 11:13:21 -0700 Subject: [PATCH 03/37] bench: add fineweb FTS end-to-end benchmark MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit New rust/lance/benches/mem_wal_fineweb_fts.rs covers three metrics across the 12 configs in the design doc: - write throughput at memtable sizes 100k / 500k / 1M - MemTable FTS query latency (avg/p50/p95) over 100 high-frequency tokens + 50 sampled phrases - consistency: |memtable_top10 ∩ post_flush_disk_top10| / |union| as a user-approved replacement for recall@k The bench downloads HuggingFaceFW/fineweb sample/10BT shards, caches them, and is fully env-driven so a single binary handles every config. Driver script bench/run_fineweb_fts.sh loops the 12 configs, uploads each result.json to S3, and prints a summary. Also: make `dataset::mem_wal::index` public so the bench can call `FtsMemIndex::search_with_options` directly to time the MemTable read path. --- bench/run_fineweb_fts.sh | 121 +++ rust/lance/Cargo.toml | 5 +- rust/lance/benches/mem_wal_fineweb_fts.rs | 952 ++++++++++++++++++++++ rust/lance/src/dataset/mem_wal.rs | 2 +- 4 files changed, 1078 insertions(+), 2 deletions(-) create mode 100755 bench/run_fineweb_fts.sh create mode 100644 rust/lance/benches/mem_wal_fineweb_fts.rs diff --git a/bench/run_fineweb_fts.sh b/bench/run_fineweb_fts.sh new file mode 100755 index 00000000000..5709052e86e --- /dev/null +++ b/bench/run_fineweb_fts.sh @@ -0,0 +1,121 @@ +#!/usr/bin/env bash +# Driver for the fineweb FTS benchmark. +# +# Runs the 12 configs (3 memtable sizes × durable yes/no × FTS yes/no), saves +# each result.json locally and to S3, and at the end prints a small summary. +# +# Usage: +# ./bench/run_fineweb_fts.sh [run_id] +# +# Env vars (optional): +# DATASET_PREFIX default: s3://jack-devland-build/bench/mem-fts-fineweb +# BENCH_BASE_ROWS default: 1000000 +# BENCH_INGEST_ROWS default: 1000000 +# BENCH_BATCH_SIZE default: 1000 +# AWS_DEFAULT_REGION default: us-east-1 + +set -euo pipefail + +cd "$(dirname "${BASH_SOURCE[0]}")/.." + +RUN_ID="${1:-$(date -u +%Y%m%dT%H%M%SZ)}" +DATASET_PREFIX="${DATASET_PREFIX:-s3://jack-devland-build/bench/mem-fts-fineweb}" +BENCH_BASE_ROWS="${BENCH_BASE_ROWS:-1000000}" +BENCH_INGEST_ROWS="${BENCH_INGEST_ROWS:-1000000}" +BENCH_BATCH_SIZE="${BENCH_BATCH_SIZE:-1000}" +export AWS_DEFAULT_REGION="${AWS_DEFAULT_REGION:-us-east-1}" + +LOCAL_DIR="bench/results/${RUN_ID}" +mkdir -p "$LOCAL_DIR" + +BIN="target/release/mem_wal_fineweb_fts" +if [ ! -x "$BIN" ]; then + echo "building bench binary..." + cargo build --release -p lance --bench mem_wal_fineweb_fts + # criterion-style bench output goes to deps/; resolve it. + BIN="$(ls -t target/release/deps/mem_wal_fineweb_fts-* | grep -v '\.d$' | head -1)" +fi +echo "using bench binary: $BIN" + +CONFIGS=( + "100000 0 0" + "100000 0 1" + "100000 1 0" + "100000 1 1" + "500000 0 0" + "500000 0 1" + "500000 1 0" + "500000 1 1" + "1000000 0 0" + "1000000 0 1" + "1000000 1 0" + "1000000 1 1" +) + +echo "=== Run $RUN_ID ==" +echo " prefix: $DATASET_PREFIX" +echo " base_rows: $BENCH_BASE_ROWS ingest_rows: $BENCH_INGEST_ROWS batch_size: $BENCH_BATCH_SIZE" +echo "" + +for cfg in "${CONFIGS[@]}"; do + read -r MT D F <<< "$cfg" + if [ "$MT" = "1000000" ]; then SZ="1M"; elif [ "$MT" = "500000" ]; then SZ="500k"; else SZ="100k"; fi + NAME="mt${SZ}_durable${D}_fts${F}" + OUT="$LOCAL_DIR/${NAME}.json" + LOG="$LOCAL_DIR/${NAME}.log" + echo ">>> $NAME" + if [ -f "$OUT" ]; then + echo " result already exists, skipping" + continue + fi + set +e + BENCH_RUN_ID="$RUN_ID" \ + DATASET_PREFIX="$DATASET_PREFIX" \ + BENCH_MAX_MEMTABLE_ROWS="$MT" \ + DURABLE_WRITE="$D" \ + FTS_ENABLED="$F" \ + BENCH_BASE_ROWS="$BENCH_BASE_ROWS" \ + BENCH_INGEST_ROWS="$BENCH_INGEST_ROWS" \ + BENCH_BATCH_SIZE="$BENCH_BATCH_SIZE" \ + BENCH_CACHE_DIR="${BENCH_CACHE_DIR:-/mnt/data/fineweb}" \ + RESULT_FILE="$OUT" \ + "$BIN" --bench --nocapture 2>&1 | tee "$LOG" + RC=${PIPESTATUS[0]} + set -e + if [ "$RC" -ne 0 ]; then + echo " !!! config failed (rc=$RC); see $LOG" + fi + # Upload to S3 alongside the dataset. + if [ -f "$OUT" ]; then + aws s3 cp "$OUT" "$DATASET_PREFIX/$RUN_ID/results/${NAME}.json" || true + aws s3 cp "$LOG" "$DATASET_PREFIX/$RUN_ID/results/${NAME}.log" || true + fi +done + +echo "" +echo "=== summary ===" +python3 - <10} {'p95_ms':>7} {'mt_p95_ms':>10} {'cons_mean':>10}") +for r in results: + name = r["config_name"] + tp = r["ingest"]["rows_per_sec"] + p95 = r["ingest"]["put_p95_ms"] + rd = r.get("read") + mt = rd["mt_latency_p95_ms"] if rd else 0 + cm = rd["consistency_mean"] if rd else 0 + print(f"{name:30s} {tp:>10.0f} {p95:>7.2f} {mt:>10.2f} {cm:>10.3f}") +PY + +echo "" +echo "Results:" +echo " local: $LOCAL_DIR" +echo " s3: $DATASET_PREFIX/$RUN_ID/results/" diff --git a/rust/lance/Cargo.toml b/rust/lance/Cargo.toml index e1e5a3bc823..19eb1aa9c8a 100644 --- a/rust/lance/Cargo.toml +++ b/rust/lance/Cargo.toml @@ -68,7 +68,6 @@ rayon.workspace = true futures.workspace = true uuid.workspace = true arrow.workspace = true -arc-swap.workspace = true # TODO: use datafusion sub-modules to reduce build size? datafusion.workspace = true datafusion-functions.workspace = true @@ -237,5 +236,9 @@ harness = false name = "mem_wal_shard_writer_backpressure" harness = false +[[bench]] +name = "mem_wal_fineweb_fts" +harness = false + [lints] workspace = true diff --git a/rust/lance/benches/mem_wal_fineweb_fts.rs b/rust/lance/benches/mem_wal_fineweb_fts.rs new file mode 100644 index 00000000000..066502b7a7a --- /dev/null +++ b/rust/lance/benches/mem_wal_fineweb_fts.rs @@ -0,0 +1,952 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The Lance Authors + +//! End-to-end benchmark for the SWMR FTS mem index using HuggingFace fineweb. +//! +//! For a single configuration, this binary: +//! 1. Downloads (and caches) one fineweb sample shard. +//! 2. Slices `BENCH_BASE_ROWS + BENCH_INGEST_ROWS` rows from it. +//! 3. (Once per `BENCH_RUN_ID`) writes the base 1M-row Lance dataset to +//! `${DATASET_PREFIX}/${BENCH_RUN_ID}/base/`. +//! 4. Ingests `BENCH_INGEST_ROWS` rows through `ShardWriter` with the +//! configured `max_memtable_rows`, `DURABLE_WRITE`, and `FTS_ENABLED`. +//! Records write throughput. +//! 5. If FTS is enabled and `--with-read-test` is set: ingests +//! `max_memtable_rows` rows into a fresh dataset with auto-flush +//! disabled, queries the MemTable for 150 prebuilt queries (latency), +//! forces a flush, queries the on-disk FTS for the same queries, and +//! reports the top-10 set overlap (the user-approved "consistency" +//! proxy for recall). +//! +//! Writes a structured `result.json` to `${RESULT_FILE}`. All paths, +//! credentials, and tunables are env-var driven so the same binary drives +//! all 12 configs from a shell loop. +//! +//! See `~/ai/analysis/lance/jack-MemTableFTSBetter/fineweb-fts-bench/DESIGN.md`. + +#![recursion_limit = "256"] +#![allow(clippy::print_stdout, clippy::print_stderr)] + +use std::collections::{BTreeMap, HashMap, HashSet}; +use std::sync::Arc; +use std::time::{Duration, Instant}; + +use arrow_array::{Array, ArrayRef, Int64Array, RecordBatch, RecordBatchIterator, StringArray}; +use arrow_schema::{DataType, Field, Schema as ArrowSchema}; +use futures::TryStreamExt; +use lance::dataset::mem_wal::index::{FtsQueryExpr, SearchOptions}; +use lance::dataset::mem_wal::write::ShardWriterConfig; +use lance::dataset::mem_wal::{DatasetMemWalExt, MemWalConfig}; +use lance::dataset::{Dataset, WriteParams}; +use lance::index::DatasetIndexExt; +use lance_index::IndexType; +use lance_index::scalar::{FullTextSearchQuery, ScalarIndexParams}; +use lance_index::scalar::inverted::tokenizer::InvertedIndexParams; +use parquet::arrow::async_reader::ParquetRecordBatchStreamBuilder; +use serde::Serialize; +use uuid::Uuid; + +const TEXT_COL: &str = "text"; +const FTS_INDEX_NAME: &str = "text_fts"; +const HF_API_LISTING: &str = + "https://huggingface.co/api/datasets/HuggingFaceFW/fineweb/tree/main/sample/10BT"; +const HF_FILE_BASE: &str = "https://huggingface.co/datasets/HuggingFaceFW/fineweb/resolve/main/"; + +// ---------------------------------------------------------------------- +// Configuration (env-driven) +// ---------------------------------------------------------------------- + +#[derive(Debug, Clone)] +struct Config { + dataset_prefix: String, + run_id: String, + config_name: String, + max_memtable_rows: usize, + durable_write: bool, + fts_enabled: bool, + base_rows: usize, + ingest_rows: usize, + batch_size: usize, + cache_dir: std::path::PathBuf, + result_file: std::path::PathBuf, + /// When true, run the read-perf + consistency sub-test in addition to + /// the throughput test. Auto-disabled when `fts_enabled = false`. + with_read_test: bool, + /// How many high-frequency single-token queries to include. + num_token_queries: usize, + /// How many random 2-token phrase queries to include. + num_phrase_queries: usize, + /// Top-K used for read latency and consistency. + top_k: usize, +} + +impl Config { + fn from_env() -> Self { + let dataset_prefix = std::env::var("DATASET_PREFIX") + .unwrap_or_else(|_| "/tmp/bench/mem_fts_fineweb".to_string()); + let run_id = std::env::var("BENCH_RUN_ID").unwrap_or_else(|_| "dev".to_string()); + let max_memtable_rows = env_usize("BENCH_MAX_MEMTABLE_ROWS", 100_000); + let durable_write = env_bool("DURABLE_WRITE", false); + let fts_enabled = env_bool("FTS_ENABLED", false); + let base_rows = env_usize("BENCH_BASE_ROWS", 1_000_000); + let ingest_rows = env_usize("BENCH_INGEST_ROWS", 1_000_000); + let batch_size = env_usize("BENCH_BATCH_SIZE", 1000); + let cache_dir = std::env::var("BENCH_CACHE_DIR") + .unwrap_or_else(|_| { + std::env::temp_dir() + .join("mem_wal_fineweb_fts_cache") + .to_string_lossy() + .into_owned() + }) + .into(); + let result_file = std::env::var("RESULT_FILE") + .unwrap_or_else(|_| "result.json".to_string()) + .into(); + let with_read_test = env_bool("BENCH_WITH_READ_TEST", true) && fts_enabled; + let num_token_queries = env_usize("BENCH_NUM_TOKEN_QUERIES", 100); + let num_phrase_queries = env_usize("BENCH_NUM_PHRASE_QUERIES", 50); + let top_k = env_usize("BENCH_TOP_K", 10); + + let config_name = format!( + "mt{}_durable{}_fts{}", + human_size(max_memtable_rows), + if durable_write { "1" } else { "0" }, + if fts_enabled { "1" } else { "0" }, + ); + + Self { + dataset_prefix, + run_id, + config_name, + max_memtable_rows, + durable_write, + fts_enabled, + base_rows, + ingest_rows, + batch_size, + cache_dir, + result_file, + with_read_test, + num_token_queries, + num_phrase_queries, + top_k, + } + } + + #[allow(dead_code)] + fn base_uri(&self) -> String { + format!("{}/{}/base", self.dataset_prefix, self.run_id) + } + + fn ingest_uri(&self) -> String { + format!( + "{}/{}/ingest_{}", + self.dataset_prefix, self.run_id, self.config_name + ) + } + + fn read_test_uri(&self) -> String { + format!( + "{}/{}/readtest_{}", + self.dataset_prefix, self.run_id, self.config_name + ) + } +} + +fn env_usize(key: &str, default: usize) -> usize { + std::env::var(key) + .ok() + .and_then(|s| s.parse().ok()) + .unwrap_or(default) +} + +fn env_bool(key: &str, default: bool) -> bool { + match std::env::var(key).ok().as_deref() { + Some("1") | Some("yes") | Some("true") | Some("YES") | Some("TRUE") => true, + Some("0") | Some("no") | Some("false") | Some("NO") | Some("FALSE") => false, + _ => default, + } +} + +fn human_size(n: usize) -> String { + if n % 1_000_000 == 0 { + format!("{}M", n / 1_000_000) + } else if n % 1_000 == 0 { + format!("{}k", n / 1_000) + } else { + n.to_string() + } +} + +// ---------------------------------------------------------------------- +// HF fineweb shard loading +// ---------------------------------------------------------------------- + +#[derive(serde::Deserialize)] +struct HfTreeEntry { + #[serde(rename = "type")] + kind: String, + path: String, +} + +async fn list_shard_paths() -> lance_core::Result> { + let entries: Vec = reqwest::get(HF_API_LISTING) + .await + .map_err(|e| lance_core::Error::io(format!("listing HTTP: {}", e)))? + .json() + .await + .map_err(|e| lance_core::Error::io(format!("listing JSON: {}", e)))?; + let mut shards: Vec = entries + .into_iter() + .filter(|e| e.kind == "file" && e.path.ends_with(".parquet")) + .map(|e| e.path) + .collect(); + shards.sort(); + Ok(shards) +} + +async fn download_shard(rel_path: &str, dest: &std::path::Path) -> lance_core::Result<()> { + if dest.exists() { + return Ok(()); + } + let url = format!("{}{}", HF_FILE_BASE, rel_path); + let max_attempts = 5; + for attempt in 1..=max_attempts { + println!( + "downloading {} (attempt {}/{}) ...", + rel_path, attempt, max_attempts + ); + let result: lance_core::Result = async { + let resp = reqwest::get(&url) + .await + .map_err(|e| lance_core::Error::io(format!("download HTTP: {}", e)))?; + if !resp.status().is_success() { + return Err(lance_core::Error::io(format!( + "download {} → status {}", + url, + resp.status() + ))); + } + resp.bytes() + .await + .map_err(|e| lance_core::Error::io(format!("read body: {}", e))) + } + .await; + match result { + Ok(bytes) => { + std::fs::write(dest, &bytes) + .map_err(|e| lance_core::Error::io(format!("write: {}", e)))?; + println!( + " wrote {:.1} MB to {}", + bytes.len() as f64 / 1024.0 / 1024.0, + dest.display() + ); + return Ok(()); + } + Err(e) if attempt < max_attempts => { + let backoff = Duration::from_secs(2u64.pow(attempt as u32)); + eprintln!( + " attempt {} failed: {}; retrying in {:?}", + attempt, e, backoff + ); + tokio::time::sleep(backoff).await; + } + Err(e) => return Err(e), + } + } + unreachable!() +} + +async fn read_shard_text( + path: &std::path::Path, + out: &mut Vec, + max_rows: usize, +) -> lance_core::Result { + let file = tokio::fs::File::open(path) + .await + .map_err(|e| lance_core::Error::io(format!("open parquet: {}", e)))?; + let builder = ParquetRecordBatchStreamBuilder::new(file) + .await + .map_err(|e| lance_core::Error::io(format!("parquet builder: {}", e)))?; + let mut stream = builder + .build() + .map_err(|e| lance_core::Error::io(format!("parquet stream: {}", e)))?; + + let mut taken = 0usize; + while taken < max_rows { + let Some(rb) = stream + .try_next() + .await + .map_err(|e| lance_core::Error::io(format!("parquet read: {}", e)))? + else { + break; + }; + let col = rb + .column_by_name("text") + .ok_or_else(|| lance_core::Error::io("text column missing".to_string()))?; + let strs = col + .as_any() + .downcast_ref::() + .ok_or_else(|| lance_core::Error::io("text column not StringArray".to_string()))?; + for i in 0..strs.len() { + if taken >= max_rows { + break; + } + if strs.is_null(i) { + continue; + } + out.push(strs.value(i).to_string()); + taken += 1; + } + } + Ok(taken) +} + +async fn load_corpus( + needed_rows: usize, + cache_dir: &std::path::Path, +) -> lance_core::Result> { + std::fs::create_dir_all(cache_dir) + .map_err(|e| lance_core::Error::io(format!("mkdir cache: {}", e)))?; + let shards = list_shard_paths().await?; + println!("fineweb sample/10BT has {} parquet shards", shards.len()); + + let mut buf: Vec = Vec::with_capacity(needed_rows); + for rel_path in &shards { + if buf.len() >= needed_rows { + break; + } + let local_name = rel_path.rsplit('/').next().unwrap_or(rel_path); + let local = cache_dir.join(local_name); + download_shard(rel_path, &local).await?; + let want = needed_rows - buf.len(); + let got = read_shard_text(&local, &mut buf, want).await?; + println!( + " shard {} → {} text rows (cumulative {})", + local_name, + got, + buf.len() + ); + } + if buf.len() < needed_rows { + eprintln!( + " warning: dataset exhausted at {} rows (asked {})", + buf.len(), + needed_rows + ); + } + Ok(buf) +} + +// ---------------------------------------------------------------------- +// Schema + batch helpers +// ---------------------------------------------------------------------- + +fn make_schema() -> Arc { + let mut id_meta = HashMap::new(); + id_meta.insert( + "lance-schema:unenforced-primary-key".to_string(), + "true".to_string(), + ); + let id = Field::new("id", DataType::Int64, false).with_metadata(id_meta); + Arc::new(ArrowSchema::new(vec![ + id, + Field::new(TEXT_COL, DataType::Utf8, true), + ])) +} + +fn make_batch(start_id: i64, texts: &[String], schema: Arc) -> RecordBatch { + let n = texts.len(); + let ids: Vec = (start_id..start_id + n as i64).collect(); + let id_arr: ArrayRef = Arc::new(Int64Array::from(ids)); + let text_arr: ArrayRef = Arc::new(StringArray::from(texts.to_vec())); + RecordBatch::try_new(schema, vec![id_arr, text_arr]).unwrap() +} + +// ---------------------------------------------------------------------- +// Base dataset +// ---------------------------------------------------------------------- + +async fn build_base_if_absent( + base_uri: &str, + schema: Arc, + base_texts: &[String], + batch_size: usize, + fts_enabled: bool, +) -> lance_core::Result<()> { + if Dataset::open(base_uri).await.is_ok() { + println!("base dataset already exists at {}, skipping build", base_uri); + return Ok(()); + } + println!( + "building base dataset at {} ({} rows, batch_size {})", + base_uri, + base_texts.len(), + batch_size + ); + let total = base_texts.len(); + let mut batches = Vec::with_capacity(total.div_ceil(batch_size)); + let mut start = 0usize; + while start < total { + let end = (start + batch_size).min(total); + batches.push(Ok(make_batch( + start as i64, + &base_texts[start..end], + schema.clone(), + ))); + start = end; + } + let reader = RecordBatchIterator::new(batches.into_iter(), schema.clone()); + let mut dataset = Dataset::write(reader, base_uri, Some(WriteParams::default())).await?; + if fts_enabled { + let fts_params = InvertedIndexParams::default(); + dataset + .create_index( + &[TEXT_COL], + IndexType::Inverted, + Some(FTS_INDEX_NAME.to_string()), + &fts_params, + true, + ) + .await?; + } else { + // Even when MemWAL is configured without FTS, we still need a + // BTree index on `id` so MemWAL has at least one maintained + // index to reference. + let pk_params = ScalarIndexParams::default(); + dataset + .create_index( + &["id"], + IndexType::BTree, + Some("id_btree".to_string()), + &pk_params, + true, + ) + .await?; + } + let maintained = if fts_enabled { + vec![FTS_INDEX_NAME.to_string()] + } else { + vec!["id_btree".to_string()] + }; + dataset + .initialize_mem_wal(MemWalConfig { + shard_spec: None, + maintained_indexes: maintained, + }) + .await?; + Ok(()) +} + +// ---------------------------------------------------------------------- +// Ingest +// ---------------------------------------------------------------------- + +#[derive(Debug, Clone, Serialize)] +struct IngestStats { + rows: usize, + wall_seconds: f64, + rows_per_sec: f64, + /// p95 per-`put` latency in milliseconds. + put_p95_ms: f64, + put_p50_ms: f64, + put_max_ms: f64, + num_puts: usize, +} + +async fn ingest_via_shard_writer( + target_uri: &str, + schema: Arc, + base_texts: &[String], + ingest_texts: &[String], + cfg: &Config, + disable_auto_flush: bool, +) -> lance_core::Result { + // Build a fresh ingest dataset by cloning the base. + println!("preparing ingest dataset at {}", target_uri); + build_base_if_absent( + target_uri, + schema.clone(), + base_texts, + cfg.batch_size, + cfg.fts_enabled, + ) + .await?; + let dataset = Arc::new(Dataset::open(target_uri).await?); + + let shard_id = Uuid::new_v4(); + let max_memtable_rows = if disable_auto_flush { + cfg.ingest_rows.saturating_mul(2).max(2_000_000) + } else { + cfg.max_memtable_rows + }; + let max_memtable_size = if disable_auto_flush { + 16 * 1024 * 1024 * 1024 // 16 GiB + } else { + 16 * 1024 * 1024 * 1024 + }; + let writer_config = ShardWriterConfig { + shard_id, + shard_spec_id: 0, + durable_write: cfg.durable_write, + sync_indexed_write: true, + max_memtable_size, + max_memtable_rows, + max_memtable_batches: 4_000_000, + max_wal_flush_interval: Some(Duration::from_millis(200)), + max_unflushed_memtable_bytes: usize::MAX / 2, + ..ShardWriterConfig::default() + }; + let writer = dataset + .as_ref() + .mem_wal_writer(shard_id, writer_config) + .await?; + + // Ingest IDs start above the base table's last id to keep PK unique. + let id_offset: i64 = cfg.base_rows as i64; + let n = ingest_texts.len(); + let bs = cfg.batch_size; + let total_batches = n.div_ceil(bs); + + let mut put_latencies: Vec = Vec::with_capacity(total_batches); + let start = Instant::now(); + for i in 0..total_batches { + let lo = i * bs; + let hi = (lo + bs).min(n); + let batch = make_batch( + id_offset + lo as i64, + &ingest_texts[lo..hi], + schema.clone(), + ); + let put_t = Instant::now(); + writer.put(vec![batch]).await?; + put_latencies.push(put_t.elapsed().as_micros()); + if (i + 1) % 100 == 0 { + let so_far = start.elapsed().as_secs_f64(); + let rate = (i + 1) as f64 * bs as f64 / so_far.max(1e-9); + println!( + " ingest progress: {}/{} batches ({:.0} rows/s)", + i + 1, + total_batches, + rate + ); + } + } + // Wait for index update to catch up if sync_indexed_write didn't fully drain. + let target_batch_pos = total_batches.saturating_sub(1); + loop { + let active = writer.active_memtable_ref().await?; + if active.index_store.max_indexed_batch_position() >= target_batch_pos { + break; + } + drop(active); + tokio::time::sleep(Duration::from_millis(50)).await; + } + let elapsed = start.elapsed(); + drop(writer); + + put_latencies.sort_unstable(); + let p50 = put_latencies[put_latencies.len() / 2] as f64 / 1000.0; + let p95 = put_latencies[put_latencies.len() * 95 / 100] as f64 / 1000.0; + let max = *put_latencies.iter().max().unwrap_or(&0) as f64 / 1000.0; + Ok(IngestStats { + rows: n, + wall_seconds: elapsed.as_secs_f64(), + rows_per_sec: n as f64 / elapsed.as_secs_f64().max(1e-9), + put_p50_ms: p50, + put_p95_ms: p95, + put_max_ms: max, + num_puts: total_batches, + }) +} + +// ---------------------------------------------------------------------- +// Query set +// ---------------------------------------------------------------------- + +#[derive(Debug, Clone, Serialize)] +struct QuerySet { + tokens: Vec, + phrases: Vec, +} + +fn build_query_set(sample_texts: &[&str], cfg: &Config) -> QuerySet { + use lance_tokenizer::TokenStream; + // ASCII stop-word-ish list used by the default English analyzer; we only + // need a coarse filter for query selection here. + const STOPWORDS: &[&str] = &[ + "the", "a", "an", "and", "or", "of", "to", "in", "on", "for", "with", "as", "by", "is", + "was", "are", "were", "be", "been", "being", "this", "that", "these", "those", "it", "its", + "but", "not", "no", "if", "then", "than", "so", "do", "does", "did", "have", "has", "had", + "will", "would", "should", "could", "can", "may", "might", "must", "i", "you", "he", "she", + "we", "they", "them", "his", "her", "their", "our", "us", "me", "my", "your", "him", + ]; + let mut tokenizer = InvertedIndexParams::default() + .build() + .expect("default tokenizer builds"); + let mut freq: HashMap = HashMap::new(); + for t in sample_texts.iter().take(50_000) { + let mut stream = tokenizer.token_stream_for_doc(t); + while let Some(tok) = stream.next() { + if tok.text.len() < 3 || tok.text.len() > 24 { + continue; + } + if STOPWORDS.contains(&tok.text.as_str()) { + continue; + } + *freq.entry(tok.text.clone()).or_default() += 1; + } + } + let mut by_freq: Vec<(String, u64)> = freq.into_iter().collect(); + by_freq.sort_by(|a, b| b.1.cmp(&a.1).then(a.0.cmp(&b.0))); + let tokens: Vec = by_freq + .into_iter() + .map(|(t, _)| t) + .take(cfg.num_token_queries) + .collect(); + + // Phrase queries: walk a deterministic stride of rows, take the first + // two consecutive non-stopword non-short tokens. + let mut phrases = Vec::with_capacity(cfg.num_phrase_queries); + let stride = sample_texts.len().max(1) / cfg.num_phrase_queries.max(1); + let mut idx = 0usize; + while phrases.len() < cfg.num_phrase_queries && idx < sample_texts.len() { + let t = sample_texts[idx]; + let mut stream = tokenizer.token_stream_for_doc(t); + let mut acc: Vec = Vec::new(); + while let Some(tok) = stream.next() { + if tok.text.len() < 3 || tok.text.len() > 24 { + continue; + } + if STOPWORDS.contains(&tok.text.as_str()) { + continue; + } + acc.push(tok.text.clone()); + if acc.len() == 2 { + phrases.push(format!("{} {}", acc[0], acc[1])); + break; + } + } + idx = idx.saturating_add(stride.max(1)); + } + + QuerySet { tokens, phrases } +} + +// ---------------------------------------------------------------------- +// Read test (FTS only): MemTable query latency + post-flush consistency +// ---------------------------------------------------------------------- + +#[derive(Debug, Clone, Serialize)] +struct ReadStats { + rows: usize, + /// Average across all queries (token + phrase). + mt_latency_avg_ms: f64, + mt_latency_p50_ms: f64, + mt_latency_p95_ms: f64, + consistency_mean: f64, + consistency_min: f64, + num_queries: usize, +} + +async fn run_read_test( + target_uri: &str, + schema: Arc, + base_texts: &[String], + ingest_texts: &[String], + queries: &QuerySet, + cfg: &Config, +) -> lance_core::Result { + println!( + " read test: ingesting {} rows with auto-flush disabled", + ingest_texts.len() + ); + build_base_if_absent( + target_uri, + schema.clone(), + base_texts, + cfg.batch_size, + true, // FTS index on the base, since this path is FTS-only. + ) + .await?; + let dataset = Arc::new(Dataset::open(target_uri).await?); + let shard_id = Uuid::new_v4(); + let writer_config = ShardWriterConfig { + shard_id, + shard_spec_id: 0, + durable_write: cfg.durable_write, + sync_indexed_write: true, + // Effectively disable auto-flush triggers so the MemTable holds + // the full ingest_texts.len() rows for the query phase. + max_memtable_size: 64 * 1024 * 1024 * 1024, + max_memtable_rows: ingest_texts.len().saturating_mul(2), + max_memtable_batches: 4_000_000, + max_wal_flush_interval: Some(Duration::from_millis(200)), + max_unflushed_memtable_bytes: usize::MAX / 2, + ..ShardWriterConfig::default() + }; + let writer = dataset + .as_ref() + .mem_wal_writer(shard_id, writer_config) + .await?; + + let id_offset: i64 = cfg.base_rows as i64; + let bs = cfg.batch_size; + let n = ingest_texts.len(); + let total_batches = n.div_ceil(bs); + for i in 0..total_batches { + let lo = i * bs; + let hi = (lo + bs).min(n); + let batch = make_batch( + id_offset + lo as i64, + &ingest_texts[lo..hi], + schema.clone(), + ); + writer.put(vec![batch]).await?; + } + let target_batch_pos = total_batches.saturating_sub(1); + loop { + let active = writer.active_memtable_ref().await?; + if active.index_store.max_indexed_batch_position() >= target_batch_pos { + break; + } + drop(active); + tokio::time::sleep(Duration::from_millis(50)).await; + } + + // ----- MemTable phase ----- + let active = writer.active_memtable_ref().await?; + let fts = active + .index_store + .get_fts(FTS_INDEX_NAME) + .ok_or_else(|| lance_core::Error::invalid_input("FTS mem index not found"))?; + + let mut latencies_us: Vec = Vec::new(); + let mut mt_top10: Vec> = Vec::new(); + + let all_queries: Vec<(FtsQueryExpr, String)> = queries + .tokens + .iter() + .map(|t| (FtsQueryExpr::match_query(t.clone()), t.clone())) + .chain( + queries + .phrases + .iter() + .map(|p| (FtsQueryExpr::phrase(p.clone()), format!("\"{p}\""))), + ) + .collect(); + + // Build a row_position -> id map by scanning the active batches. + // This is needed because the MemTable returns row_positions; the + // post-flush on-disk FTS returns row_ids that match the `id` column. + let mut row_to_id: HashMap = HashMap::new(); + for stored in active.batch_store.iter() { + let id_arr = stored + .data + .column_by_name("id") + .and_then(|c| c.as_any().downcast_ref::()) + .ok_or_else(|| lance_core::Error::invalid_input("id col missing"))?; + for r in 0..id_arr.len() { + row_to_id.insert(stored.row_offset + r as u64, id_arr.value(r)); + } + } + + for (q, label) in &all_queries { + let opts = SearchOptions::new().with_limit(cfg.top_k); + let t0 = Instant::now(); + let entries = fts.search_with_options(q, opts); + latencies_us.push(t0.elapsed().as_micros()); + let mut ids = HashSet::with_capacity(cfg.top_k); + for e in entries.iter().take(cfg.top_k) { + if let Some(id) = row_to_id.get(&e.row_position) { + ids.insert(*id); + } + } + if mt_top10.len() < 3 { + println!( + " [mt] {label}: {} hits, ids={:?}", + entries.len(), + ids.iter().take(3).collect::>() + ); + } + mt_top10.push(ids); + } + drop(active); + + latencies_us.sort_unstable(); + let avg_us = + latencies_us.iter().sum::() as f64 / latencies_us.len().max(1) as f64; + let p50 = latencies_us[latencies_us.len() / 2] as f64 / 1000.0; + let p95 = latencies_us[latencies_us.len() * 95 / 100] as f64 / 1000.0; + + // ----- Force flush, then on-disk phase ----- + println!(" read test: closing writer to force flush"); + writer.close().await?; + let flushed_dataset = Dataset::open(target_uri).await?; + + let mut consistencies: Vec = Vec::with_capacity(all_queries.len()); + for ((q, label), mt_ids) in all_queries.iter().zip(mt_top10.iter()) { + let fts_query = match q { + FtsQueryExpr::Match { query, .. } => FullTextSearchQuery::new(query.clone()), + FtsQueryExpr::Phrase { query, .. } => { + FullTextSearchQuery::new(format!("\"{}\"", query)) + } + _ => unreachable!("only match/phrase queries in this set"), + }; + let mut scanner = flushed_dataset.scan(); + scanner.full_text_search(fts_query)?; + scanner.limit(Some(cfg.top_k as i64), None)?; + scanner.project(&["id"])?; + let stream = scanner.try_into_stream().await?; + let batches: Vec = stream.try_collect().await?; + let mut disk_ids = HashSet::new(); + for b in &batches { + let id_arr = b + .column_by_name("id") + .and_then(|c| c.as_any().downcast_ref::()) + .ok_or_else(|| lance_core::Error::invalid_input("disk id col missing"))?; + for i in 0..id_arr.len() { + disk_ids.insert(id_arr.value(i)); + } + } + let inter: usize = mt_ids.intersection(&disk_ids).count(); + let denom = mt_ids.len().max(disk_ids.len()).max(1); + let cons = inter as f64 / denom as f64; + if consistencies.len() < 3 { + println!( + " [disk] {label}: {} hits; mt={} disk={} ∩={} cons={:.3}", + disk_ids.len(), + mt_ids.len(), + disk_ids.len(), + inter, + cons + ); + } + consistencies.push(cons); + } + + let cons_mean = consistencies.iter().sum::() / consistencies.len().max(1) as f64; + let cons_min = consistencies + .iter() + .copied() + .fold(f64::INFINITY, f64::min); + + Ok(ReadStats { + rows: n, + mt_latency_avg_ms: avg_us / 1000.0, + mt_latency_p50_ms: p50, + mt_latency_p95_ms: p95, + consistency_mean: cons_mean, + consistency_min: if cons_min.is_finite() { cons_min } else { 0.0 }, + num_queries: all_queries.len(), + }) +} + +// ---------------------------------------------------------------------- +// Top-level orchestration +// ---------------------------------------------------------------------- + +#[derive(Debug, Clone, Serialize)] +struct RunResult { + config_name: String, + max_memtable_rows: usize, + durable_write: bool, + fts_enabled: bool, + base_rows: usize, + ingest_rows: usize, + batch_size: usize, + ingest: IngestStats, + read: Option, + timestamp_utc: String, +} + +#[tokio::main(flavor = "multi_thread")] +async fn main() -> lance_core::Result<()> { + let cfg = Config::from_env(); + println!("=== mem_wal_fineweb_fts === config = {:?}", cfg); + + let total_rows = cfg.base_rows + cfg.ingest_rows; + let texts = load_corpus(total_rows, &cfg.cache_dir).await?; + if texts.len() < total_rows { + return Err(lance_core::Error::io(format!( + "fineweb shards yielded only {} rows, need {}", + texts.len(), + total_rows + ))); + } + let base_texts = &texts[..cfg.base_rows]; + let ingest_texts = &texts[cfg.base_rows..cfg.base_rows + cfg.ingest_rows]; + + let schema = make_schema(); + + // Build query set once from the ingest slice (deterministic). + let sample_refs: Vec<&str> = ingest_texts.iter().take(50_000).map(|s| s.as_str()).collect(); + let queries = build_query_set(&sample_refs, &cfg); + println!( + "query set: {} tokens + {} phrases", + queries.tokens.len(), + queries.phrases.len() + ); + + // Throughput sub-test: ingest 1M with the configured params. + println!("\n--- throughput sub-test ---"); + let ingest_stats = ingest_via_shard_writer( + &cfg.ingest_uri(), + schema.clone(), + base_texts, + ingest_texts, + &cfg, + false, // auto-flush enabled (per max_memtable_rows) + ) + .await?; + println!("throughput: {:.1} rows/s", ingest_stats.rows_per_sec); + + // Read sub-test: only when FTS enabled and read test requested. + let read_stats = if cfg.with_read_test { + println!("\n--- read sub-test ---"); + let n_for_read = cfg.max_memtable_rows.min(ingest_texts.len()); + let read_ingest = &ingest_texts[..n_for_read]; + Some( + run_read_test( + &cfg.read_test_uri(), + schema.clone(), + base_texts, + read_ingest, + &queries, + &cfg, + ) + .await?, + ) + } else { + None + }; + + let timestamp_utc = chrono::Utc::now().to_rfc3339(); + let result = RunResult { + config_name: cfg.config_name.clone(), + max_memtable_rows: cfg.max_memtable_rows, + durable_write: cfg.durable_write, + fts_enabled: cfg.fts_enabled, + base_rows: cfg.base_rows, + ingest_rows: cfg.ingest_rows, + batch_size: cfg.batch_size, + ingest: ingest_stats, + read: read_stats, + timestamp_utc, + }; + let json = serde_json::to_string_pretty(&result) + .map_err(|e| lance_core::Error::io(format!("serialize result: {}", e)))?; + if let Some(parent) = cfg.result_file.parent() { + if !parent.as_os_str().is_empty() { + std::fs::create_dir_all(parent).ok(); + } + } + std::fs::write(&cfg.result_file, json.as_bytes()) + .map_err(|e| lance_core::Error::io(format!("write result: {}", e)))?; + println!("\nwrote result to {}", cfg.result_file.display()); + println!("=== DONE ==="); + let _ = result; // silence unused with no read test + let _ = sample_refs; + let _ = BTreeMap::::new(); + Ok(()) +} diff --git a/rust/lance/src/dataset/mem_wal.rs b/rust/lance/src/dataset/mem_wal.rs index 3f2345d22f5..1cea7e8b9e5 100644 --- a/rust/lance/src/dataset/mem_wal.rs +++ b/rust/lance/src/dataset/mem_wal.rs @@ -34,7 +34,7 @@ mod api; mod hnsw; -mod index; +pub mod index; mod manifest; pub mod memtable; pub mod scanner; From d70daeb479fa55f381259a7d1a5bc7ac80bc551e Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sun, 10 May 2026 18:21:19 -0700 Subject: [PATCH 04/37] fix(bench): replace post-ingest index-catchup spin with writer.close() MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The previous spin on max_indexed_batch_position never terminated when max_memtable_rows triggers auto-flushes during ingest: the counter is reset on each new active memtable, so target_batch_pos = total_batches - 1 is unreachable from the active generation. Close the writer instead — it drains the final WAL flush and any outstanding memtable flush; the inline sync_indexed_write covers the per-put index updates. close() time is included in the measured elapsed so configs with different flush cadences are compared apples-to-apples. --- rust/lance/benches/mem_wal_fineweb_fts.rs | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/rust/lance/benches/mem_wal_fineweb_fts.rs b/rust/lance/benches/mem_wal_fineweb_fts.rs index 066502b7a7a..d93dc625003 100644 --- a/rust/lance/benches/mem_wal_fineweb_fts.rs +++ b/rust/lance/benches/mem_wal_fineweb_fts.rs @@ -532,18 +532,19 @@ async fn ingest_via_shard_writer( ); } } - // Wait for index update to catch up if sync_indexed_write didn't fully drain. - let target_batch_pos = total_batches.saturating_sub(1); - loop { - let active = writer.active_memtable_ref().await?; - if active.index_store.max_indexed_batch_position() >= target_batch_pos { - break; - } - drop(active); - tokio::time::sleep(Duration::from_millis(50)).await; - } + // Close the writer to drain the final WAL/memtable flush. With + // sync_indexed_write = true the inline index update is already + // complete, but auto-flush leaves background memtable->disk work + // outstanding that close() awaits. We deliberately include close() + // in the elapsed measurement so the reported throughput reflects + // "rows fully ingested + their pending flush", not just "puts + // returned". This is the apples-to-apples figure across configs + // with different `max_memtable_rows` (which drive the flush + // cadence). The previous post-ingest spin on + // `max_indexed_batch_position` was incorrect under auto-flush + // because the counter resets on each new active memtable. + writer.close().await?; let elapsed = start.elapsed(); - drop(writer); put_latencies.sort_unstable(); let p50 = put_latencies[put_latencies.len() / 2] as f64 / 1000.0; From af62c36722c7046b5fe9374d45029f4b1ad8384f Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sat, 16 May 2026 00:43:34 -0700 Subject: [PATCH 05/37] bench: rework fineweb FTS bench to mirror the ShardWriter backpressure bench MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Rewrites mem_wal_fineweb_fts as a CLI-arg bench modeled on the upstream mem_wal_shard_writer_backpressure bench: same Mode matrix (async/sync × index/no-index), same ShardWriter wiring, JSON output. Payload is real HuggingFace FineWeb text and the maintained index is the in-memory FTS index. Splits the run into independent --phase write|read invocations so a process never holds two ShardWriter lifecycles in sequence — that was the deadlock in the first iteration. The driver runs every config under a timeout watchdog so a hang costs one window, not days. --- bench/run_fineweb_fts.sh | 176 ++-- rust/lance/benches/mem_wal_fineweb_fts.rs | 1165 ++++++++++----------- 2 files changed, 618 insertions(+), 723 deletions(-) diff --git a/bench/run_fineweb_fts.sh b/bench/run_fineweb_fts.sh index 5709052e86e..c6dedd96cf9 100755 --- a/bench/run_fineweb_fts.sh +++ b/bench/run_fineweb_fts.sh @@ -1,121 +1,113 @@ #!/usr/bin/env bash -# Driver for the fineweb FTS benchmark. +# Driver for the FineWeb FTS benchmark panel. # -# Runs the 12 configs (3 memtable sizes × durable yes/no × FTS yes/no), saves -# each result.json locally and to S3, and at the end prints a small summary. +# Write panel : 12 configs = 4 modes (async/sync × idx/no-idx) × 3 memtable +# sizes (100k / 500k / 1M). Each config ingests 1M rows. +# Read panel : 6 configs = 2 indexed modes × 3 memtable sizes. Each +# ingests `size` rows into an auto-flush-disabled MemTable, +# times the FTS queries, flushes, and replays on disk. # -# Usage: -# ./bench/run_fineweb_fts.sh [run_id] +# Every config runs as its own process under a `timeout` watchdog, so a +# hang costs one timeout window, not days. result.json is uploaded to S3. # -# Env vars (optional): -# DATASET_PREFIX default: s3://jack-devland-build/bench/mem-fts-fineweb -# BENCH_BASE_ROWS default: 1000000 -# BENCH_INGEST_ROWS default: 1000000 -# BENCH_BATCH_SIZE default: 1000 -# AWS_DEFAULT_REGION default: us-east-1 +# Usage: ./bench/run_fineweb_fts.sh [run_id] -set -euo pipefail +set -uo pipefail cd "$(dirname "${BASH_SOURCE[0]}")/.." RUN_ID="${1:-$(date -u +%Y%m%dT%H%M%SZ)}" DATASET_PREFIX="${DATASET_PREFIX:-s3://jack-devland-build/bench/mem-fts-fineweb}" -BENCH_BASE_ROWS="${BENCH_BASE_ROWS:-1000000}" -BENCH_INGEST_ROWS="${BENCH_INGEST_ROWS:-1000000}" -BENCH_BATCH_SIZE="${BENCH_BATCH_SIZE:-1000}" +SEED_ROWS="${SEED_ROWS:-1000000}" +BATCH_ROWS="${BATCH_ROWS:-1000}" +CALLS="${CALLS:-1000}" +CACHE_DIR="${CACHE_DIR:-/mnt/data/fineweb}" +CONFIG_TIMEOUT="${CONFIG_TIMEOUT:-3600}" export AWS_DEFAULT_REGION="${AWS_DEFAULT_REGION:-us-east-1}" LOCAL_DIR="bench/results/${RUN_ID}" mkdir -p "$LOCAL_DIR" -BIN="target/release/mem_wal_fineweb_fts" -if [ ! -x "$BIN" ]; then +BIN="$(find target/release/deps -maxdepth 1 -type f -perm -111 -name 'mem_wal_fineweb_fts-*' ! -name '*.d' 2>/dev/null | sort | tail -1)" +if [ -z "$BIN" ]; then echo "building bench binary..." - cargo build --release -p lance --bench mem_wal_fineweb_fts - # criterion-style bench output goes to deps/; resolve it. - BIN="$(ls -t target/release/deps/mem_wal_fineweb_fts-* | grep -v '\.d$' | head -1)" + cargo bench -p lance --bench mem_wal_fineweb_fts --no-run + BIN="$(find target/release/deps -maxdepth 1 -type f -perm -111 -name 'mem_wal_fineweb_fts-*' ! -name '*.d' 2>/dev/null | sort | tail -1)" fi -echo "using bench binary: $BIN" - -CONFIGS=( - "100000 0 0" - "100000 0 1" - "100000 1 0" - "100000 1 1" - "500000 0 0" - "500000 0 1" - "500000 1 0" - "500000 1 1" - "1000000 0 0" - "1000000 0 1" - "1000000 1 0" - "1000000 1 1" -) - -echo "=== Run $RUN_ID ==" -echo " prefix: $DATASET_PREFIX" -echo " base_rows: $BENCH_BASE_ROWS ingest_rows: $BENCH_INGEST_ROWS batch_size: $BENCH_BATCH_SIZE" +echo "bench binary: $BIN" +echo "run id: $RUN_ID" echo "" -for cfg in "${CONFIGS[@]}"; do - read -r MT D F <<< "$cfg" - if [ "$MT" = "1000000" ]; then SZ="1M"; elif [ "$MT" = "500000" ]; then SZ="500k"; else SZ="100k"; fi - NAME="mt${SZ}_durable${D}_fts${F}" - OUT="$LOCAL_DIR/${NAME}.json" - LOG="$LOCAL_DIR/${NAME}.log" - echo ">>> $NAME" - if [ -f "$OUT" ]; then - echo " result already exists, skipping" - continue +run_one() { + local name="$1"; shift + local out="$LOCAL_DIR/${name}.json" + local log="$LOCAL_DIR/${name}.log" + echo ">>> $name" + if [ -f "$out" ]; then + echo " already done, skipping" + return fi - set +e - BENCH_RUN_ID="$RUN_ID" \ - DATASET_PREFIX="$DATASET_PREFIX" \ - BENCH_MAX_MEMTABLE_ROWS="$MT" \ - DURABLE_WRITE="$D" \ - FTS_ENABLED="$F" \ - BENCH_BASE_ROWS="$BENCH_BASE_ROWS" \ - BENCH_INGEST_ROWS="$BENCH_INGEST_ROWS" \ - BENCH_BATCH_SIZE="$BENCH_BATCH_SIZE" \ - BENCH_CACHE_DIR="${BENCH_CACHE_DIR:-/mnt/data/fineweb}" \ - RESULT_FILE="$OUT" \ - "$BIN" --bench --nocapture 2>&1 | tee "$LOG" - RC=${PIPESTATUS[0]} - set -e - if [ "$RC" -ne 0 ]; then - echo " !!! config failed (rc=$RC); see $LOG" - fi - # Upload to S3 alongside the dataset. - if [ -f "$OUT" ]; then - aws s3 cp "$OUT" "$DATASET_PREFIX/$RUN_ID/results/${NAME}.json" || true - aws s3 cp "$LOG" "$DATASET_PREFIX/$RUN_ID/results/${NAME}.log" || true + timeout "$CONFIG_TIMEOUT" "$BIN" --bench "$@" --output "$out" > "$log" 2>&1 + local rc=$? + if [ "$rc" -eq 124 ]; then + echo " !!! TIMED OUT after ${CONFIG_TIMEOUT}s" + elif [ "$rc" -ne 0 ]; then + echo " !!! failed rc=$rc (see $log)" + else + echo " ok" fi + [ -f "$out" ] && aws s3 cp "$out" "$DATASET_PREFIX/$RUN_ID/results/${name}.json" >/dev/null 2>&1 + aws s3 cp "$log" "$DATASET_PREFIX/$RUN_ID/results/${name}.log" >/dev/null 2>&1 +} + +# ---- write panel: 4 modes × 3 sizes ---- +for mode in async_noidx async_idx sync_noidx sync_idx; do + for sz in 100000 500000 1000000; do + case "$sz" in + 1000000) tag=1M ;; + 500000) tag=500k ;; + *) tag=100k ;; + esac + run_one "write_${mode}_mt${tag}" \ + --phase write --mode "$mode" \ + --uri "$DATASET_PREFIX/$RUN_ID/w_${mode}_mt${tag}" \ + --seed-rows "$SEED_ROWS" --batch-rows "$BATCH_ROWS" --calls "$CALLS" \ + --max-memtable-rows "$sz" --cache-dir "$CACHE_DIR" + done +done + +# ---- read panel: 2 indexed modes × 3 sizes ---- +for mode in async_idx sync_idx; do + for sz in 100000 500000 1000000; do + case "$sz" in + 1000000) tag=1M ;; + 500000) tag=500k ;; + *) tag=100k ;; + esac + run_one "read_${mode}_mt${tag}" \ + --phase read --mode "$mode" \ + --uri "$DATASET_PREFIX/$RUN_ID/r_${mode}_mt${tag}" \ + --seed-rows "$SEED_ROWS" --batch-rows "$BATCH_ROWS" \ + --read-rows "$sz" --cache-dir "$CACHE_DIR" + done done echo "" echo "=== summary ===" -python3 - <10} {'put_p99_ms':>11} {'mt_p95_ms':>10} {'cons_mean':>10}") +for p in sorted(glob.glob(os.path.join(d, "*.json"))): try: - with open(p) as f: r = json.load(f) - results.append(r) + r = json.load(open(p)) except Exception as e: - print(f" failed to read {p}: {e}") - -print(f"{'config':30s} {'rows/s':>10} {'p95_ms':>7} {'mt_p95_ms':>10} {'cons_mean':>10}") -for r in results: - name = r["config_name"] - tp = r["ingest"]["rows_per_sec"] - p95 = r["ingest"]["put_p95_ms"] - rd = r.get("read") - mt = rd["mt_latency_p95_ms"] if rd else 0 - cm = rd["consistency_mean"] if rd else 0 - print(f"{name:30s} {tp:>10.0f} {p95:>7.2f} {mt:>10.2f} {cm:>10.3f}") + print(f" bad {p}: {e}"); continue + name = os.path.basename(p)[:-5] + if r.get("phase") == "write": + print(f"{name:28s} {r['throughput_rows_per_sec']:>10.0f} {r['put_p99_ms']:>11.2f} {'-':>10} {'-':>10}") + else: + print(f"{name:28s} {'-':>10} {'-':>11} {r['mt_latency_p95_ms']:>10.3f} {r['consistency_mean']:>10.3f}") PY - echo "" -echo "Results:" -echo " local: $LOCAL_DIR" -echo " s3: $DATASET_PREFIX/$RUN_ID/results/" +echo "results: $LOCAL_DIR + $DATASET_PREFIX/$RUN_ID/results/" diff --git a/rust/lance/benches/mem_wal_fineweb_fts.rs b/rust/lance/benches/mem_wal_fineweb_fts.rs index d93dc625003..afeed61c5de 100644 --- a/rust/lance/benches/mem_wal_fineweb_fts.rs +++ b/rust/lance/benches/mem_wal_fineweb_fts.rs @@ -1,33 +1,44 @@ // SPDX-License-Identifier: Apache-2.0 // SPDX-FileCopyrightText: Copyright The Lance Authors -//! End-to-end benchmark for the SWMR FTS mem index using HuggingFace fineweb. +//! MemWAL FTS benchmark on real FineWeb text. //! -//! For a single configuration, this binary: -//! 1. Downloads (and caches) one fineweb sample shard. -//! 2. Slices `BENCH_BASE_ROWS + BENCH_INGEST_ROWS` rows from it. -//! 3. (Once per `BENCH_RUN_ID`) writes the base 1M-row Lance dataset to -//! `${DATASET_PREFIX}/${BENCH_RUN_ID}/base/`. -//! 4. Ingests `BENCH_INGEST_ROWS` rows through `ShardWriter` with the -//! configured `max_memtable_rows`, `DURABLE_WRITE`, and `FTS_ENABLED`. -//! Records write throughput. -//! 5. If FTS is enabled and `--with-read-test` is set: ingests -//! `max_memtable_rows` rows into a fresh dataset with auto-flush -//! disabled, queries the MemTable for 150 prebuilt queries (latency), -//! forces a flush, queries the on-disk FTS for the same queries, and -//! reports the top-10 set overlap (the user-approved "consistency" -//! proxy for recall). +//! FTS-specialized sibling of `mem_wal_shard_writer_backpressure`: same +//! CLI-arg shape, same `Mode` matrix (async/sync × index/no-index), same +//! `ShardWriter` wiring and JSON output, but the payload is real +//! HuggingFace FineWeb `text` and the maintained index is the in-memory +//! FTS index instead of IVF/PQ. //! -//! Writes a structured `result.json` to `${RESULT_FILE}`. All paths, -//! credentials, and tunables are env-var driven so the same binary drives -//! all 12 configs from a shell loop. +//! Two phases, selected with `--phase`; each invocation does exactly one +//! phase so a process never holds two `ShardWriter` lifecycles (that is +//! what deadlocked the first iteration of this bench): //! -//! See `~/ai/analysis/lance/jack-MemTableFTSBetter/fineweb-fts-bench/DESIGN.md`. +//! --phase write throughput panel: ingest `calls × batch-rows` rows +//! through `ShardWriter`, report rows/s + latency +//! percentiles. +//! --phase read MemTable FTS read panel: ingest `read-rows` rows into +//! an auto-flush-disabled MemTable, time the FTS queries +//! against the live MemTable, force a flush, replay the +//! queries against the on-disk FTS index, and report the +//! per-query top-K overlap ("consistency"). +//! +//! Example: +//! +//! ```bash +//! AWS_DEFAULT_REGION=us-east-1 \ +//! cargo bench -p lance --bench mem_wal_fineweb_fts -- \ +//! --phase write --mode async_idx \ +//! --uri s3://jack-devland-build/bench/mem-fts-fineweb/run1/w_async_idx_mt100k \ +//! --seed-rows 1000000 --batch-rows 1000 --calls 1000 \ +//! --max-memtable-rows 100000 \ +//! --cache-dir /mnt/data/fineweb --output result.json +//! ``` #![recursion_limit = "256"] #![allow(clippy::print_stdout, clippy::print_stderr)] -use std::collections::{BTreeMap, HashMap, HashSet}; +use std::collections::{HashMap, HashSet}; +use std::path::PathBuf; use std::sync::Arc; use std::time::{Duration, Instant}; @@ -35,15 +46,16 @@ use arrow_array::{Array, ArrayRef, Int64Array, RecordBatch, RecordBatchIterator, use arrow_schema::{DataType, Field, Schema as ArrowSchema}; use futures::TryStreamExt; use lance::dataset::mem_wal::index::{FtsQueryExpr, SearchOptions}; -use lance::dataset::mem_wal::write::ShardWriterConfig; -use lance::dataset::mem_wal::{DatasetMemWalExt, MemWalConfig}; +use lance::dataset::mem_wal::{DatasetMemWalExt, MemWalConfig, ShardWriterConfig}; use lance::dataset::{Dataset, WriteParams}; use lance::index::DatasetIndexExt; +use lance_core::Result; use lance_index::IndexType; -use lance_index::scalar::{FullTextSearchQuery, ScalarIndexParams}; +use lance_index::scalar::FullTextSearchQuery; use lance_index::scalar::inverted::tokenizer::InvertedIndexParams; +use lance_tokenizer::TokenStream; use parquet::arrow::async_reader::ParquetRecordBatchStreamBuilder; -use serde::Serialize; +use serde_json::json; use uuid::Uuid; const TEXT_COL: &str = "text"; @@ -53,133 +65,130 @@ const HF_API_LISTING: &str = const HF_FILE_BASE: &str = "https://huggingface.co/datasets/HuggingFaceFW/fineweb/resolve/main/"; // ---------------------------------------------------------------------- -// Configuration (env-driven) +// Mode / Phase // ---------------------------------------------------------------------- -#[derive(Debug, Clone)] -struct Config { - dataset_prefix: String, - run_id: String, - config_name: String, - max_memtable_rows: usize, - durable_write: bool, - fts_enabled: bool, - base_rows: usize, - ingest_rows: usize, - batch_size: usize, - cache_dir: std::path::PathBuf, - result_file: std::path::PathBuf, - /// When true, run the read-perf + consistency sub-test in addition to - /// the throughput test. Auto-disabled when `fts_enabled = false`. - with_read_test: bool, - /// How many high-frequency single-token queries to include. - num_token_queries: usize, - /// How many random 2-token phrase queries to include. - num_phrase_queries: usize, - /// Top-K used for read latency and consistency. - top_k: usize, +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +enum Mode { + AsyncNoIndex, + AsyncIndexed, + SyncNoIndex, + SyncIndexed, } -impl Config { - fn from_env() -> Self { - let dataset_prefix = std::env::var("DATASET_PREFIX") - .unwrap_or_else(|_| "/tmp/bench/mem_fts_fineweb".to_string()); - let run_id = std::env::var("BENCH_RUN_ID").unwrap_or_else(|_| "dev".to_string()); - let max_memtable_rows = env_usize("BENCH_MAX_MEMTABLE_ROWS", 100_000); - let durable_write = env_bool("DURABLE_WRITE", false); - let fts_enabled = env_bool("FTS_ENABLED", false); - let base_rows = env_usize("BENCH_BASE_ROWS", 1_000_000); - let ingest_rows = env_usize("BENCH_INGEST_ROWS", 1_000_000); - let batch_size = env_usize("BENCH_BATCH_SIZE", 1000); - let cache_dir = std::env::var("BENCH_CACHE_DIR") - .unwrap_or_else(|_| { - std::env::temp_dir() - .join("mem_wal_fineweb_fts_cache") - .to_string_lossy() - .into_owned() - }) - .into(); - let result_file = std::env::var("RESULT_FILE") - .unwrap_or_else(|_| "result.json".to_string()) - .into(); - let with_read_test = env_bool("BENCH_WITH_READ_TEST", true) && fts_enabled; - let num_token_queries = env_usize("BENCH_NUM_TOKEN_QUERIES", 100); - let num_phrase_queries = env_usize("BENCH_NUM_PHRASE_QUERIES", 50); - let top_k = env_usize("BENCH_TOP_K", 10); - - let config_name = format!( - "mt{}_durable{}_fts{}", - human_size(max_memtable_rows), - if durable_write { "1" } else { "0" }, - if fts_enabled { "1" } else { "0" }, - ); +impl Mode { + fn parse(value: &str) -> std::result::Result { + match value { + "async_noidx" => Ok(Self::AsyncNoIndex), + "async_idx" => Ok(Self::AsyncIndexed), + "sync_noidx" => Ok(Self::SyncNoIndex), + "sync_idx" => Ok(Self::SyncIndexed), + _ => Err(format!( + "unknown mode '{value}', expected async_noidx|async_idx|sync_noidx|sync_idx" + )), + } + } - Self { - dataset_prefix, - run_id, - config_name, - max_memtable_rows, - durable_write, - fts_enabled, - base_rows, - ingest_rows, - batch_size, - cache_dir, - result_file, - with_read_test, - num_token_queries, - num_phrase_queries, - top_k, + fn as_str(self) -> &'static str { + match self { + Self::AsyncNoIndex => "async_noidx", + Self::AsyncIndexed => "async_idx", + Self::SyncNoIndex => "sync_noidx", + Self::SyncIndexed => "sync_idx", } } - #[allow(dead_code)] - fn base_uri(&self) -> String { - format!("{}/{}/base", self.dataset_prefix, self.run_id) + /// FTS index maintained in the MemTable. + fn indexed(self) -> bool { + matches!(self, Self::AsyncIndexed | Self::SyncIndexed) } - fn ingest_uri(&self) -> String { - format!( - "{}/{}/ingest_{}", - self.dataset_prefix, self.run_id, self.config_name - ) + /// Each `put` waits for WAL durability before returning. + fn durable_write(self) -> bool { + matches!(self, Self::SyncNoIndex | Self::SyncIndexed) } - fn read_test_uri(&self) -> String { - format!( - "{}/{}/readtest_{}", - self.dataset_prefix, self.run_id, self.config_name - ) + /// Index update happens inline in `put` (only meaningful when indexed). + fn sync_indexed_write(self) -> bool { + matches!(self, Self::SyncIndexed) } } -fn env_usize(key: &str, default: usize) -> usize { - std::env::var(key) - .ok() - .and_then(|s| s.parse().ok()) - .unwrap_or(default) +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +enum Phase { + Write, + Read, } -fn env_bool(key: &str, default: bool) -> bool { - match std::env::var(key).ok().as_deref() { - Some("1") | Some("yes") | Some("true") | Some("YES") | Some("TRUE") => true, - Some("0") | Some("no") | Some("false") | Some("NO") | Some("FALSE") => false, - _ => default, +impl Phase { + fn parse(value: &str) -> std::result::Result { + match value { + "write" => Ok(Self::Write), + "read" => Ok(Self::Read), + _ => Err(format!("unknown phase '{value}', expected write|read")), + } + } + + fn as_str(self) -> &'static str { + match self { + Self::Write => "write", + Self::Read => "read", + } } } -fn human_size(n: usize) -> String { - if n % 1_000_000 == 0 { - format!("{}M", n / 1_000_000) - } else if n % 1_000 == 0 { - format!("{}k", n / 1_000) - } else { - n.to_string() +// ---------------------------------------------------------------------- +// Args +// ---------------------------------------------------------------------- + +#[derive(Debug, Clone)] +struct Args { + phase: Phase, + mode: Mode, + uri: Option, + seed_rows: usize, + batch_rows: usize, + calls: usize, + read_rows: usize, + max_memtable_rows: Option, + max_memtable_size: usize, + max_unflushed_memtable_bytes: usize, + max_wal_flush_interval_ms: u64, + cache_dir: PathBuf, + num_token_queries: usize, + num_phrase_queries: usize, + top_k: usize, + tokio_threads: usize, + output: Option, +} + +impl Default for Args { + fn default() -> Self { + let threads = std::thread::available_parallelism().map_or(1, usize::from); + Self { + phase: Phase::Write, + mode: Mode::AsyncIndexed, + uri: None, + seed_rows: 1_000_000, + batch_rows: 1_000, + calls: 1_000, + read_rows: 100_000, + max_memtable_rows: None, + max_memtable_size: 16 * 1024 * 1024 * 1024, + max_unflushed_memtable_bytes: 8 * 1024 * 1024 * 1024, + max_wal_flush_interval_ms: 100, + cache_dir: std::env::temp_dir().join("mem_wal_fineweb_fts_cache"), + num_token_queries: 100, + num_phrase_queries: 50, + top_k: 10, + tokio_threads: threads, + output: None, + } } } // ---------------------------------------------------------------------- -// HF fineweb shard loading +// HuggingFace FineWeb loading // ---------------------------------------------------------------------- #[derive(serde::Deserialize)] @@ -189,13 +198,13 @@ struct HfTreeEntry { path: String, } -async fn list_shard_paths() -> lance_core::Result> { +async fn list_shard_paths() -> Result> { let entries: Vec = reqwest::get(HF_API_LISTING) .await - .map_err(|e| lance_core::Error::io(format!("listing HTTP: {}", e)))? + .map_err(|e| lance_core::Error::io(format!("listing HTTP: {e}")))? .json() .await - .map_err(|e| lance_core::Error::io(format!("listing JSON: {}", e)))?; + .map_err(|e| lance_core::Error::io(format!("listing JSON: {e}")))?; let mut shards: Vec = entries .into_iter() .filter(|e| e.kind == "file" && e.path.ends_with(".parquet")) @@ -205,37 +214,35 @@ async fn list_shard_paths() -> lance_core::Result> { Ok(shards) } -async fn download_shard(rel_path: &str, dest: &std::path::Path) -> lance_core::Result<()> { +async fn download_shard(rel_path: &str, dest: &std::path::Path) -> Result<()> { if dest.exists() { return Ok(()); } - let url = format!("{}{}", HF_FILE_BASE, rel_path); - let max_attempts = 5; - for attempt in 1..=max_attempts { - println!( - "downloading {} (attempt {}/{}) ...", - rel_path, attempt, max_attempts - ); - let result: lance_core::Result = async { + let url = format!("{HF_FILE_BASE}{rel_path}"); + let tmp = dest.with_extension("part"); + for attempt in 1..=5u32 { + println!("downloading {rel_path} (attempt {attempt}/5) ..."); + let result: Result = async { let resp = reqwest::get(&url) .await - .map_err(|e| lance_core::Error::io(format!("download HTTP: {}", e)))?; + .map_err(|e| lance_core::Error::io(format!("download HTTP: {e}")))?; if !resp.status().is_success() { return Err(lance_core::Error::io(format!( - "download {} → status {}", - url, + "download {url} -> status {}", resp.status() ))); } resp.bytes() .await - .map_err(|e| lance_core::Error::io(format!("read body: {}", e))) + .map_err(|e| lance_core::Error::io(format!("read body: {e}"))) } .await; match result { Ok(bytes) => { - std::fs::write(dest, &bytes) - .map_err(|e| lance_core::Error::io(format!("write: {}", e)))?; + std::fs::write(&tmp, &bytes) + .map_err(|e| lance_core::Error::io(format!("write: {e}")))?; + std::fs::rename(&tmp, dest) + .map_err(|e| lance_core::Error::io(format!("rename: {e}")))?; println!( " wrote {:.1} MB to {}", bytes.len() as f64 / 1024.0 / 1024.0, @@ -243,13 +250,9 @@ async fn download_shard(rel_path: &str, dest: &std::path::Path) -> lance_core::R ); return Ok(()); } - Err(e) if attempt < max_attempts => { - let backoff = Duration::from_secs(2u64.pow(attempt as u32)); - eprintln!( - " attempt {} failed: {}; retrying in {:?}", - attempt, e, backoff - ); - tokio::time::sleep(backoff).await; + Err(e) if attempt < 5 => { + eprintln!(" attempt {attempt} failed: {e}; retrying"); + tokio::time::sleep(Duration::from_secs(2u64.pow(attempt))).await; } Err(e) => return Err(e), } @@ -261,23 +264,22 @@ async fn read_shard_text( path: &std::path::Path, out: &mut Vec, max_rows: usize, -) -> lance_core::Result { +) -> Result { let file = tokio::fs::File::open(path) .await - .map_err(|e| lance_core::Error::io(format!("open parquet: {}", e)))?; + .map_err(|e| lance_core::Error::io(format!("open parquet: {e}")))?; let builder = ParquetRecordBatchStreamBuilder::new(file) .await - .map_err(|e| lance_core::Error::io(format!("parquet builder: {}", e)))?; + .map_err(|e| lance_core::Error::io(format!("parquet builder: {e}")))?; let mut stream = builder .build() - .map_err(|e| lance_core::Error::io(format!("parquet stream: {}", e)))?; - + .map_err(|e| lance_core::Error::io(format!("parquet stream: {e}")))?; let mut taken = 0usize; while taken < max_rows { let Some(rb) = stream .try_next() .await - .map_err(|e| lance_core::Error::io(format!("parquet read: {}", e)))? + .map_err(|e| lance_core::Error::io(format!("parquet read: {e}")))? else { break; }; @@ -287,7 +289,7 @@ async fn read_shard_text( let strs = col .as_any() .downcast_ref::() - .ok_or_else(|| lance_core::Error::io("text column not StringArray".to_string()))?; + .ok_or_else(|| lance_core::Error::io("text not StringArray".to_string()))?; for i in 0..strs.len() { if taken >= max_rows { break; @@ -302,44 +304,36 @@ async fn read_shard_text( Ok(taken) } -async fn load_corpus( - needed_rows: usize, - cache_dir: &std::path::Path, -) -> lance_core::Result> { +/// Load `needed_rows` text rows from cached FineWeb shards, downloading +/// shards on demand. +async fn load_corpus(needed_rows: usize, cache_dir: &std::path::Path) -> Result> { std::fs::create_dir_all(cache_dir) - .map_err(|e| lance_core::Error::io(format!("mkdir cache: {}", e)))?; + .map_err(|e| lance_core::Error::io(format!("mkdir cache: {e}")))?; let shards = list_shard_paths().await?; - println!("fineweb sample/10BT has {} parquet shards", shards.len()); - + println!("fineweb sample/10BT: {} shards", shards.len()); let mut buf: Vec = Vec::with_capacity(needed_rows); - for rel_path in &shards { + for rel in &shards { if buf.len() >= needed_rows { break; } - let local_name = rel_path.rsplit('/').next().unwrap_or(rel_path); - let local = cache_dir.join(local_name); - download_shard(rel_path, &local).await?; + let name = rel.rsplit('/').next().unwrap_or(rel); + let local = cache_dir.join(name); + download_shard(rel, &local).await?; let want = needed_rows - buf.len(); let got = read_shard_text(&local, &mut buf, want).await?; - println!( - " shard {} → {} text rows (cumulative {})", - local_name, - got, - buf.len() - ); + println!(" shard {name} -> {got} rows (cumulative {})", buf.len()); } if buf.len() < needed_rows { - eprintln!( - " warning: dataset exhausted at {} rows (asked {})", - buf.len(), - needed_rows - ); + return Err(lance_core::Error::io(format!( + "fineweb yielded only {} rows, need {needed_rows}", + buf.len() + ))); } Ok(buf) } // ---------------------------------------------------------------------- -// Schema + batch helpers +// Schema / batches // ---------------------------------------------------------------------- fn make_schema() -> Arc { @@ -348,251 +342,121 @@ fn make_schema() -> Arc { "lance-schema:unenforced-primary-key".to_string(), "true".to_string(), ); - let id = Field::new("id", DataType::Int64, false).with_metadata(id_meta); Arc::new(ArrowSchema::new(vec![ - id, + Field::new("id", DataType::Int64, false).with_metadata(id_meta), Field::new(TEXT_COL, DataType::Utf8, true), ])) } -fn make_batch(start_id: i64, texts: &[String], schema: Arc) -> RecordBatch { - let n = texts.len(); - let ids: Vec = (start_id..start_id + n as i64).collect(); +fn make_batch(schema: Arc, start_id: i64, texts: &[&str]) -> RecordBatch { + let ids: Vec = (start_id..start_id + texts.len() as i64).collect(); let id_arr: ArrayRef = Arc::new(Int64Array::from(ids)); - let text_arr: ArrayRef = Arc::new(StringArray::from(texts.to_vec())); + let text_arr: ArrayRef = Arc::new(StringArray::from_iter_values(texts.iter().copied())); RecordBatch::try_new(schema, vec![id_arr, text_arr]).unwrap() } -// ---------------------------------------------------------------------- -// Base dataset -// ---------------------------------------------------------------------- - -async fn build_base_if_absent( - base_uri: &str, +/// Write a seed dataset of `seed_texts.len()` rows, optionally create the +/// base FTS index, and initialize MemWAL. +async fn build_seed_dataset( + uri: &str, schema: Arc, - base_texts: &[String], - batch_size: usize, - fts_enabled: bool, -) -> lance_core::Result<()> { - if Dataset::open(base_uri).await.is_ok() { - println!("base dataset already exists at {}, skipping build", base_uri); - return Ok(()); - } - println!( - "building base dataset at {} ({} rows, batch_size {})", - base_uri, - base_texts.len(), - batch_size - ); - let total = base_texts.len(); - let mut batches = Vec::with_capacity(total.div_ceil(batch_size)); - let mut start = 0usize; - while start < total { - let end = (start + batch_size).min(total); - batches.push(Ok(make_batch( - start as i64, - &base_texts[start..end], - schema.clone(), - ))); - start = end; + seed_texts: &[String], + batch_rows: usize, + indexed: bool, +) -> Result { + let start = Instant::now(); + let mut batches = Vec::with_capacity(seed_texts.len().div_ceil(batch_rows)); + let mut lo = 0usize; + while lo < seed_texts.len() { + let hi = (lo + batch_rows).min(seed_texts.len()); + let slice: Vec<&str> = seed_texts[lo..hi].iter().map(|s| s.as_str()).collect(); + batches.push(Ok(make_batch(schema.clone(), lo as i64, &slice))); + lo = hi; } let reader = RecordBatchIterator::new(batches.into_iter(), schema.clone()); - let mut dataset = Dataset::write(reader, base_uri, Some(WriteParams::default())).await?; - if fts_enabled { - let fts_params = InvertedIndexParams::default(); + let mut dataset = Dataset::write(reader, uri, Some(WriteParams::default())).await?; + if indexed { dataset .create_index( &[TEXT_COL], IndexType::Inverted, Some(FTS_INDEX_NAME.to_string()), - &fts_params, - true, - ) - .await?; - } else { - // Even when MemWAL is configured without FTS, we still need a - // BTree index on `id` so MemWAL has at least one maintained - // index to reference. - let pk_params = ScalarIndexParams::default(); - dataset - .create_index( - &["id"], - IndexType::BTree, - Some("id_btree".to_string()), - &pk_params, + &InvertedIndexParams::default(), true, ) .await?; } - let maintained = if fts_enabled { - vec![FTS_INDEX_NAME.to_string()] - } else { - vec!["id_btree".to_string()] - }; dataset .initialize_mem_wal(MemWalConfig { shard_spec: None, - maintained_indexes: maintained, + maintained_indexes: if indexed { + vec![FTS_INDEX_NAME.to_string()] + } else { + vec![] + }, }) .await?; - Ok(()) -} - -// ---------------------------------------------------------------------- -// Ingest -// ---------------------------------------------------------------------- - -#[derive(Debug, Clone, Serialize)] -struct IngestStats { - rows: usize, - wall_seconds: f64, - rows_per_sec: f64, - /// p95 per-`put` latency in milliseconds. - put_p95_ms: f64, - put_p50_ms: f64, - put_max_ms: f64, - num_puts: usize, + Ok(start.elapsed().as_secs_f64()) } -async fn ingest_via_shard_writer( - target_uri: &str, - schema: Arc, - base_texts: &[String], - ingest_texts: &[String], - cfg: &Config, - disable_auto_flush: bool, -) -> lance_core::Result { - // Build a fresh ingest dataset by cloning the base. - println!("preparing ingest dataset at {}", target_uri); - build_base_if_absent( - target_uri, - schema.clone(), - base_texts, - cfg.batch_size, - cfg.fts_enabled, - ) - .await?; - let dataset = Arc::new(Dataset::open(target_uri).await?); - - let shard_id = Uuid::new_v4(); - let max_memtable_rows = if disable_auto_flush { - cfg.ingest_rows.saturating_mul(2).max(2_000_000) +fn shard_writer_config(args: &Args, shard_id: Uuid, disable_auto_flush: bool) -> ShardWriterConfig { + let max_rows = if disable_auto_flush { + args.read_rows.saturating_mul(4).max(4_000_000) } else { - cfg.max_memtable_rows + args.max_memtable_rows.unwrap_or(usize::MAX / 2) }; - let max_memtable_size = if disable_auto_flush { - 16 * 1024 * 1024 * 1024 // 16 GiB + let mut config = ShardWriterConfig::new(shard_id) + .with_durable_write(args.mode.durable_write()) + .with_sync_indexed_write(args.mode.sync_indexed_write()) + .with_max_memtable_size(args.max_memtable_size) + .with_max_unflushed_memtable_bytes(args.max_unflushed_memtable_bytes) + .with_max_memtable_rows(max_rows) + .with_max_memtable_batches(max_rows.div_ceil(args.batch_rows).saturating_add(64)); + if args.max_wal_flush_interval_ms == 0 { + config.max_wal_flush_interval = None; } else { - 16 * 1024 * 1024 * 1024 - }; - let writer_config = ShardWriterConfig { - shard_id, - shard_spec_id: 0, - durable_write: cfg.durable_write, - sync_indexed_write: true, - max_memtable_size, - max_memtable_rows, - max_memtable_batches: 4_000_000, - max_wal_flush_interval: Some(Duration::from_millis(200)), - max_unflushed_memtable_bytes: usize::MAX / 2, - ..ShardWriterConfig::default() - }; - let writer = dataset - .as_ref() - .mem_wal_writer(shard_id, writer_config) - .await?; - - // Ingest IDs start above the base table's last id to keep PK unique. - let id_offset: i64 = cfg.base_rows as i64; - let n = ingest_texts.len(); - let bs = cfg.batch_size; - let total_batches = n.div_ceil(bs); + config = config + .with_max_wal_flush_interval(Duration::from_millis(args.max_wal_flush_interval_ms)); + } + config +} - let mut put_latencies: Vec = Vec::with_capacity(total_batches); - let start = Instant::now(); - for i in 0..total_batches { - let lo = i * bs; - let hi = (lo + bs).min(n); - let batch = make_batch( - id_offset + lo as i64, - &ingest_texts[lo..hi], - schema.clone(), - ); - let put_t = Instant::now(); - writer.put(vec![batch]).await?; - put_latencies.push(put_t.elapsed().as_micros()); - if (i + 1) % 100 == 0 { - let so_far = start.elapsed().as_secs_f64(); - let rate = (i + 1) as f64 * bs as f64 / so_far.max(1e-9); - println!( - " ingest progress: {}/{} batches ({:.0} rows/s)", - i + 1, - total_batches, - rate - ); - } +fn percentile(values: &[f64], pct: f64) -> f64 { + if values.is_empty() { + return f64::NAN; } - // Close the writer to drain the final WAL/memtable flush. With - // sync_indexed_write = true the inline index update is already - // complete, but auto-flush leaves background memtable->disk work - // outstanding that close() awaits. We deliberately include close() - // in the elapsed measurement so the reported throughput reflects - // "rows fully ingested + their pending flush", not just "puts - // returned". This is the apples-to-apples figure across configs - // with different `max_memtable_rows` (which drive the flush - // cadence). The previous post-ingest spin on - // `max_indexed_batch_position` was incorrect under auto-flush - // because the counter resets on each new active memtable. - writer.close().await?; - let elapsed = start.elapsed(); - - put_latencies.sort_unstable(); - let p50 = put_latencies[put_latencies.len() / 2] as f64 / 1000.0; - let p95 = put_latencies[put_latencies.len() * 95 / 100] as f64 / 1000.0; - let max = *put_latencies.iter().max().unwrap_or(&0) as f64 / 1000.0; - Ok(IngestStats { - rows: n, - wall_seconds: elapsed.as_secs_f64(), - rows_per_sec: n as f64 / elapsed.as_secs_f64().max(1e-9), - put_p50_ms: p50, - put_p95_ms: p95, - put_max_ms: max, - num_puts: total_batches, - }) + let mut sorted = values.to_vec(); + sorted.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)); + let idx = ((pct / 100.0) * (sorted.len().saturating_sub(1)) as f64).round() as usize; + sorted[idx.min(sorted.len() - 1)] } // ---------------------------------------------------------------------- // Query set // ---------------------------------------------------------------------- -#[derive(Debug, Clone, Serialize)] struct QuerySet { tokens: Vec, phrases: Vec, } -fn build_query_set(sample_texts: &[&str], cfg: &Config) -> QuerySet { - use lance_tokenizer::TokenStream; - // ASCII stop-word-ish list used by the default English analyzer; we only - // need a coarse filter for query selection here. - const STOPWORDS: &[&str] = &[ - "the", "a", "an", "and", "or", "of", "to", "in", "on", "for", "with", "as", "by", "is", - "was", "are", "were", "be", "been", "being", "this", "that", "these", "those", "it", "its", - "but", "not", "no", "if", "then", "than", "so", "do", "does", "did", "have", "has", "had", - "will", "would", "should", "could", "can", "may", "might", "must", "i", "you", "he", "she", - "we", "they", "them", "his", "her", "their", "our", "us", "me", "my", "your", "him", - ]; +const STOPWORDS: &[&str] = &[ + "the", "a", "an", "and", "or", "of", "to", "in", "on", "for", "with", "as", "by", "is", "was", + "are", "were", "be", "been", "being", "this", "that", "these", "those", "it", "its", "but", + "not", "no", "if", "then", "than", "so", "do", "does", "did", "have", "has", "had", "will", + "would", "should", "could", "can", "may", "might", "must", "i", "you", "he", "she", "we", + "they", "them", "his", "her", "their", "our", "us", "me", "my", "your", "him", "at", "from", +]; + +fn build_query_set(sample: &[&str], args: &Args) -> QuerySet { let mut tokenizer = InvertedIndexParams::default() .build() .expect("default tokenizer builds"); let mut freq: HashMap = HashMap::new(); - for t in sample_texts.iter().take(50_000) { + for t in sample.iter().take(50_000) { let mut stream = tokenizer.token_stream_for_doc(t); while let Some(tok) = stream.next() { - if tok.text.len() < 3 || tok.text.len() > 24 { - continue; - } - if STOPWORDS.contains(&tok.text.as_str()) { + if tok.text.len() < 3 || tok.text.len() > 24 || STOPWORDS.contains(&tok.text.as_str()) { continue; } *freq.entry(tok.text.clone()).or_default() += 1; @@ -603,23 +467,17 @@ fn build_query_set(sample_texts: &[&str], cfg: &Config) -> QuerySet { let tokens: Vec = by_freq .into_iter() .map(|(t, _)| t) - .take(cfg.num_token_queries) + .take(args.num_token_queries) .collect(); - // Phrase queries: walk a deterministic stride of rows, take the first - // two consecutive non-stopword non-short tokens. - let mut phrases = Vec::with_capacity(cfg.num_phrase_queries); - let stride = sample_texts.len().max(1) / cfg.num_phrase_queries.max(1); + let mut phrases = Vec::with_capacity(args.num_phrase_queries); + let stride = (sample.len().max(1) / args.num_phrase_queries.max(1)).max(1); let mut idx = 0usize; - while phrases.len() < cfg.num_phrase_queries && idx < sample_texts.len() { - let t = sample_texts[idx]; - let mut stream = tokenizer.token_stream_for_doc(t); + while phrases.len() < args.num_phrase_queries && idx < sample.len() { + let mut stream = tokenizer.token_stream_for_doc(sample[idx]); let mut acc: Vec = Vec::new(); while let Some(tok) = stream.next() { - if tok.text.len() < 3 || tok.text.len() > 24 { - continue; - } - if STOPWORDS.contains(&tok.text.as_str()) { + if tok.text.len() < 3 || tok.text.len() > 24 || STOPWORDS.contains(&tok.text.as_str()) { continue; } acc.push(tok.text.clone()); @@ -628,326 +486,371 @@ fn build_query_set(sample_texts: &[&str], cfg: &Config) -> QuerySet { break; } } - idx = idx.saturating_add(stride.max(1)); + idx += stride; } - QuerySet { tokens, phrases } } // ---------------------------------------------------------------------- -// Read test (FTS only): MemTable query latency + post-flush consistency +// Write phase // ---------------------------------------------------------------------- -#[derive(Debug, Clone, Serialize)] -struct ReadStats { - rows: usize, - /// Average across all queries (token + phrase). - mt_latency_avg_ms: f64, - mt_latency_p50_ms: f64, - mt_latency_p95_ms: f64, - consistency_mean: f64, - consistency_min: f64, - num_queries: usize, -} +async fn run_write(args: &Args, uri: &str, corpus: &[String]) -> Result { + let schema = make_schema(); + let indexed = args.mode.indexed(); -async fn run_read_test( - target_uri: &str, - schema: Arc, - base_texts: &[String], - ingest_texts: &[String], - queries: &QuerySet, - cfg: &Config, -) -> lance_core::Result { - println!( - " read test: ingesting {} rows with auto-flush disabled", - ingest_texts.len() - ); - build_base_if_absent( - target_uri, - schema.clone(), - base_texts, - cfg.batch_size, - true, // FTS index on the base, since this path is FTS-only. - ) - .await?; - let dataset = Arc::new(Dataset::open(target_uri).await?); - let shard_id = Uuid::new_v4(); - let writer_config = ShardWriterConfig { - shard_id, - shard_spec_id: 0, - durable_write: cfg.durable_write, - sync_indexed_write: true, - // Effectively disable auto-flush triggers so the MemTable holds - // the full ingest_texts.len() rows for the query phase. - max_memtable_size: 64 * 1024 * 1024 * 1024, - max_memtable_rows: ingest_texts.len().saturating_mul(2), - max_memtable_batches: 4_000_000, - max_wal_flush_interval: Some(Duration::from_millis(200)), - max_unflushed_memtable_bytes: usize::MAX / 2, - ..ShardWriterConfig::default() - }; - let writer = dataset - .as_ref() - .mem_wal_writer(shard_id, writer_config) - .await?; + let seed = &corpus[..args.seed_rows.min(corpus.len())]; + let setup_s = build_seed_dataset(uri, schema.clone(), seed, args.batch_rows, indexed).await?; - let id_offset: i64 = cfg.base_rows as i64; - let bs = cfg.batch_size; - let n = ingest_texts.len(); - let total_batches = n.div_ceil(bs); - for i in 0..total_batches { - let lo = i * bs; - let hi = (lo + bs).min(n); + let dataset = Dataset::open(uri).await?; + let shard_id = Uuid::new_v4(); + let config = shard_writer_config(args, shard_id, false); + let writer = dataset.mem_wal_writer(shard_id, config).await?; + + // Ingest rows come from the corpus after the seed, cycled if needed. + let ingest_pool = &corpus[args.seed_rows.min(corpus.len())..]; + let pool_len = ingest_pool.len().max(1); + let mut latencies_ms = Vec::with_capacity(args.calls); + let id_base = args.seed_rows as i64; + + let puts_start = Instant::now(); + for call in 0..args.calls { + let lo = (call * args.batch_rows) % pool_len; + let mut slice: Vec<&str> = Vec::with_capacity(args.batch_rows); + for j in 0..args.batch_rows { + slice.push(ingest_pool[(lo + j) % pool_len].as_str()); + } let batch = make_batch( - id_offset + lo as i64, - &ingest_texts[lo..hi], schema.clone(), + id_base + (call * args.batch_rows) as i64, + &slice, ); + let put_start = Instant::now(); writer.put(vec![batch]).await?; - } - let target_batch_pos = total_batches.saturating_sub(1); - loop { - let active = writer.active_memtable_ref().await?; - if active.index_store.max_indexed_batch_position() >= target_batch_pos { - break; + latencies_ms.push(put_start.elapsed().as_secs_f64() * 1000.0); + if (call + 1) % 100 == 0 { + let rate = ((call + 1) * args.batch_rows) as f64 / puts_start.elapsed().as_secs_f64(); + println!(" put {}/{} ({rate:.0} rows/s)", call + 1, args.calls); } - drop(active); - tokio::time::sleep(Duration::from_millis(50)).await; } + let elapsed_puts_s = puts_start.elapsed().as_secs_f64(); - // ----- MemTable phase ----- - let active = writer.active_memtable_ref().await?; - let fts = active - .index_store - .get_fts(FTS_INDEX_NAME) - .ok_or_else(|| lance_core::Error::invalid_input("FTS mem index not found"))?; + let close_start = Instant::now(); + writer.close().await?; + let elapsed_close_s = close_start.elapsed().as_secs_f64(); + let elapsed_total_s = puts_start.elapsed().as_secs_f64(); + + let rows = args.calls * args.batch_rows; + Ok(json!({ + "phase": "write", + "mode": args.mode.as_str(), + "uri": uri, + "seed_rows": args.seed_rows, + "batch_rows": args.batch_rows, + "calls": args.calls, + "ingested_rows": rows, + "max_memtable_rows": args.max_memtable_rows, + "setup_seconds": setup_s, + "elapsed_puts_seconds": elapsed_puts_s, + "elapsed_close_seconds": elapsed_close_s, + "elapsed_total_seconds": elapsed_total_s, + // Throughput including the final close()/flush — comparable across + // configs with different flush cadences. + "throughput_rows_per_sec": rows as f64 / elapsed_total_s, + // Loop-only throughput (puts returned, flush may be outstanding). + "throughput_puts_rows_per_sec": rows as f64 / elapsed_puts_s, + "put_p50_ms": percentile(&latencies_ms, 50.0), + "put_p90_ms": percentile(&latencies_ms, 90.0), + "put_p99_ms": percentile(&latencies_ms, 99.0), + "put_max_ms": latencies_ms.iter().copied().fold(0.0_f64, f64::max), + })) +} + +// ---------------------------------------------------------------------- +// Read phase +// ---------------------------------------------------------------------- + +async fn run_read(args: &Args, uri: &str, corpus: &[String]) -> Result { + let schema = make_schema(); - let mut latencies_us: Vec = Vec::new(); - let mut mt_top10: Vec> = Vec::new(); + // Seed dataset with an FTS base index (read phase is FTS-only). + let seed = &corpus[..args.seed_rows.min(corpus.len())]; + let setup_s = build_seed_dataset(uri, schema.clone(), seed, args.batch_rows, true).await?; + + let dataset = Dataset::open(uri).await?; + let shard_id = Uuid::new_v4(); + // Auto-flush disabled so the MemTable holds the full read_rows. + let config = shard_writer_config(args, shard_id, true); + let writer = dataset.mem_wal_writer(shard_id, config).await?; + + let ingest_pool = &corpus[args.seed_rows.min(corpus.len())..]; + let n = args.read_rows.min(ingest_pool.len()); + let id_base = args.seed_rows as i64; + let total_batches = n.div_ceil(args.batch_rows); + let ingest_start = Instant::now(); + for b in 0..total_batches { + let lo = b * args.batch_rows; + let hi = (lo + args.batch_rows).min(n); + let slice: Vec<&str> = ingest_pool[lo..hi].iter().map(|s| s.as_str()).collect(); + writer + .put(vec![make_batch( + schema.clone(), + id_base + lo as i64, + &slice, + )]) + .await?; + } + let ingest_s = ingest_start.elapsed().as_secs_f64(); + println!(" read phase: ingested {n} rows in {ingest_s:.1}s"); - let all_queries: Vec<(FtsQueryExpr, String)> = queries + // Build the query set from the ingested slice. + let sample: Vec<&str> = ingest_pool[..n].iter().map(|s| s.as_str()).collect(); + let queries = build_query_set(&sample, args); + let all_queries: Vec = queries .tokens .iter() - .map(|t| (FtsQueryExpr::match_query(t.clone()), t.clone())) + .map(|t| FtsQueryExpr::match_query(t.clone())) .chain( queries .phrases .iter() - .map(|p| (FtsQueryExpr::phrase(p.clone()), format!("\"{p}\""))), + .map(|p| FtsQueryExpr::phrase(p.clone())), ) .collect(); + println!( + " query set: {} tokens + {} phrases", + queries.tokens.len(), + queries.phrases.len() + ); - // Build a row_position -> id map by scanning the active batches. - // This is needed because the MemTable returns row_positions; the - // post-flush on-disk FTS returns row_ids that match the `id` column. + // ---- MemTable read phase ---- + let active = writer.active_memtable_ref().await?; + let fts = active + .index_store + .get_fts(FTS_INDEX_NAME) + .ok_or_else(|| lance_core::Error::invalid_input("FTS mem index missing"))?; + + // row_position -> id, recovered from the MemTable batches. let mut row_to_id: HashMap = HashMap::new(); for stored in active.batch_store.iter() { let id_arr = stored .data .column_by_name("id") .and_then(|c| c.as_any().downcast_ref::()) - .ok_or_else(|| lance_core::Error::invalid_input("id col missing"))?; + .ok_or_else(|| lance_core::Error::invalid_input("id column missing"))?; for r in 0..id_arr.len() { row_to_id.insert(stored.row_offset + r as u64, id_arr.value(r)); } } - for (q, label) in &all_queries { - let opts = SearchOptions::new().with_limit(cfg.top_k); + let mut latencies_us = Vec::with_capacity(all_queries.len()); + let mut mt_top: Vec> = Vec::with_capacity(all_queries.len()); + for q in &all_queries { + let opts = SearchOptions::new().with_limit(args.top_k); let t0 = Instant::now(); - let entries = fts.search_with_options(q, opts); - latencies_us.push(t0.elapsed().as_micros()); - let mut ids = HashSet::with_capacity(cfg.top_k); - for e in entries.iter().take(cfg.top_k) { - if let Some(id) = row_to_id.get(&e.row_position) { - ids.insert(*id); - } - } - if mt_top10.len() < 3 { - println!( - " [mt] {label}: {} hits, ids={:?}", - entries.len(), - ids.iter().take(3).collect::>() - ); - } - mt_top10.push(ids); + let hits = fts.search_with_options(q, opts); + latencies_us.push(t0.elapsed().as_micros() as f64); + let ids: HashSet = hits + .iter() + .take(args.top_k) + .filter_map(|e| row_to_id.get(&e.row_position).copied()) + .collect(); + mt_top.push(ids); } drop(active); - latencies_us.sort_unstable(); - let avg_us = - latencies_us.iter().sum::() as f64 / latencies_us.len().max(1) as f64; - let p50 = latencies_us[latencies_us.len() / 2] as f64 / 1000.0; - let p95 = latencies_us[latencies_us.len() * 95 / 100] as f64 / 1000.0; + let lat_ms: Vec = latencies_us.iter().map(|us| us / 1000.0).collect(); + let mt_avg_ms = lat_ms.iter().sum::() / lat_ms.len().max(1) as f64; - // ----- Force flush, then on-disk phase ----- - println!(" read test: closing writer to force flush"); + // ---- Force flush, on-disk replay ---- + let flush_start = Instant::now(); writer.close().await?; - let flushed_dataset = Dataset::open(target_uri).await?; - - let mut consistencies: Vec = Vec::with_capacity(all_queries.len()); - for ((q, label), mt_ids) in all_queries.iter().zip(mt_top10.iter()) { - let fts_query = match q { - FtsQueryExpr::Match { query, .. } => FullTextSearchQuery::new(query.clone()), - FtsQueryExpr::Phrase { query, .. } => { - FullTextSearchQuery::new(format!("\"{}\"", query)) - } - _ => unreachable!("only match/phrase queries in this set"), + let flush_s = flush_start.elapsed().as_secs_f64(); + let flushed = Dataset::open(uri).await?; + + let mut consistencies = Vec::with_capacity(all_queries.len()); + for (q, mt_ids) in all_queries.iter().zip(mt_top.iter()) { + let query_str = match q { + FtsQueryExpr::Match { query, .. } => query.clone(), + FtsQueryExpr::Phrase { query, .. } => format!("\"{query}\""), + _ => unreachable!("only match/phrase queries are generated"), }; - let mut scanner = flushed_dataset.scan(); - scanner.full_text_search(fts_query)?; - scanner.limit(Some(cfg.top_k as i64), None)?; + let mut scanner = flushed.scan(); + scanner.full_text_search(FullTextSearchQuery::new(query_str))?; + scanner.limit(Some(args.top_k as i64), None)?; scanner.project(&["id"])?; let stream = scanner.try_into_stream().await?; let batches: Vec = stream.try_collect().await?; - let mut disk_ids = HashSet::new(); + let mut disk_ids: HashSet = HashSet::new(); for b in &batches { let id_arr = b .column_by_name("id") .and_then(|c| c.as_any().downcast_ref::()) - .ok_or_else(|| lance_core::Error::invalid_input("disk id col missing"))?; + .ok_or_else(|| lance_core::Error::invalid_input("disk id column missing"))?; for i in 0..id_arr.len() { disk_ids.insert(id_arr.value(i)); } } let inter: usize = mt_ids.intersection(&disk_ids).count(); let denom = mt_ids.len().max(disk_ids.len()).max(1); - let cons = inter as f64 / denom as f64; - if consistencies.len() < 3 { - println!( - " [disk] {label}: {} hits; mt={} disk={} ∩={} cons={:.3}", - disk_ids.len(), - mt_ids.len(), - disk_ids.len(), - inter, - cons - ); - } - consistencies.push(cons); + consistencies.push(inter as f64 / denom as f64); } - let cons_mean = consistencies.iter().sum::() / consistencies.len().max(1) as f64; - let cons_min = consistencies - .iter() - .copied() - .fold(f64::INFINITY, f64::min); - - Ok(ReadStats { - rows: n, - mt_latency_avg_ms: avg_us / 1000.0, - mt_latency_p50_ms: p50, - mt_latency_p95_ms: p95, - consistency_mean: cons_mean, - consistency_min: if cons_min.is_finite() { cons_min } else { 0.0 }, - num_queries: all_queries.len(), - }) + let cons_min = consistencies.iter().copied().fold(1.0_f64, f64::min); + + Ok(json!({ + "phase": "read", + "mode": args.mode.as_str(), + "uri": uri, + "seed_rows": args.seed_rows, + "read_rows": n, + "max_memtable_rows": args.max_memtable_rows, + "setup_seconds": setup_s, + "ingest_seconds": ingest_s, + "flush_seconds": flush_s, + "num_queries": all_queries.len(), + "num_token_queries": queries.tokens.len(), + "num_phrase_queries": queries.phrases.len(), + "mt_latency_avg_ms": mt_avg_ms, + "mt_latency_p50_ms": percentile(&lat_ms, 50.0), + "mt_latency_p95_ms": percentile(&lat_ms, 95.0), + "mt_latency_p99_ms": percentile(&lat_ms, 99.0), + "consistency_mean": cons_mean, + "consistency_min": cons_min, + })) } // ---------------------------------------------------------------------- -// Top-level orchestration +// CLI // ---------------------------------------------------------------------- -#[derive(Debug, Clone, Serialize)] -struct RunResult { - config_name: String, - max_memtable_rows: usize, - durable_write: bool, - fts_enabled: bool, - base_rows: usize, - ingest_rows: usize, - batch_size: usize, - ingest: IngestStats, - read: Option, - timestamp_utc: String, +fn parse(flag: &str, value: &str) -> Result +where + T: std::str::FromStr, + T::Err: std::fmt::Display, +{ + value + .parse() + .map_err(|e| lance_core::Error::invalid_input(format!("invalid {flag}: {value} ({e})"))) } -#[tokio::main(flavor = "multi_thread")] -async fn main() -> lance_core::Result<()> { - let cfg = Config::from_env(); - println!("=== mem_wal_fineweb_fts === config = {:?}", cfg); - - let total_rows = cfg.base_rows + cfg.ingest_rows; - let texts = load_corpus(total_rows, &cfg.cache_dir).await?; - if texts.len() < total_rows { - return Err(lance_core::Error::io(format!( - "fineweb shards yielded only {} rows, need {}", - texts.len(), - total_rows - ))); +fn parse_args() -> Result { + let mut args = Args::default(); + let mut iter = std::env::args().skip(1); + while let Some(flag) = iter.next() { + if flag == "--bench" { + continue; + } + let value = iter + .next() + .ok_or_else(|| lance_core::Error::invalid_input(format!("missing value for {flag}")))?; + match flag.as_str() { + "--phase" => { + args.phase = Phase::parse(&value).map_err(lance_core::Error::invalid_input)? + } + "--mode" => { + args.mode = Mode::parse(&value).map_err(lance_core::Error::invalid_input)? + } + "--uri" => args.uri = Some(value), + "--seed-rows" => args.seed_rows = parse(&flag, &value)?, + "--batch-rows" => args.batch_rows = parse(&flag, &value)?, + "--calls" => args.calls = parse(&flag, &value)?, + "--read-rows" => args.read_rows = parse(&flag, &value)?, + "--max-memtable-rows" => { + let v: usize = parse(&flag, &value)?; + args.max_memtable_rows = (v != 0).then_some(v); + } + "--max-memtable-size" => args.max_memtable_size = parse(&flag, &value)?, + "--max-unflushed-memtable-bytes" => { + args.max_unflushed_memtable_bytes = parse(&flag, &value)? + } + "--max-wal-flush-interval-ms" => args.max_wal_flush_interval_ms = parse(&flag, &value)?, + "--cache-dir" => args.cache_dir = PathBuf::from(value), + "--num-token-queries" => args.num_token_queries = parse(&flag, &value)?, + "--num-phrase-queries" => args.num_phrase_queries = parse(&flag, &value)?, + "--top-k" => args.top_k = parse(&flag, &value)?, + "--tokio-threads" => args.tokio_threads = parse(&flag, &value)?, + "--output" => args.output = Some(PathBuf::from(value)), + _ => { + return Err(lance_core::Error::invalid_input(format!( + "unknown argument: {flag}" + ))); + } + } } - let base_texts = &texts[..cfg.base_rows]; - let ingest_texts = &texts[cfg.base_rows..cfg.base_rows + cfg.ingest_rows]; + if args.batch_rows == 0 || args.calls == 0 || args.seed_rows == 0 { + return Err(lance_core::Error::invalid_input( + "seed-rows, batch-rows, calls must be > 0", + )); + } + Ok(args) +} - let schema = make_schema(); +async fn run(args: Args) -> Result<()> { + let temp = if args.uri.is_none() { + Some(tempfile::tempdir().map_err(|e| lance_core::Error::io(format!("tempdir: {e}")))?) + } else { + None + }; + let uri = match &args.uri { + Some(u) => u.clone(), + None => temp + .as_ref() + .unwrap() + .path() + .join("fineweb_fts.lance") + .display() + .to_string(), + }; - // Build query set once from the ingest slice (deterministic). - let sample_refs: Vec<&str> = ingest_texts.iter().take(50_000).map(|s| s.as_str()).collect(); - let queries = build_query_set(&sample_refs, &cfg); println!( - "query set: {} tokens + {} phrases", - queries.tokens.len(), - queries.phrases.len() + "bench=mem_wal_fineweb_fts phase={} mode={} uri={} seed_rows={} batch_rows={} calls={} read_rows={} max_memtable_rows={:?}", + args.phase.as_str(), + args.mode.as_str(), + uri, + args.seed_rows, + args.batch_rows, + args.calls, + args.read_rows, + args.max_memtable_rows, ); - // Throughput sub-test: ingest 1M with the configured params. - println!("\n--- throughput sub-test ---"); - let ingest_stats = ingest_via_shard_writer( - &cfg.ingest_uri(), - schema.clone(), - base_texts, - ingest_texts, - &cfg, - false, // auto-flush enabled (per max_memtable_rows) - ) - .await?; - println!("throughput: {:.1} rows/s", ingest_stats.rows_per_sec); - - // Read sub-test: only when FTS enabled and read test requested. - let read_stats = if cfg.with_read_test { - println!("\n--- read sub-test ---"); - let n_for_read = cfg.max_memtable_rows.min(ingest_texts.len()); - let read_ingest = &ingest_texts[..n_for_read]; - Some( - run_read_test( - &cfg.read_test_uri(), - schema.clone(), - base_texts, - read_ingest, - &queries, - &cfg, - ) - .await?, - ) - } else { - None + let ingest_needed = match args.phase { + Phase::Write => args.calls * args.batch_rows, + Phase::Read => args.read_rows, }; - - let timestamp_utc = chrono::Utc::now().to_rfc3339(); - let result = RunResult { - config_name: cfg.config_name.clone(), - max_memtable_rows: cfg.max_memtable_rows, - durable_write: cfg.durable_write, - fts_enabled: cfg.fts_enabled, - base_rows: cfg.base_rows, - ingest_rows: cfg.ingest_rows, - batch_size: cfg.batch_size, - ingest: ingest_stats, - read: read_stats, - timestamp_utc, + // Cap downloaded corpus; the write phase cycles the ingest pool if it + // needs more rows than were loaded. + let corpus_rows = (args.seed_rows + ingest_needed).min(2_000_000); + let corpus = load_corpus(corpus_rows, &args.cache_dir).await?; + + let result = match args.phase { + Phase::Write => run_write(&args, &uri, &corpus).await?, + Phase::Read => run_read(&args, &uri, &corpus).await?, }; - let json = serde_json::to_string_pretty(&result) - .map_err(|e| lance_core::Error::io(format!("serialize result: {}", e)))?; - if let Some(parent) = cfg.result_file.parent() { - if !parent.as_os_str().is_empty() { + + let text = serde_json::to_string_pretty(&result) + .map_err(|e| lance_core::Error::io(format!("serialize: {e}")))?; + println!("{text}"); + if let Some(path) = &args.output { + if let Some(parent) = path.parent() + && !parent.as_os_str().is_empty() + { std::fs::create_dir_all(parent).ok(); } + std::fs::write(path, text.as_bytes()) + .map_err(|e| lance_core::Error::io(format!("write {}: {e}", path.display())))?; } - std::fs::write(&cfg.result_file, json.as_bytes()) - .map_err(|e| lance_core::Error::io(format!("write result: {}", e)))?; - println!("\nwrote result to {}", cfg.result_file.display()); println!("=== DONE ==="); - let _ = result; // silence unused with no read test - let _ = sample_refs; - let _ = BTreeMap::::new(); Ok(()) } + +fn main() -> Result<()> { + let args = parse_args()?; + let runtime = tokio::runtime::Builder::new_multi_thread() + .worker_threads(args.tokio_threads.max(1)) + .enable_all() + .build() + .map_err(|e| lance_core::Error::io(format!("build runtime: {e}")))?; + runtime.block_on(run(args)) +} From c6e1e2278a74531b3e4dc92f6bb13ef696f3b9ff Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sat, 16 May 2026 02:29:14 -0700 Subject: [PATCH 06/37] fix(bench): wait for FTS index catchup before the read-phase queries MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit In async-index modes the FTS index is updated in the background WAL-flush handler, so querying the MemTable right after the put loop hit an empty index — consistency scored 0 because MemTable top-10 was always empty. The read phase now waits for the IndexStore visibility watermark to reach the last buffered batch before querying. Auto-flush is disabled in the read phase so the watermark is a stable, monotonic target. Reports the catchup time as index_catchup_seconds. --- rust/lance/benches/mem_wal_fineweb_fts.rs | 29 +++++++++++++++++++++++ 1 file changed, 29 insertions(+) diff --git a/rust/lance/benches/mem_wal_fineweb_fts.rs b/rust/lance/benches/mem_wal_fineweb_fts.rs index afeed61c5de..affe902d8c5 100644 --- a/rust/lance/benches/mem_wal_fineweb_fts.rs +++ b/rust/lance/benches/mem_wal_fineweb_fts.rs @@ -603,6 +603,34 @@ async fn run_read(args: &Args, uri: &str, corpus: &[String]) -> Result break, + Some(t) if visible >= t => break, + Some(_) => {} + } + if catchup_start.elapsed() > catchup_deadline { + return Err(lance_core::Error::io( + "FTS index did not catch up within 900s".to_string(), + )); + } + tokio::time::sleep(Duration::from_millis(100)).await; + } + let index_catchup_s = catchup_start.elapsed().as_secs_f64(); + println!(" read phase: index caught up in {index_catchup_s:.1}s"); + // Build the query set from the ingested slice. let sample: Vec<&str> = ingest_pool[..n].iter().map(|s| s.as_str()).collect(); let queries = build_query_set(&sample, args); @@ -707,6 +735,7 @@ async fn run_read(args: &Args, uri: &str, corpus: &[String]) -> Result Date: Sat, 16 May 2026 03:41:10 -0700 Subject: [PATCH 07/37] fix(bench): force sync_indexed_write in the FTS read phase The watermark-based catchup wait never completed for async-index mode with auto-flush disabled. The read panel measures FTS read latency and MemTable/on-disk consistency, both of which are properties of the populated index and independent of whether ingestion indexed sync or async. So the read phase now forces sync_indexed_write: the FTS index is fully populated when the put loop returns and the query phase can start immediately, no watermark polling. --- rust/lance/benches/mem_wal_fineweb_fts.rs | 37 ++++------------------- 1 file changed, 6 insertions(+), 31 deletions(-) diff --git a/rust/lance/benches/mem_wal_fineweb_fts.rs b/rust/lance/benches/mem_wal_fineweb_fts.rs index affe902d8c5..64daa907488 100644 --- a/rust/lance/benches/mem_wal_fineweb_fts.rs +++ b/rust/lance/benches/mem_wal_fineweb_fts.rs @@ -579,8 +579,12 @@ async fn run_read(args: &Args, uri: &str, corpus: &[String]) -> Result Result break, - Some(t) if visible >= t => break, - Some(_) => {} - } - if catchup_start.elapsed() > catchup_deadline { - return Err(lance_core::Error::io( - "FTS index did not catch up within 900s".to_string(), - )); - } - tokio::time::sleep(Duration::from_millis(100)).await; - } - let index_catchup_s = catchup_start.elapsed().as_secs_f64(); - println!(" read phase: index caught up in {index_catchup_s:.1}s"); - // Build the query set from the ingested slice. let sample: Vec<&str> = ingest_pool[..n].iter().map(|s| s.as_str()).collect(); let queries = build_query_set(&sample, args); @@ -735,7 +711,6 @@ async fn run_read(args: &Args, uri: &str, corpus: &[String]) -> Result Date: Sat, 16 May 2026 03:50:15 -0700 Subject: [PATCH 08/37] fix(bench): tiny read-phase seed + id prefilter for consistency MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The read-phase consistency scored 0 because the 1M-row seed polluted the on-disk comparison: the MemTable FTS index covers only the ingested rows, but `full_text_search` ranked over all 1.1M rows and its top-10 was dominated by seed rows the MemTable never held — disjoint by construction. The read phase now seeds with only READ_SEED_ROWS (1000) rows and prefilters the on-disk query to `id >= READ_SEED_ROWS`, so both the MemTable and on-disk top-10 cover exactly the ingested population and BM25 stats are effectively identical. --- rust/lance/benches/mem_wal_fineweb_fts.rs | 31 +++++++++++++++-------- 1 file changed, 21 insertions(+), 10 deletions(-) diff --git a/rust/lance/benches/mem_wal_fineweb_fts.rs b/rust/lance/benches/mem_wal_fineweb_fts.rs index 64daa907488..3f03f9f3f64 100644 --- a/rust/lance/benches/mem_wal_fineweb_fts.rs +++ b/rust/lance/benches/mem_wal_fineweb_fts.rs @@ -60,6 +60,12 @@ use uuid::Uuid; const TEXT_COL: &str = "text"; const FTS_INDEX_NAME: &str = "text_fts"; +/// Seed-row count for the read phase. Kept tiny so the on-disk corpus is +/// effectively just the ingested rows: the MemTable FTS index covers only +/// the ingested rows, so the on-disk `full_text_search` it is compared +/// against must too. The on-disk query is additionally prefiltered to +/// `id >= READ_SEED_ROWS` to drop these few seed rows entirely. +const READ_SEED_ROWS: usize = 1000; const HF_API_LISTING: &str = "https://huggingface.co/api/datasets/HuggingFaceFW/fineweb/tree/main/sample/10BT"; const HF_FILE_BASE: &str = "https://huggingface.co/datasets/HuggingFaceFW/fineweb/resolve/main/"; @@ -573,8 +579,10 @@ async fn run_write(args: &Args, uri: &str, corpus: &[String]) -> Result Result { let schema = make_schema(); - // Seed dataset with an FTS base index (read phase is FTS-only). - let seed = &corpus[..args.seed_rows.min(corpus.len())]; + // Tiny seed dataset with an FTS base index (read phase is FTS-only). + // See READ_SEED_ROWS for why the seed is kept small. + let read_seed = READ_SEED_ROWS.min(corpus.len()); + let seed = &corpus[..read_seed]; let setup_s = build_seed_dataset(uri, schema.clone(), seed, args.batch_rows, true).await?; let dataset = Dataset::open(uri).await?; @@ -587,9 +595,9 @@ async fn run_read(args: &Args, uri: &str, corpus: &[String]) -> Result Result= {read_seed}"))?; scanner.limit(Some(args.top_k as i64), None)?; scanner.project(&["id"])?; let stream = scanner.try_into_stream().await?; @@ -819,13 +830,13 @@ async fn run(args: Args) -> Result<()> { args.max_memtable_rows, ); - let ingest_needed = match args.phase { - Phase::Write => args.calls * args.batch_rows, - Phase::Read => args.read_rows, + // Corpus size differs by phase: the write phase seeds with + // `seed_rows` and cycles the ingest pool; the read phase uses only a + // tiny `READ_SEED_ROWS` seed plus `read_rows` ingested rows. + let corpus_rows = match args.phase { + Phase::Write => (args.seed_rows + args.calls * args.batch_rows).min(2_000_000), + Phase::Read => (READ_SEED_ROWS + args.read_rows).min(2_000_000), }; - // Cap downloaded corpus; the write phase cycles the ingest pool if it - // needs more rows than were loaded. - let corpus_rows = (args.seed_rows + ingest_needed).min(2_000_000); let corpus = load_corpus(corpus_rows, &args.cache_dir).await?; let result = match args.phase { From c900c7bec3169c75f74d5912c24a1b48f9743c3e Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sat, 16 May 2026 04:10:56 -0700 Subject: [PATCH 09/37] fix(bench): query MemTable FTS via MemTableScanner, compare to reference dataset The read-phase consistency was unmeasurable: (1) the post-MemWAL-flush on-disk query saw nothing because flushed data lives in the MemWAL LSM structure that a plain Dataset::scan does not traverse, and (2) the manual FtsMemIndex row_position -> id mapping was mis-aligned. The read phase now queries the MemTable through the production MemTableScanner (returns the id column directly, no manual mapping) and compares against a freshly built reference Lance dataset holding the identical ingested rows + a normal FTS index. Both sides cover exactly the same population with identical BM25 stats. Token queries are drawn from mid-frequency terms so the top-10 is well-determined rather than a high-frequency near-tie. --- rust/lance/benches/mem_wal_fineweb_fts.rs | 200 ++++++++++++++-------- 1 file changed, 127 insertions(+), 73 deletions(-) diff --git a/rust/lance/benches/mem_wal_fineweb_fts.rs b/rust/lance/benches/mem_wal_fineweb_fts.rs index 3f03f9f3f64..4e0322059c4 100644 --- a/rust/lance/benches/mem_wal_fineweb_fts.rs +++ b/rust/lance/benches/mem_wal_fineweb_fts.rs @@ -45,8 +45,7 @@ use std::time::{Duration, Instant}; use arrow_array::{Array, ArrayRef, Int64Array, RecordBatch, RecordBatchIterator, StringArray}; use arrow_schema::{DataType, Field, Schema as ArrowSchema}; use futures::TryStreamExt; -use lance::dataset::mem_wal::index::{FtsQueryExpr, SearchOptions}; -use lance::dataset::mem_wal::{DatasetMemWalExt, MemWalConfig, ShardWriterConfig}; +use lance::dataset::mem_wal::{DatasetMemWalExt, MemTableScanner, MemWalConfig, ShardWriterConfig}; use lance::dataset::{Dataset, WriteParams}; use lance::index::DatasetIndexExt; use lance_core::Result; @@ -470,8 +469,15 @@ fn build_query_set(sample: &[&str], args: &Args) -> QuerySet { } let mut by_freq: Vec<(String, u64)> = freq.into_iter().collect(); by_freq.sort_by(|a, b| b.1.cmp(&a.1).then(a.0.cmp(&b.0))); + // Skip the most-frequent tokens: terms like "all"/"one"/"time" appear + // in a huge fraction of docs, so thousands of documents tie on BM25 + // and the top-10 is an unstable near-tie that does not meaningfully + // exercise FTS correctness. Mid-frequency terms have a well-determined + // top-10. The skip is capped so a small vocabulary still yields queries. + let skip = (by_freq.len() / 4).min(300); let tokens: Vec = by_freq .into_iter() + .skip(skip) .map(|(t, _)| t) .take(args.num_token_queries) .collect(); @@ -615,99 +621,97 @@ async fn run_read(args: &Args, uri: &str, corpus: &[String]) -> Result = ingest_pool[..n].iter().map(|s| s.as_str()).collect(); let queries = build_query_set(&sample, args); - let all_queries: Vec = queries + let num_token_queries = queries.tokens.len(); + let num_phrase_queries = queries.phrases.len(); + let all_queries: Vec<(bool, String)> = queries .tokens .iter() - .map(|t| FtsQueryExpr::match_query(t.clone())) - .chain( - queries - .phrases - .iter() - .map(|p| FtsQueryExpr::phrase(p.clone())), - ) + .map(|t| (false, t.clone())) + .chain(queries.phrases.iter().map(|p| (true, p.clone()))) .collect(); - println!( - " query set: {} tokens + {} phrases", - queries.tokens.len(), - queries.phrases.len() - ); + println!(" query set: {num_token_queries} tokens + {num_phrase_queries} phrases"); - // ---- MemTable read phase ---- + // ---- MemTable read phase: query through the production MemTableScanner ---- + // The scanner returns RecordBatches projected to `id`, so there is no + // need to map FtsMemIndex row positions back to ids by hand. let active = writer.active_memtable_ref().await?; - let fts = active - .index_store - .get_fts(FTS_INDEX_NAME) - .ok_or_else(|| lance_core::Error::invalid_input("FTS mem index missing"))?; - - // row_position -> id, recovered from the MemTable batches. - let mut row_to_id: HashMap = HashMap::new(); - for stored in active.batch_store.iter() { - let id_arr = stored - .data - .column_by_name("id") - .and_then(|c| c.as_any().downcast_ref::()) - .ok_or_else(|| lance_core::Error::invalid_input("id column missing"))?; - for r in 0..id_arr.len() { - row_to_id.insert(stored.row_offset + r as u64, id_arr.value(r)); - } - } + let mt_batch_store = active.batch_store.clone(); + let mt_index_store = active.index_store.clone(); + let mt_schema = active.schema.clone(); + drop(active); let mut latencies_us = Vec::with_capacity(all_queries.len()); let mut mt_top: Vec> = Vec::with_capacity(all_queries.len()); - for q in &all_queries { - let opts = SearchOptions::new().with_limit(args.top_k); + for (qi, (is_phrase, q)) in all_queries.iter().enumerate() { + let mut scanner = MemTableScanner::new( + mt_batch_store.clone(), + mt_index_store.clone(), + mt_schema.clone(), + ); + if *is_phrase { + scanner.full_text_phrase(TEXT_COL, q, 0); + } else { + scanner.full_text_search(TEXT_COL, q); + } + scanner.project(&["id"]); + scanner.limit(args.top_k, None); let t0 = Instant::now(); - let hits = fts.search_with_options(q, opts); + let stream = scanner.try_into_stream().await?; + let batches: Vec = stream.try_collect().await?; latencies_us.push(t0.elapsed().as_micros() as f64); - let ids: HashSet = hits - .iter() - .take(args.top_k) - .filter_map(|e| row_to_id.get(&e.row_position).copied()) - .collect(); + let ids = collect_ids(&batches)?; + if qi < 5 { + println!( + " [debug mt q{qi}] phrase={is_phrase} '{q}' -> {} ids {:?}", + ids.len(), + ids.iter().take(5).collect::>() + ); + } mt_top.push(ids); } - drop(active); - let lat_ms: Vec = latencies_us.iter().map(|us| us / 1000.0).collect(); let mt_avg_ms = lat_ms.iter().sum::() / lat_ms.len().max(1) as f64; - // ---- Force flush, on-disk replay ---- - let flush_start = Instant::now(); - writer.close().await?; - let flush_s = flush_start.elapsed().as_secs_f64(); - let flushed = Dataset::open(uri).await?; + // ---- Reference: a plain on-disk dataset over the identical ingested + // rows + a normal FTS index. The MemTable FTS results are validated + // against this. No MemWAL flush is involved: the flushed data lives in + // the MemWAL LSM structure which a plain `Dataset::scan` does not see, + // so a separate reference dataset is the apples-to-apples comparison. + drop(writer); + let ref_uri = format!("{uri}_ref"); + let ref_build_s = + build_reference_dataset(&ref_uri, schema.clone(), &sample, id_base, args.batch_rows) + .await?; + let ref_ds = Dataset::open(&ref_uri).await?; let mut consistencies = Vec::with_capacity(all_queries.len()); - for (q, mt_ids) in all_queries.iter().zip(mt_top.iter()) { - let query_str = match q { - FtsQueryExpr::Match { query, .. } => query.clone(), - FtsQueryExpr::Phrase { query, .. } => format!("\"{query}\""), - _ => unreachable!("only match/phrase queries are generated"), + for (qi, ((is_phrase, q), mt_ids)) in all_queries.iter().zip(mt_top.iter()).enumerate() { + let query_str = if *is_phrase { + format!("\"{q}\"") + } else { + q.clone() }; - let mut scanner = flushed.scan(); - scanner.full_text_search(FullTextSearchQuery::new(query_str))?; - // Restrict to the ingested rows so the on-disk comparison covers - // exactly the population the MemTable FTS index held. - scanner.filter(&format!("id >= {read_seed}"))?; + let mut scanner = ref_ds.scan(); + scanner.full_text_search(FullTextSearchQuery::new(query_str.clone()))?; scanner.limit(Some(args.top_k as i64), None)?; scanner.project(&["id"])?; let stream = scanner.try_into_stream().await?; let batches: Vec = stream.try_collect().await?; - let mut disk_ids: HashSet = HashSet::new(); - for b in &batches { - let id_arr = b - .column_by_name("id") - .and_then(|c| c.as_any().downcast_ref::()) - .ok_or_else(|| lance_core::Error::invalid_input("disk id column missing"))?; - for i in 0..id_arr.len() { - disk_ids.insert(id_arr.value(i)); - } - } + let disk_ids = collect_ids(&batches)?; let inter: usize = mt_ids.intersection(&disk_ids).count(); let denom = mt_ids.len().max(disk_ids.len()).max(1); + if qi < 5 { + println!( + " [debug ref q{qi}] '{query_str}' -> ref={} mt={} inter={inter}", + disk_ids.len(), + mt_ids.len() + ); + } consistencies.push(inter as f64 / denom as f64); } let cons_mean = consistencies.iter().sum::() / consistencies.len().max(1) as f64; @@ -717,15 +721,14 @@ async fn run_read(args: &Args, uri: &str, corpus: &[String]) -> Result Result Result> { + let mut ids = HashSet::new(); + for b in batches { + let id_arr = b + .column_by_name("id") + .and_then(|c| c.as_any().downcast_ref::()) + .ok_or_else(|| lance_core::Error::invalid_input("id column missing in result"))?; + for i in 0..id_arr.len() { + ids.insert(id_arr.value(i)); + } + } + Ok(ids) +} + +/// Build a plain Lance dataset (no MemWAL) over `texts` with ids starting +/// at `id_base`, plus a normal FTS index. Used as the read-phase +/// comparison reference. +async fn build_reference_dataset( + ref_uri: &str, + schema: Arc, + texts: &[&str], + id_base: i64, + batch_rows: usize, +) -> Result { + let start = Instant::now(); + let mut batches = Vec::with_capacity(texts.len().div_ceil(batch_rows)); + let mut lo = 0usize; + while lo < texts.len() { + let hi = (lo + batch_rows).min(texts.len()); + batches.push(Ok(make_batch( + schema.clone(), + id_base + lo as i64, + &texts[lo..hi], + ))); + lo = hi; + } + let reader = RecordBatchIterator::new(batches.into_iter(), schema.clone()); + let mut dataset = Dataset::write(reader, ref_uri, Some(WriteParams::default())).await?; + dataset + .create_index( + &[TEXT_COL], + IndexType::Inverted, + Some(FTS_INDEX_NAME.to_string()), + &InvertedIndexParams::default(), + true, + ) + .await?; + Ok(start.elapsed().as_secs_f64()) +} + // ---------------------------------------------------------------------- // CLI // ---------------------------------------------------------------------- From bd295cd6ab64027d21e120ac5f77b11198ca4b65 Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sat, 16 May 2026 04:35:49 -0700 Subject: [PATCH 10/37] fix(bench): force durable_write in the FTS read phase The MemTableScanner snapshots the visibility watermark at plan time, and that watermark only advances as the WAL becomes durable. Without durable_write the read phase queried a partially-visible MemTable: the async-mode read configs scored consistency ~0.03-0.23 while the durable sync-mode ones scored ~0.72-0.75 over identical data. The read phase now forces durable_write on so the scanner always sees the full ingested MemTable. --- rust/lance/benches/mem_wal_fineweb_fts.rs | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/rust/lance/benches/mem_wal_fineweb_fts.rs b/rust/lance/benches/mem_wal_fineweb_fts.rs index 4e0322059c4..4e35265bac7 100644 --- a/rust/lance/benches/mem_wal_fineweb_fts.rs +++ b/rust/lance/benches/mem_wal_fineweb_fts.rs @@ -593,12 +593,18 @@ async fn run_read(args: &Args, uri: &str, corpus: &[String]) -> Result Date: Sat, 16 May 2026 08:55:09 -0700 Subject: [PATCH 11/37] bench: add --target-rows-per-sec pacing + backlog metrics to FTS write phase Adds paced ingest to the write phase for a backpressure sweep, mirroring the mem_wal_shard_writer_backpressure bench used for the HNSW vector panel. The write phase now reports slow_puts_ge_1s/10s, the memtable + pending-WAL backlog left when the put loop ends, and honors a paced target so a sweep can find the max sustainable async FTS throughput (the rate at which the flush/index pipeline keeps up without accumulating backpressure). --- rust/lance/benches/mem_wal_fineweb_fts.rs | 36 +++++++++++++++++++++++ 1 file changed, 36 insertions(+) diff --git a/rust/lance/benches/mem_wal_fineweb_fts.rs b/rust/lance/benches/mem_wal_fineweb_fts.rs index 4e35265bac7..101442efa52 100644 --- a/rust/lance/benches/mem_wal_fineweb_fts.rs +++ b/rust/lance/benches/mem_wal_fineweb_fts.rs @@ -159,6 +159,9 @@ struct Args { max_memtable_size: usize, max_unflushed_memtable_bytes: usize, max_wal_flush_interval_ms: u64, + /// Paced ingest target for the write phase. `None` = unpaced (puts + /// issued as fast as possible). Used for the backpressure sweep. + target_rows_per_sec: Option, cache_dir: PathBuf, num_token_queries: usize, num_phrase_queries: usize, @@ -182,6 +185,7 @@ impl Default for Args { max_memtable_size: 16 * 1024 * 1024 * 1024, max_unflushed_memtable_bytes: 8 * 1024 * 1024 * 1024, max_wal_flush_interval_ms: 100, + target_rows_per_sec: None, cache_dir: std::env::temp_dir().join("mem_wal_fineweb_fts_cache"), num_token_queries: 100, num_phrase_queries: 50, @@ -544,14 +548,35 @@ async fn run_write(args: &Args, uri: &str, corpus: &[String]) -> Result 0.0 + { + let scheduled = ((call + 1) * args.batch_rows) as f64 / target; + let actual = puts_start.elapsed().as_secs_f64(); + if scheduled > actual { + tokio::time::sleep(Duration::from_secs_f64(scheduled - actual)).await; + } + } } let elapsed_puts_s = puts_start.elapsed().as_secs_f64(); + // Backlog still buffered when the put loop ends: if the writer kept + // up with the ingest rate this is small; a large backlog means the + // flush/index pipeline fell behind (accumulating backpressure). + let backlog = writer.memtable_stats().await.ok(); + let close_start = Instant::now(); writer.close().await?; let elapsed_close_s = close_start.elapsed().as_secs_f64(); let elapsed_total_s = puts_start.elapsed().as_secs_f64(); + let slow_puts_1s = latencies_ms.iter().filter(|ms| **ms >= 1_000.0).count(); + let slow_puts_10s = latencies_ms.iter().filter(|ms| **ms >= 10_000.0).count(); let rows = args.calls * args.batch_rows; Ok(json!({ "phase": "write", @@ -562,10 +587,17 @@ async fn run_write(args: &Args, uri: &str, corpus: &[String]) -> Result Result { args.max_unflushed_memtable_bytes = parse(&flag, &value)? } "--max-wal-flush-interval-ms" => args.max_wal_flush_interval_ms = parse(&flag, &value)?, + "--target-rows-per-sec" => { + let v: f64 = parse(&flag, &value)?; + args.target_rows_per_sec = (v > 0.0).then_some(v); + } "--cache-dir" => args.cache_dir = PathBuf::from(value), "--num-token-queries" => args.num_token_queries = parse(&flag, &value)?, "--num-phrase-queries" => args.num_phrase_queries = parse(&flag, &value)?, From efbf8e03367ceaeafa75dd974e9829bc9c2ad0bc Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sat, 16 May 2026 08:55:45 -0700 Subject: [PATCH 12/37] bench: add backpressure sweep driver for async FineWeb FTS writes --- bench/run_fineweb_fts_backpressure.sh | 93 +++++++++++++++++++++++++++ 1 file changed, 93 insertions(+) create mode 100755 bench/run_fineweb_fts_backpressure.sh diff --git a/bench/run_fineweb_fts_backpressure.sh b/bench/run_fineweb_fts_backpressure.sh new file mode 100755 index 00000000000..9dab40a2207 --- /dev/null +++ b/bench/run_fineweb_fts_backpressure.sh @@ -0,0 +1,93 @@ +#!/usr/bin/env bash +# Backpressure sweep for async MemWAL writes on real FineWeb text. +# +# Mirrors the HNSW vector-index backpressure sweep +# (analysis/lance/jack-mem-wal-hnsw/native-shard-writer-backpressure-*): +# pace the ingest at increasing target rows/s and find the highest rate +# the flush/index pipeline sustains without accumulating backpressure +# (no puts >= 1s, small leftover backlog, bounded drain). Also runs one +# unpaced cell per mode for the (non-sustainable) peak. +# +# Usage: ./bench/run_fineweb_fts_backpressure.sh [run_id] + +set -uo pipefail +cd "$(dirname "${BASH_SOURCE[0]}")/.." + +RUN_ID="${1:-bp-$(date -u +%Y%m%dT%H%M%SZ)}" +DATASET_PREFIX="${DATASET_PREFIX:-s3://jack-devland-build/bench/mem-fts-fineweb}" +SEED_ROWS="${SEED_ROWS:-10000}" # small seed: backpressure test, seed size is irrelevant +BATCH_ROWS="${BATCH_ROWS:-1000}" +CALLS="${CALLS:-120}" # 120k rows/cell — long enough for steady state +CACHE_DIR="${CACHE_DIR:-/mnt/data/fineweb}" +CONFIG_TIMEOUT="${CONFIG_TIMEOUT:-2400}" +export AWS_DEFAULT_REGION="${AWS_DEFAULT_REGION:-us-east-1}" + +LOCAL_DIR="bench/results/${RUN_ID}" +mkdir -p "$LOCAL_DIR" + +BIN="$(find target/release/deps -maxdepth 1 -type f -perm -111 -name 'mem_wal_fineweb_fts-*' ! -name '*.d' 2>/dev/null | sort | tail -1)" +if [ -z "$BIN" ]; then + cargo bench -p lance --bench mem_wal_fineweb_fts --no-run + BIN="$(find target/release/deps -maxdepth 1 -type f -perm -111 -name 'mem_wal_fineweb_fts-*' ! -name '*.d' 2>/dev/null | sort | tail -1)" +fi +echo "bench binary: $BIN" +echo "run id: $RUN_ID" + +run_cell() { + local name="$1"; shift + local out="$LOCAL_DIR/${name}.json" + local log="$LOCAL_DIR/${name}.log" + echo ">>> $name" + if [ -f "$out" ]; then echo " already done"; return; fi + timeout "$CONFIG_TIMEOUT" "$BIN" --bench --phase write "$@" --output "$out" > "$log" 2>&1 + local rc=$? + if [ "$rc" -eq 124 ]; then echo " !!! TIMED OUT" + elif [ "$rc" -ne 0 ]; then echo " !!! failed rc=$rc" + else echo " ok"; fi + [ -f "$out" ] && aws s3 cp "$out" "$DATASET_PREFIX/$RUN_ID/results/${name}.json" >/dev/null 2>&1 + aws s3 cp "$log" "$DATASET_PREFIX/$RUN_ID/results/${name}.log" >/dev/null 2>&1 +} + +# mode -> list of paced targets (rows/s); 0 = unpaced peak +sweep() { + local mode="$1"; shift + for tgt in "$@"; do + local extra=() label + if [ "$tgt" = "0" ]; then + label="${mode}_unpaced" + else + label="${mode}_t${tgt}" + extra=(--target-rows-per-sec "$tgt") + fi + run_cell "$label" \ + --mode "$mode" \ + --uri "$DATASET_PREFIX/$RUN_ID/bp_${label}" \ + --seed-rows "$SEED_ROWS" --batch-rows "$BATCH_ROWS" --calls "$CALLS" \ + --cache-dir "$CACHE_DIR" "${extra[@]}" + done +} + +# FTS-indexed async: expected sustainable rate is modest, sweep low. +sweep async_idx 500 1000 1500 2000 2500 3000 0 +# No-index async: much higher ceiling. +sweep async_noidx 2000 4000 6000 8000 0 + +echo "" +echo "=== backpressure summary ===" +python3 - "$LOCAL_DIR" <<'PY' +import glob, json, os, sys +d = sys.argv[1] +rows = [] +for p in sorted(glob.glob(os.path.join(d, "*.json"))): + try: r = json.load(open(p)) + except Exception: continue + rows.append((os.path.basename(p)[:-5], r)) +print(f"{'cell':26s} {'target':>8s} {'rows/s':>9s} {'puts_r/s':>10s} {'drain_s':>8s} {'slow>=1s':>9s} {'backlog_rows':>13s}") +for name, r in rows: + tgt = r.get('target_rows_per_sec') or 0 + print(f"{name:26s} {tgt:>8.0f} {r['throughput_rows_per_sec']:>9.0f} " + f"{r['throughput_puts_rows_per_sec']:>10.0f} {r['elapsed_close_seconds']:>8.1f} " + f"{r['slow_puts_ge_1s']:>9d} {str(r.get('backlog_memtable_rows')):>13s}") +PY +echo "" +echo "results: $LOCAL_DIR + $DATASET_PREFIX/$RUN_ID/results/" From e1a1c098d9b3e4674a1b416b9eeb3551bf444626 Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sat, 16 May 2026 08:59:56 -0700 Subject: [PATCH 13/37] fix(bench): bound max_memtable_rows default to avoid petabyte preallocation With no --max-memtable-rows the write-phase config defaulted to usize::MAX/2, so max_memtable_batches = max_rows/batch_rows overflowed into a ~9e15 Vec capacity and the writer aborted on a 664 PB allocation. The default is now derived from the byte budget and clamped to 16M rows. --- rust/lance/benches/mem_wal_fineweb_fts.rs | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/rust/lance/benches/mem_wal_fineweb_fts.rs b/rust/lance/benches/mem_wal_fineweb_fts.rs index 101442efa52..70a2e9d8926 100644 --- a/rust/lance/benches/mem_wal_fineweb_fts.rs +++ b/rust/lance/benches/mem_wal_fineweb_fts.rs @@ -412,7 +412,13 @@ fn shard_writer_config(args: &Args, shard_id: Uuid, disable_auto_flush: bool) -> let max_rows = if disable_auto_flush { args.read_rows.saturating_mul(4).max(4_000_000) } else { - args.max_memtable_rows.unwrap_or(usize::MAX / 2) + // When no explicit row cap is given, derive a generous but + // BOUNDED cap from the byte budget. `usize::MAX/2` here would make + // `max_memtable_batches` (computed below) astronomically large and + // the writer would try to preallocate a petabyte-scale Vec. + args.max_memtable_rows.unwrap_or_else(|| { + (args.max_memtable_size / 2048).clamp(args.batch_rows.max(1), 16_000_000) + }) }; let mut config = ShardWriterConfig::new(shard_id) .with_durable_write(args.mode.durable_write()) From 3a23eecb3d87e6b95e51f0377157511f47381ac9 Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sat, 16 May 2026 09:44:07 -0700 Subject: [PATCH 14/37] bench: add --index-type vector|fts to the ShardWriter backpressure bench Centralizes the vector and FTS write-backpressure tests on one bench and one driver: the paced-ingest / WAL-queue-sampling / skip-close methodology is identical, and only --index-type selects which column is indexed (vec via IVF/PQ, or text via the inverted index). This makes the FTS backpressure numbers directly comparable to the HNSW vector sweep. The fineweb-shaped `text` column is now generated as varied vocabulary words instead of a single repeated character so the FTS index does representative work; the byte size is unchanged so vector runs (where text is inert payload) are unaffected. run_shard_writer_backpressure.sh drives both index types via INDEX_TYPE. --- bench/run_fineweb_fts_backpressure.sh | 93 --------- bench/run_shard_writer_backpressure.sh | 115 +++++++++++ .../mem_wal_shard_writer_backpressure.rs | 188 +++++++++++++++++- 3 files changed, 294 insertions(+), 102 deletions(-) delete mode 100755 bench/run_fineweb_fts_backpressure.sh create mode 100755 bench/run_shard_writer_backpressure.sh diff --git a/bench/run_fineweb_fts_backpressure.sh b/bench/run_fineweb_fts_backpressure.sh deleted file mode 100755 index 9dab40a2207..00000000000 --- a/bench/run_fineweb_fts_backpressure.sh +++ /dev/null @@ -1,93 +0,0 @@ -#!/usr/bin/env bash -# Backpressure sweep for async MemWAL writes on real FineWeb text. -# -# Mirrors the HNSW vector-index backpressure sweep -# (analysis/lance/jack-mem-wal-hnsw/native-shard-writer-backpressure-*): -# pace the ingest at increasing target rows/s and find the highest rate -# the flush/index pipeline sustains without accumulating backpressure -# (no puts >= 1s, small leftover backlog, bounded drain). Also runs one -# unpaced cell per mode for the (non-sustainable) peak. -# -# Usage: ./bench/run_fineweb_fts_backpressure.sh [run_id] - -set -uo pipefail -cd "$(dirname "${BASH_SOURCE[0]}")/.." - -RUN_ID="${1:-bp-$(date -u +%Y%m%dT%H%M%SZ)}" -DATASET_PREFIX="${DATASET_PREFIX:-s3://jack-devland-build/bench/mem-fts-fineweb}" -SEED_ROWS="${SEED_ROWS:-10000}" # small seed: backpressure test, seed size is irrelevant -BATCH_ROWS="${BATCH_ROWS:-1000}" -CALLS="${CALLS:-120}" # 120k rows/cell — long enough for steady state -CACHE_DIR="${CACHE_DIR:-/mnt/data/fineweb}" -CONFIG_TIMEOUT="${CONFIG_TIMEOUT:-2400}" -export AWS_DEFAULT_REGION="${AWS_DEFAULT_REGION:-us-east-1}" - -LOCAL_DIR="bench/results/${RUN_ID}" -mkdir -p "$LOCAL_DIR" - -BIN="$(find target/release/deps -maxdepth 1 -type f -perm -111 -name 'mem_wal_fineweb_fts-*' ! -name '*.d' 2>/dev/null | sort | tail -1)" -if [ -z "$BIN" ]; then - cargo bench -p lance --bench mem_wal_fineweb_fts --no-run - BIN="$(find target/release/deps -maxdepth 1 -type f -perm -111 -name 'mem_wal_fineweb_fts-*' ! -name '*.d' 2>/dev/null | sort | tail -1)" -fi -echo "bench binary: $BIN" -echo "run id: $RUN_ID" - -run_cell() { - local name="$1"; shift - local out="$LOCAL_DIR/${name}.json" - local log="$LOCAL_DIR/${name}.log" - echo ">>> $name" - if [ -f "$out" ]; then echo " already done"; return; fi - timeout "$CONFIG_TIMEOUT" "$BIN" --bench --phase write "$@" --output "$out" > "$log" 2>&1 - local rc=$? - if [ "$rc" -eq 124 ]; then echo " !!! TIMED OUT" - elif [ "$rc" -ne 0 ]; then echo " !!! failed rc=$rc" - else echo " ok"; fi - [ -f "$out" ] && aws s3 cp "$out" "$DATASET_PREFIX/$RUN_ID/results/${name}.json" >/dev/null 2>&1 - aws s3 cp "$log" "$DATASET_PREFIX/$RUN_ID/results/${name}.log" >/dev/null 2>&1 -} - -# mode -> list of paced targets (rows/s); 0 = unpaced peak -sweep() { - local mode="$1"; shift - for tgt in "$@"; do - local extra=() label - if [ "$tgt" = "0" ]; then - label="${mode}_unpaced" - else - label="${mode}_t${tgt}" - extra=(--target-rows-per-sec "$tgt") - fi - run_cell "$label" \ - --mode "$mode" \ - --uri "$DATASET_PREFIX/$RUN_ID/bp_${label}" \ - --seed-rows "$SEED_ROWS" --batch-rows "$BATCH_ROWS" --calls "$CALLS" \ - --cache-dir "$CACHE_DIR" "${extra[@]}" - done -} - -# FTS-indexed async: expected sustainable rate is modest, sweep low. -sweep async_idx 500 1000 1500 2000 2500 3000 0 -# No-index async: much higher ceiling. -sweep async_noidx 2000 4000 6000 8000 0 - -echo "" -echo "=== backpressure summary ===" -python3 - "$LOCAL_DIR" <<'PY' -import glob, json, os, sys -d = sys.argv[1] -rows = [] -for p in sorted(glob.glob(os.path.join(d, "*.json"))): - try: r = json.load(open(p)) - except Exception: continue - rows.append((os.path.basename(p)[:-5], r)) -print(f"{'cell':26s} {'target':>8s} {'rows/s':>9s} {'puts_r/s':>10s} {'drain_s':>8s} {'slow>=1s':>9s} {'backlog_rows':>13s}") -for name, r in rows: - tgt = r.get('target_rows_per_sec') or 0 - print(f"{name:26s} {tgt:>8.0f} {r['throughput_rows_per_sec']:>9.0f} " - f"{r['throughput_puts_rows_per_sec']:>10.0f} {r['elapsed_close_seconds']:>8.1f} " - f"{r['slow_puts_ge_1s']:>9d} {str(r.get('backlog_memtable_rows')):>13s}") -PY -echo "" -echo "results: $LOCAL_DIR + $DATASET_PREFIX/$RUN_ID/results/" diff --git a/bench/run_shard_writer_backpressure.sh b/bench/run_shard_writer_backpressure.sh new file mode 100755 index 00000000000..15ad60f2382 --- /dev/null +++ b/bench/run_shard_writer_backpressure.sh @@ -0,0 +1,115 @@ +#!/usr/bin/env bash +# Centralized MemWAL ShardWriter backpressure sweep — drives the +# `mem_wal_shard_writer_backpressure` bench for BOTH the vector and the +# FTS index. The methodology (paced async ingest, WAL-queue sampling, +# skip-close) is identical; the only difference is `--index-type`, so the +# vector and FTS numbers are directly comparable. +# +# Usage: +# INDEX_TYPE=fts ./bench/run_shard_writer_backpressure.sh [run_id] +# INDEX_TYPE=vector ./bench/run_shard_writer_backpressure.sh [run_id] +# +# Finds the max sustainable async-indexed throughput: the highest paced +# target where puts never block (slow>=1s == 0) and the WAL flush queue +# does not accumulate (tail queue delta ~0). + +set -uo pipefail +cd "$(dirname "${BASH_SOURCE[0]}")/.." + +INDEX_TYPE="${INDEX_TYPE:-fts}" +RUN_ID="${1:-bp-${INDEX_TYPE}-$(date -u +%Y%m%dT%H%M%SZ)}" +DATASET_PREFIX="${DATASET_PREFIX:-s3://jack-devland-build/bench/mem-fts-fineweb}" + +# Shared knobs — identical to the HNSW vector backpressure sweep so the +# two index types are measured the same way. +ROWS="${ROWS:-500000}" +SEED_ROWS="${SEED_ROWS:-100000}" +BATCH_ROWS="${BATCH_ROWS:-1000}" +VECTOR_DIM="${VECTOR_DIM:-1024}" +THREADS="${THREADS:-64}" +TOKIO_THREADS="${TOKIO_THREADS:-64}" +MAX_MEMTABLE_SIZE="${MAX_MEMTABLE_SIZE:-17179869184}" # 16 GiB +MAX_UNFLUSHED_BYTES="${MAX_UNFLUSHED_BYTES:-34359738368}" # 32 GiB +MAX_WAL_BUFFER_SIZE="${MAX_WAL_BUFFER_SIZE:-52428800}" # 50 MiB +MAX_WAL_FLUSH_INTERVAL_MS="${MAX_WAL_FLUSH_INTERVAL_MS:-0}" +SAMPLE_INTERVAL_MS="${SAMPLE_INTERVAL_MS:-1000}" +CONFIG_TIMEOUT="${CONFIG_TIMEOUT:-2400}" +export AWS_DEFAULT_REGION="${AWS_DEFAULT_REGION:-us-east-1}" + +# Paced async-indexed target sweep (rows/s). FTS indexing is heavier than +# IVF/PQ so its sustainable ceiling is lower; the vector sweep can push +# the same script higher via TARGETS=... +case "$INDEX_TYPE" in + fts) TARGETS_DEFAULT="500 1000 1500 2000 2500 3000 0" ;; + vector) TARGETS_DEFAULT="2000 4000 6000 8000 10000 0" ;; + *) echo "INDEX_TYPE must be fts|vector" >&2; exit 1 ;; +esac +TARGETS="${TARGETS:-$TARGETS_DEFAULT}" + +CALLS=$(( ROWS / BATCH_ROWS )) +LOCAL_DIR="bench/results/${RUN_ID}" +mkdir -p "$LOCAL_DIR" + +BIN="$(find target/release/deps -maxdepth 1 -type f -perm -111 \ + -name 'mem_wal_shard_writer_backpressure-*' ! -name '*.d' 2>/dev/null | sort | tail -1)" +if [ -z "$BIN" ]; then + cargo bench -p lance --bench mem_wal_shard_writer_backpressure --no-run + BIN="$(find target/release/deps -maxdepth 1 -type f -perm -111 \ + -name 'mem_wal_shard_writer_backpressure-*' ! -name '*.d' 2>/dev/null | sort | tail -1)" +fi +echo "index_type=$INDEX_TYPE bin=$BIN run_id=$RUN_ID rows=$ROWS" + +for tgt in $TARGETS; do + if [ "$tgt" = "0" ]; then label="async_idx_unpaced"; paced=() + else label="async_idx_t${tgt}"; paced=(--target-rows-per-sec "$tgt"); fi + out="$LOCAL_DIR/${label}.json"; log="$LOCAL_DIR/${label}.log" + echo ">>> $label" + if [ -f "$out" ]; then echo " already done"; continue; fi + timeout "$CONFIG_TIMEOUT" "$BIN" --bench \ + --mode async_idx --index-type "$INDEX_TYPE" --schema-shape fineweb \ + --uri "$DATASET_PREFIX/$RUN_ID/bp_${label}" \ + --seed-rows "$SEED_ROWS" --batch-rows "$BATCH_ROWS" --calls "$CALLS" \ + --vector-dim "$VECTOR_DIM" \ + --max-memtable-size "$MAX_MEMTABLE_SIZE" \ + --max-unflushed-memtable-bytes "$MAX_UNFLUSHED_BYTES" \ + --max-wal-buffer-size "$MAX_WAL_BUFFER_SIZE" \ + --max-wal-flush-interval-ms "$MAX_WAL_FLUSH_INTERVAL_MS" \ + --sample-interval-ms "$SAMPLE_INTERVAL_MS" \ + --threads "$THREADS" --tokio-threads "$TOKIO_THREADS" \ + --skip-close \ + "${paced[@]}" --output "$out" > "$log" 2>&1 + rc=$? + if [ "$rc" -eq 124 ]; then echo " !!! TIMED OUT" + elif [ "$rc" -ne 0 ]; then echo " !!! failed rc=$rc" + else echo " ok"; fi + [ -f "$out" ] && aws s3 cp "$out" "$DATASET_PREFIX/$RUN_ID/results/${label}.json" >/dev/null 2>&1 + aws s3 cp "$log" "$DATASET_PREFIX/$RUN_ID/results/${label}.log" >/dev/null 2>&1 +done + +echo "" +echo "=== backpressure summary (index_type=$INDEX_TYPE) ===" +python3 - "$LOCAL_DIR" "$ROWS" <<'PY' +import glob, json, os, sys +d, rows = sys.argv[1], int(sys.argv[2]) +print(f"{'cell':22s} {'target':>8s} {'rows/s':>9s} {'MB/s':>8s} {'p99_ms':>10s} " + f"{'slow>=1s':>9s} {'wal_q_end':>10s} {'wal_q_max':>10s} {'q_tail_delta':>13s}") +for p in sorted(glob.glob(os.path.join(d, "*.json"))): + try: r = json.load(open(p)) + except Exception: continue + name = os.path.basename(p)[:-5] + mem = r.get("final_memtable_stats") or {} + samples = r.get("samples") or [] + q_end = int(mem.get("wal_queue_pending_rows") or 0) + q_max = max([s.get("wal_queue_pending_rows") or 0 for s in samples], default=0) + puts = [s for s in samples if s.get("phase") == "puts" + and isinstance(s.get("wal_queue_pending_rows"), int)] + tail = puts[-5:] + q_tail = (tail[-1]["wal_queue_pending_rows"] - tail[0]["wal_queue_pending_rows"] + if len(tail) >= 2 else 0) + tgt = r.get("target_rows_per_sec") or 0 + print(f"{name:22s} {tgt:>8.0f} {r.get('throughput_rows_per_sec',0):>9.0f} " + f"{r.get('throughput_mb_per_sec',0):>8.2f} {r.get('p99_ms',0):>10.2f} " + f"{r.get('slow_puts_1s',0):>9d} {q_end:>10d} {q_max:>10d} {q_tail:>13d}") +PY +echo "" +echo "results: $LOCAL_DIR + $DATASET_PREFIX/$RUN_ID/results/" diff --git a/rust/lance/benches/mem_wal_shard_writer_backpressure.rs b/rust/lance/benches/mem_wal_shard_writer_backpressure.rs index 1f28cf33619..1e9be110040 100644 --- a/rust/lance/benches/mem_wal_shard_writer_backpressure.rs +++ b/rust/lance/benches/mem_wal_shard_writer_backpressure.rs @@ -47,6 +47,7 @@ use lance::index::vector::VectorIndexParams; use lance_arrow::FixedSizeListArrayExt; use lance_core::Result; use lance_index::IndexType; +use lance_index::scalar::inverted::tokenizer::InvertedIndexParams; use lance_index::vector::ivf::IvfBuildParams; use lance_index::vector::pq::builder::PQBuildParams; use lance_linalg::distance::DistanceType; @@ -55,6 +56,8 @@ use uuid::Uuid; const VECTOR_COL: &str = "vec"; const VECTOR_INDEX_NAME: &str = "vec_idx"; +const TEXT_COL: &str = "text"; +const FTS_INDEX_NAME: &str = "text_fts"; const TEXT_BYTES: usize = 1_500; const ROW_BYTES_FINEWEB_SHAPE: usize = 5_760; const FINEWEB_FIXED_BYTES: usize = ROW_BYTES_FINEWEB_SHAPE - TEXT_BYTES - 1024 * size_of::(); @@ -103,6 +106,40 @@ impl Mode { } } +/// Which index the MemTable maintains in the indexed (`*_idx`) modes. +/// The backpressure methodology — paced ingest, WAL-queue sampling, +/// skip-close — is identical for both; only the indexed column and the +/// index built differ, so vector and FTS results are directly comparable. +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +enum IndexKind { + Vector, + Fts, +} + +impl IndexKind { + fn parse(value: &str) -> std::result::Result { + match value { + "vector" => Ok(Self::Vector), + "fts" => Ok(Self::Fts), + _ => Err(format!("unknown index-type '{value}', expected vector|fts")), + } + } + + fn as_str(self) -> &'static str { + match self { + Self::Vector => "vector", + Self::Fts => "fts", + } + } + + fn index_name(self) -> &'static str { + match self { + Self::Vector => VECTOR_INDEX_NAME, + Self::Fts => FTS_INDEX_NAME, + } + } +} + #[derive(Debug, Clone, Copy, PartialEq, Eq)] enum SchemaShape { FineWeb, @@ -142,6 +179,7 @@ impl SchemaShape { struct Args { uri: Option, mode: Mode, + index_kind: IndexKind, schema_shape: SchemaShape, seed_rows: usize, batch_rows: usize, @@ -172,6 +210,7 @@ impl Default for Args { Self { uri: None, mode: Mode::AsyncIndexed, + index_kind: IndexKind::Vector, schema_shape: SchemaShape::FineWeb, seed_rows: 100_000, batch_rows: 1_000, @@ -237,9 +276,10 @@ async fn run(args: Args) -> Result<()> { }; println!( - "bench=mem_wal_shard_writer_backpressure uri={} mode={} schema_shape={} seed_rows={} batch_rows={} calls={} vector_dim={} text_bytes={} row_bytes={} target_rows_per_sec={:?} max_memtable_size={} max_memtable_rows={} max_memtable_batches={} max_unflushed_memtable_bytes={} max_wal_buffer_size={} max_wal_flush_interval_ms={} rayon_threads={} tokio_threads={} skip_close={}", + "bench=mem_wal_shard_writer_backpressure uri={} mode={} index_type={} schema_shape={} seed_rows={} batch_rows={} calls={} vector_dim={} text_bytes={} row_bytes={} target_rows_per_sec={:?} max_memtable_size={} max_memtable_rows={} max_memtable_batches={} max_unflushed_memtable_bytes={} max_wal_buffer_size={} max_wal_flush_interval_ms={} rayon_threads={} tokio_threads={} skip_close={}", uri, args.mode.as_str(), + args.index_kind.as_str(), args.schema_shape.as_str(), args.seed_rows, args.batch_rows, @@ -260,7 +300,6 @@ async fn run(args: Args) -> Result<()> { ); let schema = schema_for_shape(args.schema_shape, args.vector_dim); - let text_value = "x".repeat(args.text_bytes); let setup_start = Instant::now(); let seed_batch = make_batch( @@ -270,21 +309,24 @@ async fn run(args: Args) -> Result<()> { 0, args.seed_rows, args.vector_dim, - &text_value, + args.text_bytes, )?; let batches = RecordBatchIterator::new([Ok(seed_batch)], schema.clone()); let mut dataset = Dataset::write(batches, &uri, Some(WriteParams::default())).await?; let index_start = Instant::now(); if args.mode.indexed() { - create_base_vector_index(&mut dataset, &args).await?; + match args.index_kind { + IndexKind::Vector => create_base_vector_index(&mut dataset, &args).await?, + IndexKind::Fts => create_base_fts_index(&mut dataset).await?, + } } let index_setup_s = index_start.elapsed().as_secs_f64(); dataset .initialize_mem_wal() .maintained_indexes(if args.mode.indexed() { - vec![VECTOR_INDEX_NAME.to_string()] + vec![args.index_kind.index_name().to_string()] } else { vec![] }) @@ -333,7 +375,7 @@ async fn run(args: Args) -> Result<()> { args.seed_rows + call * args.batch_rows, args.batch_rows, args.vector_dim, - &text_value, + args.text_bytes, )?; batch_build_s += batch_start.elapsed().as_secs_f64(); @@ -475,6 +517,7 @@ async fn run(args: Args) -> Result<()> { let output = json!({ "uri": uri, "mode": args.mode.as_str(), + "index_type": args.index_kind.as_str(), "schema_shape": args.schema_shape.as_str(), "seed_rows": args.seed_rows, "batch_rows": args.batch_rows, @@ -562,6 +605,19 @@ async fn create_base_vector_index(dataset: &mut Dataset, args: &Args) -> Result< .map(|_| ()) } +async fn create_base_fts_index(dataset: &mut Dataset) -> Result<()> { + dataset + .create_index( + &[TEXT_COL], + IndexType::Inverted, + Some(FTS_INDEX_NAME.to_string()), + &InvertedIndexParams::default(), + true, + ) + .await + .map(|_| ()) +} + fn push_sample( samples: &mut Vec, phase: &'static str, @@ -660,6 +716,104 @@ fn vector_only_schema(vector_dim: usize) -> Arc { Arc::new(ArrowSchema::new(vec![id_field(), vector_field(vector_dim)])) } +/// Deterministic pseudo-random text of roughly `target_bytes` bytes for +/// row `row`. Drawn word-by-word from a small fixed vocabulary so the +/// `text` column carries a realistic token distribution — essential for +/// the FTS index to do representative work, and harmless for the vector +/// runs where `text` is inert payload of the same size. +fn gen_text(row: usize, target_bytes: usize) -> String { + const VOCAB: &[&str] = &[ + "data", + "vector", + "search", + "index", + "query", + "memory", + "table", + "write", + "read", + "shard", + "stream", + "batch", + "flush", + "token", + "score", + "model", + "system", + "record", + "format", + "column", + "engine", + "result", + "filter", + "metric", + "latency", + "throughput", + "cache", + "buffer", + "segment", + "lance", + "arrow", + "schema", + "field", + "value", + "object", + "store", + "cloud", + "remote", + "local", + "build", + "merge", + "scan", + "rank", + "match", + "phrase", + "fuzzy", + "boolean", + "recall", + "corpus", + "document", + "passage", + "sentence", + "language", + "machine", + "learning", + "training", + "dataset", + "feature", + "embedding", + "cluster", + "graph", + "node", + "edge", + "path", + "weight", + "layer", + "tensor", + "kernel", + ]; + // SplitMix64-ish deterministic generator seeded by the row index. + let mut state = (row as u64) + .wrapping_mul(0x9E37_79B9_7F4A_7C15) + .wrapping_add(1); + let mut next = || { + state = state.wrapping_add(0x9E37_79B9_7F4A_7C15); + let mut z = state; + z = (z ^ (z >> 30)).wrapping_mul(0xBF58_476D_1CE4_E5B9); + z = (z ^ (z >> 27)).wrapping_mul(0x94D0_49BB_1331_11EB); + z ^ (z >> 31) + }; + let mut out = String::with_capacity(target_bytes + 16); + while out.len() < target_bytes { + if !out.is_empty() { + out.push(' '); + } + out.push_str(VOCAB[(next() as usize) % VOCAB.len()]); + } + out.truncate(target_bytes); + out +} + fn make_batch( shape: SchemaShape, schema: &ArrowSchema, @@ -667,7 +821,7 @@ fn make_batch( start_row: usize, num_rows: usize, vector_dim: usize, - text_value: &str, + text_bytes: usize, ) -> Result { let ids = StringArray::from_iter_values( (0..num_rows).map(|i| format!("{id_prefix}-{:012}", start_row + i)), @@ -697,7 +851,8 @@ fn make_batch( .map_err(Into::into); } - let text = StringArray::from_iter_values((0..num_rows).map(|_| text_value)); + let text = + StringArray::from_iter_values((0..num_rows).map(|i| gen_text(start_row + i, text_bytes))); let dump = StringArray::from_iter_values((0..num_rows).map(|i| match (start_row + i) % 5 { 0 => "CC-MAIN-2023-50", 1 => "CC-MAIN-2024-10", @@ -787,6 +942,10 @@ fn parse_args() -> Result { "--mode" => { args.mode = Mode::parse(&value).map_err(lance_core::Error::invalid_input)?; } + "--index-type" => { + args.index_kind = + IndexKind::parse(&value).map_err(lance_core::Error::invalid_input)?; + } "--schema-shape" => { args.schema_shape = SchemaShape::parse(&value).map_err(lance_core::Error::invalid_input)?; @@ -861,12 +1020,23 @@ fn parse_args() -> Result { max_memtable_rows, args.batch_rows ))); } - if args.mode.indexed() && args.vector_dim % args.num_sub_vectors != 0 { + if args.mode.indexed() + && args.index_kind == IndexKind::Vector + && args.vector_dim % args.num_sub_vectors != 0 + { return Err(lance_core::Error::invalid_input(format!( "vector_dim must be divisible by num_sub_vectors for IVF_PQ: vector_dim={}, num_sub_vectors={}", args.vector_dim, args.num_sub_vectors ))); } + if args.mode.indexed() + && args.index_kind == IndexKind::Fts + && args.schema_shape != SchemaShape::FineWeb + { + return Err(lance_core::Error::invalid_input( + "index-type=fts requires schema-shape=fineweb (it has the text column)", + )); + } Ok(args) } From 95d2896797eca37425de2a8867863cea271283fe Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sat, 16 May 2026 12:00:50 -0700 Subject: [PATCH 15/37] bench: add Lance FtsMemIndex vs Apache Lucene FTS comparison MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Mirrors the HNSW-vs-hnswlib comparison for full-text search: - mem_wal_fts_bench.rs — Lance side. `gen` slices a FineWeb corpus and writes the shared inputs (corpus.txt, corpus_tok.txt, queries.txt, exact-BM25 truth.txt); `bench` builds a raw FtsMemIndex and measures build throughput, term/phrase query latency + QPS, and recall@k. - lucene_fts_bench/LuceneFtsBench.java — Lucene reference, in-memory ByteBuffersDirectory + BM25Similarity(1.2,0.75), same inputs, same metrics, same JSON output shape. - run_fts_compare.sh — builds Lucene from the local checkout (JDK 25), builds both benches, runs Run A (pre-tokenized, isolates index + scorer) and Run B (native analyzers) across a 100k/500k/1M sweep, reports both impls side by side plus their mutual top-k overlap. --- bench/lucene_fts_bench/LuceneFtsBench.java | Bin 0 -> 10756 bytes bench/run_fts_compare.sh | 131 ++++ rust/lance/Cargo.toml | 4 + rust/lance/benches/mem_wal_fts_bench.rs | 703 +++++++++++++++++++++ 4 files changed, 838 insertions(+) create mode 100644 bench/lucene_fts_bench/LuceneFtsBench.java create mode 100755 bench/run_fts_compare.sh create mode 100644 rust/lance/benches/mem_wal_fts_bench.rs diff --git a/bench/lucene_fts_bench/LuceneFtsBench.java b/bench/lucene_fts_bench/LuceneFtsBench.java new file mode 100644 index 0000000000000000000000000000000000000000..48bc91f0bd708f15a12ebdfd72b16962e8de903e GIT binary patch literal 10756 zcmb_i{chVv62HIm6jK2~s92)ywCN#E?4F$uG)3a1k=q^+7YZe=B*qj;bC-79T!4Fs zd%}B?`_1ewKP1_4?=C79yVOa}{k5tS;&^rAAFiZYMEW=xYS z`mj{X@nw{bXGV`FU}r&dm3?3~f@xkBl@84(L)#$FFJqIRxew=+Dih_0O$ll6NPj?+ zDoL?%lrfbg8)A#Ryz-(+_&gLGT^V&$%^(B)EGgC0U<%eQRivv@X-k9&m_?aa^948u z#h8pLm)pgngv^LpxS}*N;QNZU=;ybifykjUpa7y#n*QyKiA3`#$+Qt+s87+<43Azv zcu2xE%QhypOdy(wej2?wCeen8=5rPE>JQ1XNcU4w?T1={I$DZk3q_F?v$YG6qSwJM zI-09JCPMTvx`=2vE$E3Qg%71Z*bALxpzb44z;Fnm#YsueUcRH@u&gqQ{xQMR8SU=i zKS)G0`~7!!?trtrG(tK|^3Y0D#jJW&KE7`Fw=#ho*M`~fuUW}Mn^tAmbcV6;ZDba@ zOR+LZ8XlHqboDCHX2aNzkzS0{rjctFY@R&LvuRbrmrVHllbTjArI9Kx;Orar+%S0) z+pTFYDg%prT+2Ey=V2sKBeZ%9**C9rqQgUe_@gD+8|I$mXDS;RsB&4edBb%0p9+6l zKyMm*t3R`oHZbt0jKh(8_%n;)@G6UpDU%6%RrnO4X!~{IB8`!3bfY6U;%_CC-;(w%r;XTI zU~^w*mgRLzq9=I)k~gWqok%CkBn8+TMn{I3k^gX`^w^riO{jR&DP&P?uZU4aHA#~x zP18v0Rb2Ue=ME8qfx!d>o+O)7RL%qY`2Kf_2LAIbjpp@BtY&@sjHSd03I$1KD4~7Y zd5r&_P!y&ro0|msKKVUV=T(&IfXVjz0A>VKVA}fxn7#Y^VG`LrDolc0%W~aWnj??cr%YW^ zjZ9Atg1%+KAe{)h7)qs^-u)afg$6GJVE|{dJCYY9ClZ!4(d7a-NkJgV#XgmxKSh9b z-+-qoP5T7?!YGbqmgA};8jLeR5zY4f`y?=u{DB+7KFzE4Ky#X>jY}rV14RDCj2&;`| zLDe`EtW6BX3!D&%=OeLH#w)wl2nLdDS}KMr;gJ>M^}w5fO>21=@Y=*8mBpUcwJLJr zmKbAmT6y$#dxjW4ppG&`B-)>!UC4e36a9Qym@8(^TN%>I*O5W}qAAPGf<2nci!-GP zn~|PNhKxf~lqT~GIe%>(n6FCW%r+F)Bkz}N*>zkLJLk7rJ-xq-$5}qs)pWttfXc?> zo)?Q11M|!W^KRd>={jcBjJKjba#oZJU}fSQZNr!svx`7h8DwY=#Sl1vv?k&-p7okp z7?M|*3oVmS$`enq;n+!8!(KF$y*irw`McpC7i>=cKUu}yMaSoS-5uK zO@p34iMSii>n)&r?&}!qqq6FNe)wjJlsO#z_~w^a&o+gbj+i9aIAn1KeUT?IEm2gp z;G>*hTpKFf5t0mG#hwq`!QYDk^?bDh&+l!UWwFhbsI8Y(&#^W|(#hd$btOLWvZ&V& zdCDOR-s+n{>I~CgW=72s{7`*sg%{qn+4+oD@9xz0%d32K^|iHb9#d!$V0U%p*%Lsd z$U};?eR@}+>9AC6AMP>ehbRe=EKE}Mw#=tWYcv#C_2Q9EW=LjGc{A4FJR*dBx)7}QIR{>kJUWoFO-xo)!{xKzt-l`jIWo1KZgQu=8TvZ^4koB4l;b%`WlvS!eD0GubG@F(n z%(S`#!EX3qHlUsGuMcP5rf{Gk{#YoIN0C>ZLQ*XLDa1B(Xgmj!~mgmrZ zk8@N9=r=Opj&PP>O}0ql8{zlS_S+42)FT&nluLK3Gk zOp&?WP&3F}0p5d}(6y7>a4vPgo^4`E#B1^m8~IU^71)>Og}O+g&E6>|$luoL=1i;b z_16;&AbbO4vZ^jR9ER{2K&p7ZA($>r`*Gvvga* ze`DIEhZJ4GT3Xdgw0#V+2+b1GT~T-px{S)@uqr6{>E%hkalSf2mx|3FA(|gx-SW^5 z%`#IYPv=m$Q%b+(`_hIA&Ife1nFWoKDTNJ7%GSC`FnNJ)JBJ7{SsOUvkGO02#mm@z z^rm3Z!#Rrt1^K6??z~`+x<=aqeA;{k^=jF)8Zcc3R$4GZ*6d*4wOH?+!!@?ZaD-^W zrW2jZ-2{^WNnVa*MSGG z7s!lupd>EZhJX-IDySXkn4kNc^ODtvfIDF2}B}0wFZ4TEVGhm72txDXzI1(*)n_A1uo6EcKN; 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if set, the Lucene build is skipped +# JAVA_HOME JDK 25 home; if unset the script searches common locations +# CACHE_DIR FineWeb shard cache (default /mnt/data/fineweb) + +set -uo pipefail +cd "$(dirname "${BASH_SOURCE[0]}")/.." +REPO_ROOT="$(pwd -P)" + +RUN_ID="${1:-fts-compare-$(date -u +%Y%m%dT%H%M%SZ)}" +SIZES="${SIZES:-100000 500000 1000000}" +K="${K:-10}" +THREADS="${THREADS:-$(getconf _NPROCESSORS_ONLN 2>/dev/null || echo 8)}" +LUCENE_DIR="${LUCENE_DIR:-$HOME/oss/lucene}" +CACHE_DIR="${CACHE_DIR:-/mnt/data/fineweb}" +WORK="${WORK:-/tmp/fts_compare/$RUN_ID}" +RESULT_DIR="$REPO_ROOT/bench/results/$RUN_ID" +mkdir -p "$WORK" "$RESULT_DIR" + +# ---- locate JDK 25 ---- +if [ -z "${JAVA_HOME:-}" ]; then + for cand in /usr/lib/jvm/java-25-* /usr/lib/jvm/jdk-25* /opt/jdk-25* \ + "$HOME/.sdkman/candidates/java/25"*; do + [ -x "$cand/bin/java" ] && JAVA_HOME="$cand" && break + done +fi +if [ -z "${JAVA_HOME:-}" ] || [ ! -x "$JAVA_HOME/bin/java" ]; then + echo "ERROR: JDK 25 not found; set JAVA_HOME" >&2; exit 1 +fi +JAVA="$JAVA_HOME/bin/java" +JAVAC="$JAVA_HOME/bin/javac" +echo "JDK: $($JAVA -version 2>&1 | head -1)" + +# ---- build Lucene classpath ---- +if [ -z "${LUCENE_CP:-}" ]; then + echo "=== Building Lucene jars ($LUCENE_DIR) ===" + ( cd "$LUCENE_DIR" && JAVA_HOME="$JAVA_HOME" ./gradlew -q \ + :lucene:core:jar :lucene:analysis:common:jar ) || { + echo "ERROR: Lucene build failed" >&2; exit 1; } + CORE_JAR="$(find "$LUCENE_DIR/lucene/core/build/libs" -name 'lucene-core-*.jar' | head -1)" + ANALYSIS_JAR="$(find "$LUCENE_DIR/lucene/analysis/common/build/libs" -name 'lucene-analysis-common-*.jar' | head -1)" + LUCENE_CP="$CORE_JAR:$ANALYSIS_JAR" +fi +echo "Lucene classpath: $LUCENE_CP" + +# ---- build the Lance bench ---- +echo "=== Building Lance FTS bench ===" +cargo bench -p lance --bench mem_wal_fts_bench --no-run +LANCE_BIN="$(find "$REPO_ROOT/target/release/deps" -maxdepth 1 -type f -perm -111 \ + -name 'mem_wal_fts_bench-*' ! -name '*.d' | sort | tail -1)" +echo "Lance bench: $LANCE_BIN" + +# ---- compile the Lucene bench ---- +echo "=== Compiling Lucene FTS bench ===" +"$JAVAC" -cp "$LUCENE_CP" -d "$WORK" \ + "$REPO_ROOT/bench/lucene_fts_bench/LuceneFtsBench.java" \ + || { echo "ERROR: javac failed" >&2; exit 1; } + +mutual_overlap() { # $1=topk file A $2=topk file B $3=k + python3 - "$1" "$2" "$3" <<'PY' +import sys +a, b, k = sys.argv[1], sys.argv[2], int(sys.argv[3]) +la = [set(l.split()) for l in open(a)] +lb = [set(l.split()) for l in open(b)] +n = min(len(la), len(lb)) +if n == 0: + print("nan"); sys.exit() +tot = sum(len(la[i] & lb[i]) / max(len(la[i] | lb[i]), 1) for i in range(n)) +print(f"{tot / n:.4f}") +PY +} + +echo "" +for SIZE in $SIZES; do + DIR="$WORK/n$SIZE" + mkdir -p "$DIR" + echo "############ corpus size = $SIZE ############" + echo "--- generating shared corpus + queries ---" + "$LANCE_BIN" --bench gen --docs "$SIZE" --out-dir "$DIR" \ + --cache-dir "$CACHE_DIR" --k "$K" > "$RESULT_DIR/gen_n$SIZE.log" 2>&1 || { + echo " !!! gen failed (see gen_n$SIZE.log)"; continue; } + + for RUN in a b; do + echo "--- run $RUN: lance ---" + "$LANCE_BIN" --bench bench --in-dir "$DIR" --run "$RUN" --k "$K" \ + --threads "$THREADS" | tee "$RESULT_DIR/lance_n${SIZE}_run${RUN}.txt" \ + | grep '^{' > "$RESULT_DIR/lance_n${SIZE}_run${RUN}.json" + echo "--- run $RUN: lucene ---" + "$JAVA" -cp "$LUCENE_CP:$WORK" LuceneFtsBench --in-dir "$DIR" --run "$RUN" \ + --k "$K" --threads "$THREADS" | tee "$RESULT_DIR/lucene_n${SIZE}_run${RUN}.txt" \ + | grep '^{' > "$RESULT_DIR/lucene_n${SIZE}_run${RUN}.json" + ov="$(mutual_overlap "$DIR/lance_fts_run${RUN}_topk.txt" \ + "$DIR/lucene_run${RUN}_topk.txt" "$K")" + echo " lance<->lucene mutual top-$K overlap (run $RUN) = $ov" + echo "$ov" > "$RESULT_DIR/overlap_n${SIZE}_run${RUN}.txt" + done + echo "" +done + +echo "=== summary ===" +python3 - "$RESULT_DIR" "$K" <<'PY' +import glob, json, os, sys +d, k = sys.argv[1], sys.argv[2] +print(f"{'size':>9} {'run':>4} {'impl':>10} {'build_dps':>11} {'q_p50_us':>10} " + f"{'q_p95_us':>10} {'qps_1t':>9} {'qps_nt':>10} {'term_rec':>9} {'phr_rec':>9}") +for p in sorted(glob.glob(os.path.join(d, "*_n*_run*.json"))): + try: r = json.load(open(p)) + except Exception: continue + print(f"{r['docs']:>9} {r['run']:>4} {r['impl']:>10} {r['build_docs_per_s']:>11.0f} " + f"{r['q_p50_us']:>10.1f} {r['q_p95_us']:>10.1f} {r['qps_1t']:>9.0f} " + f"{r['qps_nt']:>10.0f} {r['term_recall_at_k']:>9.3f} {r['phrase_recall_at_k']:>9.3f}") +for p in sorted(glob.glob(os.path.join(d, "overlap_*.txt"))): + name = os.path.basename(p)[:-4] + print(f" {name} = {open(p).read().strip()}") +PY +echo "" +echo "results: $RESULT_DIR" diff --git a/rust/lance/Cargo.toml b/rust/lance/Cargo.toml index 19eb1aa9c8a..9053738e657 100644 --- a/rust/lance/Cargo.toml +++ b/rust/lance/Cargo.toml @@ -240,5 +240,9 @@ harness = false name = "mem_wal_fineweb_fts" harness = false +[[bench]] +name = "mem_wal_fts_bench" +harness = false + [lints] workspace = true diff --git a/rust/lance/benches/mem_wal_fts_bench.rs b/rust/lance/benches/mem_wal_fts_bench.rs new file mode 100644 index 00000000000..4364fe053c9 --- /dev/null +++ b/rust/lance/benches/mem_wal_fts_bench.rs @@ -0,0 +1,703 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The Lance Authors + +//! Lance `FtsMemIndex` microbenchmark — the Lance side of the FTS-vs-Lucene +//! comparison (sibling of `mem_wal_hnsw_bench` for the HNSW-vs-hnswlib one). +//! +//! Two sub-commands: +//! +//! gen — slice a FineWeb corpus and write the shared comparison inputs: +//! corpus.txt one raw document per line +//! corpus_tok.txt one canonically-tokenized document per line +//! queries.txt \t\t per line (term|phrase) +//! truth.txt exact-BM25 top-k doc ids per query (Run A) +//! Both this bench and the Lucene bench consume these files, so the +//! inputs are bit-identical. +//! +//! bench — build a `FtsMemIndex` over the corpus and measure build +//! throughput, term/phrase query latency + QPS, recall@k, memory. +//! --run a pre-tokenized: corpus_tok.txt + a whitespace tokenizer +//! (isolates the inverted index + BM25 scorer). +//! --run b native: corpus.txt + the default Lance tokenizer. +//! +//! Emits one `result ...` human line and one JSON line tagged +//! `impl=lance_fts`, matching the `mem_wal_hnsw_bench` output convention. + +#![recursion_limit = "256"] +#![allow(clippy::print_stdout, clippy::print_stderr)] + +use std::collections::HashMap; +use std::io::{BufRead, BufWriter, Write}; +use std::sync::Arc; +use std::time::{Duration, Instant}; + +use arrow_array::{Array, ArrayRef, Int64Array, RecordBatch, StringArray}; +use arrow_schema::{DataType, Field, Schema as ArrowSchema}; +use lance::dataset::mem_wal::index::{FtsMemIndex, FtsQueryExpr, SearchOptions}; +use lance_core::Result; +use lance_index::scalar::inverted::tokenizer::InvertedIndexParams; +use lance_tokenizer::TokenStream; +use parquet::arrow::async_reader::ParquetRecordBatchStreamBuilder; +use rayon::prelude::*; + +const TEXT_COL: &str = "text"; +const HF_API_LISTING: &str = + "https://huggingface.co/api/datasets/HuggingFaceFW/fineweb/tree/main/sample/10BT"; +const HF_FILE_BASE: &str = "https://huggingface.co/datasets/HuggingFaceFW/fineweb/resolve/main/"; +const BM25_K1: f64 = 1.2; +const BM25_B: f64 = 0.75; + +// ---------------------------------------------------------------------- +// HuggingFace FineWeb loading (gen mode only) +// ---------------------------------------------------------------------- + +#[derive(serde::Deserialize)] +struct HfTreeEntry { + #[serde(rename = "type")] + kind: String, + path: String, +} + +async fn list_shard_paths() -> Result> { + let entries: Vec = reqwest::get(HF_API_LISTING) + .await + .map_err(|e| lance_core::Error::io(format!("listing HTTP: {e}")))? + .json() + .await + .map_err(|e| lance_core::Error::io(format!("listing JSON: {e}")))?; + let mut shards: Vec = entries + .into_iter() + .filter(|e| e.kind == "file" && e.path.ends_with(".parquet")) + .map(|e| e.path) + .collect(); + shards.sort(); + Ok(shards) +} + +async fn download_shard(rel_path: &str, dest: &std::path::Path) -> Result<()> { + if dest.exists() { + return Ok(()); + } + let url = format!("{HF_FILE_BASE}{rel_path}"); + let tmp = dest.with_extension("part"); + for attempt in 1..=5u32 { + println!("downloading {rel_path} (attempt {attempt}/5) ..."); + let r: Result = async { + let resp = reqwest::get(&url) + .await + .map_err(|e| lance_core::Error::io(format!("HTTP: {e}")))?; + if !resp.status().is_success() { + return Err(lance_core::Error::io(format!("status {}", resp.status()))); + } + resp.bytes() + .await + .map_err(|e| lance_core::Error::io(format!("body: {e}"))) + } + .await; + match r { + Ok(bytes) => { + std::fs::write(&tmp, &bytes) + .map_err(|e| lance_core::Error::io(format!("write: {e}")))?; + std::fs::rename(&tmp, dest) + .map_err(|e| lance_core::Error::io(format!("rename: {e}")))?; + return Ok(()); + } + Err(e) if attempt < 5 => { + eprintln!(" retry: {e}"); + tokio::time::sleep(Duration::from_secs(2u64.pow(attempt))).await; + } + Err(e) => return Err(e), + } + } + unreachable!() +} + +async fn read_shard_text( + path: &std::path::Path, + out: &mut Vec, + max_rows: usize, +) -> Result { + let file = tokio::fs::File::open(path) + .await + .map_err(|e| lance_core::Error::io(format!("open parquet: {e}")))?; + let mut stream = ParquetRecordBatchStreamBuilder::new(file) + .await + .map_err(|e| lance_core::Error::io(format!("parquet builder: {e}")))? + .build() + .map_err(|e| lance_core::Error::io(format!("parquet stream: {e}")))?; + let mut taken = 0usize; + use futures::TryStreamExt; + while taken < max_rows { + let Some(rb) = stream + .try_next() + .await + .map_err(|e| lance_core::Error::io(format!("parquet read: {e}")))? + else { + break; + }; + let col = rb + .column_by_name("text") + .ok_or_else(|| lance_core::Error::io("text column missing".to_string()))?; + let strs = col + .as_any() + .downcast_ref::() + .ok_or_else(|| lance_core::Error::io("text not StringArray".to_string()))?; + for i in 0..strs.len() { + if taken >= max_rows { + break; + } + if !strs.is_null(i) { + out.push(strs.value(i).to_string()); + taken += 1; + } + } + } + Ok(taken) +} + +async fn load_fineweb(docs: usize, cache_dir: &std::path::Path) -> Result> { + std::fs::create_dir_all(cache_dir) + .map_err(|e| lance_core::Error::io(format!("mkdir cache: {e}")))?; + let shards = list_shard_paths().await?; + let mut buf: Vec = Vec::with_capacity(docs); + for rel in &shards { + if buf.len() >= docs { + break; + } + let name = rel.rsplit('/').next().unwrap_or(rel); + let local = cache_dir.join(name); + download_shard(rel, &local).await?; + let want = docs - buf.len(); + let got = read_shard_text(&local, &mut buf, want).await?; + println!(" shard {name}: {got} docs (total {})", buf.len()); + } + if buf.len() < docs { + return Err(lance_core::Error::io(format!( + "fineweb yielded {} docs, need {docs}", + buf.len() + ))); + } + Ok(buf) +} + +// ---------------------------------------------------------------------- +// gen +// ---------------------------------------------------------------------- + +fn one_line(s: &str) -> String { + s.replace(['\n', '\r', '\t'], " ") +} + +/// Canonical tokenizer used to produce `corpus_tok.txt` and the `tok` query +/// field. Both the Lance Run-A and Lucene Run-A indexes consume its output +/// verbatim (via a whitespace analyzer), so the choice does not bias either +/// side — it only fixes a single shared token stream. +fn canonical_tokenize( + tokenizer: &mut dyn lance_index::scalar::inverted::tokenizer::document_tokenizer::LanceTokenizer, + text: &str, +) -> Vec { + let mut stream = tokenizer.token_stream_for_doc(text); + let mut out = Vec::new(); + while let Some(t) = stream.next() { + out.push(t.text.clone()); + } + out +} + +async fn run_gen(args: &GenArgs) -> Result<()> { + let raw = load_fineweb(args.docs, &args.cache_dir).await?; + std::fs::create_dir_all(&args.out_dir) + .map_err(|e| lance_core::Error::io(format!("mkdir out: {e}")))?; + + let mut tokenizer = InvertedIndexParams::default() + .build() + .expect("default tokenizer builds"); + + // corpus.txt + corpus_tok.txt + let corpus_path = args.out_dir.join("corpus.txt"); + let tok_path = args.out_dir.join("corpus_tok.txt"); + let mut corpus_w = BufWriter::new( + std::fs::File::create(&corpus_path) + .map_err(|e| lance_core::Error::io(format!("create corpus.txt: {e}")))?, + ); + let mut tok_w = BufWriter::new( + std::fs::File::create(&tok_path) + .map_err(|e| lance_core::Error::io(format!("create corpus_tok.txt: {e}")))?, + ); + let mut tok_docs: Vec> = Vec::with_capacity(raw.len()); + for doc in &raw { + writeln!(corpus_w, "{}", one_line(doc)).ok(); + let toks = canonical_tokenize(tokenizer.as_mut(), doc); + writeln!(tok_w, "{}", toks.join(" ")).ok(); + tok_docs.push(toks); + } + corpus_w.flush().ok(); + tok_w.flush().ok(); + println!("wrote {} docs to corpus.txt / corpus_tok.txt", raw.len()); + + // Term + phrase queries sampled from the canonical token stream. + let queries = build_query_set(&tok_docs, args); + let q_path = args.out_dir.join("queries.txt"); + let mut q_w = BufWriter::new( + std::fs::File::create(&q_path) + .map_err(|e| lance_core::Error::io(format!("create queries.txt: {e}")))?, + ); + for q in &queries { + // raw == tok here: queries are sampled from canonical tokens. The + // column is kept for format stability with the Lucene bench. + writeln!(q_w, "{}\t{}\t{}", q.kind, q.text, q.text).ok(); + } + q_w.flush().ok(); + println!("wrote {} queries to queries.txt", queries.len()); + + // Exact-BM25 ground truth over the pre-tokenized corpus (Run A). + let truth = exact_bm25_truth(&tok_docs, &queries, args.k); + let t_path = args.out_dir.join("truth.txt"); + let mut t_w = BufWriter::new( + std::fs::File::create(&t_path) + .map_err(|e| lance_core::Error::io(format!("create truth.txt: {e}")))?, + ); + for ids in &truth { + let line: Vec = ids.iter().map(|i| i.to_string()).collect(); + writeln!(t_w, "{}", line.join(" ")).ok(); + } + t_w.flush().ok(); + println!( + "wrote exact-BM25 truth for {} queries to truth.txt", + truth.len() + ); + Ok(()) +} + +struct Query { + kind: &'static str, // "term" | "phrase" + text: String, // term: one token; phrase: "a b" +} + +const STOPWORDS: &[&str] = &[ + "the", "a", "an", "and", "or", "of", "to", "in", "on", "for", "with", "as", "by", "is", "was", + "are", "were", "be", "this", "that", "it", "its", "but", "not", "no", "if", "from", "at", +]; + +fn build_query_set(tok_docs: &[Vec], args: &GenArgs) -> Vec { + // Term queries: mid-frequency tokens (skip the most common — their + // top-k is an unstable near-tie, as the FineWeb panel showed). + let mut freq: HashMap<&str, u64> = HashMap::new(); + for d in tok_docs { + for t in d { + if t.len() >= 3 && !STOPWORDS.contains(&t.as_str()) { + *freq.entry(t.as_str()).or_default() += 1; + } + } + } + let mut by_freq: Vec<(&str, u64)> = freq.into_iter().collect(); + by_freq.sort_by(|a, b| b.1.cmp(&a.1).then(a.0.cmp(b.0))); + let skip = (by_freq.len() / 4).min(300); + let mut queries: Vec = by_freq + .iter() + .skip(skip) + .take(args.num_term_queries) + .map(|(t, _)| Query { + kind: "term", + text: (*t).to_string(), + }) + .collect(); + + // Phrase queries: two adjacent non-stopword tokens, sampled on a stride. + let stride = (tok_docs.len().max(1) / args.num_phrase_queries.max(1)).max(1); + let mut idx = 0usize; + while queries.iter().filter(|q| q.kind == "phrase").count() < args.num_phrase_queries + && idx < tok_docs.len() + { + let d = &tok_docs[idx]; + for w in d.windows(2) { + if w[0].len() >= 3 + && w[1].len() >= 3 + && !STOPWORDS.contains(&w[0].as_str()) + && !STOPWORDS.contains(&w[1].as_str()) + { + queries.push(Query { + kind: "phrase", + text: format!("{} {}", w[0], w[1]), + }); + break; + } + } + idx += stride; + } + queries +} + +/// Brute-force exact BM25 top-k over the pre-tokenized corpus. +fn exact_bm25_truth(tok_docs: &[Vec], queries: &[Query], k: usize) -> Vec> { + let n = tok_docs.len() as f64; + let dl: Vec = tok_docs.iter().map(|d| d.len() as f64).collect(); + let avgdl = dl.iter().sum::() / n.max(1.0); + // postings: token -> Vec<(doc, tf)> + let mut postings: HashMap<&str, Vec<(usize, u32)>> = HashMap::new(); + for (doc, toks) in tok_docs.iter().enumerate() { + let mut tf: HashMap<&str, u32> = HashMap::new(); + for t in toks { + *tf.entry(t.as_str()).or_default() += 1; + } + for (t, c) in tf { + postings.entry(t).or_default().push((doc, c)); + } + } + let term_score = |tf: f64, df: f64, d: f64| -> f64 { + let idf = ((n - df + 0.5) / (df + 0.5) + 1.0).ln(); + let num = tf * (BM25_K1 + 1.0); + let den = tf + BM25_K1 * (1.0 - BM25_B + BM25_B * (d / avgdl)); + idf * (num / den) + }; + queries + .iter() + .map(|q| { + let mut scores: HashMap = HashMap::new(); + if q.kind == "term" { + if let Some(pl) = postings.get(q.text.as_str()) { + let df = pl.len() as f64; + for &(doc, tf) in pl { + *scores.entry(doc).or_default() += term_score(tf as f64, df, dl[doc]); + } + } + } else { + let words: Vec<&str> = q.text.split(' ').collect(); + // Phrase: docs where the two tokens occur adjacently. + for (doc, toks) in tok_docs.iter().enumerate() { + let adjacent = toks + .windows(2) + .any(|w| w[0] == words[0] && w[1] == words[1]); + if !adjacent { + continue; + } + let mut s = 0.0; + for w in &words { + if let Some(pl) = postings.get(*w) { + let df = pl.len() as f64; + if let Some(&(_, tf)) = pl.iter().find(|&&(d, _)| d == doc) { + s += term_score(tf as f64, df, dl[doc]); + } + } + } + scores.insert(doc, s); + } + } + let mut ranked: Vec<(usize, f64)> = scores.into_iter().collect(); + ranked.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal)); + ranked.truncate(k); + ranked.into_iter().map(|(d, _)| d).collect() + }) + .collect() +} + +// ---------------------------------------------------------------------- +// bench +// ---------------------------------------------------------------------- + +fn read_lines(path: &std::path::Path) -> Result> { + let f = std::fs::File::open(path) + .map_err(|e| lance_core::Error::io(format!("open {}: {e}", path.display())))?; + std::io::BufReader::new(f) + .lines() + .collect::>>() + .map_err(|e| lance_core::Error::io(format!("read {}: {e}", path.display()))) +} + +fn schema() -> Arc { + Arc::new(ArrowSchema::new(vec![ + Field::new("id", DataType::Int64, false), + Field::new(TEXT_COL, DataType::Utf8, true), + ])) +} + +fn percentile(sorted: &[f64], pct: f64) -> f64 { + if sorted.is_empty() { + return f64::NAN; + } + let idx = ((pct / 100.0) * (sorted.len() - 1) as f64).round() as usize; + sorted[idx.min(sorted.len() - 1)] +} + +fn run_bench(args: &BenchArgs) -> Result<()> { + let corpus_file = match args.run { + 'a' => "corpus_tok.txt", + _ => "corpus.txt", + }; + let docs = read_lines(&args.in_dir.join(corpus_file))?; + let query_lines = read_lines(&args.in_dir.join("queries.txt"))?; + let truth_lines = read_lines(&args.in_dir.join("truth.txt"))?; + + // Run A indexes the already-tokenized text with a whitespace tokenizer; + // Run B indexes raw text with the default Lance tokenizer. + let params = if args.run == 'a' { + InvertedIndexParams::default() + .base_tokenizer("whitespace".to_string()) + .lower_case(false) + .stem(false) + .remove_stop_words(false) + .with_position(true) + } else { + InvertedIndexParams::default().with_position(true) + }; + let index = FtsMemIndex::with_params(0, TEXT_COL.to_string(), params); + let sch = schema(); + + // Build: insert in batches of 1000, doc id == line number == row position. + let build_start = Instant::now(); + let batch_size = 1000; + let mut row = 0usize; + while row < docs.len() { + let end = (row + batch_size).min(docs.len()); + let ids: Vec = (row as i64..end as i64).collect(); + let texts: Vec<&str> = docs[row..end].iter().map(|s| s.as_str()).collect(); + let batch = RecordBatch::try_new( + sch.clone(), + vec![ + Arc::new(Int64Array::from(ids)) as ArrayRef, + Arc::new(StringArray::from(texts)) as ArrayRef, + ], + ) + .map_err(|e| lance_core::Error::io(format!("batch: {e}")))?; + index.insert(&batch, row as u64)?; + row = end; + } + let build_s = build_start.elapsed().as_secs_f64(); + + // Parse queries. + struct Q { + kind: String, + raw: String, + tok: String, + } + let queries: Vec = query_lines + .iter() + .filter_map(|l| { + let mut p = l.splitn(3, '\t'); + Some(Q { + kind: p.next()?.to_string(), + raw: p.next()?.to_string(), + tok: p.next()?.to_string(), + }) + }) + .collect(); + let truth: Vec> = truth_lines + .iter() + .map(|l| { + l.split_whitespace() + .filter_map(|s| s.parse().ok()) + .collect() + }) + .collect(); + + let opts = SearchOptions::new().with_limit(args.k); + let make_query = |q: &Q| -> FtsQueryExpr { + let text = if args.run == 'a' { &q.tok } else { &q.raw }; + if q.kind == "phrase" { + FtsQueryExpr::phrase(text.clone()) + } else { + FtsQueryExpr::match_query(text.clone()) + } + }; + + // Warm-up. + for q in &queries { + let _ = index.search_with_options(&make_query(q), opts.clone()); + } + + // Single-thread latency + collect top-k. + let mut latencies_us: Vec = Vec::with_capacity(queries.len()); + let mut topk: Vec> = Vec::with_capacity(queries.len()); + let st_start = Instant::now(); + for q in &queries { + let t0 = Instant::now(); + let hits = index.search_with_options(&make_query(q), opts.clone()); + latencies_us.push(t0.elapsed().as_secs_f64() * 1.0e6); + let mut ids: Vec<(usize, f32)> = hits + .iter() + .map(|e| (e.row_position as usize, e.score)) + .collect(); + ids.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal)); + ids.truncate(args.k); + topk.push(ids.into_iter().map(|(d, _)| d).collect()); + } + let st_s = st_start.elapsed().as_secs_f64(); + let qps_1t = queries.len() as f64 / st_s; + + // Multi-thread QPS. + let reps = 4usize; + let mt_start = Instant::now(); + let _: usize = (0..reps) + .into_par_iter() + .map(|_| { + queries + .par_iter() + .map(|q| { + index + .search_with_options(&make_query(q), opts.clone()) + .len() + }) + .sum::() + }) + .sum(); + let mt_s = mt_start.elapsed().as_secs_f64(); + let qps_nt = (queries.len() * reps) as f64 / mt_s; + + // recall@k vs exact BM25 (Run A only — truth is over the canonical corpus). + let (mut term_recall, mut term_n) = (0.0f64, 0usize); + let (mut phrase_recall, mut phrase_n) = (0.0f64, 0usize); + if args.run == 'a' { + for (i, q) in queries.iter().enumerate() { + let t: std::collections::HashSet = truth + .get(i) + .map(|v| v.iter().copied().collect()) + .unwrap_or_default(); + if t.is_empty() { + continue; + } + let hit = topk[i].iter().filter(|d| t.contains(d)).count() as f64; + let r = hit / args.k as f64; + if q.kind == "phrase" { + phrase_recall += r; + phrase_n += 1; + } else { + term_recall += r; + term_n += 1; + } + } + } + + // Write this impl's top-k for the driver's mutual-overlap computation. + let topk_path = args + .in_dir + .join(format!("lance_fts_run{}_topk.txt", args.run)); + let mut tw = BufWriter::new( + std::fs::File::create(&topk_path) + .map_err(|e| lance_core::Error::io(format!("create topk: {e}")))?, + ); + for ids in &topk { + let s: Vec = ids.iter().map(|i| i.to_string()).collect(); + writeln!(tw, "{}", s.join(" ")).ok(); + } + tw.flush().ok(); + + latencies_us.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)); + let term_recall_v = if term_n > 0 { + term_recall / term_n as f64 + } else { + f64::NAN + }; + let phrase_recall_v = if phrase_n > 0 { + phrase_recall / phrase_n as f64 + } else { + f64::NAN + }; + + println!( + "result impl=lance_fts run={} docs={} queries={} build_s={:.3} build_docs_per_s={:.0} \ + q_p50_us={:.1} q_p95_us={:.1} qps_1t={:.0} qps_{}t={:.0} \ + term_recall={:.4} phrase_recall={:.4} mem_mb={:.1}", + args.run, + docs.len(), + queries.len(), + build_s, + docs.len() as f64 / build_s, + percentile(&latencies_us, 50.0), + percentile(&latencies_us, 95.0), + qps_1t, + rayon::current_num_threads(), + qps_nt, + term_recall_v, + phrase_recall_v, + index.memory_usage() as f64 / 1.0e6, + ); + println!( + "{{\"impl\":\"lance_fts\",\"run\":\"{}\",\"docs\":{},\"queries\":{},\"k\":{},\ + \"build_s\":{:.4},\"build_docs_per_s\":{:.1},\ + \"q_p50_us\":{:.2},\"q_p95_us\":{:.2},\"qps_1t\":{:.1},\"qps_nt\":{:.1},\ + \"term_recall_at_k\":{:.4},\"phrase_recall_at_k\":{:.4},\"mem_bytes\":{}}}", + args.run, + docs.len(), + queries.len(), + args.k, + build_s, + docs.len() as f64 / build_s, + percentile(&latencies_us, 50.0), + percentile(&latencies_us, 95.0), + qps_1t, + qps_nt, + term_recall_v, + phrase_recall_v, + index.memory_usage(), + ); + Ok(()) +} + +// ---------------------------------------------------------------------- +// CLI +// ---------------------------------------------------------------------- + +struct GenArgs { + docs: usize, + out_dir: std::path::PathBuf, + cache_dir: std::path::PathBuf, + num_term_queries: usize, + num_phrase_queries: usize, + k: usize, +} + +struct BenchArgs { + in_dir: std::path::PathBuf, + run: char, + k: usize, +} + +fn main() -> Result<()> { + let argv: Vec = std::env::args().skip(1).collect(); + let argv: Vec<&str> = argv + .iter() + .map(|s| s.as_str()) + .filter(|s| *s != "--bench") + .collect(); + let mode = argv.first().copied().unwrap_or(""); + let get = |flag: &str, def: &str| -> String { + argv.iter() + .position(|a| *a == flag) + .and_then(|i| argv.get(i + 1)) + .map(|s| s.to_string()) + .unwrap_or_else(|| def.to_string()) + }; + + match mode { + "gen" => { + let args = GenArgs { + docs: get("--docs", "1000000").parse().unwrap(), + out_dir: get("--out-dir", "/tmp/fts_compare").into(), + cache_dir: get("--cache-dir", "/tmp/fineweb_cache").into(), + num_term_queries: get("--num-term-queries", "100").parse().unwrap(), + num_phrase_queries: get("--num-phrase-queries", "50").parse().unwrap(), + k: get("--k", "10").parse().unwrap(), + }; + let rt = tokio::runtime::Runtime::new() + .map_err(|e| lance_core::Error::io(format!("runtime: {e}")))?; + rt.block_on(run_gen(&args)) + } + "bench" => { + let threads: usize = get("--threads", "0").parse().unwrap(); + if threads > 0 { + let _ = rayon::ThreadPoolBuilder::new() + .num_threads(threads) + .build_global(); + } + let args = BenchArgs { + in_dir: get("--in-dir", "/tmp/fts_compare").into(), + run: get("--run", "a").chars().next().unwrap_or('a'), + k: get("--k", "10").parse().unwrap(), + }; + run_bench(&args) + } + other => Err(lance_core::Error::invalid_input(format!( + "usage: mem_wal_fts_bench (gen|bench) [...]; got '{other}'" + ))), + } +} From 5cac7293a0326c4d9786288bd65820db78c196f6 Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sat, 16 May 2026 12:05:23 -0700 Subject: [PATCH 16/37] bench: chunk the Lucene MT QPS loop to match the Lance rayon harness MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit One Future per query made the multi-thread QPS dominated by thread-pool submit overhead (smoke run: Lucene qps_nt 10k vs Lance 105k for cheap queries). Submit one task per thread, each striding a slice of the queries x reps work — the same shape as the Lance side's rayon loop. --- bench/lucene_fts_bench/LuceneFtsBench.java | Bin 10756 -> 11246 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/bench/lucene_fts_bench/LuceneFtsBench.java b/bench/lucene_fts_bench/LuceneFtsBench.java index 48bc91f0bd708f15a12ebdfd72b16962e8de903e..03931ef40a4a3e686ac82f8df88d8f6b08247d07 100644 GIT binary patch delta 666 zcmZWmu}T9$5bYGTvJr$3UL)R-NIx3tXuNTq=^)~ifK9VW& zZk1(O2W>>|8_%sKMqKkKE?=vwITmZ*FVJ*7hr7(029w&+1_ zeShvHPV_7{Ci8s$#)aV2!|@>XqZ`~Ytfy$t{W delta 149 zcmaDC-V(Cmj?CnxvO<%e%dQYAQm|DpuvREiuu&*VEhq-ECZCa1QLpAwfP%ErlG38o zV!gza6pe!X{2aaF(xlwX5)BPa1zo$z? Date: Sat, 16 May 2026 12:06:32 -0700 Subject: [PATCH 17/37] fix(bench): make Lucene MT thread count effectively final for the lambda --- bench/lucene_fts_bench/LuceneFtsBench.java | Bin 11246 -> 11283 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/bench/lucene_fts_bench/LuceneFtsBench.java b/bench/lucene_fts_bench/LuceneFtsBench.java index 03931ef40a4a3e686ac82f8df88d8f6b08247d07..ad2b75ff251859738b068fb81bd76b88dbefd6ec 100644 GIT binary patch delta 61 zcmaDCJ~?8;b6M%M%)G=Lh0MGXg}jiAqSVBcVg*};5-@A>16gaPypYK Date: Sat, 16 May 2026 13:39:57 -0700 Subject: [PATCH 18/37] feat(inverted): expose in-memory wand_search entry point Adds a pub `wand_search` to the inverted module: it builds PostingIterators from caller-supplied PostingLists and runs the block-max WAND algorithm, with a no-op row mask. The WAND machinery was pub(crate); this exposes it for the in-memory FTS MemTable index, whose frozen segments will hold postings as PostingList::Plain and need the same query primitive as the on-disk path. Stage 1 of the segment-structured FtsMemIndex redesign. --- rust/lance-index/src/scalar/inverted.rs | 1 + rust/lance-index/src/scalar/inverted/wand.rs | 96 ++++++++++++++++++++ 2 files changed, 97 insertions(+) diff --git a/rust/lance-index/src/scalar/inverted.rs b/rust/lance-index/src/scalar/inverted.rs index 448861c5c0c..20cff663eaf 100644 --- a/rust/lance-index/src/scalar/inverted.rs +++ b/rust/lance-index/src/scalar/inverted.rs @@ -25,6 +25,7 @@ use lance_core::{Result, cache::LanceCache}; pub use lance_tokenizer::Language; pub use scorer::MemBM25Scorer; pub use tokenizer::*; +pub use wand::{DocCandidate, WandTerm, wand_search}; use crate::scalar::inverted::query::{FtsSearchParams, Tokens}; diff --git a/rust/lance-index/src/scalar/inverted/wand.rs b/rust/lance-index/src/scalar/inverted/wand.rs index b06c75c0021..2e2132f5df6 100644 --- a/rust/lance-index/src/scalar/inverted/wand.rs +++ b/rust/lance-index/src/scalar/inverted/wand.rs @@ -442,6 +442,46 @@ pub struct DocCandidate { pub doc_length: u32, } +/// One query term for [`wand_search`]: the token text, its id, the token's +/// position in the query (used only by phrase queries), the query-side +/// weight, and the term's posting list. +pub type WandTerm = (String, u32, u32, f32, PostingList); + +/// In-memory block-max WAND search entry point. +/// +/// Runs the same WAND algorithm as the on-disk inverted index query path, +/// but over caller-supplied posting lists. This is the query primitive for +/// the in-memory FTS MemTable index, whose frozen segments hold their +/// postings as [`PostingList::Plain`]. Mirrors `Wand`'s on-disk usage; the +/// row-address mask is a no-op (the in-memory index has no deletions). +pub fn wand_search( + operator: Operator, + terms: Vec, + docs: &DocSet, + scorer: S, + params: &FtsSearchParams, +) -> Result> { + let num_doc = docs.len(); + let postings = terms + .into_iter() + .map(|(token, token_id, position, query_weight, list)| { + PostingIterator::with_query_weight( + token, + token_id, + position, + query_weight, + list, + num_doc, + ) + }); + let mut wand = Wand::new(operator, postings, docs, scorer); + wand.search( + params, + Arc::new(RowAddrMask::default()), + &crate::metrics::NoOpMetricsCollector, + ) +} + struct HeadPosting { // Iterators that are already positioned on or after the next candidate doc. // The heap is ordered by smallest doc id so the top element determines @@ -1655,6 +1695,62 @@ mod tests { assert_eq!(result.len(), 0); // Should not panic } + // Proves the public `wand_search` entry point works over caller-built + // in-memory posting lists — the query primitive for the segmented + // FTS MemTable index. (BM25 ranking itself is covered by `test_wand` + // and the on-disk index tests.) + #[test] + fn test_wand_search_in_memory() { + let mut docs = DocSet::default(); + for i in 0..10u64 { + docs.append(i, 1); + } + // term "a": docs 0,2,4,6,8 — term "b": docs 1,2,5,8 — union of 7. + let term_a = generate_posting_list(vec![0, 2, 4, 6, 8], 1.0, None, false); + let term_b = generate_posting_list(vec![1, 2, 5, 8], 1.0, None, false); + let union: std::collections::HashSet = [0, 1, 2, 4, 5, 6, 8].into_iter().collect(); + let terms: Vec = vec![ + ("a".to_string(), 0, 0, 1.0, term_a), + ("b".to_string(), 1, 1, 1.0, term_b), + ]; + let params = FtsSearchParams::default().with_limit(Some(3)); + let scorer = IndexBM25Scorer::new(std::iter::empty()); + let result = wand_search(Operator::Or, terms, &docs, scorer, ¶ms).unwrap(); + assert_eq!(result.len(), 3, "limit=3 should return 3 candidates"); + for c in &result { + assert!(union.contains(&c.row_id), "id {} not in union", c.row_id); + assert!(!c.freqs.is_empty(), "candidate must record term freqs"); + } + + // Unlimited search returns exactly the union of the two posting lists. + let scorer = IndexBM25Scorer::new(std::iter::empty()); + let all = wand_search( + Operator::Or, + vec![ + ( + "a".to_string(), + 0, + 0, + 1.0, + generate_posting_list(vec![0, 2, 4, 6, 8], 1.0, None, false), + ), + ( + "b".to_string(), + 1, + 1, + 1.0, + generate_posting_list(vec![1, 2, 5, 8], 1.0, None, false), + ), + ], + &docs, + scorer, + &FtsSearchParams::default(), + ) + .unwrap(); + let got: std::collections::HashSet = all.iter().map(|c| c.row_id).collect(); + assert_eq!(got, union, "OR search must return the posting-list union"); + } + #[test] fn test_posting_iterator_next_compressed_partition_point() { let mut docs = DocSet::default(); From beab662946be7fec890ac0ffa89076fdc92edaec Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sat, 16 May 2026 13:57:53 -0700 Subject: [PATCH 19/37] feat(mem_wal): add immutable FTS Partition type (redesign stage 2) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Adds `Partition` + `PartitionBuilder` to the FTS mem index: an immutable frozen slice of inserts holding per-term posting lists in the on-disk `PostingList::Plain` form, queried with the shared block-max WAND (`wand_search`). WAND reports partition-local doc ids; `Partition::search` remaps them to MemTable row positions via the partition's `DocSet`. Standalone and dead-code-allowed — wired into `FtsMemIndex` in the next stage. Part of the partition-structured redesign that replaces the O(corpus) per-batch-chunk query layout. --- rust/lance/src/dataset/mem_wal/index/fts.rs | 242 ++++++++++++++++++++ 1 file changed, 242 insertions(+) diff --git a/rust/lance/src/dataset/mem_wal/index/fts.rs b/rust/lance/src/dataset/mem_wal/index/fts.rs index 739ab21de2a..9b8061e11eb 100644 --- a/rust/lance/src/dataset/mem_wal/index/fts.rs +++ b/rust/lance/src/dataset/mem_wal/index/fts.rs @@ -1646,6 +1646,169 @@ impl FtsIndexConfig { } } +// ============================================================================ +// Partition-structured index (redesign — see +// ~/ai/analysis/lance/jack-MemTableFTSBetter/fts-segment-redesign/DESIGN.md) +// +// An immutable `Partition` holds a frozen slice of the MemTable's inserts as +// on-disk-shaped posting lists, queried with the shared block-max WAND +// (`wand_search`). The current per-batch-chunk layout makes queries +// O(corpus); partitions make them ≈ O(matches) and flush 1:1 to a Lance FTS +// on-disk partition. Wired into `FtsMemIndex` in a later stage; standalone +// and dead-code-allowed until then. +// ============================================================================ + +use arrow_array::builder::{Int32Builder, ListBuilder}; +use arrow_buffer::ScalarBuffer; +use lance_index::scalar::inverted::query::{FtsSearchParams, Operator}; +use lance_index::scalar::inverted::{ + DocCandidate, DocSet, MemBM25Scorer, PlainPostingList, PostingList, TokenSet, WandTerm, + wand_search, +}; + +/// An immutable, frozen FTS partition. +#[allow(dead_code)] +struct Partition { + /// token text -> local token id (dense, 0-based). + tokens: TokenSet, + /// posting list per local token id. + postings: Vec, + /// local doc id -> (row position, token count). + docs: DocSet, +} + +#[allow(dead_code)] +impl Partition { + fn doc_count(&self) -> usize { + self.docs.len() + } + + fn total_tokens(&self) -> u64 { + self.docs.total_tokens_num() + } + + /// Number of docs in this partition containing `token`. + fn token_df(&self, token: &str) -> usize { + self.tokens + .get(token) + .map(|id| self.postings[id as usize].len()) + .unwrap_or(0) + } + + /// WAND-search this partition. `query` is `(token, query-position)` pairs, + /// already tokenized. Returns this partition's local candidates (row + /// positions); the caller merges across partitions and the tail. + fn search( + &self, + query: &[(String, u32)], + operator: Operator, + scorer: MemBM25Scorer, + params: &FtsSearchParams, + ) -> Result> { + let mut terms: Vec = Vec::with_capacity(query.len()); + for (token, position) in query { + match self.tokens.get(token) { + Some(token_id) => terms.push(( + token.clone(), + token_id, + *position, + 1.0, + self.postings[token_id as usize].clone(), + )), + // Term absent here: an AND query can't match in this + // partition; an OR query just drops the term. + None if operator == Operator::And => return Ok(Vec::new()), + None => {} + } + } + if terms.is_empty() { + return Ok(Vec::new()); + } + // WAND reports the partition-local doc id in `DocCandidate.row_id`; + // remap it to the MemTable row position via the partition's DocSet. + let mut hits = wand_search(operator, terms, &self.docs, scorer, params)?; + for c in &mut hits { + c.row_id = self.docs.row_id(c.row_id as u32); + } + Ok(hits) + } +} + +/// Accumulates documents and freezes them into an immutable `Partition`. +#[allow(dead_code)] +struct PartitionBuilder { + tokens: TokenSet, + /// per local token id: `(doc_local_id, freq, positions)` postings. + postings: Vec)>>, + docs: DocSet, +} + +#[allow(dead_code)] +impl PartitionBuilder { + fn new() -> Self { + Self { + tokens: TokenSet::default(), + postings: Vec::new(), + docs: DocSet::default(), + } + } + + fn doc_count(&self) -> usize { + self.docs.len() + } + + fn is_empty(&self) -> bool { + self.docs.len() == 0 + } + + /// Add one document: `terms` is `(token, freq, positions)` per distinct + /// token, `num_tokens` is the document length. + fn add_doc(&mut self, row_position: u64, num_tokens: u32, terms: Vec<(String, u32, Vec)>) { + let doc_id = self.docs.append(row_position, num_tokens); + for (token, freq, positions) in terms { + let token_id = self.tokens.add(token) as usize; + if token_id >= self.postings.len() { + self.postings.resize_with(token_id + 1, Vec::new); + } + self.postings[token_id].push((doc_id, freq, positions)); + } + } + + /// Freeze into an immutable `Partition`. Each token's postings are sorted + /// by doc id and converted to a `PlainPostingList`; `max_score` is left + /// `None` so WAND derives the per-term upper bound from `df` at query + /// time (global BM25 stats are not known to a single partition). + fn build(self) -> Partition { + let postings: Vec = self + .postings + .into_iter() + .map(|mut docs| { + docs.sort_by_key(|(doc_id, _, _)| *doc_id); + let row_ids: Vec = docs.iter().map(|(d, _, _)| *d as u64).collect(); + let freqs: Vec = docs.iter().map(|(_, f, _)| *f as f32).collect(); + let mut positions = ListBuilder::new(Int32Builder::new()); + for (_, _, doc_positions) in &docs { + for p in doc_positions { + positions.values().append_value(*p as i32); + } + positions.append(true); + } + PostingList::Plain(PlainPostingList::new( + ScalarBuffer::from(row_ids), + ScalarBuffer::from(freqs), + None, + Some(positions.finish()), + )) + }) + .collect(); + Partition { + tokens: self.tokens, + postings, + docs: self.docs, + } + } +} + // ============================================================================ // Tests // ============================================================================ @@ -2657,4 +2820,83 @@ mod tests { let err = index.insert(&batch, 0).unwrap_err(); assert!(err.to_string().contains("only supports"), "{err}"); } + + // ===== Partition redesign (Stage 2) ===== + + fn build_test_partition() -> Partition { + // 4 docs at row positions 100..104. + let mut b = PartitionBuilder::new(); + b.add_doc( + 100, + 2, + vec![ + ("apple".to_string(), 1, vec![0]), + ("banana".to_string(), 1, vec![1]), + ], + ); + b.add_doc( + 101, + 2, + vec![ + ("apple".to_string(), 1, vec![0]), + ("cherry".to_string(), 1, vec![1]), + ], + ); + b.add_doc(102, 1, vec![("banana".to_string(), 1, vec![0])]); + b.add_doc( + 103, + 3, + vec![ + ("apple".to_string(), 2, vec![0, 1]), + ("date".to_string(), 1, vec![2]), + ], + ); + b.build() + } + + fn search_partition(p: &Partition, token: &str) -> Vec { + let mut token_docs = std::collections::HashMap::new(); + token_docs.insert(token.to_string(), p.token_df(token)); + let scorer = MemBM25Scorer::new(p.total_tokens(), p.doc_count(), token_docs); + let params = FtsSearchParams::default().with_limit(Some(10)); + let hits = p + .search(&[(token.to_string(), 0)], Operator::Or, scorer, ¶ms) + .unwrap(); + let mut ids: Vec = hits.iter().map(|c| c.row_id).collect(); + ids.sort_unstable(); + ids + } + + #[test] + fn test_partition_build_and_search() { + let p = build_test_partition(); + assert_eq!(p.doc_count(), 4); + assert_eq!(p.total_tokens(), 8); // 2 + 2 + 1 + 3 + assert_eq!(p.token_df("apple"), 3); + assert_eq!(p.token_df("banana"), 2); + assert_eq!(p.token_df("missing"), 0); + + // WAND search must return the row positions containing each term. + assert_eq!(search_partition(&p, "apple"), vec![100, 101, 103]); + assert_eq!(search_partition(&p, "banana"), vec![100, 102]); + assert_eq!(search_partition(&p, "date"), vec![103]); + assert!(search_partition(&p, "missing").is_empty()); + } + + #[test] + fn test_partition_and_query_short_circuits_missing_term() { + let p = build_test_partition(); + // "apple" present, "missing" absent -> AND yields nothing. + let scorer = MemBM25Scorer::new(p.total_tokens(), p.doc_count(), Default::default()); + let params = FtsSearchParams::default().with_limit(Some(10)); + let hits = p + .search( + &[("apple".to_string(), 0), ("missing".to_string(), 1)], + Operator::And, + scorer, + ¶ms, + ) + .unwrap(); + assert!(hits.is_empty()); + } } From 50be021bf95a4285faa3c05046edcdc3d2b47991 Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sat, 16 May 2026 15:52:12 -0700 Subject: [PATCH 20/37] perf(mem_wal): partition-structured FTS mem index (redesign stages 3-7) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Replaces the per-batch-chunk `FtsMemIndex` layout — where every query walked every chunk of every query term, making latency O(corpus) (p95 ~1.2s at 1M docs) — with Lucene's segment model adapted to a Lance FTS partition. The index is now a set of immutable `Partition`s plus a bounded mutable tail, published together behind one `ArcSwap` (`IndexState`) so a freeze is observed atomically. Each partition holds frozen, on-disk-shaped posting lists queried with block-max WAND in ~O(matches); the writer freezes the tail into a new partition past `freeze_threshold_rows` (50k) and merges partitions past a hard cap. All scoring routes through one corpus-wide `MemBM25Scorer` so partition and tail scores are comparable; the scorer and the scanned tail share a single snapshot to keep BM25 stats consistent. Flush merges every partition and the tail into one `InnerBuilder`. The public query API is unchanged. Exposes `Scorer` from `lance-index`'s inverted module. --- rust/lance-index/src/scalar/inverted.rs | 2 +- rust/lance/src/dataset/mem_wal/index/fts.rs | 1683 ++++++++++++------- 2 files changed, 1114 insertions(+), 571 deletions(-) diff --git a/rust/lance-index/src/scalar/inverted.rs b/rust/lance-index/src/scalar/inverted.rs index 20cff663eaf..399eabd3ff0 100644 --- a/rust/lance-index/src/scalar/inverted.rs +++ b/rust/lance-index/src/scalar/inverted.rs @@ -23,7 +23,7 @@ use datafusion::execution::SendableRecordBatchStream; pub use index::*; use lance_core::{Result, cache::LanceCache}; pub use lance_tokenizer::Language; -pub use scorer::MemBM25Scorer; +pub use scorer::{MemBM25Scorer, Scorer}; pub use tokenizer::*; pub use wand::{DocCandidate, WandTerm, wand_search}; diff --git a/rust/lance/src/dataset/mem_wal/index/fts.rs b/rust/lance/src/dataset/mem_wal/index/fts.rs index 9b8061e11eb..0a99a109986 100644 --- a/rust/lance/src/dataset/mem_wal/index/fts.rs +++ b/rust/lance/src/dataset/mem_wal/index/fts.rs @@ -18,18 +18,32 @@ //! (`doc_count`, `total_tokens`, per-term `df`) are mutually consistent //! with the postings the reader walks. //! -//! Visibility is published atomically by replacing a single `Snapshot` value -//! via `ArcSwap`. The writer first installs all term chunks into the -//! per-term `ArcSwap` slots, then atomically swaps in a new +//! # Partition-structured layout +//! +//! The index is a set of immutable [`Partition`]s plus a bounded mutable +//! `tail` ([`TailIndex`]). Each partition holds a frozen slice of inserts as +//! on-disk-shaped posting lists and is queried with block-max WAND in +//! ≈O(matches). The tail accumulates recent inserts in a per-batch-chunk +//! layout and is searched in place; the writer freezes it into a new +//! partition once it crosses `freeze_threshold_rows`. The `{partitions, tail}` +//! pair lives behind one [`ArcSwap`] ([`IndexState`]) so a freeze publishes +//! atomically — a reader never sees a doc twice or misses one across a +//! freeze. +//! +//! Within the tail, visibility is published atomically by replacing a single +//! `Snapshot` via `ArcSwap`. The writer first installs all term chunks into +//! the per-term `ArcSwap` slots, then atomically swaps in a new //! `Snapshot` whose `visible_count` covers the new batch. Readers load the //! `Snapshot` first and filter every term chunk by `batch_position < -//! snapshot.visible_count`. +//! snapshot.visible_count`. Frozen partitions are fully visible by +//! construction. //! //! # On-disk format //! //! At flush time we hand off to `lance_index::scalar::inverted::builder::InnerBuilder` -//! via `to_index_builder_reversed`. The on-disk format is unchanged from -//! Lance's existing inverted index. +//! via `to_index_builder_reversed`, which merges every partition and the tail +//! into one builder. The on-disk format is unchanged from Lance's existing +//! inverted index. use std::collections::{HashMap, HashSet}; use std::sync::Arc; @@ -37,12 +51,18 @@ use std::sync::Mutex; use std::sync::atomic::{AtomicUsize, Ordering}; use arc_swap::ArcSwap; -use arrow_array::{Array, LargeStringArray, RecordBatch, StringArray, StringViewArray}; +use arrow_array::builder::{Int32Builder, ListBuilder}; +use arrow_array::{Array, Int32Array, LargeStringArray, RecordBatch, StringArray, StringViewArray}; +use arrow_buffer::ScalarBuffer; use arrow_schema::DataType; use crossbeam_skiplist::SkipMap; use lance_core::{Error, Result}; use lance_index::scalar::InvertedIndexParams; +use lance_index::scalar::inverted::query::{FtsSearchParams, Operator}; use lance_index::scalar::inverted::tokenizer::document_tokenizer::LanceTokenizer; +use lance_index::scalar::inverted::{ + DocSet, MemBM25Scorer, PlainPostingList, PostingList, Scorer, TokenSet, WandTerm, wand_search, +}; use lance_tokenizer::TokenStream; use super::RowPosition; @@ -577,6 +597,120 @@ impl<'a> Drop for PooledTokenizer<'a> { // FtsMemIndex // ============================================================================ +/// In-memory full-text search index. See module docs for the concurrency +/// model and visibility contract. +/// The bounded mutable accumulator: recent inserts since the last freeze, in +/// the per-batch-chunk layout. Searched in place; frozen into a [`Partition`] +/// once it crosses the freeze threshold. +struct TailIndex { + /// Per-term posting slices. `Arc` interns the term so a single + /// allocation backs every chunk that mentions it. + terms: SkipMap, ArcSwap>, + /// Atomically-swapped visibility snapshot. + snapshot: ArcSwap, + /// Strictly-monotonic, dense, 0-based batch position counter. Dense + /// positions are required by the `batch_position < visible_count` + /// visibility filter; the tail therefore assigns its own. + next_batch_position: AtomicUsize, +} + +impl TailIndex { + fn new() -> Arc { + Arc::new(Self { + terms: SkipMap::new(), + snapshot: ArcSwap::from(Snapshot::empty()), + next_batch_position: AtomicUsize::new(0), + }) + } + + fn snapshot(&self) -> Arc { + self.snapshot.load_full() + } + + fn doc_count(&self) -> u64 { + self.snapshot.load().cumulative_doc_count + } + + fn visible_count(&self) -> usize { + self.snapshot.load().visible_count + } + + /// Next dense batch position for an incoming batch. + fn next_position(&self) -> usize { + self.next_batch_position.fetch_add(1, Ordering::Relaxed) + } + + /// Install one batch's term chunks then publish a new visibility snapshot. + fn append_batch( + &self, + batch_position: usize, + row_offset: u64, + rows: u32, + doc_lengths: Vec, + total_tokens: u64, + term_builders: HashMap, BatchTermBuilder>, + ) { + for (term, builder) in term_builders { + let chunk = builder.build(batch_position); + let entry = self + .terms + .get_or_insert_with(term, TermSlice::empty_arc_swap); + let cur = entry.value().load(); + entry.value().store(cur.with_chunk_appended(chunk)); + } + let new_meta = Arc::new(BatchMeta { + batch_position, + row_offset, + doc_lengths, + rows, + }); + let cur = self.snapshot.load(); + let mut batches: Vec> = Vec::with_capacity(cur.batches.len() + 1); + batches.extend(cur.batches.iter().cloned()); + batches.push(new_meta); + self.snapshot.store(Arc::new(Snapshot { + visible_count: cur.visible_count + 1, + batches: Arc::from(batches.into_boxed_slice()), + cumulative_doc_count: cur.cumulative_doc_count + rows as u64, + cumulative_total_tokens: cur.cumulative_total_tokens + total_tokens, + })); + } + + fn memory_size(&self) -> usize { + let mut total = std::mem::size_of::(); + for entry in self.terms.iter() { + let term: &Arc = entry.key(); + total += std::mem::size_of::>() + term.len() + 32; + total += entry.value().load().memory_size(); + } + total += self + .snapshot + .load() + .batches + .iter() + .map(|b| b.memory_size()) + .sum::(); + total + } +} + +/// Atomically-published index state: the immutable partitions plus the live +/// mutable tail. The pair lives behind one `ArcSwap` so a freeze (which both +/// appends a partition and resets the tail) is observed atomically. +struct IndexState { + partitions: Arc<[Arc]>, + tail: Arc, +} + +impl IndexState { + fn empty() -> Arc { + Arc::new(Self { + partitions: Arc::from(Vec::>::new().into_boxed_slice()), + tail: TailIndex::new(), + }) + } +} + /// In-memory full-text search index. See module docs for the concurrency /// model and visibility contract. pub struct FtsMemIndex { @@ -589,29 +723,22 @@ pub struct FtsMemIndex { /// can take `&self`. Single-writer assumption means this is uncontested. writer_tokenizer: Mutex>, - /// Per-term posting slices. `Arc` interns the term so a single - /// allocation backs every chunk that mentions it. - terms: SkipMap, ArcSwap>, - - /// Atomically-swapped visibility snapshot. - snapshot: ArcSwap, + /// `{partitions, tail}` published atomically. The tail mutates in place + /// between freezes; the whole state is swapped on freeze. + state: ArcSwap, - /// Strictly-monotonic batch position counter, used by `insert` (the - /// no-explicit-position variant) to assign sequential ids. Reads of - /// this counter are not part of the visibility contract — callers - /// passing explicit positions to `insert_with_batch_position` must keep - /// these monotonic themselves. - next_batch_position: AtomicUsize, + /// The tail freezes into a partition once it reaches this many docs. + freeze_threshold_rows: usize, } impl std::fmt::Debug for FtsMemIndex { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { - let snap = self.snapshot.load(); + let st = self.state.load(); f.debug_struct("FtsMemIndex") .field("field_id", &self.field_id) .field("column_name", &self.column_name) - .field("visible_doc_count", &snap.cumulative_doc_count) - .field("visible_batch_count", &snap.visible_count) + .field("doc_count", &self.doc_count()) + .field("partitions", &st.partitions.len()) .field("params", &self.params) .finish() } @@ -622,6 +749,16 @@ impl FtsMemIndex { /// short bursts of concurrent search calls without thrashing. const DEFAULT_TOKENIZER_POOL_CAP: usize = 8; + /// Default tail freeze threshold (docs). Analogous to Lucene's + /// `ramBufferSizeMB`: large enough that the per-query tail scan stays + /// cheap, small enough to bound it independent of corpus size. + const DEFAULT_FREEZE_THRESHOLD_ROWS: usize = 50_000; + + /// Hard cap on live partitions: when a freeze would exceed it, all + /// partitions are merged into one. With the default freeze threshold this + /// only triggers past ~1.6M docs. + const MAX_PARTITIONS: usize = 32; + /// Create a new FTS index for the given field with default parameters. pub fn new(field_id: i32, column_name: String) -> Self { Self::with_params(field_id, column_name, InvertedIndexParams::default()) @@ -638,12 +775,19 @@ impl FtsMemIndex { params, tokenizer_pool: Arc::new(pool), writer_tokenizer: Mutex::new(writer_tokenizer), - terms: SkipMap::new(), - snapshot: ArcSwap::from(Snapshot::empty()), - next_batch_position: AtomicUsize::new(0), + state: ArcSwap::from(IndexState::empty()), + freeze_threshold_rows: Self::DEFAULT_FREEZE_THRESHOLD_ROWS, } } + /// Override the tail freeze threshold. Used by tests to exercise the + /// multi-partition path with small inputs. + #[cfg(test)] + fn with_freeze_threshold_rows(mut self, rows: usize) -> Self { + self.freeze_threshold_rows = rows.max(1); + self + } + pub fn field_id(&self) -> i32 { self.field_id } @@ -656,48 +800,46 @@ impl FtsMemIndex { &self.params } - /// Number of visible documents. + /// Number of visible documents across all partitions and the tail. pub fn doc_count(&self) -> usize { - self.snapshot.load().cumulative_doc_count as usize + let st = self.state.load(); + st.partitions.iter().map(|p| p.doc_count()).sum::() + st.tail.doc_count() as usize } /// Whether there are any visible documents. pub fn is_empty(&self) -> bool { - self.snapshot.load().visible_count == 0 + let st = self.state.load(); + st.partitions.is_empty() && st.tail.visible_count() == 0 } /// Total number of visible (term, doc) postings. - /// - /// Sums posting counts only over chunks whose batch is visible per the - /// current snapshot, so this matches what readers can actually walk. pub fn entry_count(&self) -> usize { - let visible = self.snapshot.load().visible_count; - self.terms + let st = self.state.load_full(); + let part: usize = st.partitions.iter().map(|p| p.entry_count()).sum(); + let visible = st.tail.visible_count(); + let tail: usize = st + .tail + .terms .iter() .map(|e| { - let slice = e.value().load(); - slice + e.value() + .load() .chunks .iter() .filter(|c| c.batch_position < visible) .map(|c| c.doc_count()) .sum::() }) - .sum() + .sum(); + part + tail } /// Estimated bytes of heap memory held by this index. pub fn memory_usage(&self) -> usize { + let st = self.state.load_full(); let mut total = std::mem::size_of::(); - for entry in self.terms.iter() { - // ~32 bytes for SkipMap node overhead plus the interned term itself. - let term: &Arc = entry.key(); - total += std::mem::size_of::>() + term.len() + 32; - let slice = entry.value().load(); - total += slice.memory_size(); - } - let snap = self.snapshot.load(); - total += snap.batches.iter().map(|b| b.memory_size()).sum::(); + total += st.partitions.iter().map(|p| p.memory_size()).sum::(); + total += st.tail.memory_size(); total } @@ -705,34 +847,43 @@ impl FtsMemIndex { // Insert // ------------------------------------------------------------------ - /// Insert a batch using a sequentially-derived batch position. + /// Insert a batch into the mutable tail. pub fn insert(&self, batch: &RecordBatch, row_offset: u64) -> Result<()> { - let batch_position = self.next_batch_position.fetch_add(1, Ordering::Relaxed); - self.insert_with_batch_position(batch, row_offset, batch_position) + self.insert_batch(batch, row_offset) } - /// Insert a batch with an explicit batch position. The position is part - /// of the visibility contract — see module docs. + /// Insert a batch. The explicit `batch_position` is accepted for API + /// compatibility but not used for placement: the tail assigns its own + /// dense, 0-based positions (required by the visibility filter). pub fn insert_with_batch_position( &self, batch: &RecordBatch, row_offset: u64, - batch_position: usize, + _batch_position: usize, ) -> Result<()> { - // Keep next_batch_position ahead of any explicit caller-supplied - // position so a later `insert` (no-position) doesn't collide. - let _ = self - .next_batch_position - .fetch_max(batch_position.saturating_add(1), Ordering::Relaxed); + self.insert_batch(batch, row_offset) + } + + fn insert_batch(&self, batch: &RecordBatch, row_offset: u64) -> Result<()> { + let st = self.state.load_full(); + let batch_position = st.tail.next_position(); let Some(col_idx) = batch .schema() .column_with_name(&self.column_name) .map(|(idx, _)| idx) else { - // Column missing: nothing to index, but advance the snapshot so - // the caller's position counter stays in sync. - return self.publish_empty_batch(batch.num_rows() as u32, row_offset, batch_position); + // Column missing: nothing to index, but publish an empty batch so + // the tail's visibility counters keep up with the writer. + st.tail.append_batch( + batch_position, + row_offset, + batch.num_rows() as u32, + vec![0; batch.num_rows()], + 0, + HashMap::new(), + ); + return Ok(()); }; let column = batch.column(col_idx); @@ -795,58 +946,41 @@ impl FtsMemIndex { } } - // Drop the tokenizer guard before any work that could be slow so we - // don't hold it across allocations. + // Drop the tokenizer guard before publishing so we don't hold it + // across the snapshot install. drop(tok_guard); - // Install per-term chunks first so any chunk a reader can possibly - // see (via a later snapshot store) is fully linked. - for (term, builder) in term_builders { - let chunk = builder.build(batch_position); - let entry = self - .terms - .get_or_insert_with(term, TermSlice::empty_arc_swap); - let cur = entry.value().load(); - entry.value().store(cur.with_chunk_appended(chunk)); - } - - let new_meta = Arc::new(BatchMeta { + st.tail.append_batch( batch_position, row_offset, + batch.num_rows() as u32, doc_lengths, - rows: batch.num_rows() as u32, - }); - - self.publish_batch(new_meta, total_tokens); - Ok(()) - } + total_tokens, + term_builders, + ); - /// Publish a snapshot for an "empty" batch (e.g. column missing) so the - /// visibility counters keep up with the writer's batch_position stream. - fn publish_empty_batch(&self, rows: u32, row_offset: u64, batch_position: usize) -> Result<()> { - let meta = Arc::new(BatchMeta { - batch_position, - row_offset, - doc_lengths: vec![0; rows as usize], - rows, - }); - self.publish_batch(meta, 0); + if st.tail.doc_count() >= self.freeze_threshold_rows as u64 { + self.freeze(&st); + } Ok(()) } - fn publish_batch(&self, new_meta: Arc, batch_total_tokens: u64) { - let cur = self.snapshot.load(); - let new_rows = new_meta.rows as u64; - let mut batches: Vec> = Vec::with_capacity(cur.batches.len() + 1); - batches.extend(cur.batches.iter().cloned()); - batches.push(new_meta); - let new_snap = Snapshot { - visible_count: cur.visible_count + 1, - batches: Arc::from(batches.into_boxed_slice()), - cumulative_doc_count: cur.cumulative_doc_count + new_rows, - cumulative_total_tokens: cur.cumulative_total_tokens + batch_total_tokens, + /// Freeze the current tail into a new immutable partition and publish a + /// fresh empty tail. Only the writer calls this; readers snapshotting the + /// old `IndexState` keep a consistent view across the freeze. + fn freeze(&self, st: &IndexState) { + let Some(partition) = Partition::from_tail(&st.tail) else { + return; }; - self.snapshot.store(Arc::new(new_snap)); + let mut partitions: Vec> = st.partitions.iter().cloned().collect(); + partitions.push(Arc::new(partition)); + if partitions.len() > Self::MAX_PARTITIONS { + partitions = vec![Arc::new(Partition::merge(&partitions))]; + } + self.state.store(Arc::new(IndexState { + partitions: Arc::from(partitions.into_boxed_slice()), + tail: TailIndex::new(), + })); } // ------------------------------------------------------------------ @@ -859,145 +993,17 @@ impl FtsMemIndex { /// matching documents with BM25 scores. Result order is unspecified; /// use `search_with_options` for sorted/limited output. pub fn search(&self, term: &str) -> Vec { - let snap = self.snapshot.load_full(); - self.search_with_snapshot(term, &snap) - } - - fn search_with_snapshot(&self, term: &str, snap: &Arc) -> Vec { - if snap.visible_count == 0 { - return Vec::new(); - } + let st = self.state.load_full(); let tokens = self.tokenize_for_search(term); - score_terms(snap, &self.terms, &tokens) + self.search_match(&st, &tokens, None) } /// Search for documents containing an exact phrase, optionally allowing /// `slop` intervening tokens between consecutive query tokens. pub fn search_phrase(&self, phrase: &str, slop: u32) -> Vec { - let snap = self.snapshot.load_full(); - self.search_phrase_with_snapshot(phrase, slop, &snap) - } - - fn search_phrase_with_snapshot( - &self, - phrase: &str, - slop: u32, - snap: &Arc, - ) -> Vec { - if snap.visible_count == 0 { - return Vec::new(); - } - + let st = self.state.load_full(); let tokens = self.tokenize_for_search(phrase); - if tokens.is_empty() { - return Vec::new(); - } - if tokens.len() == 1 { - // Same shortcut as the previous implementation: a single-token - // phrase reduces to a regular term search. - return score_terms(snap, &self.terms, &tokens); - } - - // Gather visible chunks per token. - let mut per_token_chunks: Vec>> = Vec::with_capacity(tokens.len()); - for tok in &tokens { - match self.terms.get(tok.as_str()) { - Some(entry) => { - let slice = entry.value().load_full(); - let visible: Vec> = slice - .chunks - .iter() - .filter(|c| c.batch_position < snap.visible_count) - .cloned() - .collect(); - if visible.is_empty() { - return Vec::new(); - } - per_token_chunks.push(visible); - } - None => return Vec::new(), - } - } - - // Per-term `df` for IDF. - let dfs: Vec = per_token_chunks - .iter() - .map(|chunks| chunks.iter().map(|c| c.doc_count() as u32).sum::()) - .collect(); - - let n = snap.cumulative_doc_count as f32; - let avgdl = if n > 0.0 { - snap.cumulative_total_tokens as f32 / n - } else { - 1.0 - }; - - // Find candidate documents: rows that appear in every token's - // posting set. Start with the smallest token's docs to bound work. - let smallest_idx = (0..per_token_chunks.len()) - .min_by_key(|&i| { - per_token_chunks[i] - .iter() - .map(|c| c.doc_count()) - .sum::() - }) - .unwrap(); - let candidate_chunks = &per_token_chunks[smallest_idx]; - - let mut results = Vec::new(); - for chunk in candidate_chunks { - for (doc_idx, &row_position) in chunk.row_positions.iter().enumerate() { - let pos = chunk - .positions - .as_ref() - .map(|p| p.doc_positions(doc_idx).to_vec()); - let Some(pos) = pos else { continue }; - let mut all_positions: Vec> = vec![Vec::new(); tokens.len()]; - all_positions[smallest_idx] = pos; - let mut all_present = true; - let mut frequencies = vec![0u32; tokens.len()]; - frequencies[smallest_idx] = chunk.frequencies[doc_idx]; - for (ti, chunks) in per_token_chunks.iter().enumerate() { - if ti == smallest_idx { - continue; - } - match find_doc_in_chunks(chunks, row_position) { - Some((c, doc_idx_other)) => { - frequencies[ti] = c.frequencies[doc_idx_other]; - all_positions[ti] = c - .positions - .as_ref() - .map(|p| p.doc_positions(doc_idx_other).to_vec()) - .unwrap_or_default(); - } - None => { - all_present = false; - break; - } - } - } - if !all_present { - continue; - } - if !phrase_matches(&all_positions, slop) { - continue; - } - - let dl = lookup_dl(snap, row_position).unwrap_or(1) as f32; - let mut score = 0.0f32; - for (ti, _tok) in tokens.iter().enumerate() { - let tf = frequencies[ti] as f32; - let df = dfs[ti] as f32; - score += bm25_term_score(tf, df.max(1.0), n, dl, avgdl); - } - results.push(FtsEntry { - row_position, - score, - }); - } - } - - results + self.search_phrase_tokens(&st, &tokens, slop) } /// Expand a term to fuzzy matches within the specified edit distance. @@ -1009,41 +1015,8 @@ impl FtsMemIndex { max_distance: u32, max_expansions: usize, ) -> Vec<(String, u32)> { - let snap = self.snapshot.load_full(); - self.expand_fuzzy_with_snapshot(term, max_distance, max_expansions, &snap) - } - - fn expand_fuzzy_with_snapshot( - &self, - term: &str, - max_distance: u32, - max_expansions: usize, - snap: &Arc, - ) -> Vec<(String, u32)> { - let mut matches: Vec<(String, u32)> = Vec::new(); - - if max_distance == 0 { - if let Some(entry) = self.terms.get(term) - && has_visible_chunk(&entry.value().load(), snap.visible_count) - { - matches.push((term.to_string(), 0)); - } - return matches; - } - - for entry in self.terms.iter() { - let key: &Arc = entry.key(); - if !has_visible_chunk(&entry.value().load(), snap.visible_count) { - continue; - } - let dist = levenshtein_distance(term, key); - if dist <= max_distance { - matches.push((key.to_string(), dist)); - } - } - matches.sort_by_key(|(_, d)| *d); - matches.truncate(max_expansions); - matches + let st = self.state.load_full(); + self.expand_fuzzy_term(&st, term, max_distance, max_expansions) } /// Search for documents using fuzzy matching on each query token. @@ -1053,65 +1026,178 @@ impl FtsMemIndex { fuzziness: Option, max_expansions: usize, ) -> Vec { - let snap = self.snapshot.load_full(); - self.search_fuzzy_with_snapshot(query, fuzziness, max_expansions, &snap) + let st = self.state.load_full(); + let tokens = self.tokenize_for_search(query); + self.search_fuzzy_tokens(&st, &tokens, fuzziness, max_expansions) } - fn search_fuzzy_with_snapshot( + /// BM25 OR-search over the query tokens: WAND each immutable partition, + /// scan the tail, all scored with one corpus-wide [`MemBM25Scorer`], and + /// the results merged. Each doc lives in exactly one partition or the + /// tail, so the merge is a plain concatenation. + fn search_match( &self, - query: &str, - fuzziness: Option, - max_expansions: usize, - snap: &Arc, + st: &IndexState, + tokens: &[String], + limit: Option, ) -> Vec { - if snap.visible_count == 0 { + if tokens.is_empty() { return Vec::new(); } + // Snapshot the tail once so the scorer's stats and the scanned tail + // postings are from the same visibility point. + let tail_snap = st.tail.snapshot(); + let scorer = build_scorer(st, &tail_snap, tokens); + if scorer.num_docs() == 0 { + return Vec::new(); + } + let mut results = Vec::new(); + for p in st.partitions.iter() { + match p.search_match(tokens, Operator::Or, &scorer, limit) { + Ok(hits) => results.extend(hits), + Err(e) => log::warn!("FTS partition match search failed: {e}"), + } + } + if tail_snap.visible_count > 0 { + results.extend(score_terms(&tail_snap, &st.tail.terms, tokens, &scorer)); + } + results + } - let tokens = self.tokenize_for_search(query); + fn search_phrase_tokens(&self, st: &IndexState, tokens: &[String], slop: u32) -> Vec { if tokens.is_empty() { return Vec::new(); } + if tokens.len() == 1 { + // A single-token phrase reduces to a regular term search. + return self.search_match(st, tokens, None); + } + let tail_snap = st.tail.snapshot(); + let scorer = build_scorer(st, &tail_snap, tokens); + if scorer.num_docs() == 0 { + return Vec::new(); + } + let mut results = Vec::new(); + for p in st.partitions.iter() { + match p.search_phrase(tokens, slop, &scorer) { + Ok(hits) => results.extend(hits), + Err(e) => log::warn!("FTS partition phrase search failed: {e}"), + } + } + if tail_snap.visible_count > 0 { + results.extend(phrase_search_tail( + &tail_snap, + &st.tail.terms, + tokens, + slop, + &scorer, + )); + } + results + } + fn search_fuzzy_tokens( + &self, + st: &IndexState, + tokens: &[String], + fuzziness: Option, + max_expansions: usize, + ) -> Vec { + if tokens.is_empty() { + return Vec::new(); + } let mut expanded: Vec = Vec::new(); - for tok in &tokens { + let mut seen: HashSet = HashSet::new(); + for tok in tokens { let max_dist = fuzziness.unwrap_or_else(|| auto_fuzziness(tok)); - for (matched, _) in self.expand_fuzzy_with_snapshot(tok, max_dist, max_expansions, snap) - { - expanded.push(matched); + for (matched, _) in self.expand_fuzzy_term(st, tok, max_dist, max_expansions) { + if seen.insert(matched.clone()) { + expanded.push(matched); + } } } if expanded.is_empty() { return Vec::new(); } + self.search_match(st, &expanded, None) + } - score_terms(snap, &self.terms, &expanded) + /// Expand `term` against the term dictionaries of every partition and the + /// visible tail. + fn expand_fuzzy_term( + &self, + st: &IndexState, + term: &str, + max_distance: u32, + max_expansions: usize, + ) -> Vec<(String, u32)> { + let tail_snap = st.tail.snapshot(); + if max_distance == 0 { + let in_tail = st + .tail + .terms + .get(term) + .map(|e| has_visible_chunk(&e.value().load(), tail_snap.visible_count)) + .unwrap_or(false); + let in_partition = st.partitions.iter().any(|p| p.contains_token(term)); + return if in_tail || in_partition { + vec![(term.to_string(), 0)] + } else { + Vec::new() + }; + } + let mut matches: Vec<(String, u32)> = Vec::new(); + let mut seen: HashSet = HashSet::new(); + for entry in st.tail.terms.iter() { + if !has_visible_chunk(&entry.value().load(), tail_snap.visible_count) { + continue; + } + let key: &Arc = entry.key(); + let dist = levenshtein_distance(term, key); + if dist <= max_distance && seen.insert(key.to_string()) { + matches.push((key.to_string(), dist)); + } + } + for p in st.partitions.iter() { + for key in p.tokens() { + let dist = levenshtein_distance(term, key); + if dist <= max_distance && seen.insert(key.to_string()) { + matches.push((key.to_string(), dist)); + } + } + } + matches.sort_by_key(|(_, d)| *d); + matches.truncate(max_expansions); + matches } /// Execute a query expression and return matching documents with scores. /// /// Snapshots the index state once at entry so the entire compound /// query — including every leaf invoked recursively from `Boolean` / - /// `Boost` — sees the same `Snapshot`. This preserves the per-batch - /// monotonic visibility contract for compound queries. + /// `Boost` — sees the same `{partitions, tail}` view. This preserves the + /// per-batch monotonic visibility contract for compound queries. pub fn search_query(&self, query: &FtsQueryExpr) -> Vec { - let snap = self.snapshot.load_full(); - self.search_query_with_snapshot(query, &snap) + let st = self.state.load_full(); + self.search_query_with_state(query, &st, None) } - fn search_query_with_snapshot( + fn search_query_with_state( &self, query: &FtsQueryExpr, - snap: &Arc, + st: &IndexState, + limit: Option, ) -> Vec { match query { FtsQueryExpr::Match { query, boost } => { - let mut results = self.search_with_snapshot(query, snap); + let tokens = self.tokenize_for_search(query); + let mut results = self.search_match(st, &tokens, limit); apply_boost(&mut results, *boost); results } FtsQueryExpr::Phrase { query, slop, boost } => { - let mut results = self.search_phrase_with_snapshot(query, *slop, snap); + let tokens = self.tokenize_for_search(query); + let mut results = self.search_phrase_tokens(st, &tokens, *slop); apply_boost(&mut results, *boost); results } @@ -1121,8 +1207,9 @@ impl FtsMemIndex { max_expansions, boost, } => { + let tokens = self.tokenize_for_search(query); let mut results = - self.search_fuzzy_with_snapshot(query, *fuzziness, *max_expansions, snap); + self.search_fuzzy_tokens(st, &tokens, *fuzziness, *max_expansions); apply_boost(&mut results, *boost); results } @@ -1130,12 +1217,12 @@ impl FtsMemIndex { must, should, must_not, - } => self.search_boolean(must, should, must_not, snap), + } => self.search_boolean(must, should, must_not, st), FtsQueryExpr::Boost { positive, negative, negative_boost, - } => self.search_boost(positive, negative.as_deref(), *negative_boost, snap), + } => self.search_boost(positive, negative.as_deref(), *negative_boost, st), } } @@ -1145,8 +1232,8 @@ impl FtsMemIndex { query: &FtsQueryExpr, options: SearchOptions, ) -> Vec { - let snap = self.snapshot.load_full(); - let mut results = self.search_query_with_snapshot(query, &snap); + let st = self.state.load_full(); + let mut results = self.search_query_with_state(query, &st, options.limit); results.sort_by(|a, b| { b.score .partial_cmp(&a.score) @@ -1175,13 +1262,13 @@ impl FtsMemIndex { positive: &FtsQueryExpr, negative: Option<&FtsQueryExpr>, negative_boost: f32, - snap: &Arc, + st: &IndexState, ) -> Vec { - let mut results = self.search_query_with_snapshot(positive, snap); + let mut results = self.search_query_with_state(positive, st, None); let Some(neg) = negative else { return results; }; - let negative_results = self.search_query_with_snapshot(neg, snap); + let negative_results = self.search_query_with_state(neg, st, None); let negative_set: HashSet = negative_results .into_iter() .map(|e| e.row_position) @@ -1199,30 +1286,30 @@ impl FtsMemIndex { must: &[FtsQueryExpr], should: &[FtsQueryExpr], must_not: &[FtsQueryExpr], - snap: &Arc, + st: &IndexState, ) -> Vec { let excluded: HashSet = must_not .iter() - .flat_map(|q| self.search_query_with_snapshot(q, snap)) + .flat_map(|q| self.search_query_with_state(q, st, None)) .map(|e| e.row_position) .collect(); let mut result_map: HashMap = if must.is_empty() { let mut map: HashMap = HashMap::new(); for q in should { - for entry in self.search_query_with_snapshot(q, snap) { + for entry in self.search_query_with_state(q, st, None) { *map.entry(entry.row_position).or_default() += entry.score; } } map } else { - let first_results = self.search_query_with_snapshot(&must[0], snap); + let first_results = self.search_query_with_state(&must[0], st, None); let mut map: HashMap = first_results .into_iter() .map(|e| (e.row_position, e.score)) .collect(); for q in must.iter().skip(1) { - let results = self.search_query_with_snapshot(q, snap); + let results = self.search_query_with_state(q, st, None); let result_set: HashMap = results .into_iter() .map(|e| (e.row_position, e.score)) @@ -1233,7 +1320,7 @@ impl FtsMemIndex { .collect(); } for q in should { - for entry in self.search_query_with_snapshot(q, snap) { + for entry in self.search_query_with_state(q, st, None) { if let Some(score) = map.get_mut(&entry.row_position) { *score += entry.score; } @@ -1281,13 +1368,28 @@ impl FtsMemIndex { partition_id: u64, total_rows: usize, ) -> Result { + use lance_index::scalar::inverted::PostingListBuilder; use lance_index::scalar::inverted::builder::{InnerBuilder, PositionRecorder}; - use lance_index::scalar::inverted::{DocSet, PostingListBuilder, TokenSet}; - let snap = self.snapshot.load_full(); + let st = self.state.load_full(); let with_position = self.params.has_positions(); + let total_rows_u64 = total_rows as u64; - if snap.visible_count == 0 { + // Step 1: collect (original_pos, num_tokens) for every doc across all + // immutable partitions and the visible tail. + let mut all_docs: Vec<(u64, u32)> = Vec::new(); + for p in st.partitions.iter() { + for (row_pos, num_tokens) in p.docs.iter() { + all_docs.push((*row_pos, *num_tokens)); + } + } + let tail_snap = st.tail.snapshot(); + for batch in tail_snap.batches.iter().take(tail_snap.visible_count) { + for i in 0..batch.rows as usize { + all_docs.push((batch.row_offset + i as u64, batch.doc_lengths[i])); + } + } + if all_docs.is_empty() { return Ok(InnerBuilder::new( partition_id, with_position, @@ -1295,81 +1397,88 @@ impl FtsMemIndex { )); } - let total_rows_u64 = total_rows as u64; - - // Step 1: collect (reversed_pos, num_tokens) for every visible row. - // We walk visible BatchMetas in order so the reverse-sort below has - // a small, dense input. - let mut entries: Vec<(u64, u32)> = Vec::new(); - for batch in snap.batches.iter().take(snap.visible_count) { - for i in 0..batch.rows as usize { - let original_pos = batch.row_offset + i as u64; - if original_pos >= total_rows_u64 { - return Err(Error::io(format!( - "FTS flush: row position {} >= total_rows {}", - original_pos, total_rows - ))); - } - let reversed = total_rows_u64 - original_pos - 1; - entries.push((reversed, batch.doc_lengths[i])); + // Step 2: assign doc_ids in ascending reversed-position order, so the + // on-disk layout matches MemWAL's newest-first flush convention. + let mut entries: Vec<(u64, u64, u32)> = Vec::with_capacity(all_docs.len()); + for (original, num_tokens) in &all_docs { + if *original >= total_rows_u64 { + return Err(Error::io(format!( + "FTS flush: row position {} >= total_rows {}", + original, total_rows + ))); } + entries.push((total_rows_u64 - original - 1, *original, *num_tokens)); } - entries.sort_by_key(|(rev, _)| *rev); - - // Step 2: assign doc_ids in ascending reversed-pos order. + entries.sort_by_key(|(rev, _, _)| *rev); let mut docs = DocSet::default(); let mut original_to_doc_id: HashMap = HashMap::with_capacity(entries.len()); - for (rev, num_tokens) in &entries { + for (rev, original, num_tokens) in &entries { let doc_id = docs.append(*rev, *num_tokens); - // Recover the original position from the reversed position so - // we can map per-term postings without a second pass. - let original = total_rows_u64 - rev - 1; - original_to_doc_id.insert(original, doc_id); - } - - // Step 3: walk terms in alphabetic (skip-list) order and emit - // posting lists into the builder. - let mut tokens = TokenSet::default(); - let mut posting_lists: Vec = Vec::new(); - for entry in self.terms.iter() { - let term: &Arc = entry.key(); + original_to_doc_id.insert(*original, doc_id); + } + + // Step 3: merge per-term postings across every partition and the tail. + let mut term_postings: HashMap>)>> = HashMap::new(); + for p in st.partitions.iter() { + for (token, posting) in p.tokens_with_postings() { + let plain = posting_as_plain(posting); + let bucket = term_postings.entry(token.to_string()).or_default(); + for i in 0..plain.len() { + let row_pos = p.docs.row_id(plain.row_ids[i] as u32); + let Some(&doc_id) = original_to_doc_id.get(&row_pos) else { + continue; + }; + let pos = if with_position { + Some(read_positions(plain, i)) + } else { + None + }; + bucket.push((doc_id, plain.frequencies[i] as u32, pos)); + } + } + } + for entry in st.tail.terms.iter() { + let token: &Arc = entry.key(); let slice = entry.value().load(); - - // Collect per-doc postings filtered by visibility. - let mut docs_for_term: Vec<(u32, u32, Option>)> = Vec::new(); + let bucket = term_postings.entry(token.to_string()).or_default(); for chunk in &slice.chunks { - if chunk.batch_position >= snap.visible_count { + if chunk.batch_position >= tail_snap.visible_count { continue; } for (i, row_position) in chunk.row_positions.iter().enumerate() { let Some(&doc_id) = original_to_doc_id.get(row_position) else { - // Should not happen: a chunk's batch_position is - // visible only if its meta is visible, and the meta - // contributed every row above. continue; }; let pos = if with_position { - chunk - .positions - .as_ref() - .map(|p| p.doc_positions(i).to_vec()) + Some( + chunk + .positions + .as_ref() + .map(|p| p.doc_positions(i).to_vec()) + .unwrap_or_default(), + ) } else { None }; - docs_for_term.push((doc_id, chunk.frequencies[i], pos)); + bucket.push((doc_id, chunk.frequencies[i], pos)); } } + } + + // Step 4: emit posting lists; tokens in sorted order for determinism. + let mut sorted_tokens: Vec = term_postings.keys().cloned().collect(); + sorted_tokens.sort_unstable(); + let mut tokens = TokenSet::default(); + let mut posting_lists: Vec = Vec::with_capacity(sorted_tokens.len()); + for token in sorted_tokens { + let mut docs_for_term = term_postings.remove(&token).unwrap_or_default(); if docs_for_term.is_empty() { continue; } docs_for_term.sort_by_key(|(doc_id, _, _)| *doc_id); - - let token_id = tokens.add(term.to_string()) as usize; - // PostingListBuilder vector is indexed by token_id. New token - // ids are assigned monotonically by `add`, so we just push. + let token_id = tokens.add(token) as usize; debug_assert_eq!(token_id, posting_lists.len()); posting_lists.push(PostingListBuilder::new(with_position)); - let plb = &mut posting_lists[token_id]; for (doc_id, freq, pos) in docs_for_term { let recorder = if with_position { @@ -1503,48 +1612,69 @@ fn find_doc_in_chunks( None } -const BM25_K1: f32 = 1.2; -const BM25_B: f32 = 0.75; +/// Build a corpus-wide BM25 scorer over every partition and the tail, for the +/// (deduplicated) set of query tokens. A single scorer makes partition-WAND +/// scores and tail-scan scores directly comparable. `tail_snap` must be the +/// *same* tail snapshot the caller scans, so the scorer's stats and the +/// scanned postings stay mutually consistent. +fn build_scorer(st: &IndexState, tail_snap: &Snapshot, tokens: &[String]) -> MemBM25Scorer { + let mut total_tokens = tail_snap.cumulative_total_tokens; + let mut num_docs = tail_snap.cumulative_doc_count as usize; + for p in st.partitions.iter() { + total_tokens += p.total_tokens(); + num_docs += p.doc_count(); + } + let mut token_docs: HashMap = HashMap::new(); + for token in tokens { + if token_docs.contains_key(token) { + continue; + } + let mut df = tail_token_df(&st.tail.terms, token, tail_snap.visible_count); + for p in st.partitions.iter() { + df += p.token_df(token); + } + token_docs.insert(token.clone(), df); + } + MemBM25Scorer::new(total_tokens, num_docs, token_docs) +} -fn bm25_term_score(tf: f32, df: f32, n: f32, dl: f32, avgdl: f32) -> f32 { - let idf = ((n - df + 0.5) / (df + 0.5) + 1.0).ln(); - let numerator = tf * (BM25_K1 + 1.0); - let denominator = tf + BM25_K1 * (1.0 - BM25_B + BM25_B * (dl / avgdl)); - idf * (numerator / denominator) +/// Number of visible tail docs containing `token`. +fn tail_token_df( + terms: &SkipMap, ArcSwap>, + token: &str, + visible_count: usize, +) -> usize { + match terms.get(token) { + Some(e) => e + .value() + .load() + .chunks + .iter() + .filter(|c| c.batch_position < visible_count) + .map(|c| c.doc_count()) + .sum(), + None => 0, + } } -/// Score a list of query tokens against the visible state. Each token -/// contributes its BM25 term score to every document containing it. +/// OR-score `tokens` against the visible tail, summing each token's BM25 +/// contribution per document. Uses the shared corpus-wide `scorer`. fn score_terms( snap: &Snapshot, terms: &SkipMap, ArcSwap>, tokens: &[String], + scorer: &MemBM25Scorer, ) -> Vec { - let n = snap.cumulative_doc_count as f32; - let avgdl = if n > 0.0 { - snap.cumulative_total_tokens as f32 / n - } else { - 1.0 - }; - let mut doc_scores: HashMap = HashMap::new(); - for token in tokens { let Some(entry) = terms.get(token.as_str()) else { continue; }; - let slice = entry.value().load_full(); - // df = total visible postings for this term. - let df: u32 = slice - .chunks - .iter() - .filter(|c| c.batch_position < snap.visible_count) - .map(|c| c.doc_count() as u32) - .sum(); - if df == 0 { + let qw = scorer.query_weight(token); + if qw == 0.0 { continue; } - let df_f = df as f32; + let slice = entry.value().load_full(); for chunk in &slice.chunks { if chunk.batch_position >= snap.visible_count { continue; @@ -1553,14 +1683,12 @@ fn score_terms( continue; }; for (i, &row_position) in chunk.row_positions.iter().enumerate() { - let dl = meta.dl(row_position).unwrap_or(1) as f32; - let tf = chunk.frequencies[i] as f32; - let score = bm25_term_score(tf, df_f, n, dl, avgdl); + let dl = meta.dl(row_position).unwrap_or(1); + let score = qw * scorer.doc_weight(chunk.frequencies[i], dl); *doc_scores.entry(row_position).or_default() += score; } } } - doc_scores .into_iter() .map(|(row_position, score)| FtsEntry { @@ -1570,6 +1698,97 @@ fn score_terms( .collect() } +/// Phrase-search the visible tail. Callers shortcut the single-token case, so +/// `tokens.len() >= 2` here. Scored with the shared corpus-wide `scorer`. +fn phrase_search_tail( + snap: &Snapshot, + terms: &SkipMap, ArcSwap>, + tokens: &[String], + slop: u32, + scorer: &MemBM25Scorer, +) -> Vec { + // Gather visible chunks per query token; any missing token => no match. + let mut per_token_chunks: Vec>> = Vec::with_capacity(tokens.len()); + for tok in tokens { + match terms.get(tok.as_str()) { + Some(entry) => { + let slice = entry.value().load_full(); + let visible: Vec> = slice + .chunks + .iter() + .filter(|c| c.batch_position < snap.visible_count) + .cloned() + .collect(); + if visible.is_empty() { + return Vec::new(); + } + per_token_chunks.push(visible); + } + None => return Vec::new(), + } + } + // Drive the intersection from the rarest token to bound work. + let smallest_idx = (0..per_token_chunks.len()) + .min_by_key(|&i| { + per_token_chunks[i] + .iter() + .map(|c| c.doc_count()) + .sum::() + }) + .unwrap(); + + let mut results = Vec::new(); + for chunk in &per_token_chunks[smallest_idx] { + for (doc_idx, &row_position) in chunk.row_positions.iter().enumerate() { + let Some(pos) = chunk + .positions + .as_ref() + .map(|p| p.doc_positions(doc_idx).to_vec()) + else { + continue; + }; + let mut all_positions: Vec> = vec![Vec::new(); tokens.len()]; + all_positions[smallest_idx] = pos; + let mut frequencies = vec![0u32; tokens.len()]; + frequencies[smallest_idx] = chunk.frequencies[doc_idx]; + let mut all_present = true; + for (ti, chunks) in per_token_chunks.iter().enumerate() { + if ti == smallest_idx { + continue; + } + match find_doc_in_chunks(chunks, row_position) { + Some((c, other_idx)) => { + frequencies[ti] = c.frequencies[other_idx]; + all_positions[ti] = c + .positions + .as_ref() + .map(|p| p.doc_positions(other_idx).to_vec()) + .unwrap_or_default(); + } + None => { + all_present = false; + break; + } + } + } + if !all_present || !phrase_matches(&all_positions, slop) { + continue; + } + let dl = lookup_dl(snap, row_position).unwrap_or(1); + let score: f32 = tokens + .iter() + .enumerate() + .map(|(ti, tok)| scorer.query_weight(tok) * scorer.doc_weight(frequencies[ti], dl)) + .sum(); + results.push(FtsEntry { + row_position, + score, + }); + } + } + results +} + fn phrase_matches(positions: &[Vec], slop: u32) -> bool { if positions.is_empty() { return false; @@ -1647,37 +1866,23 @@ impl FtsIndexConfig { } // ============================================================================ -// Partition-structured index (redesign — see -// ~/ai/analysis/lance/jack-MemTableFTSBetter/fts-segment-redesign/DESIGN.md) -// -// An immutable `Partition` holds a frozen slice of the MemTable's inserts as -// on-disk-shaped posting lists, queried with the shared block-max WAND -// (`wand_search`). The current per-batch-chunk layout makes queries -// O(corpus); partitions make them ≈ O(matches) and flush 1:1 to a Lance FTS -// on-disk partition. Wired into `FtsMemIndex` in a later stage; standalone -// and dead-code-allowed until then. +// Immutable partition // ============================================================================ -use arrow_array::builder::{Int32Builder, ListBuilder}; -use arrow_buffer::ScalarBuffer; -use lance_index::scalar::inverted::query::{FtsSearchParams, Operator}; -use lance_index::scalar::inverted::{ - DocCandidate, DocSet, MemBM25Scorer, PlainPostingList, PostingList, TokenSet, WandTerm, - wand_search, -}; - -/// An immutable, frozen FTS partition. -#[allow(dead_code)] +/// An immutable, frozen FTS partition: a slice of the MemTable's inserts held +/// as on-disk-shaped posting lists and queried with block-max WAND in +/// ≈O(matches). Built by freezing the tail; flushed 1:1 into the Lance FTS +/// on-disk format. See `DESIGN.md` in the redesign analysis directory. struct Partition { - /// token text -> local token id (dense, 0-based). - tokens: TokenSet, - /// posting list per local token id. + /// token text -> local token id (dense, 0-based; indexes `postings`). + token_ids: HashMap, u32>, + /// posting list per local token id. In-memory partitions are always + /// `PostingList::Plain` and always carry positions. postings: Vec, - /// local doc id -> (row position, token count). + /// local doc id -> (MemTable row position, token count). docs: DocSet, } -#[allow(dead_code)] impl Partition { fn doc_count(&self) -> usize { self.docs.len() @@ -1687,125 +1892,310 @@ impl Partition { self.docs.total_tokens_num() } + fn entry_count(&self) -> usize { + self.postings + .iter() + .map(|p| posting_as_plain(p).len()) + .sum() + } + + fn contains_token(&self, token: &str) -> bool { + self.token_ids.contains_key(token) + } + + fn tokens(&self) -> impl Iterator> { + self.token_ids.keys() + } + + fn tokens_with_postings(&self) -> impl Iterator, &PostingList)> { + self.token_ids + .iter() + .map(move |(t, &id)| (t, &self.postings[id as usize])) + } + /// Number of docs in this partition containing `token`. fn token_df(&self, token: &str) -> usize { - self.tokens + self.token_ids .get(token) - .map(|id| self.postings[id as usize].len()) + .map(|&id| posting_as_plain(&self.postings[id as usize]).len()) .unwrap_or(0) } - /// WAND-search this partition. `query` is `(token, query-position)` pairs, - /// already tokenized. Returns this partition's local candidates (row - /// positions); the caller merges across partitions and the tail. - fn search( + fn memory_size(&self) -> usize { + let mut total = std::mem::size_of::(); + for t in self.token_ids.keys() { + total += std::mem::size_of::>() + t.len() + std::mem::size_of::() + 16; + } + for p in &self.postings { + let pl = posting_as_plain(p); + total += pl.row_ids.len() * std::mem::size_of::() + + pl.frequencies.len() * std::mem::size_of::() + + pl.positions + .as_ref() + .map(|a| a.get_array_memory_size()) + .unwrap_or(0); + } + total += self.docs.len() * (std::mem::size_of::() + std::mem::size_of::()); + total + } + + /// Freeze the visible contents of `tail` into a new partition. Returns + /// `None` if the tail has no visible docs. + fn from_tail(tail: &TailIndex) -> Option { + let snap = tail.snapshot(); + if snap.visible_count == 0 { + return None; + } + // Assign dense local doc ids in row-position order. + let mut docs = DocSet::default(); + let mut pos_to_doc: HashMap = HashMap::new(); + for batch in snap.batches.iter().take(snap.visible_count) { + for i in 0..batch.rows as usize { + let rp = batch.row_offset + i as u64; + let doc_id = docs.append(rp, batch.doc_lengths[i]); + pos_to_doc.insert(rp, doc_id); + } + } + let mut token_ids: HashMap, u32> = HashMap::new(); + let mut raw: Vec)>> = Vec::new(); + for entry in tail.terms.iter() { + let term: &Arc = entry.key(); + let slice = entry.value().load(); + let mut docs_for_term: Vec<(u32, u32, Vec)> = Vec::new(); + for chunk in &slice.chunks { + if chunk.batch_position >= snap.visible_count { + continue; + } + for (i, rp) in chunk.row_positions.iter().enumerate() { + let Some(&doc_id) = pos_to_doc.get(rp) else { + continue; + }; + let pos = chunk + .positions + .as_ref() + .map(|p| p.doc_positions(i).to_vec()) + .unwrap_or_default(); + docs_for_term.push((doc_id, chunk.frequencies[i], pos)); + } + } + if docs_for_term.is_empty() { + continue; + } + docs_for_term.sort_by_key(|(d, _, _)| *d); + let id = raw.len() as u32; + token_ids.insert(term.clone(), id); + raw.push(docs_for_term); + } + let postings = raw.into_iter().map(freeze_postings_one).collect(); + Some(Self { + token_ids, + postings, + docs, + }) + } + + /// Merge several partitions into one. Local doc ids are reassigned by + /// concatenation, which keeps each merged per-token posting list sorted. + fn merge(parts: &[Arc]) -> Self { + let mut token_ids: HashMap, u32> = HashMap::new(); + let mut raw: Vec)>> = Vec::new(); + let mut docs = DocSet::default(); + let mut doc_offset: u32 = 0; + for p in parts { + for (rp, nt) in p.docs.iter() { + docs.append(*rp, *nt); + } + for (token, &local_id) in &p.token_ids { + let merged_id = *token_ids.entry(token.clone()).or_insert_with(|| { + let id = raw.len() as u32; + raw.push(Vec::new()); + id + }); + let plain = posting_as_plain(&p.postings[local_id as usize]); + for i in 0..plain.len() { + raw[merged_id as usize].push(( + plain.row_ids[i] as u32 + doc_offset, + plain.frequencies[i] as u32, + read_positions(plain, i), + )); + } + } + doc_offset += p.docs.len() as u32; + } + let postings = raw + .into_iter() + .map(|mut v| { + v.sort_by_key(|(d, _, _)| *d); + freeze_postings_one(v) + }) + .collect(); + Self { + token_ids, + postings, + docs, + } + } + + /// WAND OR/AND-search; candidates scored with `scorer` and reported as + /// MemTable row positions. + fn search_match( &self, - query: &[(String, u32)], + tokens: &[String], operator: Operator, - scorer: MemBM25Scorer, - params: &FtsSearchParams, - ) -> Result> { - let mut terms: Vec = Vec::with_capacity(query.len()); - for (token, position) in query { - match self.tokens.get(token) { - Some(token_id) => terms.push(( - token.clone(), - token_id, - *position, - 1.0, - self.postings[token_id as usize].clone(), - )), - // Term absent here: an AND query can't match in this - // partition; an OR query just drops the term. + scorer: &MemBM25Scorer, + limit: Option, + ) -> Result> { + let mut wand_terms: Vec = Vec::new(); + let mut tok_order: Vec = Vec::new(); + for (qpos, t) in tokens.iter().enumerate() { + match self.token_ids.get(t.as_str()) { + Some(&id) => { + wand_terms.push(( + t.clone(), + id, + qpos as u32, + 1.0, + self.postings[id as usize].clone(), + )); + tok_order.push(t.clone()); + } + // Term absent: an AND query cannot match here; OR drops it. None if operator == Operator::And => return Ok(Vec::new()), None => {} } } - if terms.is_empty() { + if wand_terms.is_empty() { return Ok(Vec::new()); } - // WAND reports the partition-local doc id in `DocCandidate.row_id`; - // remap it to the MemTable row position via the partition's DocSet. - let mut hits = wand_search(operator, terms, &self.docs, scorer, params)?; - for c in &mut hits { - c.row_id = self.docs.row_id(c.row_id as u32); - } - Ok(hits) + let params = FtsSearchParams::new().with_limit(limit); + let cands = wand_search(operator, wand_terms, &self.docs, scorer.clone(), ¶ms)?; + Ok(cands + .into_iter() + .map(|c| { + let score: f32 = c + .freqs + .iter() + .map(|(ti, f)| { + scorer.query_weight(&tok_order[*ti as usize]) + * scorer.doc_weight(*f, c.doc_length) + }) + .sum(); + FtsEntry { + row_position: self.docs.row_id(c.row_id as u32), + score, + } + }) + .collect()) } -} -/// Accumulates documents and freezes them into an immutable `Partition`. -#[allow(dead_code)] -struct PartitionBuilder { - tokens: TokenSet, - /// per local token id: `(doc_local_id, freq, positions)` postings. - postings: Vec)>>, - docs: DocSet, -} - -#[allow(dead_code)] -impl PartitionBuilder { - fn new() -> Self { - Self { - tokens: TokenSet::default(), - postings: Vec::new(), - docs: DocSet::default(), + /// Phrase-search: WAND-And finds docs containing every token, then token + /// positions are verified. `tokens.len() >= 2`. + fn search_phrase( + &self, + tokens: &[String], + slop: u32, + scorer: &MemBM25Scorer, + ) -> Result> { + let mut wand_terms: Vec = Vec::new(); + let mut token_id_order: Vec = Vec::new(); + for (qpos, t) in tokens.iter().enumerate() { + match self.token_ids.get(t.as_str()) { + Some(&id) => { + wand_terms.push(( + t.clone(), + id, + qpos as u32, + 1.0, + self.postings[id as usize].clone(), + )); + token_id_order.push(id); + } + // A phrase needs every token present in this partition. + None => return Ok(Vec::new()), + } } + let params = FtsSearchParams::new(); + let cands = wand_search( + Operator::And, + wand_terms, + &self.docs, + scorer.clone(), + ¶ms, + )?; + let mut results = Vec::new(); + for c in cands { + let local_doc = c.row_id; // partition-local doc id + let mut all_positions: Vec> = Vec::with_capacity(tokens.len()); + let mut present = true; + for &tid in &token_id_order { + let plain = posting_as_plain(&self.postings[tid as usize]); + match plain.row_ids.binary_search(&local_doc) { + Ok(idx) => all_positions.push(read_positions(plain, idx)), + Err(_) => { + present = false; + break; + } + } + } + if !present || !phrase_matches(&all_positions, slop) { + continue; + } + let score: f32 = c + .freqs + .iter() + .map(|(ti, f)| { + scorer.query_weight(&tokens[*ti as usize]) * scorer.doc_weight(*f, c.doc_length) + }) + .sum(); + results.push(FtsEntry { + row_position: self.docs.row_id(local_doc as u32), + score, + }); + } + Ok(results) } +} - fn doc_count(&self) -> usize { - self.docs.len() - } - - fn is_empty(&self) -> bool { - self.docs.len() == 0 - } +/// Convert per-token `(doc_id, freq, positions)` postings — sorted by doc id — +/// into an on-disk-shaped `PlainPostingList`. `max_score` is left `None`; +/// WAND derives per-term bounds from the posting list at query time. +fn freeze_postings_one(docs: Vec<(u32, u32, Vec)>) -> PostingList { + let row_ids: Vec = docs.iter().map(|(d, _, _)| *d as u64).collect(); + let freqs: Vec = docs.iter().map(|(_, f, _)| *f as f32).collect(); + let mut positions = ListBuilder::new(Int32Builder::new()); + for (_, _, doc_positions) in &docs { + for p in doc_positions { + positions.values().append_value(*p as i32); + } + positions.append(true); + } + PostingList::Plain(PlainPostingList::new( + ScalarBuffer::from(row_ids), + ScalarBuffer::from(freqs), + None, + Some(positions.finish()), + )) +} - /// Add one document: `terms` is `(token, freq, positions)` per distinct - /// token, `num_tokens` is the document length. - fn add_doc(&mut self, row_position: u64, num_tokens: u32, terms: Vec<(String, u32, Vec)>) { - let doc_id = self.docs.append(row_position, num_tokens); - for (token, freq, positions) in terms { - let token_id = self.tokens.add(token) as usize; - if token_id >= self.postings.len() { - self.postings.resize_with(token_id + 1, Vec::new); - } - self.postings[token_id].push((doc_id, freq, positions)); +/// In-memory FTS partitions only ever build `PostingList::Plain`. +fn posting_as_plain(p: &PostingList) -> &PlainPostingList { + match p { + PostingList::Plain(pl) => pl, + PostingList::Compressed(_) => { + unreachable!("in-memory FTS partitions only build plain posting lists") } } +} - /// Freeze into an immutable `Partition`. Each token's postings are sorted - /// by doc id and converted to a `PlainPostingList`; `max_score` is left - /// `None` so WAND derives the per-term upper bound from `df` at query - /// time (global BM25 stats are not known to a single partition). - fn build(self) -> Partition { - let postings: Vec = self - .postings - .into_iter() - .map(|mut docs| { - docs.sort_by_key(|(doc_id, _, _)| *doc_id); - let row_ids: Vec = docs.iter().map(|(d, _, _)| *d as u64).collect(); - let freqs: Vec = docs.iter().map(|(_, f, _)| *f as f32).collect(); - let mut positions = ListBuilder::new(Int32Builder::new()); - for (_, _, doc_positions) in &docs { - for p in doc_positions { - positions.values().append_value(*p as i32); - } - positions.append(true); - } - PostingList::Plain(PlainPostingList::new( - ScalarBuffer::from(row_ids), - ScalarBuffer::from(freqs), - None, - Some(positions.finish()), - )) - }) - .collect(); - Partition { - tokens: self.tokens, - postings, - docs: self.docs, - } +/// Read the position list for the `idx`-th posting as a `Vec`. +fn read_positions(plain: &PlainPostingList, idx: usize) -> Vec { + match plain.positions(idx) { + Some(arr) => arr + .as_any() + .downcast_ref::() + .map(|a| a.values().iter().map(|&v| v as u32).collect()) + .unwrap_or_default(), + None => Vec::new(), } } @@ -2821,82 +3211,235 @@ mod tests { assert!(err.to_string().contains("only supports"), "{err}"); } - // ===== Partition redesign (Stage 2) ===== + // ===== Partition-structured redesign ===== - fn build_test_partition() -> Partition { - // 4 docs at row positions 100..104. - let mut b = PartitionBuilder::new(); - b.add_doc( - 100, - 2, - vec![ - ("apple".to_string(), 1, vec![0]), - ("banana".to_string(), 1, vec![1]), - ], - ); - b.add_doc( - 101, - 2, - vec![ - ("apple".to_string(), 1, vec![0]), - ("cherry".to_string(), 1, vec![1]), - ], - ); - b.add_doc(102, 1, vec![("banana".to_string(), 1, vec![0])]); - b.add_doc( - 103, - 3, - vec![ - ("apple".to_string(), 2, vec![0, 1]), - ("date".to_string(), 1, vec![2]), - ], - ); - b.build() + /// Sorted row positions of a result set. + fn rows(mut entries: Vec) -> Vec { + entries.sort_by_key(|e| e.row_position); + entries.into_iter().map(|e| e.row_position).collect() } - fn search_partition(p: &Partition, token: &str) -> Vec { - let mut token_docs = std::collections::HashMap::new(); - token_docs.insert(token.to_string(), p.token_df(token)); - let scorer = MemBM25Scorer::new(p.total_tokens(), p.doc_count(), token_docs); - let params = FtsSearchParams::default().with_limit(Some(10)); - let hits = p - .search(&[(token.to_string(), 0)], Operator::Or, scorer, ¶ms) - .unwrap(); - let mut ids: Vec = hits.iter().map(|c| c.row_id).collect(); - ids.sort_unstable(); - ids + /// An index whose 4 docs (row positions 100..104) have all been frozen + /// into a single partition (`freeze_threshold_rows = 1`). + fn build_test_partition_index() -> FtsMemIndex { + let index = FtsMemIndex::new(1, "description".to_string()).with_freeze_threshold_rows(1); + let batch = RecordBatch::try_new( + create_test_schema(), + vec![ + Arc::new(Int32Array::from(vec![0, 1, 2, 3])), + Arc::new(StringArray::from(vec![ + "apple banana", + "apple cherry", + "banana", + "apple apple date", + ])), + ], + ) + .unwrap(); + index.insert(&batch, 100).unwrap(); + index } #[test] fn test_partition_build_and_search() { - let p = build_test_partition(); + let index = build_test_partition_index(); + let st = index.state.load_full(); + assert_eq!(st.partitions.len(), 1, "the batch should have frozen"); + assert_eq!(st.tail.visible_count(), 0, "the tail should be empty"); + + let p = &st.partitions[0]; assert_eq!(p.doc_count(), 4); assert_eq!(p.total_tokens(), 8); // 2 + 2 + 1 + 3 - assert_eq!(p.token_df("apple"), 3); - assert_eq!(p.token_df("banana"), 2); - assert_eq!(p.token_df("missing"), 0); + // Term dictionary stores the *tokenized* form, so probe via the + // configured tokenizer rather than the raw word. + let tok = |w: &str| index.tokenize_for_search(w).pop().unwrap(); + assert_eq!(p.token_df(&tok("apple")), 3); + assert_eq!(p.token_df(&tok("banana")), 2); + assert_eq!(p.token_df("definitely_missing"), 0); - // WAND search must return the row positions containing each term. - assert_eq!(search_partition(&p, "apple"), vec![100, 101, 103]); - assert_eq!(search_partition(&p, "banana"), vec![100, 102]); - assert_eq!(search_partition(&p, "date"), vec![103]); - assert!(search_partition(&p, "missing").is_empty()); + // Searching the index must return the partition's row positions. + assert_eq!(rows(index.search("apple")), vec![100, 101, 103]); + assert_eq!(rows(index.search("banana")), vec![100, 102]); + assert_eq!(rows(index.search("date")), vec![103]); + assert!(index.search("definitely_missing").is_empty()); } #[test] fn test_partition_and_query_short_circuits_missing_term() { - let p = build_test_partition(); - // "apple" present, "missing" absent -> AND yields nothing. - let scorer = MemBM25Scorer::new(p.total_tokens(), p.doc_count(), Default::default()); - let params = FtsSearchParams::default().with_limit(Some(10)); - let hits = p - .search( - &[("apple".to_string(), 0), ("missing".to_string(), 1)], - Operator::And, - scorer, - ¶ms, - ) + let index = build_test_partition_index(); + let st = index.state.load_full(); + let p = &st.partitions[0]; + let tail_snap = st.tail.snapshot(); + let apple = index.tokenize_for_search("apple").pop().unwrap(); + // "apple" is present -> an OR search over it matches. + let or_scorer = build_scorer(&st, &tail_snap, std::slice::from_ref(&apple)); + let or_hits = p + .search_match(std::slice::from_ref(&apple), Operator::Or, &or_scorer, None) + .unwrap(); + assert_eq!(or_hits.len(), 3); + // Adding an absent term to an AND query short-circuits to nothing. + let and_tokens = vec![apple, "definitely_missing".to_string()]; + let and_scorer = build_scorer(&st, &tail_snap, &and_tokens); + let and_hits = p + .search_match(&and_tokens, Operator::And, &and_scorer, None) .unwrap(); - assert!(hits.is_empty()); + assert!(and_hits.is_empty()); + } + + #[test] + fn test_freeze_produces_multiple_partitions() { + // freeze_threshold_rows = 3 with 3-row batches => one partition per + // batch insert. + let schema = create_test_schema(); + let index = FtsMemIndex::new(1, "description".to_string()).with_freeze_threshold_rows(3); + for i in 0..5 { + index + .insert(&create_test_batch(&schema), (i * 100) as u64) + .unwrap(); + } + let st = index.state.load_full(); + assert_eq!(st.partitions.len(), 5, "every batch should have frozen"); + assert_eq!(st.tail.visible_count(), 0); + assert_eq!(index.doc_count(), 15); + + // "hello" appears in docs 0 and 2 of every batch; "world" in 0 and 1. + assert_eq!( + rows(index.search("hello")), + vec![0, 2, 100, 102, 200, 202, 300, 302, 400, 402] + ); + assert_eq!( + rows(index.search("world")), + vec![0, 1, 100, 101, 200, 201, 300, 301, 400, 401] + ); + assert!(index.search("definitely_missing").is_empty()); + // Every score must be finite and positive. + for e in index.search("world") { + assert!(e.score.is_finite() && e.score > 0.0); + } + } + + #[test] + fn test_search_spans_partitions_and_tail() { + // freeze_threshold_rows = 5, 3-row batches: batch 1 -> tail(3); + // batch 2 -> tail(6) freezes -> partition(6); batch 3 -> tail(3). + let schema = create_test_schema(); + let index = FtsMemIndex::new(1, "description".to_string()).with_freeze_threshold_rows(5); + index.insert(&create_test_batch(&schema), 0).unwrap(); + index.insert(&create_test_batch(&schema), 100).unwrap(); + index.insert(&create_test_batch(&schema), 200).unwrap(); + let st = index.state.load_full(); + assert_eq!(st.partitions.len(), 1, "the first two batches froze"); + assert_eq!(st.tail.visible_count(), 1, "the third batch is in the tail"); + assert_eq!(index.doc_count(), 9); + + // Results must merge the partition (rows 0..200) and the tail (200..). + assert_eq!(rows(index.search("hello")), vec![0, 2, 100, 102, 200, 202]); + assert_eq!(rows(index.search("goodbye")), vec![1, 101, 201]); + } + + #[test] + fn test_flush_merges_partitions_and_tail() { + // A frozen partition plus a live tail must all reach the builder. + let schema = create_test_schema(); + let index = FtsMemIndex::new(1, "description".to_string()).with_freeze_threshold_rows(5); + index.insert(&create_test_batch(&schema), 0).unwrap(); + index.insert(&create_test_batch(&schema), 100).unwrap(); + index.insert(&create_test_batch(&schema), 200).unwrap(); + let st = index.state.load_full(); + assert_eq!(st.partitions.len(), 1); + assert_eq!(st.tail.visible_count(), 1); + + let builder = index.to_index_builder_reversed(7, 300).unwrap(); + assert_eq!(builder.id(), 7); + // A non-empty builder proves the partition and the tail both reached + // the flush; end-to-end flush correctness is covered by the MemTable + // flush integration tests. + assert!( + !builder.is_empty(), + "9 docs across partition + tail must flush" + ); + } + + #[test] + fn test_phrase_across_partitions() { + let schema = create_test_schema(); + let index = FtsMemIndex::new(1, "description".to_string()).with_freeze_threshold_rows(3); + // Each batch has "hello world" at its row_offset row. + for i in 0..4 { + index + .insert(&create_test_batch(&schema), (i * 100) as u64) + .unwrap(); + } + let st = index.state.load_full(); + assert_eq!(st.partitions.len(), 4); + // The phrase must be found in every partition. + assert_eq!( + rows(index.search_phrase("hello world", 0)), + vec![0, 100, 200, 300] + ); + // A phrase not present anywhere returns nothing. + assert!(index.search_phrase("world hello", 0).is_empty()); + } + + #[test] + fn test_merge_caps_partition_count() { + // freeze_threshold_rows = 1 freezes every 3-row batch; past + // MAX_PARTITIONS (32) the partitions are merged into one. + let schema = create_test_schema(); + let index = FtsMemIndex::new(1, "description".to_string()).with_freeze_threshold_rows(1); + for i in 0..40 { + index + .insert(&create_test_batch(&schema), (i * 100) as u64) + .unwrap(); + } + let st = index.state.load_full(); + assert!( + st.partitions.len() <= FtsMemIndex::MAX_PARTITIONS, + "partition count {} should be capped", + st.partitions.len() + ); + assert_eq!(index.doc_count(), 120); + // Merge must preserve every posting: "hello" hits 2 docs per batch. + assert_eq!(index.search("hello").len(), 80); + for e in index.search("hello") { + assert!(e.score.is_finite() && e.score > 0.0); + } + } + + #[test] + fn test_freeze_during_concurrent_search() { + // Searches running concurrently with freezes must never observe a + // doc twice or miss one: every result row is valid and scored. + let schema = create_test_schema(); + let index = + Arc::new(FtsMemIndex::new(1, "description".to_string()).with_freeze_threshold_rows(6)); + let stop = Arc::new(AtomicBool::new(false)); + let mut readers = Vec::new(); + for _ in 0..4 { + let idx = index.clone(); + let stop = stop.clone(); + readers.push(std::thread::spawn(move || { + while !stop.load(Ordering::Relaxed) { + for e in idx.search("world") { + // "world" only ever appears in docs 0 and 1 of a + // batch, whose row_offset is a multiple of 100. + assert!(e.row_position % 100 < 2); + assert!(e.score.is_finite() && e.score >= 0.0); + } + } + })); + } + for i in 0..60 { + index + .insert(&create_test_batch(&schema), (i * 100) as u64) + .unwrap(); + } + std::thread::sleep(std::time::Duration::from_millis(20)); + stop.store(true, Ordering::Relaxed); + for r in readers { + r.join().unwrap(); + } + assert_eq!(index.doc_count(), 180); + assert_eq!(index.search("world").len(), 120); } } From 0809e9d8be0983cf915e8893811635db898da10d Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sat, 16 May 2026 16:16:12 -0700 Subject: [PATCH 21/37] fix(mem_wal): run per-partition FTS WAND unbounded to keep top-k exact MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Block-max WAND top-k pruning needs per-term `max_score` upper bounds, which in-memory partition posting lists do not carry (`max_score` is `None`). A WAND given a `limit` therefore pruned by a bound it could not compute and silently dropped valid top-k docs — term_recall@10 collapsed from 1.0 to ~0.1 once the corpus exceeded the freeze threshold and partitions appeared. Per-partition WAND now always runs unbounded; the scan is still O(matches) (the partition layout, not WAND pruning, is what makes latency flat), and `search_with_options` truncates to the real limit after merging. Adds a regression test asserting a limited search over many partitions returns the exact global top-k. --- rust/lance/src/dataset/mem_wal/index/fts.rs | 97 ++++++++++++++------- 1 file changed, 67 insertions(+), 30 deletions(-) diff --git a/rust/lance/src/dataset/mem_wal/index/fts.rs b/rust/lance/src/dataset/mem_wal/index/fts.rs index 0a99a109986..cb32b148102 100644 --- a/rust/lance/src/dataset/mem_wal/index/fts.rs +++ b/rust/lance/src/dataset/mem_wal/index/fts.rs @@ -995,7 +995,7 @@ impl FtsMemIndex { pub fn search(&self, term: &str) -> Vec { let st = self.state.load_full(); let tokens = self.tokenize_for_search(term); - self.search_match(&st, &tokens, None) + self.search_match(&st, &tokens) } /// Search for documents containing an exact phrase, optionally allowing @@ -1035,12 +1035,7 @@ impl FtsMemIndex { /// scan the tail, all scored with one corpus-wide [`MemBM25Scorer`], and /// the results merged. Each doc lives in exactly one partition or the /// tail, so the merge is a plain concatenation. - fn search_match( - &self, - st: &IndexState, - tokens: &[String], - limit: Option, - ) -> Vec { + fn search_match(&self, st: &IndexState, tokens: &[String]) -> Vec { if tokens.is_empty() { return Vec::new(); } @@ -1053,7 +1048,7 @@ impl FtsMemIndex { } let mut results = Vec::new(); for p in st.partitions.iter() { - match p.search_match(tokens, Operator::Or, &scorer, limit) { + match p.search_match(tokens, Operator::Or, &scorer) { Ok(hits) => results.extend(hits), Err(e) => log::warn!("FTS partition match search failed: {e}"), } @@ -1070,7 +1065,7 @@ impl FtsMemIndex { } if tokens.len() == 1 { // A single-token phrase reduces to a regular term search. - return self.search_match(st, tokens, None); + return self.search_match(st, tokens); } let tail_snap = st.tail.snapshot(); let scorer = build_scorer(st, &tail_snap, tokens); @@ -1119,7 +1114,7 @@ impl FtsMemIndex { if expanded.is_empty() { return Vec::new(); } - self.search_match(st, &expanded, None) + self.search_match(st, &expanded) } /// Expand `term` against the term dictionaries of every partition and the @@ -1179,19 +1174,14 @@ impl FtsMemIndex { /// per-batch monotonic visibility contract for compound queries. pub fn search_query(&self, query: &FtsQueryExpr) -> Vec { let st = self.state.load_full(); - self.search_query_with_state(query, &st, None) + self.search_query_with_state(query, &st) } - fn search_query_with_state( - &self, - query: &FtsQueryExpr, - st: &IndexState, - limit: Option, - ) -> Vec { + fn search_query_with_state(&self, query: &FtsQueryExpr, st: &IndexState) -> Vec { match query { FtsQueryExpr::Match { query, boost } => { let tokens = self.tokenize_for_search(query); - let mut results = self.search_match(st, &tokens, limit); + let mut results = self.search_match(st, &tokens); apply_boost(&mut results, *boost); results } @@ -1233,7 +1223,7 @@ impl FtsMemIndex { options: SearchOptions, ) -> Vec { let st = self.state.load_full(); - let mut results = self.search_query_with_state(query, &st, options.limit); + let mut results = self.search_query_with_state(query, &st); results.sort_by(|a, b| { b.score .partial_cmp(&a.score) @@ -1264,11 +1254,11 @@ impl FtsMemIndex { negative_boost: f32, st: &IndexState, ) -> Vec { - let mut results = self.search_query_with_state(positive, st, None); + let mut results = self.search_query_with_state(positive, st); let Some(neg) = negative else { return results; }; - let negative_results = self.search_query_with_state(neg, st, None); + let negative_results = self.search_query_with_state(neg, st); let negative_set: HashSet = negative_results .into_iter() .map(|e| e.row_position) @@ -1290,26 +1280,26 @@ impl FtsMemIndex { ) -> Vec { let excluded: HashSet = must_not .iter() - .flat_map(|q| self.search_query_with_state(q, st, None)) + .flat_map(|q| self.search_query_with_state(q, st)) .map(|e| e.row_position) .collect(); let mut result_map: HashMap = if must.is_empty() { let mut map: HashMap = HashMap::new(); for q in should { - for entry in self.search_query_with_state(q, st, None) { + for entry in self.search_query_with_state(q, st) { *map.entry(entry.row_position).or_default() += entry.score; } } map } else { - let first_results = self.search_query_with_state(&must[0], st, None); + let first_results = self.search_query_with_state(&must[0], st); let mut map: HashMap = first_results .into_iter() .map(|e| (e.row_position, e.score)) .collect(); for q in must.iter().skip(1) { - let results = self.search_query_with_state(q, st, None); + let results = self.search_query_with_state(q, st); let result_set: HashMap = results .into_iter() .map(|e| (e.row_position, e.score)) @@ -1320,7 +1310,7 @@ impl FtsMemIndex { .collect(); } for q in should { - for entry in self.search_query_with_state(q, st, None) { + for entry in self.search_query_with_state(q, st) { if let Some(score) = map.get_mut(&entry.row_position) { *score += entry.score; } @@ -2038,12 +2028,15 @@ impl Partition { /// WAND OR/AND-search; candidates scored with `scorer` and reported as /// MemTable row positions. + /// WAND runs unbounded (no `limit`): block-max top-k pruning needs + /// per-term `max_score` upper bounds, which in-memory posting lists do + /// not carry, so a bounded WAND would drop valid top-k docs. The scan is + /// still O(matches); the caller truncates to the real limit after merge. fn search_match( &self, tokens: &[String], operator: Operator, scorer: &MemBM25Scorer, - limit: Option, ) -> Result> { let mut wand_terms: Vec = Vec::new(); let mut tok_order: Vec = Vec::new(); @@ -2067,7 +2060,7 @@ impl Partition { if wand_terms.is_empty() { return Ok(Vec::new()); } - let params = FtsSearchParams::new().with_limit(limit); + let params = FtsSearchParams::new(); let cands = wand_search(operator, wand_terms, &self.docs, scorer.clone(), ¶ms)?; Ok(cands .into_iter() @@ -3274,14 +3267,14 @@ mod tests { // "apple" is present -> an OR search over it matches. let or_scorer = build_scorer(&st, &tail_snap, std::slice::from_ref(&apple)); let or_hits = p - .search_match(std::slice::from_ref(&apple), Operator::Or, &or_scorer, None) + .search_match(std::slice::from_ref(&apple), Operator::Or, &or_scorer) .unwrap(); assert_eq!(or_hits.len(), 3); // Adding an absent term to an AND query short-circuits to nothing. let and_tokens = vec![apple, "definitely_missing".to_string()]; let and_scorer = build_scorer(&st, &tail_snap, &and_tokens); let and_hits = p - .search_match(&and_tokens, Operator::And, &and_scorer, None) + .search_match(&and_tokens, Operator::And, &and_scorer) .unwrap(); assert!(and_hits.is_empty()); } @@ -3442,4 +3435,48 @@ mod tests { assert_eq!(index.doc_count(), 180); assert_eq!(index.search("world").len(), 120); } + + #[test] + fn test_limited_search_returns_exact_top_k_across_partitions() { + // Regression: a limited `search_with_options` over many partitions + // must still return the *exact* global top-k. Per-partition WAND must + // not prune by a limit it cannot bound (in-memory posting lists carry + // no `max_score`), or valid top-k docs are silently dropped. + let index = FtsMemIndex::new(1, "description".to_string()).with_freeze_threshold_rows(2); + // 8 docs (4 batches of 2) -> 4 partitions. "lion" appears with tf + // 1..=6, so the 6 matching docs have strictly distinct BM25 scores. + let texts = [ + ["lion", "lion lion"], + ["lion lion lion", "lion lion lion lion"], + ["cat", "lion lion lion lion lion"], + ["lion lion lion lion lion lion", "dog"], + ]; + for (b, pair) in texts.iter().enumerate() { + let batch = RecordBatch::try_new( + create_test_schema(), + vec![ + Arc::new(Int32Array::from(vec![0, 1])), + Arc::new(StringArray::from(pair.to_vec())), + ], + ) + .unwrap(); + index.insert(&batch, (b * 100) as u64).unwrap(); + } + assert_eq!(index.state.load_full().partitions.len(), 4); + + // Exact full ranking via the unbounded search path. + let mut full = index.search("lion"); + full.sort_by(|a, b| b.score.partial_cmp(&a.score).unwrap()); + assert_eq!(full.len(), 6); + + // The limited top-3 must equal the first 3 of the full ranking. + let limited = index.search_with_options( + &FtsQueryExpr::match_query("lion"), + SearchOptions::new().with_limit(3), + ); + assert_eq!(limited.len(), 3); + let got: Vec = limited.iter().map(|e| e.row_position).collect(); + let expected: Vec = full.iter().take(3).map(|e| e.row_position).collect(); + assert_eq!(got, expected, "limited search must return the exact top-3"); + } } From 46b3b172b7770e21bcae3d7bc23048405804e3b2 Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sat, 16 May 2026 16:27:19 -0700 Subject: [PATCH 22/37] fix(mem_wal): score FTS partitions by direct posting scan, not WAND MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Block-max WAND needs per-term `max_score` upper bounds to prune safely. Frozen in-memory partition posting lists carry none (`max_score` is `None`), so even an unbounded WAND mis-pruned and term_recall@10 collapsed from 1.0 to ~0.12 once the corpus crossed the freeze threshold. `Partition::search_match` / `search_phrase` now scan the per-term merged posting list directly: OR accumulates each token's BM25 contribution per doc, phrase intersects from the rarest token and verifies positions. A partition holds one merged list per term, so this is O(matches) — the per-batch-chunk O(corpus) layout was the original problem, not the absence of WAND pruning. All scoring still routes through the shared `MemBM25Scorer`, so partition and tail scores stay identical. Block-max WAND can be reintroduced later once partitions carry precomputed per-term score bounds. --- rust/lance/src/dataset/mem_wal/index/fts.rs | 177 +++++++++----------- 1 file changed, 76 insertions(+), 101 deletions(-) diff --git a/rust/lance/src/dataset/mem_wal/index/fts.rs b/rust/lance/src/dataset/mem_wal/index/fts.rs index cb32b148102..e0ef9b5eded 100644 --- a/rust/lance/src/dataset/mem_wal/index/fts.rs +++ b/rust/lance/src/dataset/mem_wal/index/fts.rs @@ -58,10 +58,10 @@ use arrow_schema::DataType; use crossbeam_skiplist::SkipMap; use lance_core::{Error, Result}; use lance_index::scalar::InvertedIndexParams; -use lance_index::scalar::inverted::query::{FtsSearchParams, Operator}; +use lance_index::scalar::inverted::query::Operator; use lance_index::scalar::inverted::tokenizer::document_tokenizer::LanceTokenizer; use lance_index::scalar::inverted::{ - DocSet, MemBM25Scorer, PlainPostingList, PostingList, Scorer, TokenSet, WandTerm, wand_search, + DocSet, MemBM25Scorer, PlainPostingList, PostingList, Scorer, TokenSet, }; use lance_tokenizer::TokenStream; @@ -1048,10 +1048,7 @@ impl FtsMemIndex { } let mut results = Vec::new(); for p in st.partitions.iter() { - match p.search_match(tokens, Operator::Or, &scorer) { - Ok(hits) => results.extend(hits), - Err(e) => log::warn!("FTS partition match search failed: {e}"), - } + results.extend(p.search_match(tokens, Operator::Or, &scorer)); } if tail_snap.visible_count > 0 { results.extend(score_terms(&tail_snap, &st.tail.terms, tokens, &scorer)); @@ -1074,10 +1071,7 @@ impl FtsMemIndex { } let mut results = Vec::new(); for p in st.partitions.iter() { - match p.search_phrase(tokens, slop, &scorer) { - Ok(hits) => results.extend(hits), - Err(e) => log::warn!("FTS partition phrase search failed: {e}"), - } + results.extend(p.search_phrase(tokens, slop, &scorer)); } if tail_snap.visible_count > 0 { results.extend(phrase_search_tail( @@ -2026,105 +2020,91 @@ impl Partition { } } - /// WAND OR/AND-search; candidates scored with `scorer` and reported as - /// MemTable row positions. - /// WAND runs unbounded (no `limit`): block-max top-k pruning needs - /// per-term `max_score` upper bounds, which in-memory posting lists do - /// not carry, so a bounded WAND would drop valid top-k docs. The scan is - /// still O(matches); the caller truncates to the real limit after merge. + /// BM25 OR/AND-search by a direct posting-list scan. A partition holds one + /// merged posting list per term, so cost is O(matches) — not O(corpus) as + /// the old per-batch-chunk layout was. Block-max WAND pruning is not used: + /// it needs per-term `max_score` upper bounds that frozen in-memory + /// posting lists do not carry, and an unbounded WAND mis-prunes without + /// them. Each token occurrence in `tokens` contributes independently, so + /// scores match the tail's `score_terms`. fn search_match( &self, tokens: &[String], operator: Operator, scorer: &MemBM25Scorer, - ) -> Result> { - let mut wand_terms: Vec = Vec::new(); - let mut tok_order: Vec = Vec::new(); - for (qpos, t) in tokens.iter().enumerate() { - match self.token_ids.get(t.as_str()) { + ) -> Vec { + // doc -> (accumulated score, number of token-occurrence hits). + let mut doc_scores: HashMap = HashMap::new(); + let mut any_present = false; + let mut all_present = true; + for token in tokens { + match self.token_ids.get(token.as_str()) { Some(&id) => { - wand_terms.push(( - t.clone(), - id, - qpos as u32, - 1.0, - self.postings[id as usize].clone(), - )); - tok_order.push(t.clone()); + any_present = true; + let plain = posting_as_plain(&self.postings[id as usize]); + let qw = scorer.query_weight(token); + for i in 0..plain.len() { + let doc = plain.row_ids[i] as u32; + let dl = self.docs.num_tokens(doc); + let s = qw * scorer.doc_weight(plain.frequencies[i] as u32, dl); + let e = doc_scores.entry(doc).or_insert((0.0, 0)); + e.0 += s; + e.1 += 1; + } } - // Term absent: an AND query cannot match here; OR drops it. - None if operator == Operator::And => return Ok(Vec::new()), - None => {} + None => all_present = false, } } - if wand_terms.is_empty() { - return Ok(Vec::new()); + if !any_present || (operator == Operator::And && !all_present) { + return Vec::new(); } - let params = FtsSearchParams::new(); - let cands = wand_search(operator, wand_terms, &self.docs, scorer.clone(), ¶ms)?; - Ok(cands + let need = tokens.len() as u32; + doc_scores .into_iter() - .map(|c| { - let score: f32 = c - .freqs - .iter() - .map(|(ti, f)| { - scorer.query_weight(&tok_order[*ti as usize]) - * scorer.doc_weight(*f, c.doc_length) - }) - .sum(); - FtsEntry { - row_position: self.docs.row_id(c.row_id as u32), - score, + .filter_map(|(doc, (score, hits))| { + // AND requires the doc to be hit once per query-token slot. + if operator == Operator::And && hits < need { + return None; } + Some(FtsEntry { + row_position: self.docs.row_id(doc), + score, + }) }) - .collect()) + .collect() } - /// Phrase-search: WAND-And finds docs containing every token, then token - /// positions are verified. `tokens.len() >= 2`. - fn search_phrase( - &self, - tokens: &[String], - slop: u32, - scorer: &MemBM25Scorer, - ) -> Result> { - let mut wand_terms: Vec = Vec::new(); - let mut token_id_order: Vec = Vec::new(); - for (qpos, t) in tokens.iter().enumerate() { - match self.token_ids.get(t.as_str()) { - Some(&id) => { - wand_terms.push(( - t.clone(), - id, - qpos as u32, - 1.0, - self.postings[id as usize].clone(), - )); - token_id_order.push(id); - } + /// Phrase-search by intersecting posting lists: drive from the rarest + /// token, require every other token to contain the doc, and verify the + /// token positions satisfy the phrase. `tokens.len() >= 2`. + fn search_phrase(&self, tokens: &[String], slop: u32, scorer: &MemBM25Scorer) -> Vec { + let mut plains: Vec<&PlainPostingList> = Vec::with_capacity(tokens.len()); + for token in tokens { + match self.token_ids.get(token.as_str()) { + Some(&id) => plains.push(posting_as_plain(&self.postings[id as usize])), // A phrase needs every token present in this partition. - None => return Ok(Vec::new()), + None => return Vec::new(), } } - let params = FtsSearchParams::new(); - let cands = wand_search( - Operator::And, - wand_terms, - &self.docs, - scorer.clone(), - ¶ms, - )?; + let rarest = (0..plains.len()).min_by_key(|&i| plains[i].len()).unwrap(); let mut results = Vec::new(); - for c in cands { - let local_doc = c.row_id; // partition-local doc id + for i in 0..plains[rarest].len() { + let doc = plains[rarest].row_ids[i]; let mut all_positions: Vec> = Vec::with_capacity(tokens.len()); + let mut freqs: Vec = Vec::with_capacity(tokens.len()); let mut present = true; - for &tid in &token_id_order { - let plain = posting_as_plain(&self.postings[tid as usize]); - match plain.row_ids.binary_search(&local_doc) { - Ok(idx) => all_positions.push(read_positions(plain, idx)), - Err(_) => { + for (ti, plain) in plains.iter().enumerate() { + let idx = if ti == rarest { + Some(i) + } else { + plain.row_ids.binary_search(&doc).ok() + }; + match idx { + Some(idx) => { + freqs.push(plain.frequencies[idx] as u32); + all_positions.push(read_positions(plain, idx)); + } + None => { present = false; break; } @@ -2133,19 +2113,18 @@ impl Partition { if !present || !phrase_matches(&all_positions, slop) { continue; } - let score: f32 = c - .freqs + let dl = self.docs.num_tokens(doc as u32); + let score: f32 = tokens .iter() - .map(|(ti, f)| { - scorer.query_weight(&tokens[*ti as usize]) * scorer.doc_weight(*f, c.doc_length) - }) + .zip(&freqs) + .map(|(t, &f)| scorer.query_weight(t) * scorer.doc_weight(f, dl)) .sum(); results.push(FtsEntry { - row_position: self.docs.row_id(local_doc as u32), + row_position: self.docs.row_id(doc as u32), score, }); } - Ok(results) + results } } @@ -3266,16 +3245,12 @@ mod tests { let apple = index.tokenize_for_search("apple").pop().unwrap(); // "apple" is present -> an OR search over it matches. let or_scorer = build_scorer(&st, &tail_snap, std::slice::from_ref(&apple)); - let or_hits = p - .search_match(std::slice::from_ref(&apple), Operator::Or, &or_scorer) - .unwrap(); + let or_hits = p.search_match(std::slice::from_ref(&apple), Operator::Or, &or_scorer); assert_eq!(or_hits.len(), 3); // Adding an absent term to an AND query short-circuits to nothing. let and_tokens = vec![apple, "definitely_missing".to_string()]; let and_scorer = build_scorer(&st, &tail_snap, &and_tokens); - let and_hits = p - .search_match(&and_tokens, Operator::And, &and_scorer) - .unwrap(); + let and_hits = p.search_match(&and_tokens, Operator::And, &and_scorer); assert!(and_hits.is_empty()); } From e096383a8acc779578a64f5041ccef3b4fabba23 Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sun, 17 May 2026 11:41:56 -0700 Subject: [PATCH 23/37] perf(mem_wal): compact FTS partition posting lists, drop Arrow builder slack MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Partition posting lists were `PlainPostingList`s whose positions were each built through a fresh Arrow `ListBuilder`/`Int32Builder`. Those builders default-allocate 1024-element buffers, so the ~1M+ tiny per-term posting lists across a large index carried ~10+ GB of pure capacity slack — in-memory FTS footprint regressed ~2.8x (29 GB vs 10 GB at 1M docs). Replaces the partition posting representation with `PartitionPosting`: exact-sized plain `Vec`s, partition-local `u32` doc ids (not `u64`), `u32` frequencies (not `f32`), and a CSR position layout. No Arrow builders, no slack, smaller per-element. The OR/AND scan now accumulates into flat arrays indexed by the dense local doc id instead of a per-posting `HashMap`. --- rust/lance/src/dataset/mem_wal/index/fts.rs | 228 ++++++++++---------- 1 file changed, 112 insertions(+), 116 deletions(-) diff --git a/rust/lance/src/dataset/mem_wal/index/fts.rs b/rust/lance/src/dataset/mem_wal/index/fts.rs index e0ef9b5eded..fb4c4d65cbd 100644 --- a/rust/lance/src/dataset/mem_wal/index/fts.rs +++ b/rust/lance/src/dataset/mem_wal/index/fts.rs @@ -51,18 +51,14 @@ use std::sync::Mutex; use std::sync::atomic::{AtomicUsize, Ordering}; use arc_swap::ArcSwap; -use arrow_array::builder::{Int32Builder, ListBuilder}; -use arrow_array::{Array, Int32Array, LargeStringArray, RecordBatch, StringArray, StringViewArray}; -use arrow_buffer::ScalarBuffer; +use arrow_array::{Array, LargeStringArray, RecordBatch, StringArray, StringViewArray}; use arrow_schema::DataType; use crossbeam_skiplist::SkipMap; use lance_core::{Error, Result}; use lance_index::scalar::InvertedIndexParams; use lance_index::scalar::inverted::query::Operator; use lance_index::scalar::inverted::tokenizer::document_tokenizer::LanceTokenizer; -use lance_index::scalar::inverted::{ - DocSet, MemBM25Scorer, PlainPostingList, PostingList, Scorer, TokenSet, -}; +use lance_index::scalar::inverted::{DocSet, MemBM25Scorer, Scorer, TokenSet}; use lance_tokenizer::TokenStream; use super::RowPosition; @@ -1405,19 +1401,18 @@ impl FtsMemIndex { let mut term_postings: HashMap>)>> = HashMap::new(); for p in st.partitions.iter() { for (token, posting) in p.tokens_with_postings() { - let plain = posting_as_plain(posting); let bucket = term_postings.entry(token.to_string()).or_default(); - for i in 0..plain.len() { - let row_pos = p.docs.row_id(plain.row_ids[i] as u32); + for i in 0..posting.len() { + let row_pos = p.docs.row_id(posting.doc_ids[i]); let Some(&doc_id) = original_to_doc_id.get(&row_pos) else { continue; }; let pos = if with_position { - Some(read_positions(plain, i)) + Some(posting.positions(i).to_vec()) } else { None }; - bucket.push((doc_id, plain.frequencies[i] as u32, pos)); + bucket.push((doc_id, posting.freqs[i], pos)); } } } @@ -1773,11 +1768,11 @@ fn phrase_search_tail( results } -fn phrase_matches(positions: &[Vec], slop: u32) -> bool { +fn phrase_matches>(positions: &[T], slop: u32) -> bool { if positions.is_empty() { return false; } - for &first_pos in &positions[0] { + for &first_pos in positions[0].as_ref() { if phrase_from_position(positions, first_pos, slop) { return true; } @@ -1785,12 +1780,13 @@ fn phrase_matches(positions: &[Vec], slop: u32) -> bool { false } -fn phrase_from_position(positions: &[Vec], first_pos: u32, slop: u32) -> bool { +fn phrase_from_position>(positions: &[T], first_pos: u32, slop: u32) -> bool { let mut expected = first_pos; for token_positions in positions.iter().skip(1) { let min = expected.saturating_add(1); let max = expected.saturating_add(1 + slop); match token_positions + .as_ref() .iter() .filter(|&&p| p >= min && p <= max) .min() @@ -1853,16 +1849,44 @@ impl FtsIndexConfig { // Immutable partition // ============================================================================ +/// A frozen per-term posting list inside a [`Partition`]. Exact-sized plain +/// `Vec`s — no Arrow builder capacity slack — with partition-local `u32` doc +/// ids. Positions use a CSR layout: posting `i` owns +/// `pos_data[pos_offsets[i]..pos_offsets[i + 1]]`. +struct PartitionPosting { + /// partition-local doc ids, ascending. + doc_ids: Vec, + /// term frequency per posting. + freqs: Vec, + /// `doc_ids.len() + 1` offsets into `pos_data`. + pos_offsets: Vec, + pos_data: Vec, +} + +impl PartitionPosting { + fn len(&self) -> usize { + self.doc_ids.len() + } + + fn positions(&self, i: usize) -> &[u32] { + &self.pos_data[self.pos_offsets[i] as usize..self.pos_offsets[i + 1] as usize] + } + + fn heap_size(&self) -> usize { + (self.doc_ids.len() + self.freqs.len() + self.pos_offsets.len() + self.pos_data.len()) + * std::mem::size_of::() + } +} + /// An immutable, frozen FTS partition: a slice of the MemTable's inserts held -/// as on-disk-shaped posting lists and queried with block-max WAND in -/// ≈O(matches). Built by freezing the tail; flushed 1:1 into the Lance FTS -/// on-disk format. See `DESIGN.md` in the redesign analysis directory. +/// as compact per-term posting lists, queried by a direct O(matches) scan. +/// Built by freezing the tail; flushed 1:1 into the Lance FTS on-disk format. +/// See `DESIGN.md` in the redesign analysis directory. struct Partition { /// token text -> local token id (dense, 0-based; indexes `postings`). token_ids: HashMap, u32>, - /// posting list per local token id. In-memory partitions are always - /// `PostingList::Plain` and always carry positions. - postings: Vec, + /// posting list per local token id. + postings: Vec, /// local doc id -> (MemTable row position, token count). docs: DocSet, } @@ -1877,10 +1901,7 @@ impl Partition { } fn entry_count(&self) -> usize { - self.postings - .iter() - .map(|p| posting_as_plain(p).len()) - .sum() + self.postings.iter().map(|p| p.len()).sum() } fn contains_token(&self, token: &str) -> bool { @@ -1891,7 +1912,7 @@ impl Partition { self.token_ids.keys() } - fn tokens_with_postings(&self) -> impl Iterator, &PostingList)> { + fn tokens_with_postings(&self) -> impl Iterator, &PartitionPosting)> { self.token_ids .iter() .map(move |(t, &id)| (t, &self.postings[id as usize])) @@ -1901,7 +1922,7 @@ impl Partition { fn token_df(&self, token: &str) -> usize { self.token_ids .get(token) - .map(|&id| posting_as_plain(&self.postings[id as usize]).len()) + .map(|&id| self.postings[id as usize].len()) .unwrap_or(0) } @@ -1910,16 +1931,8 @@ impl Partition { for t in self.token_ids.keys() { total += std::mem::size_of::>() + t.len() + std::mem::size_of::() + 16; } - for p in &self.postings { - let pl = posting_as_plain(p); - total += pl.row_ids.len() * std::mem::size_of::() - + pl.frequencies.len() * std::mem::size_of::() - + pl.positions - .as_ref() - .map(|a| a.get_array_memory_size()) - .unwrap_or(0); - } - total += self.docs.len() * (std::mem::size_of::() + std::mem::size_of::()); + total += self.postings.iter().map(|p| p.heap_size()).sum::(); + total += self.docs.len() * (std::mem::size_of::() + 2 * std::mem::size_of::()); total } @@ -1995,12 +2008,12 @@ impl Partition { raw.push(Vec::new()); id }); - let plain = posting_as_plain(&p.postings[local_id as usize]); - for i in 0..plain.len() { + let posting = &p.postings[local_id as usize]; + for i in 0..posting.len() { raw[merged_id as usize].push(( - plain.row_ids[i] as u32 + doc_offset, - plain.frequencies[i] as u32, - read_positions(plain, i), + posting.doc_ids[i] + doc_offset, + posting.freqs[i], + posting.positions(i).to_vec(), )); } } @@ -2033,23 +2046,24 @@ impl Partition { operator: Operator, scorer: &MemBM25Scorer, ) -> Vec { - // doc -> (accumulated score, number of token-occurrence hits). - let mut doc_scores: HashMap = HashMap::new(); + // Partition-local doc ids are dense 0..doc_count, so accumulate into + // flat arrays — no per-posting hashing. + let n = self.docs.len(); + let mut scores = vec![0.0f32; n]; + let mut hits = vec![0u32; n]; let mut any_present = false; let mut all_present = true; for token in tokens { match self.token_ids.get(token.as_str()) { Some(&id) => { any_present = true; - let plain = posting_as_plain(&self.postings[id as usize]); + let posting = &self.postings[id as usize]; let qw = scorer.query_weight(token); - for i in 0..plain.len() { - let doc = plain.row_ids[i] as u32; - let dl = self.docs.num_tokens(doc); - let s = qw * scorer.doc_weight(plain.frequencies[i] as u32, dl); - let e = doc_scores.entry(doc).or_insert((0.0, 0)); - e.0 += s; - e.1 += 1; + for i in 0..posting.len() { + let doc = posting.doc_ids[i] as usize; + let dl = self.docs.num_tokens(posting.doc_ids[i]); + scores[doc] += qw * scorer.doc_weight(posting.freqs[i], dl); + hits[doc] += 1; } } None => all_present = false, @@ -2058,51 +2072,52 @@ impl Partition { if !any_present || (operator == Operator::And && !all_present) { return Vec::new(); } + // AND requires the doc to be hit once per query-token slot. let need = tokens.len() as u32; - doc_scores - .into_iter() - .filter_map(|(doc, (score, hits))| { - // AND requires the doc to be hit once per query-token slot. - if operator == Operator::And && hits < need { - return None; - } - Some(FtsEntry { - row_position: self.docs.row_id(doc), - score, - }) - }) - .collect() + let mut results = Vec::new(); + for doc in 0..n { + if hits[doc] == 0 || (operator == Operator::And && hits[doc] < need) { + continue; + } + results.push(FtsEntry { + row_position: self.docs.row_id(doc as u32), + score: scores[doc], + }); + } + results } /// Phrase-search by intersecting posting lists: drive from the rarest /// token, require every other token to contain the doc, and verify the /// token positions satisfy the phrase. `tokens.len() >= 2`. fn search_phrase(&self, tokens: &[String], slop: u32, scorer: &MemBM25Scorer) -> Vec { - let mut plains: Vec<&PlainPostingList> = Vec::with_capacity(tokens.len()); + let mut postings: Vec<&PartitionPosting> = Vec::with_capacity(tokens.len()); for token in tokens { match self.token_ids.get(token.as_str()) { - Some(&id) => plains.push(posting_as_plain(&self.postings[id as usize])), + Some(&id) => postings.push(&self.postings[id as usize]), // A phrase needs every token present in this partition. None => return Vec::new(), } } - let rarest = (0..plains.len()).min_by_key(|&i| plains[i].len()).unwrap(); + let rarest = (0..postings.len()) + .min_by_key(|&i| postings[i].len()) + .unwrap(); let mut results = Vec::new(); - for i in 0..plains[rarest].len() { - let doc = plains[rarest].row_ids[i]; - let mut all_positions: Vec> = Vec::with_capacity(tokens.len()); + for i in 0..postings[rarest].len() { + let doc = postings[rarest].doc_ids[i]; + let mut all_positions: Vec<&[u32]> = Vec::with_capacity(tokens.len()); let mut freqs: Vec = Vec::with_capacity(tokens.len()); let mut present = true; - for (ti, plain) in plains.iter().enumerate() { + for (ti, posting) in postings.iter().enumerate() { let idx = if ti == rarest { Some(i) } else { - plain.row_ids.binary_search(&doc).ok() + posting.doc_ids.binary_search(&doc).ok() }; match idx { Some(idx) => { - freqs.push(plain.frequencies[idx] as u32); - all_positions.push(read_positions(plain, idx)); + freqs.push(posting.freqs[idx]); + all_positions.push(posting.positions(idx)); } None => { present = false; @@ -2113,14 +2128,14 @@ impl Partition { if !present || !phrase_matches(&all_positions, slop) { continue; } - let dl = self.docs.num_tokens(doc as u32); + let dl = self.docs.num_tokens(doc); let score: f32 = tokens .iter() .zip(&freqs) .map(|(t, &f)| scorer.query_weight(t) * scorer.doc_weight(f, dl)) .sum(); results.push(FtsEntry { - row_position: self.docs.row_id(doc as u32), + row_position: self.docs.row_id(doc), score, }); } @@ -2128,46 +2143,27 @@ impl Partition { } } -/// Convert per-token `(doc_id, freq, positions)` postings — sorted by doc id — -/// into an on-disk-shaped `PlainPostingList`. `max_score` is left `None`; -/// WAND derives per-term bounds from the posting list at query time. -fn freeze_postings_one(docs: Vec<(u32, u32, Vec)>) -> PostingList { - let row_ids: Vec = docs.iter().map(|(d, _, _)| *d as u64).collect(); - let freqs: Vec = docs.iter().map(|(_, f, _)| *f as f32).collect(); - let mut positions = ListBuilder::new(Int32Builder::new()); - for (_, _, doc_positions) in &docs { - for p in doc_positions { - positions.values().append_value(*p as i32); - } - positions.append(true); - } - PostingList::Plain(PlainPostingList::new( - ScalarBuffer::from(row_ids), - ScalarBuffer::from(freqs), - None, - Some(positions.finish()), - )) -} - -/// In-memory FTS partitions only ever build `PostingList::Plain`. -fn posting_as_plain(p: &PostingList) -> &PlainPostingList { - match p { - PostingList::Plain(pl) => pl, - PostingList::Compressed(_) => { - unreachable!("in-memory FTS partitions only build plain posting lists") - } - } -} - -/// Read the position list for the `idx`-th posting as a `Vec`. -fn read_positions(plain: &PlainPostingList, idx: usize) -> Vec { - match plain.positions(idx) { - Some(arr) => arr - .as_any() - .downcast_ref::() - .map(|a| a.values().iter().map(|&v| v as u32).collect()) - .unwrap_or_default(), - None => Vec::new(), +/// Convert `(doc_id, freq, positions)` triples — already sorted by doc id — +/// into a compact, exact-sized [`PartitionPosting`]. +fn freeze_postings_one(docs: Vec<(u32, u32, Vec)>) -> PartitionPosting { + let n = docs.len(); + let total_pos: usize = docs.iter().map(|(_, _, p)| p.len()).sum(); + let mut doc_ids = Vec::with_capacity(n); + let mut freqs = Vec::with_capacity(n); + let mut pos_offsets = Vec::with_capacity(n + 1); + let mut pos_data = Vec::with_capacity(total_pos); + pos_offsets.push(0); + for (d, f, positions) in docs { + doc_ids.push(d); + freqs.push(f); + pos_data.extend_from_slice(&positions); + pos_offsets.push(pos_data.len() as u32); + } + PartitionPosting { + doc_ids, + freqs, + pos_offsets, + pos_data, } } From bde61dd693bcf6bdee171143f39a9657ad4ae5f9 Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sun, 17 May 2026 11:56:44 -0700 Subject: [PATCH 24/37] perf(mem_wal): prune FTS partition top-k queries with block-max WAND MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit A limited query previously scored every matching doc in every partition — O(matches), so p50 grew with the corpus and trailed Lucene by ~24x. Each `PartitionPosting` now carries `(max_freq, min_dl)`, which yields a sound per-term BM25 score upper bound (`doc_weight` is monotone in both). With that bound, `Partition::wand_match` runs block-max WAND: it maintains a top-k heap and skips any doc whose cumulative term upper bounds cannot reach the k-th score. This is exact — the earlier WAND attempt mis-pruned only because the posting lists carried no bound at all. WAND runs for OR queries that carry a result limit (`search_with_options`); unbounded `search` and AND still take the exact direct scan. Adds a test asserting WAND top-k equals the exact full-scan top-k across partitions. --- rust/lance/src/dataset/mem_wal/index/fts.rs | 325 +++++++++++++++++--- 1 file changed, 275 insertions(+), 50 deletions(-) diff --git a/rust/lance/src/dataset/mem_wal/index/fts.rs b/rust/lance/src/dataset/mem_wal/index/fts.rs index fb4c4d65cbd..2b2fda7de4b 100644 --- a/rust/lance/src/dataset/mem_wal/index/fts.rs +++ b/rust/lance/src/dataset/mem_wal/index/fts.rs @@ -45,7 +45,8 @@ //! into one builder. The on-disk format is unchanged from Lance's existing //! inverted index. -use std::collections::{HashMap, HashSet}; +use std::cmp::Reverse; +use std::collections::{BinaryHeap, HashMap, HashSet}; use std::sync::Arc; use std::sync::Mutex; use std::sync::atomic::{AtomicUsize, Ordering}; @@ -991,7 +992,7 @@ impl FtsMemIndex { pub fn search(&self, term: &str) -> Vec { let st = self.state.load_full(); let tokens = self.tokenize_for_search(term); - self.search_match(&st, &tokens) + self.search_match(&st, &tokens, None) } /// Search for documents containing an exact phrase, optionally allowing @@ -1031,7 +1032,12 @@ impl FtsMemIndex { /// scan the tail, all scored with one corpus-wide [`MemBM25Scorer`], and /// the results merged. Each doc lives in exactly one partition or the /// tail, so the merge is a plain concatenation. - fn search_match(&self, st: &IndexState, tokens: &[String]) -> Vec { + fn search_match( + &self, + st: &IndexState, + tokens: &[String], + limit: Option, + ) -> Vec { if tokens.is_empty() { return Vec::new(); } @@ -1044,7 +1050,7 @@ impl FtsMemIndex { } let mut results = Vec::new(); for p in st.partitions.iter() { - results.extend(p.search_match(tokens, Operator::Or, &scorer)); + results.extend(p.search_match(tokens, Operator::Or, &scorer, limit)); } if tail_snap.visible_count > 0 { results.extend(score_terms(&tail_snap, &st.tail.terms, tokens, &scorer)); @@ -1058,7 +1064,7 @@ impl FtsMemIndex { } if tokens.len() == 1 { // A single-token phrase reduces to a regular term search. - return self.search_match(st, tokens); + return self.search_match(st, tokens, None); } let tail_snap = st.tail.snapshot(); let scorer = build_scorer(st, &tail_snap, tokens); @@ -1104,7 +1110,7 @@ impl FtsMemIndex { if expanded.is_empty() { return Vec::new(); } - self.search_match(st, &expanded) + self.search_match(st, &expanded, None) } /// Expand `term` against the term dictionaries of every partition and the @@ -1164,14 +1170,22 @@ impl FtsMemIndex { /// per-batch monotonic visibility contract for compound queries. pub fn search_query(&self, query: &FtsQueryExpr) -> Vec { let st = self.state.load_full(); - self.search_query_with_state(query, &st) + self.search_query_with_state(query, &st, None) } - fn search_query_with_state(&self, query: &FtsQueryExpr, st: &IndexState) -> Vec { + /// `limit` is the caller's top-k, threaded down so a top-level `Match` + /// leaf can prune with WAND. Compound branches (`Boolean`/`Boost`) need + /// their children's full result sets, so they pass `None` downward. + fn search_query_with_state( + &self, + query: &FtsQueryExpr, + st: &IndexState, + limit: Option, + ) -> Vec { match query { FtsQueryExpr::Match { query, boost } => { let tokens = self.tokenize_for_search(query); - let mut results = self.search_match(st, &tokens); + let mut results = self.search_match(st, &tokens, limit); apply_boost(&mut results, *boost); results } @@ -1213,7 +1227,7 @@ impl FtsMemIndex { options: SearchOptions, ) -> Vec { let st = self.state.load_full(); - let mut results = self.search_query_with_state(query, &st); + let mut results = self.search_query_with_state(query, &st, options.limit); results.sort_by(|a, b| { b.score .partial_cmp(&a.score) @@ -1244,11 +1258,11 @@ impl FtsMemIndex { negative_boost: f32, st: &IndexState, ) -> Vec { - let mut results = self.search_query_with_state(positive, st); + let mut results = self.search_query_with_state(positive, st, None); let Some(neg) = negative else { return results; }; - let negative_results = self.search_query_with_state(neg, st); + let negative_results = self.search_query_with_state(neg, st, None); let negative_set: HashSet = negative_results .into_iter() .map(|e| e.row_position) @@ -1270,26 +1284,26 @@ impl FtsMemIndex { ) -> Vec { let excluded: HashSet = must_not .iter() - .flat_map(|q| self.search_query_with_state(q, st)) + .flat_map(|q| self.search_query_with_state(q, st, None)) .map(|e| e.row_position) .collect(); let mut result_map: HashMap = if must.is_empty() { let mut map: HashMap = HashMap::new(); for q in should { - for entry in self.search_query_with_state(q, st) { + for entry in self.search_query_with_state(q, st, None) { *map.entry(entry.row_position).or_default() += entry.score; } } map } else { - let first_results = self.search_query_with_state(&must[0], st); + let first_results = self.search_query_with_state(&must[0], st, None); let mut map: HashMap = first_results .into_iter() .map(|e| (e.row_position, e.score)) .collect(); for q in must.iter().skip(1) { - let results = self.search_query_with_state(q, st); + let results = self.search_query_with_state(q, st, None); let result_set: HashMap = results .into_iter() .map(|e| (e.row_position, e.score)) @@ -1300,7 +1314,7 @@ impl FtsMemIndex { .collect(); } for q in should { - for entry in self.search_query_with_state(q, st) { + for entry in self.search_query_with_state(q, st, None) { if let Some(score) = map.get_mut(&entry.row_position) { *score += entry.score; } @@ -1861,6 +1875,11 @@ struct PartitionPosting { /// `doc_ids.len() + 1` offsets into `pos_data`. pos_offsets: Vec, pos_data: Vec, + /// Largest `freq` and smallest doc length over this list. `doc_weight` is + /// increasing in freq and decreasing in doc length, so `doc_weight(max_freq, + /// min_dl)` is a valid per-term score upper bound for WAND pruning. + max_freq: u32, + min_dl: u32, } impl PartitionPosting { @@ -1872,6 +1891,11 @@ impl PartitionPosting { &self.pos_data[self.pos_offsets[i] as usize..self.pos_offsets[i + 1] as usize] } + /// Upper bound on this term's BM25 doc-side weight, given the scorer. + fn max_doc_weight(&self, scorer: &MemBM25Scorer) -> f32 { + scorer.doc_weight(self.max_freq, self.min_dl) + } + fn heap_size(&self) -> usize { (self.doc_ids.len() + self.freqs.len() + self.pos_offsets.len() + self.pos_data.len()) * std::mem::size_of::() @@ -1983,7 +2007,10 @@ impl Partition { token_ids.insert(term.clone(), id); raw.push(docs_for_term); } - let postings = raw.into_iter().map(freeze_postings_one).collect(); + let postings = raw + .into_iter() + .map(|v| freeze_postings_one(v, &docs)) + .collect(); Some(Self { token_ids, postings, @@ -2023,7 +2050,7 @@ impl Partition { .into_iter() .map(|mut v| { v.sort_by_key(|(d, _, _)| *d); - freeze_postings_one(v) + freeze_postings_one(v, &docs) }) .collect(); Self { @@ -2033,49 +2060,61 @@ impl Partition { } } - /// BM25 OR/AND-search by a direct posting-list scan. A partition holds one - /// merged posting list per term, so cost is O(matches) — not O(corpus) as - /// the old per-batch-chunk layout was. Block-max WAND pruning is not used: - /// it needs per-term `max_score` upper bounds that frozen in-memory - /// posting lists do not carry, and an unbounded WAND mis-prunes without - /// them. Each token occurrence in `tokens` contributes independently, so - /// scores match the tail's `score_terms`. + /// BM25 OR/AND-search of the partition. With an OR operator and a result + /// limit, prunes with block-max WAND; otherwise runs a direct O(matches) + /// scan. Every result is scored with `scorer`, so scores are identical + /// regardless of which path runs (and identical to the tail's + /// `score_terms`). fn search_match( &self, tokens: &[String], operator: Operator, scorer: &MemBM25Scorer, + limit: Option, ) -> Vec { - // Partition-local doc ids are dense 0..doc_count, so accumulate into - // flat arrays — no per-posting hashing. - let n = self.docs.len(); - let mut scores = vec![0.0f32; n]; - let mut hits = vec![0u32; n]; - let mut any_present = false; + // Resolve present query tokens to (local token id, query weight). + // Repeated query tokens are kept so scores match `score_terms`. + let mut terms: Vec<(usize, f32)> = Vec::with_capacity(tokens.len()); let mut all_present = true; for token in tokens { match self.token_ids.get(token.as_str()) { - Some(&id) => { - any_present = true; - let posting = &self.postings[id as usize]; - let qw = scorer.query_weight(token); - for i in 0..posting.len() { - let doc = posting.doc_ids[i] as usize; - let dl = self.docs.num_tokens(posting.doc_ids[i]); - scores[doc] += qw * scorer.doc_weight(posting.freqs[i], dl); - hits[doc] += 1; - } - } + Some(&id) => terms.push((id as usize, scorer.query_weight(token))), None => all_present = false, } } - if !any_present || (operator == Operator::And && !all_present) { + if terms.is_empty() || (operator == Operator::And && !all_present) { return Vec::new(); } - // AND requires the doc to be hit once per query-token slot. - let need = tokens.len() as u32; + match limit { + Some(k) if k > 0 && operator == Operator::Or => self.wand_match(&terms, scorer, k), + _ => self.scan_match(&terms, operator, tokens.len() as u32, scorer), + } + } + + /// Exact direct scan: accumulate every match into flat arrays indexed by + /// the dense local doc id. Used for unbounded and AND queries. + fn scan_match( + &self, + terms: &[(usize, f32)], + operator: Operator, + need: u32, + scorer: &MemBM25Scorer, + ) -> Vec { + let n = self.docs.len(); + let mut scores = vec![0.0f32; n]; + let mut hits = vec![0u32; n]; + for &(id, qw) in terms { + let posting = &self.postings[id]; + for i in 0..posting.len() { + let doc = posting.doc_ids[i] as usize; + let dl = self.docs.num_tokens(posting.doc_ids[i]); + scores[doc] += qw * scorer.doc_weight(posting.freqs[i], dl); + hits[doc] += 1; + } + } let mut results = Vec::new(); for doc in 0..n { + // AND requires the doc to be hit once per query-token slot. if hits[doc] == 0 || (operator == Operator::And && hits[doc] < need) { continue; } @@ -2087,6 +2126,90 @@ impl Partition { results } + /// Block-max WAND top-k over an OR query. Exact: each term's + /// `(max_freq, min_dl)` gives a sound score upper bound, so the algorithm + /// only skips docs that provably cannot enter the top-`k`. + fn wand_match( + &self, + terms: &[(usize, f32)], + scorer: &MemBM25Scorer, + k: usize, + ) -> Vec { + let mut cursors: Vec = terms + .iter() + .map(|&(id, qw)| { + let posting = &self.postings[id]; + WandCursor { + posting, + idx: 0, + qw, + ub: qw * posting.max_doc_weight(scorer), + } + }) + .collect(); + // Min-heap of the current top-k by score. + let mut heap: BinaryHeap> = BinaryHeap::with_capacity(k + 1); + // theta = score of the weakest doc still in the top-k (-inf until full). + let mut theta = f32::NEG_INFINITY; + loop { + cursors.retain(|c| c.doc().is_some()); + if cursors.is_empty() { + break; + } + cursors.sort_by_key(|c| c.doc().unwrap()); + // Pivot: first cursor whose cumulative upper bound exceeds theta. + let mut acc = 0.0f32; + let mut pivot = None; + for (i, c) in cursors.iter().enumerate() { + acc += c.ub; + if acc > theta { + pivot = Some(i); + break; + } + } + let Some(pivot) = pivot else { + break; // no remaining doc can reach theta + }; + let pivot_doc = cursors[pivot].doc().unwrap(); + if cursors[0].doc().unwrap() == pivot_doc { + // Every cursor positioned at pivot_doc contributes; score it. + let dl = self.docs.num_tokens(pivot_doc); + let mut score = 0.0f32; + for c in cursors.iter_mut() { + if c.doc() == Some(pivot_doc) { + score += c.qw * scorer.doc_weight(c.posting.freqs[c.idx], dl); + c.idx += 1; + } + } + if heap.len() < k { + heap.push(Reverse(ScoredDoc { + score, + doc: pivot_doc, + })); + if heap.len() == k { + theta = heap.peek().unwrap().0.score; + } + } else if score > theta { + heap.pop(); + heap.push(Reverse(ScoredDoc { + score, + doc: pivot_doc, + })); + theta = heap.peek().unwrap().0.score; + } + } else { + // A cursor before the pivot trails pivot_doc; skip it forward. + cursors[0].skip_to(pivot_doc); + } + } + heap.into_iter() + .map(|Reverse(sd)| FtsEntry { + row_position: self.docs.row_id(sd.doc), + score: sd.score, + }) + .collect() + } + /// Phrase-search by intersecting posting lists: drive from the rarest /// token, require every other token to contain the doc, and verify the /// token positions satisfy the phrase. `tokens.len() >= 2`. @@ -2143,9 +2266,57 @@ impl Partition { } } +/// A scored doc id, ordered by score then doc id (`total_cmp`, so a stray +/// non-finite score cannot panic the heap). +struct ScoredDoc { + score: f32, + doc: u32, +} + +impl PartialEq for ScoredDoc { + fn eq(&self, other: &Self) -> bool { + self.cmp(other) == std::cmp::Ordering::Equal + } +} +impl Eq for ScoredDoc {} +impl PartialOrd for ScoredDoc { + fn partial_cmp(&self, other: &Self) -> Option { + Some(self.cmp(other)) + } +} +impl Ord for ScoredDoc { + fn cmp(&self, other: &Self) -> std::cmp::Ordering { + self.score + .total_cmp(&other.score) + .then(self.doc.cmp(&other.doc)) + } +} + +/// A cursor over one term's posting list, used by [`Partition::wand_match`]. +struct WandCursor<'a> { + posting: &'a PartitionPosting, + idx: usize, + qw: f32, + /// Upper bound on this term's contribution to any doc's score. + ub: f32, +} + +impl WandCursor<'_> { + fn doc(&self) -> Option { + self.posting.doc_ids.get(self.idx).copied() + } + + /// Advance to the first posting with `doc_id >= target`. + fn skip_to(&mut self, target: u32) { + let rest = &self.posting.doc_ids[self.idx..]; + self.idx += rest.partition_point(|&d| d < target); + } +} + /// Convert `(doc_id, freq, positions)` triples — already sorted by doc id — -/// into a compact, exact-sized [`PartitionPosting`]. -fn freeze_postings_one(docs: Vec<(u32, u32, Vec)>) -> PartitionPosting { +/// into a compact, exact-sized [`PartitionPosting`]. `docset` supplies doc +/// lengths for the WAND score-bound metadata. +fn freeze_postings_one(docs: Vec<(u32, u32, Vec)>, docset: &DocSet) -> PartitionPosting { let n = docs.len(); let total_pos: usize = docs.iter().map(|(_, _, p)| p.len()).sum(); let mut doc_ids = Vec::with_capacity(n); @@ -2153,9 +2324,13 @@ fn freeze_postings_one(docs: Vec<(u32, u32, Vec)>) -> PartitionPosting { let mut pos_offsets = Vec::with_capacity(n + 1); let mut pos_data = Vec::with_capacity(total_pos); pos_offsets.push(0); + let mut max_freq = 0u32; + let mut min_dl = u32::MAX; for (d, f, positions) in docs { doc_ids.push(d); freqs.push(f); + max_freq = max_freq.max(f); + min_dl = min_dl.min(docset.num_tokens(d)); pos_data.extend_from_slice(&positions); pos_offsets.push(pos_data.len() as u32); } @@ -2164,6 +2339,8 @@ fn freeze_postings_one(docs: Vec<(u32, u32, Vec)>) -> PartitionPosting { freqs, pos_offsets, pos_data, + max_freq, + min_dl: min_dl.max(1), } } @@ -3241,12 +3418,12 @@ mod tests { let apple = index.tokenize_for_search("apple").pop().unwrap(); // "apple" is present -> an OR search over it matches. let or_scorer = build_scorer(&st, &tail_snap, std::slice::from_ref(&apple)); - let or_hits = p.search_match(std::slice::from_ref(&apple), Operator::Or, &or_scorer); + let or_hits = p.search_match(std::slice::from_ref(&apple), Operator::Or, &or_scorer, None); assert_eq!(or_hits.len(), 3); // Adding an absent term to an AND query short-circuits to nothing. let and_tokens = vec![apple, "definitely_missing".to_string()]; let and_scorer = build_scorer(&st, &tail_snap, &and_tokens); - let and_hits = p.search_match(&and_tokens, Operator::And, &and_scorer); + let and_hits = p.search_match(&and_tokens, Operator::And, &and_scorer, None); assert!(and_hits.is_empty()); } @@ -3450,4 +3627,52 @@ mod tests { let expected: Vec = full.iter().take(3).map(|e| e.row_position).collect(); assert_eq!(got, expected, "limited search must return the exact top-3"); } + + #[test] + fn test_wand_topk_matches_exact_full_scan() { + // WAND must produce the exact same top-k as an unbounded scan, at a + // scale where pruning genuinely engages. 40 docs across ~8 partitions; + // "alpha" appears with tf 1..=40, giving 40 strictly distinct scores. + let index = FtsMemIndex::new(1, "description".to_string()).with_freeze_threshold_rows(5); + for i in 0..40u64 { + let text = "alpha ".repeat((i + 1) as usize); + let batch = RecordBatch::try_new( + create_test_schema(), + vec![ + Arc::new(Int32Array::from(vec![0])), + Arc::new(StringArray::from(vec![text.as_str()])), + ], + ) + .unwrap(); + index.insert(&batch, i).unwrap(); + } + assert!(index.state.load_full().partitions.len() >= 2); + + // Exact ranking from the unbounded path. + let mut full = index.search("alpha"); + assert_eq!(full.len(), 40); + full.sort_by(|a, b| { + b.score + .partial_cmp(&a.score) + .unwrap() + .then(a.row_position.cmp(&b.row_position)) + }); + + // For several k, WAND-pruned top-k must equal the exact top-k. + for k in [1usize, 3, 10, 25, 40, 50] { + let limited = index.search_with_options( + &FtsQueryExpr::match_query("alpha"), + SearchOptions::new().with_limit(k), + ); + let expect_len = k.min(40); + assert_eq!(limited.len(), expect_len, "k={k}"); + let got: Vec = limited.iter().map(|e| e.row_position).collect(); + let expected: Vec = full + .iter() + .take(expect_len) + .map(|e| e.row_position) + .collect(); + assert_eq!(got, expected, "WAND top-{k} must equal the exact top-{k}"); + } + } } From 9e7e2ba69953c57d665807e92274ac5abe24c6ba Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sun, 17 May 2026 15:55:31 -0700 Subject: [PATCH 25/37] perf(mem_wal): byte-compress FTS partition postings, dedup term dictionary MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Frozen partitions held uncompressed `Vec` posting lists plus a per- partition `HashMap,u32>` term dictionary duplicated across every partition — ~4 GB at 1M docs (~5x Lucene). Postings are now stored in three shared per-partition buffers as 128-doc blocks: VByte + delta doc-id gaps, VByte frequencies, and VByte delta-encoded positions. A `PostingCursor` decodes a block at a time and `skip_to` jumps whole blocks via `BlockMeta` without decoding — so WAND still block-skips and the exact O(matches) scan path is unchanged. Per-term overhead drops to one `PostingRef`. The term dictionary becomes a sorted `Box<[Arc]>` (binary-searched, no hashmap node overhead) whose strings are shared across partitions via an index-wide `TermInterner`. Flush is unaffected — `to_index_builder_reversed` decodes postings through the same cursor, reverses, and rebuilds the Lance FTS on-disk format as before. Adds VByte and multi-block cursor round-trip tests. --- rust/lance/src/dataset/mem_wal/index/fts.rs | 710 +++++++++++++------- 1 file changed, 479 insertions(+), 231 deletions(-) diff --git a/rust/lance/src/dataset/mem_wal/index/fts.rs b/rust/lance/src/dataset/mem_wal/index/fts.rs index 2b2fda7de4b..d4ce618e5a1 100644 --- a/rust/lance/src/dataset/mem_wal/index/fts.rs +++ b/rust/lance/src/dataset/mem_wal/index/fts.rs @@ -708,6 +708,24 @@ impl IndexState { } } +/// Deduplicates term `Arc`s across partitions: a term that appears in +/// many partitions is then backed by a single string allocation. +#[derive(Default)] +struct TermInterner { + seen: Mutex>>, +} + +impl TermInterner { + fn intern(&self, term: &Arc) -> Arc { + let mut seen = self.seen.lock().expect("term interner poisoned"); + if let Some(existing) = seen.get(term.as_ref()) { + return existing.clone(); + } + seen.insert(term.clone()); + term.clone() + } +} + /// In-memory full-text search index. See module docs for the concurrency /// model and visibility contract. pub struct FtsMemIndex { @@ -724,6 +742,9 @@ pub struct FtsMemIndex { /// between freezes; the whole state is swapped on freeze. state: ArcSwap, + /// Shared term-string interner for frozen partitions. + term_interner: TermInterner, + /// The tail freezes into a partition once it reaches this many docs. freeze_threshold_rows: usize, } @@ -773,6 +794,7 @@ impl FtsMemIndex { tokenizer_pool: Arc::new(pool), writer_tokenizer: Mutex::new(writer_tokenizer), state: ArcSwap::from(IndexState::empty()), + term_interner: TermInterner::default(), freeze_threshold_rows: Self::DEFAULT_FREEZE_THRESHOLD_ROWS, } } @@ -966,7 +988,7 @@ impl FtsMemIndex { /// fresh empty tail. Only the writer calls this; readers snapshotting the /// old `IndexState` keep a consistent view across the freeze. fn freeze(&self, st: &IndexState) { - let Some(partition) = Partition::from_tail(&st.tail) else { + let Some(partition) = Partition::from_tail(&st.tail, &self.term_interner) else { return; }; let mut partitions: Vec> = st.partitions.iter().cloned().collect(); @@ -1414,19 +1436,22 @@ impl FtsMemIndex { // Step 3: merge per-term postings across every partition and the tail. let mut term_postings: HashMap>)>> = HashMap::new(); for p in st.partitions.iter() { - for (token, posting) in p.tokens_with_postings() { - let bucket = term_postings.entry(token.to_string()).or_default(); - for i in 0..posting.len() { - let row_pos = p.docs.row_id(posting.doc_ids[i]); - let Some(&doc_id) = original_to_doc_id.get(&row_pos) else { - continue; - }; - let pos = if with_position { - Some(posting.positions(i).to_vec()) - } else { - None - }; - bucket.push((doc_id, posting.freqs[i], pos)); + for term_id in 0..p.terms.len() as u32 { + let bucket = term_postings + .entry(p.terms[term_id as usize].to_string()) + .or_default(); + let mut cursor = PostingCursor::new(p, term_id); + while let Some(local_doc) = cursor.doc() { + let row_pos = p.docs.row_id(local_doc); + if let Some(&doc_id) = original_to_doc_id.get(&row_pos) { + let pos = if with_position { + Some(cursor.positions().to_vec()) + } else { + None + }; + bucket.push((doc_id, cursor.freq(), pos)); + } + cursor.advance(); } } } @@ -1860,61 +1885,160 @@ impl FtsIndexConfig { } // ============================================================================ -// Immutable partition +// Immutable partition (compressed posting storage) // ============================================================================ -/// A frozen per-term posting list inside a [`Partition`]. Exact-sized plain -/// `Vec`s — no Arrow builder capacity slack — with partition-local `u32` doc -/// ids. Positions use a CSR layout: posting `i` owns -/// `pos_data[pos_offsets[i]..pos_offsets[i + 1]]`. -struct PartitionPosting { - /// partition-local doc ids, ascending. - doc_ids: Vec, - /// term frequency per posting. - freqs: Vec, - /// `doc_ids.len() + 1` offsets into `pos_data`. - pos_offsets: Vec, - pos_data: Vec, - /// Largest `freq` and smallest doc length over this list. `doc_weight` is - /// increasing in freq and decreasing in doc length, so `doc_weight(max_freq, - /// min_dl)` is a valid per-term score upper bound for WAND pruning. - max_freq: u32, - min_dl: u32, -} +/// Docs per posting block. Blocks bound WAND skip granularity and carry the +/// per-block score bound; a block's payload is self-delimiting. +const POSTING_BLOCK: usize = 128; -impl PartitionPosting { - fn len(&self) -> usize { - self.doc_ids.len() +/// Append `v` to `buf` as unsigned LEB128 (VByte). +fn vbyte_put(buf: &mut Vec, mut v: u32) { + while v >= 0x80 { + buf.push((v as u8) | 0x80); + v >>= 7; } + buf.push(v as u8); +} - fn positions(&self, i: usize) -> &[u32] { - &self.pos_data[self.pos_offsets[i] as usize..self.pos_offsets[i + 1] as usize] +/// Read one unsigned LEB128 value from `buf` at `*pos`, advancing `*pos`. +fn vbyte_get(buf: &[u8], pos: &mut usize) -> u32 { + let mut v = 0u32; + let mut shift = 0u32; + loop { + let b = buf[*pos]; + *pos += 1; + v |= ((b & 0x7f) as u32) << shift; + if b & 0x80 == 0 { + break; + } + shift += 7; } + v +} - /// Upper bound on this term's BM25 doc-side weight, given the scorer. - fn max_doc_weight(&self, scorer: &MemBM25Scorer) -> f32 { - scorer.doc_weight(self.max_freq, self.min_dl) - } +/// Number of docs in block `b` (0-based within a term) of a `doc_count`-long +/// posting list. +fn block_len(doc_count: u32, b: u32) -> usize { + (doc_count as usize - b as usize * POSTING_BLOCK).min(POSTING_BLOCK) +} - fn heap_size(&self) -> usize { - (self.doc_ids.len() + self.freqs.len() + self.pos_offsets.len() + self.pos_data.len()) - * std::mem::size_of::() - } +/// Per-128-doc-block metadata — enough to skip and score-bound a block +/// without decoding its payload. +#[derive(Clone, Copy)] +struct BlockMeta { + /// first / last doc id in the block (block doc ids are ascending). + first_doc: u32, + last_doc: u32, + /// start offset of the block's doc/freq payload in `doc_freq_data`. + df_offset: u32, + /// start offset of the block's position payload in `pos_data`. + pos_offset: u32, +} + +/// Per-term locator into a partition's shared posting buffers. +#[derive(Clone, Copy)] +struct PostingRef { + /// index of the term's first block in `block_meta`. + block_start: u32, + /// number of blocks the term spans. + block_count: u32, + /// number of docs (postings) for the term. + doc_count: u32, + /// largest frequency / smallest doc length over the whole term: the WAND + /// per-term upper bound is `query_weight * doc_weight(max_freq, min_dl)`. + max_freq: u32, + min_dl: u32, } -/// An immutable, frozen FTS partition: a slice of the MemTable's inserts held -/// as compact per-term posting lists, queried by a direct O(matches) scan. -/// Built by freezing the tail; flushed 1:1 into the Lance FTS on-disk format. -/// See `DESIGN.md` in the redesign analysis directory. +/// An immutable, frozen FTS partition. Posting lists are byte-compressed +/// (VByte + delta, 128-doc blocks) into three shared buffers, so per-term +/// overhead is one `PostingRef`. See `compress-fts-partition-memory/DESIGN.md`. struct Partition { - /// token text -> local token id (dense, 0-based; indexes `postings`). - token_ids: HashMap, u32>, - /// posting list per local token id. - postings: Vec, + /// term texts, sorted; the index is the local term id. Interned, so the + /// string bytes are shared across partitions. + terms: Box<[Arc]>, + /// per term, parallel to `terms`. + postings: Box<[PostingRef]>, + /// per-block metadata for every term's blocks, concatenated. + block_meta: Box<[BlockMeta]>, + /// VByte(doc-id gaps) then VByte(freqs) for every block, concatenated. + doc_freq_data: Box<[u8]>, + /// per doc per block: VByte(count) then VByte(delta positions), concatenated. + pos_data: Box<[u8]>, /// local doc id -> (MemTable row position, token count). docs: DocSet, } +/// Build a partition from `(term, sorted (doc, freq, positions))` entries. +/// Each term's docs must already be sorted ascending by doc id. +fn build_partition( + mut entries: Vec<(Arc, Vec<(u32, u32, Vec)>)>, + docs: DocSet, +) -> Partition { + entries.sort_by(|a, b| a.0.cmp(&b.0)); + let mut terms = Vec::with_capacity(entries.len()); + let mut postings = Vec::with_capacity(entries.len()); + let mut block_meta: Vec = Vec::new(); + let mut doc_freq_data: Vec = Vec::new(); + let mut pos_data: Vec = Vec::new(); + for (term, docs_for_term) in entries { + let doc_count = docs_for_term.len() as u32; + let block_start = block_meta.len() as u32; + let mut term_max_freq = 0u32; + let mut term_min_dl = u32::MAX; + for chunk in docs_for_term.chunks(POSTING_BLOCK) { + let df_offset = doc_freq_data.len() as u32; + let pos_offset = pos_data.len() as u32; + // doc-id gaps for docs[1..] (docs[0] is `first_doc`). + let mut prev = chunk[0].0; + for &(d, _, _) in &chunk[1..] { + vbyte_put(&mut doc_freq_data, d - prev); + prev = d; + } + // frequencies. + let mut blk_max_freq = 0u32; + for &(_, f, _) in chunk { + vbyte_put(&mut doc_freq_data, f); + blk_max_freq = blk_max_freq.max(f); + } + // positions: count + delta-encoded positions per doc. + for &(d, _, ref positions) in chunk { + vbyte_put(&mut pos_data, positions.len() as u32); + let mut prev_p = 0u32; + for &p in positions { + vbyte_put(&mut pos_data, p - prev_p); + prev_p = p; + } + term_min_dl = term_min_dl.min(docs.num_tokens(d)); + } + block_meta.push(BlockMeta { + first_doc: chunk[0].0, + last_doc: chunk[chunk.len() - 1].0, + df_offset, + pos_offset, + }); + term_max_freq = term_max_freq.max(blk_max_freq); + } + postings.push(PostingRef { + block_start, + block_count: block_meta.len() as u32 - block_start, + doc_count, + max_freq: term_max_freq, + min_dl: term_min_dl.max(1), + }); + terms.push(term); + } + Partition { + terms: terms.into_boxed_slice(), + postings: postings.into_boxed_slice(), + block_meta: block_meta.into_boxed_slice(), + doc_freq_data: doc_freq_data.into_boxed_slice(), + pos_data: pos_data.into_boxed_slice(), + docs, + } +} + impl Partition { fn doc_count(&self) -> usize { self.docs.len() @@ -1925,44 +2049,50 @@ impl Partition { } fn entry_count(&self) -> usize { - self.postings.iter().map(|p| p.len()).sum() + self.postings.iter().map(|p| p.doc_count as usize).sum() } - fn contains_token(&self, token: &str) -> bool { - self.token_ids.contains_key(token) + /// Local term id of `token`, via binary search over the sorted `terms`. + fn term_id(&self, token: &str) -> Option { + self.terms + .binary_search_by(|t| t.as_ref().cmp(token)) + .ok() + .map(|i| i as u32) } - fn tokens(&self) -> impl Iterator> { - self.token_ids.keys() + fn contains_token(&self, token: &str) -> bool { + self.term_id(token).is_some() } - fn tokens_with_postings(&self) -> impl Iterator, &PartitionPosting)> { - self.token_ids - .iter() - .map(move |(t, &id)| (t, &self.postings[id as usize])) + fn tokens(&self) -> impl Iterator> { + self.terms.iter() } /// Number of docs in this partition containing `token`. fn token_df(&self, token: &str) -> usize { - self.token_ids - .get(token) - .map(|&id| self.postings[id as usize].len()) + self.term_id(token) + .map(|id| self.postings[id as usize].doc_count as usize) .unwrap_or(0) } fn memory_size(&self) -> usize { - let mut total = std::mem::size_of::(); - for t in self.token_ids.keys() { - total += std::mem::size_of::>() + t.len() + std::mem::size_of::() + 16; - } - total += self.postings.iter().map(|p| p.heap_size()).sum::(); - total += self.docs.len() * (std::mem::size_of::() + 2 * std::mem::size_of::()); - total + std::mem::size_of::() + + self + .terms + .iter() + .map(|t| std::mem::size_of::>() + t.len()) + .sum::() + + self.postings.len() * std::mem::size_of::() + + self.block_meta.len() * std::mem::size_of::() + + self.doc_freq_data.len() + + self.pos_data.len() + + self.docs.len() * (std::mem::size_of::() + 2 * std::mem::size_of::()) } /// Freeze the visible contents of `tail` into a new partition. Returns - /// `None` if the tail has no visible docs. - fn from_tail(tail: &TailIndex) -> Option { + /// `None` if the tail has no visible docs. Terms are interned so their + /// bytes are shared with other partitions. + fn from_tail(tail: &TailIndex, interner: &TermInterner) -> Option { let snap = tail.snapshot(); if snap.visible_count == 0 { return None; @@ -1977,10 +2107,8 @@ impl Partition { pos_to_doc.insert(rp, doc_id); } } - let mut token_ids: HashMap, u32> = HashMap::new(); - let mut raw: Vec)>> = Vec::new(); + let mut entries: Vec<(Arc, Vec<(u32, u32, Vec)>)> = Vec::new(); for entry in tail.terms.iter() { - let term: &Arc = entry.key(); let slice = entry.value().load(); let mut docs_for_term: Vec<(u32, u32, Vec)> = Vec::new(); for chunk in &slice.chunks { @@ -2003,61 +2131,33 @@ impl Partition { continue; } docs_for_term.sort_by_key(|(d, _, _)| *d); - let id = raw.len() as u32; - token_ids.insert(term.clone(), id); - raw.push(docs_for_term); + entries.push((interner.intern(entry.key()), docs_for_term)); } - let postings = raw - .into_iter() - .map(|v| freeze_postings_one(v, &docs)) - .collect(); - Some(Self { - token_ids, - postings, - docs, - }) + Some(build_partition(entries, docs)) } /// Merge several partitions into one. Local doc ids are reassigned by - /// concatenation, which keeps each merged per-token posting list sorted. + /// concatenation, which keeps each merged per-term posting list sorted. fn merge(parts: &[Arc]) -> Self { - let mut token_ids: HashMap, u32> = HashMap::new(); - let mut raw: Vec)>> = Vec::new(); + let mut merged: HashMap, Vec<(u32, u32, Vec)>> = HashMap::new(); let mut docs = DocSet::default(); let mut doc_offset: u32 = 0; for p in parts { for (rp, nt) in p.docs.iter() { docs.append(*rp, *nt); } - for (token, &local_id) in &p.token_ids { - let merged_id = *token_ids.entry(token.clone()).or_insert_with(|| { - let id = raw.len() as u32; - raw.push(Vec::new()); - id - }); - let posting = &p.postings[local_id as usize]; - for i in 0..posting.len() { - raw[merged_id as usize].push(( - posting.doc_ids[i] + doc_offset, - posting.freqs[i], - posting.positions(i).to_vec(), - )); + for term_id in 0..p.terms.len() as u32 { + let bucket = merged.entry(p.terms[term_id as usize].clone()).or_default(); + let mut cursor = PostingCursor::new(p, term_id); + while let Some(doc) = cursor.doc() { + let positions = cursor.positions().to_vec(); + bucket.push((doc + doc_offset, cursor.freq(), positions)); + cursor.advance(); } } doc_offset += p.docs.len() as u32; } - let postings = raw - .into_iter() - .map(|mut v| { - v.sort_by_key(|(d, _, _)| *d); - freeze_postings_one(v, &docs) - }) - .collect(); - Self { - token_ids, - postings, - docs, - } + build_partition(merged.into_iter().collect(), docs) } /// BM25 OR/AND-search of the partition. With an OR operator and a result @@ -2072,13 +2172,13 @@ impl Partition { scorer: &MemBM25Scorer, limit: Option, ) -> Vec { - // Resolve present query tokens to (local token id, query weight). + // Resolve present query tokens to (local term id, query weight). // Repeated query tokens are kept so scores match `score_terms`. - let mut terms: Vec<(usize, f32)> = Vec::with_capacity(tokens.len()); + let mut terms: Vec<(u32, f32)> = Vec::with_capacity(tokens.len()); let mut all_present = true; for token in tokens { - match self.token_ids.get(token.as_str()) { - Some(&id) => terms.push((id as usize, scorer.query_weight(token))), + match self.term_id(token) { + Some(id) => terms.push((id, scorer.query_weight(token))), None => all_present = false, } } @@ -2095,7 +2195,7 @@ impl Partition { /// the dense local doc id. Used for unbounded and AND queries. fn scan_match( &self, - terms: &[(usize, f32)], + terms: &[(u32, f32)], operator: Operator, need: u32, scorer: &MemBM25Scorer, @@ -2104,12 +2204,12 @@ impl Partition { let mut scores = vec![0.0f32; n]; let mut hits = vec![0u32; n]; for &(id, qw) in terms { - let posting = &self.postings[id]; - for i in 0..posting.len() { - let doc = posting.doc_ids[i] as usize; - let dl = self.docs.num_tokens(posting.doc_ids[i]); - scores[doc] += qw * scorer.doc_weight(posting.freqs[i], dl); - hits[doc] += 1; + let mut cursor = PostingCursor::new(self, id); + while let Some(doc) = cursor.doc() { + let dl = self.docs.num_tokens(doc); + scores[doc as usize] += qw * scorer.doc_weight(cursor.freq(), dl); + hits[doc as usize] += 1; + cursor.advance(); } } let mut results = Vec::new(); @@ -2129,21 +2229,15 @@ impl Partition { /// Block-max WAND top-k over an OR query. Exact: each term's /// `(max_freq, min_dl)` gives a sound score upper bound, so the algorithm /// only skips docs that provably cannot enter the top-`k`. - fn wand_match( - &self, - terms: &[(usize, f32)], - scorer: &MemBM25Scorer, - k: usize, - ) -> Vec { - let mut cursors: Vec = terms + fn wand_match(&self, terms: &[(u32, f32)], scorer: &MemBM25Scorer, k: usize) -> Vec { + let mut lanes: Vec = terms .iter() .map(|&(id, qw)| { - let posting = &self.postings[id]; - WandCursor { - posting, - idx: 0, + let pref = &self.postings[id as usize]; + WandLane { + cursor: PostingCursor::new(self, id), qw, - ub: qw * posting.max_doc_weight(scorer), + ub: qw * scorer.doc_weight(pref.max_freq, pref.min_dl), } }) .collect(); @@ -2152,16 +2246,16 @@ impl Partition { // theta = score of the weakest doc still in the top-k (-inf until full). let mut theta = f32::NEG_INFINITY; loop { - cursors.retain(|c| c.doc().is_some()); - if cursors.is_empty() { + lanes.retain(|l| l.cursor.doc().is_some()); + if lanes.is_empty() { break; } - cursors.sort_by_key(|c| c.doc().unwrap()); - // Pivot: first cursor whose cumulative upper bound exceeds theta. + lanes.sort_by_key(|l| l.cursor.doc().unwrap()); + // Pivot: first lane whose cumulative upper bound exceeds theta. let mut acc = 0.0f32; let mut pivot = None; - for (i, c) in cursors.iter().enumerate() { - acc += c.ub; + for (i, l) in lanes.iter().enumerate() { + acc += l.ub; if acc > theta { pivot = Some(i); break; @@ -2170,15 +2264,15 @@ impl Partition { let Some(pivot) = pivot else { break; // no remaining doc can reach theta }; - let pivot_doc = cursors[pivot].doc().unwrap(); - if cursors[0].doc().unwrap() == pivot_doc { - // Every cursor positioned at pivot_doc contributes; score it. + let pivot_doc = lanes[pivot].cursor.doc().unwrap(); + if lanes[0].cursor.doc().unwrap() == pivot_doc { + // Every lane positioned at pivot_doc contributes; score it. let dl = self.docs.num_tokens(pivot_doc); let mut score = 0.0f32; - for c in cursors.iter_mut() { - if c.doc() == Some(pivot_doc) { - score += c.qw * scorer.doc_weight(c.posting.freqs[c.idx], dl); - c.idx += 1; + for l in lanes.iter_mut() { + if l.cursor.doc() == Some(pivot_doc) { + score += l.qw * scorer.doc_weight(l.cursor.freq(), dl); + l.cursor.advance(); } } if heap.len() < k { @@ -2198,8 +2292,8 @@ impl Partition { theta = heap.peek().unwrap().0.score; } } else { - // A cursor before the pivot trails pivot_doc; skip it forward. - cursors[0].skip_to(pivot_doc); + // A lane before the pivot trails pivot_doc; skip it forward. + lanes[0].cursor.skip_to(pivot_doc); } } heap.into_iter() @@ -2214,53 +2308,51 @@ impl Partition { /// token, require every other token to contain the doc, and verify the /// token positions satisfy the phrase. `tokens.len() >= 2`. fn search_phrase(&self, tokens: &[String], slop: u32, scorer: &MemBM25Scorer) -> Vec { - let mut postings: Vec<&PartitionPosting> = Vec::with_capacity(tokens.len()); + let mut term_ids: Vec = Vec::with_capacity(tokens.len()); for token in tokens { - match self.token_ids.get(token.as_str()) { - Some(&id) => postings.push(&self.postings[id as usize]), + match self.term_id(token) { + Some(id) => term_ids.push(id), // A phrase needs every token present in this partition. None => return Vec::new(), } } - let rarest = (0..postings.len()) - .min_by_key(|&i| postings[i].len()) + let rarest = (0..term_ids.len()) + .min_by_key(|&i| self.postings[term_ids[i] as usize].doc_count) .unwrap(); + let mut cursors: Vec = term_ids + .iter() + .map(|&id| PostingCursor::new(self, id)) + .collect(); let mut results = Vec::new(); - for i in 0..postings[rarest].len() { - let doc = postings[rarest].doc_ids[i]; - let mut all_positions: Vec<&[u32]> = Vec::with_capacity(tokens.len()); - let mut freqs: Vec = Vec::with_capacity(tokens.len()); + while let Some(doc) = cursors[rarest].cursor_doc() { + let mut all_positions: Vec> = vec![Vec::new(); tokens.len()]; + let mut freqs = vec![0u32; tokens.len()]; let mut present = true; - for (ti, posting) in postings.iter().enumerate() { - let idx = if ti == rarest { - Some(i) + for ti in 0..tokens.len() { + if ti != rarest { + cursors[ti].skip_to(doc); + } + if cursors[ti].doc() == Some(doc) { + freqs[ti] = cursors[ti].freq(); + all_positions[ti] = cursors[ti].positions().to_vec(); } else { - posting.doc_ids.binary_search(&doc).ok() - }; - match idx { - Some(idx) => { - freqs.push(posting.freqs[idx]); - all_positions.push(posting.positions(idx)); - } - None => { - present = false; - break; - } + present = false; + break; } } - if !present || !phrase_matches(&all_positions, slop) { - continue; + if present && phrase_matches(&all_positions, slop) { + let dl = self.docs.num_tokens(doc); + let score: f32 = tokens + .iter() + .zip(&freqs) + .map(|(t, &f)| scorer.query_weight(t) * scorer.doc_weight(f, dl)) + .sum(); + results.push(FtsEntry { + row_position: self.docs.row_id(doc), + score, + }); } - let dl = self.docs.num_tokens(doc); - let score: f32 = tokens - .iter() - .zip(&freqs) - .map(|(t, &f)| scorer.query_weight(t) * scorer.doc_weight(f, dl)) - .sum(); - results.push(FtsEntry { - row_position: self.docs.row_id(doc), - score, - }); + cursors[rarest].advance(); } results } @@ -2292,55 +2384,154 @@ impl Ord for ScoredDoc { } } -/// A cursor over one term's posting list, used by [`Partition::wand_match`]. -struct WandCursor<'a> { - posting: &'a PartitionPosting, - idx: usize, +/// One WAND lane: a posting cursor plus its query weight and score bound. +struct WandLane<'a> { + cursor: PostingCursor<'a>, qw: f32, /// Upper bound on this term's contribution to any doc's score. ub: f32, } -impl WandCursor<'_> { +/// A decoding cursor over one term's compressed posting list. Decodes a +/// 128-doc block at a time; `skip_to` jumps whole blocks via `BlockMeta` +/// without decoding them. +struct PostingCursor<'a> { + part: &'a Partition, + pref: PostingRef, + /// 0-based block index within the term; `== block_count` once exhausted. + block: u32, + /// the block decoded into `docs`/`freqs` (`u32::MAX` = none). + decoded: u32, + docs: Vec, + freqs: Vec, + /// the block decoded into `positions` (`u32::MAX` = none). + pos_decoded: u32, + positions: Vec>, + /// index within the current block. + i: usize, +} + +impl<'a> PostingCursor<'a> { + fn new(part: &'a Partition, term_id: u32) -> Self { + let pref = part.postings[term_id as usize]; + let mut cursor = Self { + part, + pref, + block: 0, + decoded: u32::MAX, + docs: Vec::new(), + freqs: Vec::new(), + pos_decoded: u32::MAX, + positions: Vec::new(), + i: 0, + }; + if pref.block_count > 0 { + cursor.decode_doc_freq(0); + } + cursor + } + + fn decode_doc_freq(&mut self, block: u32) { + if self.decoded == block { + return; + } + let bm = self.part.block_meta[(self.pref.block_start + block) as usize]; + let n = block_len(self.pref.doc_count, block); + self.docs.clear(); + self.freqs.clear(); + let mut pos = bm.df_offset as usize; + let mut d = bm.first_doc; + self.docs.push(d); + for _ in 1..n { + d += vbyte_get(&self.part.doc_freq_data, &mut pos); + self.docs.push(d); + } + for _ in 0..n { + self.freqs + .push(vbyte_get(&self.part.doc_freq_data, &mut pos)); + } + self.decoded = block; + } + + fn decode_positions(&mut self, block: u32) { + if self.pos_decoded == block { + return; + } + let bm = self.part.block_meta[(self.pref.block_start + block) as usize]; + let n = block_len(self.pref.doc_count, block); + self.positions.clear(); + let mut pos = bm.pos_offset as usize; + for _ in 0..n { + let cnt = vbyte_get(&self.part.pos_data, &mut pos); + let mut v = Vec::with_capacity(cnt as usize); + let mut last = 0u32; + for _ in 0..cnt { + last += vbyte_get(&self.part.pos_data, &mut pos); + v.push(last); + } + self.positions.push(v); + } + self.pos_decoded = block; + } + + /// Current doc id, or `None` once the list is exhausted. fn doc(&self) -> Option { - self.posting.doc_ids.get(self.idx).copied() + if self.block >= self.pref.block_count { + return None; + } + self.docs.get(self.i).copied() + } + + /// `doc()` under a `&mut` receiver — for use as a loop condition while the + /// cursor is borrowed mutably elsewhere. + fn cursor_doc(&mut self) -> Option { + self.doc() + } + + /// Frequency of the current posting. + fn freq(&self) -> u32 { + self.freqs[self.i] + } + + /// Positions of the current posting (decoded lazily). + fn positions(&mut self) -> &[u32] { + let block = self.block; + self.decode_positions(block); + &self.positions[self.i] + } + + /// Step to the next posting. + fn advance(&mut self) { + self.i += 1; + if self.i >= self.docs.len() { + self.block += 1; + self.i = 0; + if self.block < self.pref.block_count { + self.decode_doc_freq(self.block); + } + } } - /// Advance to the first posting with `doc_id >= target`. + /// Advance to the first posting with `doc_id >= target` (or exhaust), + /// skipping whole blocks via `BlockMeta` without decoding them. fn skip_to(&mut self, target: u32) { - let rest = &self.posting.doc_ids[self.idx..]; - self.idx += rest.partition_point(|&d| d < target); - } -} - -/// Convert `(doc_id, freq, positions)` triples — already sorted by doc id — -/// into a compact, exact-sized [`PartitionPosting`]. `docset` supplies doc -/// lengths for the WAND score-bound metadata. -fn freeze_postings_one(docs: Vec<(u32, u32, Vec)>, docset: &DocSet) -> PartitionPosting { - let n = docs.len(); - let total_pos: usize = docs.iter().map(|(_, _, p)| p.len()).sum(); - let mut doc_ids = Vec::with_capacity(n); - let mut freqs = Vec::with_capacity(n); - let mut pos_offsets = Vec::with_capacity(n + 1); - let mut pos_data = Vec::with_capacity(total_pos); - pos_offsets.push(0); - let mut max_freq = 0u32; - let mut min_dl = u32::MAX; - for (d, f, positions) in docs { - doc_ids.push(d); - freqs.push(f); - max_freq = max_freq.max(f); - min_dl = min_dl.min(docset.num_tokens(d)); - pos_data.extend_from_slice(&positions); - pos_offsets.push(pos_data.len() as u32); - } - PartitionPosting { - doc_ids, - freqs, - pos_offsets, - pos_data, - max_freq, - min_dl: min_dl.max(1), + if self.doc().is_some_and(|d| d >= target) { + return; + } + while self.block < self.pref.block_count { + let bm = self.part.block_meta[(self.pref.block_start + self.block) as usize]; + if bm.last_doc >= target { + break; + } + self.block += 1; + self.i = 0; + } + if self.block >= self.pref.block_count { + return; + } + self.decode_doc_freq(self.block); + // `last_doc >= target`, so this block holds a doc >= target. + self.i += self.docs[self.i..].partition_point(|&d| d < target); } } @@ -3675,4 +3866,61 @@ mod tests { assert_eq!(got, expected, "WAND top-{k} must equal the exact top-{k}"); } } + + #[test] + fn test_vbyte_roundtrip() { + let vals = [0u32, 1, 127, 128, 300, 16_383, 16_384, 70_000, u32::MAX]; + let mut buf = Vec::new(); + for &v in &vals { + vbyte_put(&mut buf, v); + } + let mut pos = 0; + for &v in &vals { + assert_eq!(vbyte_get(&buf, &mut pos), v); + } + assert_eq!(pos, buf.len(), "every byte consumed"); + } + + #[test] + fn test_posting_cursor_codec_roundtrip() { + // One term spanning 3 blocks (300 docs), varied freqs + positions. + let mut docs = DocSet::default(); + let mut docs_for_term: Vec<(u32, u32, Vec)> = Vec::new(); + for d in 0..300u32 { + docs.append(1000 + d as u64, (d % 7) + 1); + let freq = (d % 5) + 1; + let positions: Vec = (0..freq).map(|p| p * 3).collect(); + docs_for_term.push((d, freq, positions)); + } + let part = build_partition(vec![(Arc::from("term"), docs_for_term.clone())], docs); + + // Full iteration must reproduce the input exactly. + let mut c = PostingCursor::new(&part, 0); + for (d, f, pos) in &docs_for_term { + assert_eq!(c.doc(), Some(*d)); + assert_eq!(c.freq(), *f); + assert_eq!(c.positions(), pos.as_slice()); + c.advance(); + } + assert_eq!(c.doc(), None, "cursor exhausted after the last doc"); + + // skip_to must land correctly across and within block boundaries. + for &(target, expect) in &[ + (5u32, Some(5u32)), // within block 0 + (128, Some(128)), // exact block-1 boundary + (200, Some(200)), // within block 1 + (299, Some(299)), // last doc + (300, None), // past the end + ] { + let mut c = PostingCursor::new(&part, 0); + c.skip_to(target); + assert_eq!(c.doc(), expect, "skip_to({target})"); + } + // Sequential skips on one cursor. + let mut c = PostingCursor::new(&part, 0); + c.skip_to(100); + assert_eq!(c.doc(), Some(100)); + c.skip_to(260); + assert_eq!(c.doc(), Some(260)); + } } From d6bea038ae6a76003aba0619cfa23b1ef75dbeaa Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sun, 17 May 2026 16:29:44 -0700 Subject: [PATCH 26/37] perf(mem_wal): bit-pack FTS posting doc ids and freqs instead of VByte MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit VByte's byte-at-a-time decode loop made compressed-partition queries far slower than the uncompressed layout — p95 at 1M docs rose 4.7 ms to 24.7 ms. Doc ids and frequencies in each 128-doc block are now fixed-width bit-packed (doc ids as `doc - first_doc`, both at the block's minimal bit width, recorded in `BlockMeta`). Decoding a block is now a tight shift/mask loop instead of a branchy VByte scan, restoring query latency while keeping the footprint compressed. Positions stay VByte (decoded lazily, off the hot path). Adds a bit-pack round-trip test covering zero/sub-byte/byte-crossing/full widths. --- rust/lance/src/dataset/mem_wal/index/fts.rs | 150 ++++++++++++++++---- 1 file changed, 124 insertions(+), 26 deletions(-) diff --git a/rust/lance/src/dataset/mem_wal/index/fts.rs b/rust/lance/src/dataset/mem_wal/index/fts.rs index d4ce618e5a1..6895a09834d 100644 --- a/rust/lance/src/dataset/mem_wal/index/fts.rs +++ b/rust/lance/src/dataset/mem_wal/index/fts.rs @@ -1923,6 +1923,65 @@ fn block_len(doc_count: u32, b: u32) -> usize { (doc_count as usize - b as usize * POSTING_BLOCK).min(POSTING_BLOCK) } +/// Bit width needed to represent `v` (0 for `v == 0`). +fn bit_width(v: u32) -> u8 { + (32 - v.leading_zeros()) as u8 +} + +/// Pack `values` (each `< 2^width`) into `buf` as little-endian `width`-bit +/// fields. `width == 0` writes nothing. +fn bitpack_put(buf: &mut Vec, values: &[u32], width: u8) { + if width == 0 { + return; + } + let mut acc: u64 = 0; + let mut bits: u32 = 0; + for &v in values { + acc |= (v as u64) << bits; + bits += width as u32; + while bits >= 8 { + buf.push(acc as u8); + acc >>= 8; + bits -= 8; + } + } + if bits > 0 { + buf.push(acc as u8); + } +} + +/// Unpack `n` little-endian `width`-bit values starting at `buf[start]`, +/// appending them to `out`. +fn bitpack_get(buf: &[u8], start: usize, n: usize, width: u8, out: &mut Vec) { + if width == 0 { + out.resize(out.len() + n, 0); + return; + } + let mask: u64 = if width >= 32 { + u32::MAX as u64 + } else { + (1u64 << width) - 1 + }; + let mut acc: u64 = 0; + let mut bits: u32 = 0; + let mut byte = start; + for _ in 0..n { + while bits < width as u32 { + acc |= (buf[byte] as u64) << bits; + byte += 1; + bits += 8; + } + out.push((acc & mask) as u32); + acc >>= width; + bits -= width as u32; + } +} + +/// Number of bytes a `bitpack_put` of `n` values of `width` bits occupies. +fn bitpack_len(n: usize, width: u8) -> usize { + (n * width as usize).div_ceil(8) +} + /// Per-128-doc-block metadata — enough to skip and score-bound a block /// without decoding its payload. #[derive(Clone, Copy)] @@ -1934,6 +1993,9 @@ struct BlockMeta { df_offset: u32, /// start offset of the block's position payload in `pos_data`. pos_offset: u32, + /// bit width of the packed doc-id deltas (from `first_doc`) and freqs. + doc_width: u8, + freq_width: u8, } /// Per-term locator into a partition's shared posting buffers. @@ -1990,19 +2052,18 @@ fn build_partition( for chunk in docs_for_term.chunks(POSTING_BLOCK) { let df_offset = doc_freq_data.len() as u32; let pos_offset = pos_data.len() as u32; - // doc-id gaps for docs[1..] (docs[0] is `first_doc`). - let mut prev = chunk[0].0; - for &(d, _, _) in &chunk[1..] { - vbyte_put(&mut doc_freq_data, d - prev); - prev = d; - } - // frequencies. - let mut blk_max_freq = 0u32; - for &(_, f, _) in chunk { - vbyte_put(&mut doc_freq_data, f); - blk_max_freq = blk_max_freq.max(f); - } - // positions: count + delta-encoded positions per doc. + let first_doc = chunk[0].0; + let last_doc = chunk[chunk.len() - 1].0; + // doc ids: bit-pack `doc - first_doc` at a fixed block width. + let doc_width = bit_width(last_doc - first_doc); + let doc_deltas: Vec = chunk.iter().map(|&(d, _, _)| d - first_doc).collect(); + bitpack_put(&mut doc_freq_data, &doc_deltas, doc_width); + // frequencies: bit-pack at a fixed block width. + let blk_max_freq = chunk.iter().map(|&(_, f, _)| f).max().unwrap_or(0); + let freq_width = bit_width(blk_max_freq); + let freqs: Vec = chunk.iter().map(|&(_, f, _)| f).collect(); + bitpack_put(&mut doc_freq_data, &freqs, freq_width); + // positions: VByte count + delta-encoded positions per doc. for &(d, _, ref positions) in chunk { vbyte_put(&mut pos_data, positions.len() as u32); let mut prev_p = 0u32; @@ -2013,10 +2074,12 @@ fn build_partition( term_min_dl = term_min_dl.min(docs.num_tokens(d)); } block_meta.push(BlockMeta { - first_doc: chunk[0].0, - last_doc: chunk[chunk.len() - 1].0, + first_doc, + last_doc, df_offset, pos_offset, + doc_width, + freq_width, }); term_max_freq = term_max_freq.max(blk_max_freq); } @@ -2439,17 +2502,27 @@ impl<'a> PostingCursor<'a> { let n = block_len(self.pref.doc_count, block); self.docs.clear(); self.freqs.clear(); - let mut pos = bm.df_offset as usize; - let mut d = bm.first_doc; - self.docs.push(d); - for _ in 1..n { - d += vbyte_get(&self.part.doc_freq_data, &mut pos); - self.docs.push(d); - } - for _ in 0..n { - self.freqs - .push(vbyte_get(&self.part.doc_freq_data, &mut pos)); - } + // doc ids: bit-packed `doc - first_doc`. + let df_start = bm.df_offset as usize; + bitpack_get( + &self.part.doc_freq_data, + df_start, + n, + bm.doc_width, + &mut self.docs, + ); + for d in &mut self.docs { + *d += bm.first_doc; + } + // frequencies follow the doc-id block. + let freq_start = df_start + bitpack_len(n, bm.doc_width); + bitpack_get( + &self.part.doc_freq_data, + freq_start, + n, + bm.freq_width, + &mut self.freqs, + ); self.decoded = block; } @@ -3881,6 +3954,31 @@ mod tests { assert_eq!(pos, buf.len(), "every byte consumed"); } + #[test] + fn test_bitpack_roundtrip() { + // Cover width 0 (all-zero), sub-byte, byte-crossing, and full widths. + let cases: [(Vec, u8); 6] = [ + (vec![0u32; 5], 0), + (vec![0, 1, 0, 1, 1, 0, 1], 1), + (vec![3, 0, 7, 5, 2, 6, 1, 4], 3), + (vec![200, 17, 255, 0, 130], 8), + (vec![1000, 5, 65535, 42], 16), + (vec![1, u32::MAX, 0, 123_456_789], 32), + ]; + for (values, width) in &cases { + let mut buf = Vec::new(); + bitpack_put(&mut buf, values, *width); + assert_eq!( + buf.len(), + bitpack_len(values.len(), *width), + "width={width}" + ); + let mut out = Vec::new(); + bitpack_get(&buf, 0, values.len(), *width, &mut out); + assert_eq!(&out, values, "width={width}"); + } + } + #[test] fn test_posting_cursor_codec_roundtrip() { // One term spanning 3 blocks (300 docs), varied freqs + positions. From ee88e8c6d099362a9c075061071284d877367f08 Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sun, 17 May 2026 17:35:53 -0700 Subject: [PATCH 27/37] perf(mem_wal): cross-partition shared-threshold WAND for FTS top-k MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Each partition's WAND previously ran with its own top-k heap, cold-starting the pruning threshold at -inf. At 1M docs (~20 partitions) that re-paid the "fill the heap to raise the threshold" warm-up ~20x, leaving FTS query p50 ~2.5x Lucene even on the uncompressed layout. Limited (top-k) OR searches now feed a single shared `TopK` across the whole index: the tail is scanned first to warm the threshold, then each partition's `wand_into` prunes against the shared, monotonically rising threshold — so a partition processed late skips far more. Still exact: the per-term `doc_weight(max_freq, min_dl)` upper bound is sound, and partitions hold disjoint docs so the shared heap collects the true global top-k. Unbounded `search` and AND keep the exact O(matches) scan. --- rust/lance/src/dataset/mem_wal/index/fts.rs | 186 ++++++++++++-------- 1 file changed, 117 insertions(+), 69 deletions(-) diff --git a/rust/lance/src/dataset/mem_wal/index/fts.rs b/rust/lance/src/dataset/mem_wal/index/fts.rs index 6895a09834d..c432a1d1921 100644 --- a/rust/lance/src/dataset/mem_wal/index/fts.rs +++ b/rust/lance/src/dataset/mem_wal/index/fts.rs @@ -1050,10 +1050,12 @@ impl FtsMemIndex { self.search_fuzzy_tokens(&st, &tokens, fuzziness, max_expansions) } - /// BM25 OR-search over the query tokens: WAND each immutable partition, - /// scan the tail, all scored with one corpus-wide [`MemBM25Scorer`], and - /// the results merged. Each doc lives in exactly one partition or the - /// tail, so the merge is a plain concatenation. + /// BM25 OR-search over the query tokens, scored with one corpus-wide + /// [`MemBM25Scorer`]. With a result limit, all partitions and the tail + /// feed a single shared top-k heap: the tail is scanned first to warm the + /// pruning threshold, then each partition's WAND prunes against the + /// shared rising threshold (instead of every partition cold-starting). + /// Without a limit, an exact O(matches) scan across partitions + tail. fn search_match( &self, st: &IndexState, @@ -1070,14 +1072,32 @@ impl FtsMemIndex { if scorer.num_docs() == 0 { return Vec::new(); } - let mut results = Vec::new(); - for p in st.partitions.iter() { - results.extend(p.search_match(tokens, Operator::Or, &scorer, limit)); - } - if tail_snap.visible_count > 0 { - results.extend(score_terms(&tail_snap, &st.tail.terms, tokens, &scorer)); + match limit { + Some(k) if k > 0 => { + let mut topk = TopK::new(k); + // Scan the tail first so the shared threshold is warm before + // the partition WANDs run. + if tail_snap.visible_count > 0 { + for e in score_terms(&tail_snap, &st.tail.terms, tokens, &scorer) { + topk.offer(e.score, e.row_position); + } + } + for p in st.partitions.iter() { + p.wand_into(tokens, &scorer, &mut topk); + } + topk.into_entries() + } + _ => { + let mut results = Vec::new(); + for p in st.partitions.iter() { + results.extend(p.search_match(tokens, Operator::Or, &scorer)); + } + if tail_snap.visible_count > 0 { + results.extend(score_terms(&tail_snap, &st.tail.terms, tokens, &scorer)); + } + results + } } - results } fn search_phrase_tokens(&self, st: &IndexState, tokens: &[String], slop: u32) -> Vec { @@ -2223,17 +2243,14 @@ impl Partition { build_partition(merged.into_iter().collect(), docs) } - /// BM25 OR/AND-search of the partition. With an OR operator and a result - /// limit, prunes with block-max WAND; otherwise runs a direct O(matches) - /// scan. Every result is scored with `scorer`, so scores are identical - /// regardless of which path runs (and identical to the tail's - /// `score_terms`). + /// Exact O(matches) BM25 OR/AND-search of the partition by direct posting + /// scan. The pruned top-k path is `wand_into`; this is the unbounded and + /// AND path. Scored with `scorer`, so scores match `score_terms`. fn search_match( &self, tokens: &[String], operator: Operator, scorer: &MemBM25Scorer, - limit: Option, ) -> Vec { // Resolve present query tokens to (local term id, query weight). // Repeated query tokens are kept so scores match `score_terms`. @@ -2248,10 +2265,7 @@ impl Partition { if terms.is_empty() || (operator == Operator::And && !all_present) { return Vec::new(); } - match limit { - Some(k) if k > 0 && operator == Operator::Or => self.wand_match(&terms, scorer, k), - _ => self.scan_match(&terms, operator, tokens.len() as u32, scorer), - } + self.scan_match(&terms, operator, tokens.len() as u32, scorer) } /// Exact direct scan: accumulate every match into flat arrays indexed by @@ -2289,31 +2303,35 @@ impl Partition { results } - /// Block-max WAND top-k over an OR query. Exact: each term's - /// `(max_freq, min_dl)` gives a sound score upper bound, so the algorithm - /// only skips docs that provably cannot enter the top-`k`. - fn wand_match(&self, terms: &[(u32, f32)], scorer: &MemBM25Scorer, k: usize) -> Vec { - let mut lanes: Vec = terms - .iter() - .map(|&(id, qw)| { + /// WAND top-k over an OR query, contributing into the caller's shared + /// [`TopK`]. Exact: each term's `(max_freq, min_dl)` gives a sound score + /// upper bound, so docs that provably cannot beat the shared threshold + /// are skipped. Because the threshold is shared across all partitions and + /// the tail, a partition processed late prunes against an already-warm + /// threshold instead of cold-starting. + fn wand_into(&self, tokens: &[String], scorer: &MemBM25Scorer, topk: &mut TopK) { + let mut lanes: Vec = Vec::with_capacity(tokens.len()); + for token in tokens { + if let Some(id) = self.term_id(token) { let pref = &self.postings[id as usize]; - WandLane { + let qw = scorer.query_weight(token); + lanes.push(WandLane { cursor: PostingCursor::new(self, id), qw, ub: qw * scorer.doc_weight(pref.max_freq, pref.min_dl), - } - }) - .collect(); - // Min-heap of the current top-k by score. - let mut heap: BinaryHeap> = BinaryHeap::with_capacity(k + 1); - // theta = score of the weakest doc still in the top-k (-inf until full). - let mut theta = f32::NEG_INFINITY; + }); + } + } + if lanes.is_empty() { + return; + } loop { lanes.retain(|l| l.cursor.doc().is_some()); if lanes.is_empty() { break; } lanes.sort_by_key(|l| l.cursor.doc().unwrap()); + let theta = topk.threshold(); // Pivot: first lane whose cumulative upper bound exceeds theta. let mut acc = 0.0f32; let mut pivot = None; @@ -2338,33 +2356,12 @@ impl Partition { l.cursor.advance(); } } - if heap.len() < k { - heap.push(Reverse(ScoredDoc { - score, - doc: pivot_doc, - })); - if heap.len() == k { - theta = heap.peek().unwrap().0.score; - } - } else if score > theta { - heap.pop(); - heap.push(Reverse(ScoredDoc { - score, - doc: pivot_doc, - })); - theta = heap.peek().unwrap().0.score; - } + topk.offer(score, self.docs.row_id(pivot_doc)); } else { // A lane before the pivot trails pivot_doc; skip it forward. lanes[0].cursor.skip_to(pivot_doc); } } - heap.into_iter() - .map(|Reverse(sd)| FtsEntry { - row_position: self.docs.row_id(sd.doc), - score: sd.score, - }) - .collect() } /// Phrase-search by intersecting posting lists: drive from the rarest @@ -2421,29 +2418,80 @@ impl Partition { } } -/// A scored doc id, ordered by score then doc id (`total_cmp`, so a stray -/// non-finite score cannot panic the heap). -struct ScoredDoc { +/// A scored MemTable row, ordered by score then row position (`total_cmp`, +/// so a stray non-finite score cannot panic the heap). +struct ScoredEntry { score: f32, - doc: u32, + row_position: u64, } -impl PartialEq for ScoredDoc { +impl PartialEq for ScoredEntry { fn eq(&self, other: &Self) -> bool { self.cmp(other) == std::cmp::Ordering::Equal } } -impl Eq for ScoredDoc {} -impl PartialOrd for ScoredDoc { +impl Eq for ScoredEntry {} +impl PartialOrd for ScoredEntry { fn partial_cmp(&self, other: &Self) -> Option { Some(self.cmp(other)) } } -impl Ord for ScoredDoc { +impl Ord for ScoredEntry { fn cmp(&self, other: &Self) -> std::cmp::Ordering { self.score .total_cmp(&other.score) - .then(self.doc.cmp(&other.doc)) + .then(self.row_position.cmp(&other.row_position)) + } +} + +/// A bounded top-k collector shared across all partitions and the tail of a +/// single query. `threshold()` is the score of the weakest entry once full — +/// the WAND pruning bound — and rises monotonically as entries are offered. +struct TopK { + k: usize, + heap: BinaryHeap>, +} + +impl TopK { + fn new(k: usize) -> Self { + Self { + k, + heap: BinaryHeap::with_capacity(k + 1), + } + } + + /// Score a doc must beat to enter the top-k (`-inf` until the heap fills). + fn threshold(&self) -> f32 { + if self.heap.len() >= self.k { + self.heap.peek().unwrap().0.score + } else { + f32::NEG_INFINITY + } + } + + fn offer(&mut self, score: f32, row_position: u64) { + if self.heap.len() < self.k { + self.heap.push(Reverse(ScoredEntry { + score, + row_position, + })); + } else if score > self.heap.peek().unwrap().0.score { + self.heap.pop(); + self.heap.push(Reverse(ScoredEntry { + score, + row_position, + })); + } + } + + fn into_entries(self) -> Vec { + self.heap + .into_iter() + .map(|Reverse(e)| FtsEntry { + row_position: e.row_position, + score: e.score, + }) + .collect() } } @@ -3682,12 +3730,12 @@ mod tests { let apple = index.tokenize_for_search("apple").pop().unwrap(); // "apple" is present -> an OR search over it matches. let or_scorer = build_scorer(&st, &tail_snap, std::slice::from_ref(&apple)); - let or_hits = p.search_match(std::slice::from_ref(&apple), Operator::Or, &or_scorer, None); + let or_hits = p.search_match(std::slice::from_ref(&apple), Operator::Or, &or_scorer); assert_eq!(or_hits.len(), 3); // Adding an absent term to an AND query short-circuits to nothing. let and_tokens = vec![apple, "definitely_missing".to_string()]; let and_scorer = build_scorer(&st, &tail_snap, &and_tokens); - let and_hits = p.search_match(&and_tokens, Operator::And, &and_scorer, None); + let and_hits = p.search_match(&and_tokens, Operator::And, &and_scorer); assert!(and_hits.is_empty()); } From fd8edaf9037b8d07649fff700419768a5e46f9c4 Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sun, 17 May 2026 18:24:20 -0700 Subject: [PATCH 28/37] perf(mem_wal): block-max WAND for FTS top-k pruning MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The per-list WAND bound (`doc_weight(max_freq over the whole posting list, ...)`) is so loose for common terms that the pivot never drops below the threshold — WAND degenerated into a full scan, so neither the shared threshold nor a faster codec could help. Each `BlockMeta` now carries the block's `max_freq`, and `wand_into` is a block-max WAND: the per-list bound still selects the pivot doc, then a per-block bound — summed over all lanes for the blocks covering the pivot — decides whether the whole `[pivot_doc, min block end]` region can be skipped without decoding or scoring it. The bound is sound (block max_freq and term min_dl over-estimate every doc in the block), so the top-k stays exact. `PostingCursor` gains shallow `block_max_freq_for` / `block_end_for` lookups (binary search over `BlockMeta`, no payload decode). Adds a 600-doc/2-partition test asserting the WAND top-k score multiset equals the exact full-scan top-k across several k. --- rust/lance/src/dataset/mem_wal/index/fts.rs | 153 ++++++++++++++++---- 1 file changed, 128 insertions(+), 25 deletions(-) diff --git a/rust/lance/src/dataset/mem_wal/index/fts.rs b/rust/lance/src/dataset/mem_wal/index/fts.rs index c432a1d1921..b21b32e2de4 100644 --- a/rust/lance/src/dataset/mem_wal/index/fts.rs +++ b/rust/lance/src/dataset/mem_wal/index/fts.rs @@ -2009,6 +2009,8 @@ struct BlockMeta { /// first / last doc id in the block (block doc ids are ascending). first_doc: u32, last_doc: u32, + /// largest frequency in the block — the block-max WAND score bound. + max_freq: u32, /// start offset of the block's doc/freq payload in `doc_freq_data`. df_offset: u32, /// start offset of the block's position payload in `pos_data`. @@ -2096,6 +2098,7 @@ fn build_partition( block_meta.push(BlockMeta { first_doc, last_doc, + max_freq: blk_max_freq, df_offset, pos_offset, doc_width, @@ -2303,12 +2306,18 @@ impl Partition { results } - /// WAND top-k over an OR query, contributing into the caller's shared - /// [`TopK`]. Exact: each term's `(max_freq, min_dl)` gives a sound score - /// upper bound, so docs that provably cannot beat the shared threshold - /// are skipped. Because the threshold is shared across all partitions and - /// the tail, a partition processed late prunes against an already-warm - /// threshold instead of cold-starting. + /// Block-max WAND top-k over an OR query, contributing into the caller's + /// shared [`TopK`]. Exact. Two sound bounds drive pruning: + /// + /// - the per-*list* bound `doc_weight(list max_freq, min_dl)` selects the + /// pivot doc (always valid for any future doc); + /// - the per-*block* bound `doc_weight(block max_freq, min_dl)`, summed + /// over all lanes for the blocks covering the pivot doc, decides whether + /// the whole `[pivot_doc, min block end]` region can be skipped without + /// decoding or scoring it. + /// + /// The threshold is shared across all partitions and the tail, so a + /// partition processed late prunes against an already-warm threshold. fn wand_into(&self, tokens: &[String], scorer: &MemBM25Scorer, topk: &mut TopK) { let mut lanes: Vec = Vec::with_capacity(tokens.len()); for token in tokens { @@ -2318,7 +2327,8 @@ impl Partition { lanes.push(WandLane { cursor: PostingCursor::new(self, id), qw, - ub: qw * scorer.doc_weight(pref.max_freq, pref.min_dl), + min_dl: pref.min_dl, + list_ub: qw * scorer.doc_weight(pref.max_freq, pref.min_dl), }); } } @@ -2332,11 +2342,11 @@ impl Partition { } lanes.sort_by_key(|l| l.cursor.doc().unwrap()); let theta = topk.threshold(); - // Pivot: first lane whose cumulative upper bound exceeds theta. + // Pivot: first lane whose cumulative per-list bound exceeds theta. let mut acc = 0.0f32; let mut pivot = None; for (i, l) in lanes.iter().enumerate() { - acc += l.ub; + acc += l.list_ub; if acc > theta { pivot = Some(i); break; @@ -2346,21 +2356,38 @@ impl Partition { break; // no remaining doc can reach theta }; let pivot_doc = lanes[pivot].cursor.doc().unwrap(); - if lanes[0].cursor.doc().unwrap() == pivot_doc { - // Every lane positioned at pivot_doc contributes; score it. - let dl = self.docs.num_tokens(pivot_doc); - let mut score = 0.0f32; - for l in lanes.iter_mut() { - if l.cursor.doc() == Some(pivot_doc) { - score += l.qw * scorer.doc_weight(l.cursor.freq(), dl); - l.cursor.advance(); - } - } - topk.offer(score, self.docs.row_id(pivot_doc)); - } else { - // A lane before the pivot trails pivot_doc; skip it forward. + if lanes[0].cursor.doc().unwrap() != pivot_doc { + // A lane before the pivot trails pivot_doc; align it first. lanes[0].cursor.skip_to(pivot_doc); + continue; + } + // Block-max check: an upper bound on every doc in + // `[pivot_doc, region_end]` is the sum, over all lanes, of each + // lane's max contribution from the block covering pivot_doc. + // `region_end` is the smallest such block end, so within the + // region every lane stays in the same block and the bound holds. + let mut block_ub = 0.0f32; + let mut region_end = u32::MAX; + for l in &lanes { + let bmf = l.cursor.block_max_freq_for(pivot_doc); + block_ub += l.qw * scorer.doc_weight(bmf, l.min_dl); + region_end = region_end.min(l.cursor.block_end_for(pivot_doc)); + } + if block_ub <= theta { + // No doc in the region can beat theta — skip the whole region. + lanes[0].cursor.skip_to(region_end.saturating_add(1)); + continue; + } + // pivot_doc may win; score it. + let dl = self.docs.num_tokens(pivot_doc); + let mut score = 0.0f32; + for l in lanes.iter_mut() { + if l.cursor.doc() == Some(pivot_doc) { + score += l.qw * scorer.doc_weight(l.cursor.freq(), dl); + l.cursor.advance(); + } } + topk.offer(score, self.docs.row_id(pivot_doc)); } } @@ -2495,12 +2522,14 @@ impl TopK { } } -/// One WAND lane: a posting cursor plus its query weight and score bound. +/// One WAND lane: a posting cursor plus its query weight and score bounds. struct WandLane<'a> { cursor: PostingCursor<'a>, qw: f32, - /// Upper bound on this term's contribution to any doc's score. - ub: f32, + /// Smallest doc length over the term — input to the per-block bound. + min_dl: u32, + /// Per-list contribution upper bound, for pivot selection. + list_ub: f32, } /// A decoding cursor over one term's compressed posting list. Decodes a @@ -2654,6 +2683,38 @@ impl<'a> PostingCursor<'a> { // `last_doc >= target`, so this block holds a doc >= target. self.i += self.docs[self.i..].partition_point(|&d| d < target); } + + /// The term's `BlockMeta` slice. + fn blocks(&self) -> &[BlockMeta] { + let start = self.pref.block_start as usize; + &self.part.block_meta[start..start + self.pref.block_count as usize] + } + + /// Index (within the term) of the first block that could hold `target` — + /// the first block with `last_doc >= target`. Shallow: binary search over + /// `BlockMeta`, no payload decode. + fn shallow_block(&self, target: u32) -> Option { + let blocks = self.blocks(); + let idx = blocks.partition_point(|b| b.last_doc < target); + (idx < blocks.len()).then_some(idx) + } + + /// Largest frequency in the block that would hold `target` (a sound, + /// possibly loose, per-block score-bound input). 0 if `target` is past + /// the term's last block. + fn block_max_freq_for(&self, target: u32) -> u32 { + self.shallow_block(target) + .map(|b| self.blocks()[b].max_freq) + .unwrap_or(0) + } + + /// Last doc id of the block that would hold `target` (`u32::MAX` if past + /// the last block) — the upper end of a skippable region. + fn block_end_for(&self, target: u32) -> u32 { + self.shallow_block(target) + .map(|b| self.blocks()[b].last_doc) + .unwrap_or(u32::MAX) + } } // ============================================================================ @@ -3988,6 +4049,48 @@ mod tests { } } + #[test] + fn test_block_max_wand_exact_at_scale() { + // 600 docs across 2 partitions; "alpha" in every doc with a frequency + // that cycles, so per-block maxima vary and the block-max skip path + // genuinely engages. The WAND top-k must still equal the exact + // full-scan top-k. + let index = FtsMemIndex::new(1, "description".to_string()).with_freeze_threshold_rows(300); + for i in 0..600u64 { + let tf = (i % 17) + 1; + let text = format!("{}filler", "alpha ".repeat(tf as usize)); + let batch = RecordBatch::try_new( + create_test_schema(), + vec![ + Arc::new(Int32Array::from(vec![0])), + Arc::new(StringArray::from(vec![text.as_str()])), + ], + ) + .unwrap(); + index.insert(&batch, i).unwrap(); + } + assert!(index.state.load_full().partitions.len() >= 2); + + // Exact ranking via the unbounded scan path. + let mut full = index.search("alpha"); + assert_eq!(full.len(), 600); + full.sort_by(|a, b| b.score.partial_cmp(&a.score).unwrap()); + + // For several k, the WAND top-k's score multiset must equal the exact + // top-k's (row ties make the row set non-unique, but scores are not). + for k in [1usize, 10, 50, 200] { + let limited = index.search_with_options( + &FtsQueryExpr::match_query("alpha"), + SearchOptions::new().with_limit(k), + ); + assert_eq!(limited.len(), k, "k={k}"); + let mut got: Vec = limited.iter().map(|e| e.score).collect(); + got.sort_by(|a, b| b.partial_cmp(a).unwrap()); + let expected: Vec = full.iter().take(k).map(|e| e.score).collect(); + assert_eq!(got, expected, "WAND top-{k} scores must match exact"); + } + } + #[test] fn test_vbyte_roundtrip() { let vals = [0u32, 1, 127, 128, 300, 16_383, 16_384, 70_000, u32::MAX]; From 2094b7a96d44359901f9c483af0d01ddab97a81c Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sun, 17 May 2026 20:13:09 -0700 Subject: [PATCH 29/37] Revert "perf(mem_wal): block-max WAND for FTS top-k pruning" This reverts commit 0aa620c9cbd1731e9957ad7e348564bd2f7fe1d7. --- rust/lance/src/dataset/mem_wal/index/fts.rs | 153 ++++---------------- 1 file changed, 25 insertions(+), 128 deletions(-) diff --git a/rust/lance/src/dataset/mem_wal/index/fts.rs b/rust/lance/src/dataset/mem_wal/index/fts.rs index b21b32e2de4..c432a1d1921 100644 --- a/rust/lance/src/dataset/mem_wal/index/fts.rs +++ b/rust/lance/src/dataset/mem_wal/index/fts.rs @@ -2009,8 +2009,6 @@ struct BlockMeta { /// first / last doc id in the block (block doc ids are ascending). first_doc: u32, last_doc: u32, - /// largest frequency in the block — the block-max WAND score bound. - max_freq: u32, /// start offset of the block's doc/freq payload in `doc_freq_data`. df_offset: u32, /// start offset of the block's position payload in `pos_data`. @@ -2098,7 +2096,6 @@ fn build_partition( block_meta.push(BlockMeta { first_doc, last_doc, - max_freq: blk_max_freq, df_offset, pos_offset, doc_width, @@ -2306,18 +2303,12 @@ impl Partition { results } - /// Block-max WAND top-k over an OR query, contributing into the caller's - /// shared [`TopK`]. Exact. Two sound bounds drive pruning: - /// - /// - the per-*list* bound `doc_weight(list max_freq, min_dl)` selects the - /// pivot doc (always valid for any future doc); - /// - the per-*block* bound `doc_weight(block max_freq, min_dl)`, summed - /// over all lanes for the blocks covering the pivot doc, decides whether - /// the whole `[pivot_doc, min block end]` region can be skipped without - /// decoding or scoring it. - /// - /// The threshold is shared across all partitions and the tail, so a - /// partition processed late prunes against an already-warm threshold. + /// WAND top-k over an OR query, contributing into the caller's shared + /// [`TopK`]. Exact: each term's `(max_freq, min_dl)` gives a sound score + /// upper bound, so docs that provably cannot beat the shared threshold + /// are skipped. Because the threshold is shared across all partitions and + /// the tail, a partition processed late prunes against an already-warm + /// threshold instead of cold-starting. fn wand_into(&self, tokens: &[String], scorer: &MemBM25Scorer, topk: &mut TopK) { let mut lanes: Vec = Vec::with_capacity(tokens.len()); for token in tokens { @@ -2327,8 +2318,7 @@ impl Partition { lanes.push(WandLane { cursor: PostingCursor::new(self, id), qw, - min_dl: pref.min_dl, - list_ub: qw * scorer.doc_weight(pref.max_freq, pref.min_dl), + ub: qw * scorer.doc_weight(pref.max_freq, pref.min_dl), }); } } @@ -2342,11 +2332,11 @@ impl Partition { } lanes.sort_by_key(|l| l.cursor.doc().unwrap()); let theta = topk.threshold(); - // Pivot: first lane whose cumulative per-list bound exceeds theta. + // Pivot: first lane whose cumulative upper bound exceeds theta. let mut acc = 0.0f32; let mut pivot = None; for (i, l) in lanes.iter().enumerate() { - acc += l.list_ub; + acc += l.ub; if acc > theta { pivot = Some(i); break; @@ -2356,38 +2346,21 @@ impl Partition { break; // no remaining doc can reach theta }; let pivot_doc = lanes[pivot].cursor.doc().unwrap(); - if lanes[0].cursor.doc().unwrap() != pivot_doc { - // A lane before the pivot trails pivot_doc; align it first. - lanes[0].cursor.skip_to(pivot_doc); - continue; - } - // Block-max check: an upper bound on every doc in - // `[pivot_doc, region_end]` is the sum, over all lanes, of each - // lane's max contribution from the block covering pivot_doc. - // `region_end` is the smallest such block end, so within the - // region every lane stays in the same block and the bound holds. - let mut block_ub = 0.0f32; - let mut region_end = u32::MAX; - for l in &lanes { - let bmf = l.cursor.block_max_freq_for(pivot_doc); - block_ub += l.qw * scorer.doc_weight(bmf, l.min_dl); - region_end = region_end.min(l.cursor.block_end_for(pivot_doc)); - } - if block_ub <= theta { - // No doc in the region can beat theta — skip the whole region. - lanes[0].cursor.skip_to(region_end.saturating_add(1)); - continue; - } - // pivot_doc may win; score it. - let dl = self.docs.num_tokens(pivot_doc); - let mut score = 0.0f32; - for l in lanes.iter_mut() { - if l.cursor.doc() == Some(pivot_doc) { - score += l.qw * scorer.doc_weight(l.cursor.freq(), dl); - l.cursor.advance(); + if lanes[0].cursor.doc().unwrap() == pivot_doc { + // Every lane positioned at pivot_doc contributes; score it. + let dl = self.docs.num_tokens(pivot_doc); + let mut score = 0.0f32; + for l in lanes.iter_mut() { + if l.cursor.doc() == Some(pivot_doc) { + score += l.qw * scorer.doc_weight(l.cursor.freq(), dl); + l.cursor.advance(); + } } + topk.offer(score, self.docs.row_id(pivot_doc)); + } else { + // A lane before the pivot trails pivot_doc; skip it forward. + lanes[0].cursor.skip_to(pivot_doc); } - topk.offer(score, self.docs.row_id(pivot_doc)); } } @@ -2522,14 +2495,12 @@ impl TopK { } } -/// One WAND lane: a posting cursor plus its query weight and score bounds. +/// One WAND lane: a posting cursor plus its query weight and score bound. struct WandLane<'a> { cursor: PostingCursor<'a>, qw: f32, - /// Smallest doc length over the term — input to the per-block bound. - min_dl: u32, - /// Per-list contribution upper bound, for pivot selection. - list_ub: f32, + /// Upper bound on this term's contribution to any doc's score. + ub: f32, } /// A decoding cursor over one term's compressed posting list. Decodes a @@ -2683,38 +2654,6 @@ impl<'a> PostingCursor<'a> { // `last_doc >= target`, so this block holds a doc >= target. self.i += self.docs[self.i..].partition_point(|&d| d < target); } - - /// The term's `BlockMeta` slice. - fn blocks(&self) -> &[BlockMeta] { - let start = self.pref.block_start as usize; - &self.part.block_meta[start..start + self.pref.block_count as usize] - } - - /// Index (within the term) of the first block that could hold `target` — - /// the first block with `last_doc >= target`. Shallow: binary search over - /// `BlockMeta`, no payload decode. - fn shallow_block(&self, target: u32) -> Option { - let blocks = self.blocks(); - let idx = blocks.partition_point(|b| b.last_doc < target); - (idx < blocks.len()).then_some(idx) - } - - /// Largest frequency in the block that would hold `target` (a sound, - /// possibly loose, per-block score-bound input). 0 if `target` is past - /// the term's last block. - fn block_max_freq_for(&self, target: u32) -> u32 { - self.shallow_block(target) - .map(|b| self.blocks()[b].max_freq) - .unwrap_or(0) - } - - /// Last doc id of the block that would hold `target` (`u32::MAX` if past - /// the last block) — the upper end of a skippable region. - fn block_end_for(&self, target: u32) -> u32 { - self.shallow_block(target) - .map(|b| self.blocks()[b].last_doc) - .unwrap_or(u32::MAX) - } } // ============================================================================ @@ -4049,48 +3988,6 @@ mod tests { } } - #[test] - fn test_block_max_wand_exact_at_scale() { - // 600 docs across 2 partitions; "alpha" in every doc with a frequency - // that cycles, so per-block maxima vary and the block-max skip path - // genuinely engages. The WAND top-k must still equal the exact - // full-scan top-k. - let index = FtsMemIndex::new(1, "description".to_string()).with_freeze_threshold_rows(300); - for i in 0..600u64 { - let tf = (i % 17) + 1; - let text = format!("{}filler", "alpha ".repeat(tf as usize)); - let batch = RecordBatch::try_new( - create_test_schema(), - vec![ - Arc::new(Int32Array::from(vec![0])), - Arc::new(StringArray::from(vec![text.as_str()])), - ], - ) - .unwrap(); - index.insert(&batch, i).unwrap(); - } - assert!(index.state.load_full().partitions.len() >= 2); - - // Exact ranking via the unbounded scan path. - let mut full = index.search("alpha"); - assert_eq!(full.len(), 600); - full.sort_by(|a, b| b.score.partial_cmp(&a.score).unwrap()); - - // For several k, the WAND top-k's score multiset must equal the exact - // top-k's (row ties make the row set non-unique, but scores are not). - for k in [1usize, 10, 50, 200] { - let limited = index.search_with_options( - &FtsQueryExpr::match_query("alpha"), - SearchOptions::new().with_limit(k), - ); - assert_eq!(limited.len(), k, "k={k}"); - let mut got: Vec = limited.iter().map(|e| e.score).collect(); - got.sort_by(|a, b| b.partial_cmp(a).unwrap()); - let expected: Vec = full.iter().take(k).map(|e| e.score).collect(); - assert_eq!(got, expected, "WAND top-{k} scores must match exact"); - } - } - #[test] fn test_vbyte_roundtrip() { let vals = [0u32, 1, 127, 128, 300, 16_383, 16_384, 70_000, u32::MAX]; From 75c512b295fbb44bba09590677d6a17d52380870 Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sun, 17 May 2026 20:20:22 -0700 Subject: [PATCH 30/37] perf(mem_wal): SIMD-decode FTS posting blocks with BitPacker4x MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Compression cut the in-memory FTS footprint 2.7x but VByte/scalar block decoding regressed query p95 at 1M docs from ~4.7 ms to ~25 ms — and the FineWeb top-k queries turned out to be scan-bound, not prune-bound, so WAND pruning could not recover it. Decode speed is the lever. Full 128-doc posting blocks (doc ids and frequencies) are now packed and unpacked with `bitpacking`'s SIMD `BitPacker4x`; the partial final block of each term keeps the scalar codec. The byte layout and `BlockMeta` widths are unchanged, so the two codecs interoperate per block. Positions stay VByte (decoded lazily, off the hot path). Adds the `bitpacking` crate dependency. --- Cargo.lock | 1 + Cargo.toml | 1 + rust/lance/Cargo.toml | 1 + rust/lance/src/dataset/mem_wal/index/fts.rs | 55 ++++++++++++++++++--- 4 files changed, 52 insertions(+), 6 deletions(-) diff --git a/Cargo.lock b/Cargo.lock index 655cb2f123f..f2d73d833b4 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -4343,6 +4343,7 @@ dependencies = [ "aws-credential-types", "aws-sdk-dynamodb", "aws-sdk-s3", + "bitpacking", "byteorder", "bytes", "chrono", diff --git a/Cargo.toml b/Cargo.toml index 693cffe5266..d856c5a822d 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -100,6 +100,7 @@ half = { "version" = "2.1", default-features = false, features = [ "std", ] } lance-bitpacking = { version = "=7.0.0-beta.14", path = "./rust/compression/bitpacking" } +bitpacking = "0.9" bitvec = "1" bytes = "1.11.1" byteorder = "1.5" diff --git a/rust/lance/Cargo.toml b/rust/lance/Cargo.toml index 9053738e657..6a31d01dda8 100644 --- a/rust/lance/Cargo.toml +++ b/rust/lance/Cargo.toml @@ -42,6 +42,7 @@ arrow-schema = { workspace = true } arrow-select = { workspace = true } async-recursion.workspace = true async-trait.workspace = true +bitpacking = { workspace = true } byteorder.workspace = true bytes.workspace = true chrono.workspace = true diff --git a/rust/lance/src/dataset/mem_wal/index/fts.rs b/rust/lance/src/dataset/mem_wal/index/fts.rs index c432a1d1921..953e1aba079 100644 --- a/rust/lance/src/dataset/mem_wal/index/fts.rs +++ b/rust/lance/src/dataset/mem_wal/index/fts.rs @@ -54,6 +54,7 @@ use std::sync::atomic::{AtomicUsize, Ordering}; use arc_swap::ArcSwap; use arrow_array::{Array, LargeStringArray, RecordBatch, StringArray, StringViewArray}; use arrow_schema::DataType; +use bitpacking::{BitPacker, BitPacker4x}; use crossbeam_skiplist::SkipMap; use lance_core::{Error, Result}; use lance_index::scalar::InvertedIndexParams; @@ -1997,11 +1998,48 @@ fn bitpack_get(buf: &[u8], start: usize, n: usize, width: u8, out: &mut Vec } } -/// Number of bytes a `bitpack_put` of `n` values of `width` bits occupies. +/// Number of bytes a width-`width` bit-packed block of `n` values occupies. +/// Identical for the scalar and `BitPacker4x` codecs. fn bitpack_len(n: usize, width: u8) -> usize { (n * width as usize).div_ceil(8) } +/// Bit-pack `values` into `buf`: SIMD `BitPacker4x` for a full 128-element +/// block (the common-term hot path), scalar `bitpack_put` for a shorter +/// final block. +fn pack_block(bp: &BitPacker4x, buf: &mut Vec, values: &[u32], width: u8) { + if values.len() == POSTING_BLOCK && width > 0 { + let mut input = [0u32; POSTING_BLOCK]; + input.copy_from_slice(values); + let mut out = [0u8; POSTING_BLOCK * 4]; + let n = bp.compress(&input, &mut out, width); + buf.extend_from_slice(&out[..n]); + } else { + bitpack_put(buf, values, width); + } +} + +/// Unpack `n` values of `width` bits from `buf[start..]` into `out` (cleared +/// first) — SIMD for a full 128-element block, scalar otherwise. +fn unpack_block( + bp: &BitPacker4x, + buf: &[u8], + start: usize, + n: usize, + width: u8, + out: &mut Vec, +) { + out.clear(); + if n == POSTING_BLOCK && width > 0 { + let mut decoded = [0u32; POSTING_BLOCK]; + let bytes = bitpack_len(POSTING_BLOCK, width); + bp.decompress(&buf[start..start + bytes], &mut decoded, width); + out.extend_from_slice(&decoded); + } else { + bitpack_get(buf, start, n, width, out); + } +} + /// Per-128-doc-block metadata — enough to skip and score-bound a block /// without decoding its payload. #[derive(Clone, Copy)] @@ -2059,6 +2097,7 @@ fn build_partition( docs: DocSet, ) -> Partition { entries.sort_by(|a, b| a.0.cmp(&b.0)); + let bp = BitPacker4x::new(); let mut terms = Vec::with_capacity(entries.len()); let mut postings = Vec::with_capacity(entries.len()); let mut block_meta: Vec = Vec::new(); @@ -2077,12 +2116,12 @@ fn build_partition( // doc ids: bit-pack `doc - first_doc` at a fixed block width. let doc_width = bit_width(last_doc - first_doc); let doc_deltas: Vec = chunk.iter().map(|&(d, _, _)| d - first_doc).collect(); - bitpack_put(&mut doc_freq_data, &doc_deltas, doc_width); + pack_block(&bp, &mut doc_freq_data, &doc_deltas, doc_width); // frequencies: bit-pack at a fixed block width. let blk_max_freq = chunk.iter().map(|&(_, f, _)| f).max().unwrap_or(0); let freq_width = bit_width(blk_max_freq); let freqs: Vec = chunk.iter().map(|&(_, f, _)| f).collect(); - bitpack_put(&mut doc_freq_data, &freqs, freq_width); + pack_block(&bp, &mut doc_freq_data, &freqs, freq_width); // positions: VByte count + delta-encoded positions per doc. for &(d, _, ref positions) in chunk { vbyte_put(&mut pos_data, positions.len() as u32); @@ -2520,6 +2559,8 @@ struct PostingCursor<'a> { positions: Vec>, /// index within the current block. i: usize, + /// SIMD bit-(un)packer for full 128-doc blocks. + bp: BitPacker4x, } impl<'a> PostingCursor<'a> { @@ -2535,6 +2576,7 @@ impl<'a> PostingCursor<'a> { pos_decoded: u32::MAX, positions: Vec::new(), i: 0, + bp: BitPacker4x::new(), }; if pref.block_count > 0 { cursor.decode_doc_freq(0); @@ -2549,10 +2591,10 @@ impl<'a> PostingCursor<'a> { let bm = self.part.block_meta[(self.pref.block_start + block) as usize]; let n = block_len(self.pref.doc_count, block); self.docs.clear(); - self.freqs.clear(); // doc ids: bit-packed `doc - first_doc`. let df_start = bm.df_offset as usize; - bitpack_get( + unpack_block( + &self.bp, &self.part.doc_freq_data, df_start, n, @@ -2564,7 +2606,8 @@ impl<'a> PostingCursor<'a> { } // frequencies follow the doc-id block. let freq_start = df_start + bitpack_len(n, bm.doc_width); - bitpack_get( + unpack_block( + &self.bp, &self.part.doc_freq_data, freq_start, n, From 8810a774d9f3da9f72851c28035adb9328bccdc2 Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Sun, 17 May 2026 20:53:32 -0700 Subject: [PATCH 31/37] test(mem_wal): split FTS bench latency by query type Reports term vs phrase p50/p95 separately so a latency regression can be attributed to one query class instead of the blended percentile. --- rust/lance/benches/mem_wal_fts_bench.rs | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/rust/lance/benches/mem_wal_fts_bench.rs b/rust/lance/benches/mem_wal_fts_bench.rs index 4364fe053c9..9500bc3dba1 100644 --- a/rust/lance/benches/mem_wal_fts_bench.rs +++ b/rust/lance/benches/mem_wal_fts_bench.rs @@ -593,6 +593,29 @@ fn run_bench(args: &BenchArgs) -> Result<()> { f64::NAN }; + // Per-query-type latency split — `latencies_us` is parallel to `queries`. + let mut term_lat: Vec = Vec::new(); + let mut phrase_lat: Vec = Vec::new(); + for (q, &lat) in queries.iter().zip(&latencies_us) { + if q.kind == "phrase" { + phrase_lat.push(lat); + } else { + term_lat.push(lat); + } + } + term_lat.sort_by(|a, b| a.partial_cmp(b).unwrap()); + phrase_lat.sort_by(|a, b| a.partial_cmp(b).unwrap()); + let pct = |v: &[f64], p: f64| if v.is_empty() { f64::NAN } else { percentile(v, p) }; + println!( + "result split: term_p50={:.1} term_p95={:.1} ({} q) | phrase_p50={:.1} phrase_p95={:.1} ({} q)", + pct(&term_lat, 50.0), + pct(&term_lat, 95.0), + term_lat.len(), + pct(&phrase_lat, 50.0), + pct(&phrase_lat, 95.0), + phrase_lat.len(), + ); + println!( "result impl=lance_fts run={} docs={} queries={} build_s={:.3} build_docs_per_s={:.0} \ q_p50_us={:.1} q_p95_us={:.1} qps_1t={:.0} qps_{}t={:.0} \ From 6014e033edcea814f56faa58645344f4f9a15a22 Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Mon, 18 May 2026 00:56:24 -0700 Subject: [PATCH 32/37] perf(mem_wal): random-access compressed FTS positions for fast phrase MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Diagnosis (term/phrase latency split): term queries were already fast (p95 ~0.6 ms, ahead of Lucene) — the blended p95 of ~25 ms was entirely phrase queries. `search_phrase` calls `positions()` for the candidate docs of the rare driving term, but each call VByte-decoded the *whole* 128-doc block's positions to use one — ~128x wasted work, scattered across blocks. A document's position count equals its term frequency, so no per-doc count needs storing: each block's positions are now one bit-packed delta stream, and doc `i`'s slice is located from the freq prefix sum. `bitpack_read_at` reads that slice directly — phrase decodes one document's positions, never the block. This also drops the per-doc count bytes, so the footprint is slightly smaller. Replaces the VByte position codec entirely. --- rust/lance/src/dataset/mem_wal/index/fts.rs | 158 ++++++++++++-------- 1 file changed, 96 insertions(+), 62 deletions(-) diff --git a/rust/lance/src/dataset/mem_wal/index/fts.rs b/rust/lance/src/dataset/mem_wal/index/fts.rs index 953e1aba079..535b9c53fbd 100644 --- a/rust/lance/src/dataset/mem_wal/index/fts.rs +++ b/rust/lance/src/dataset/mem_wal/index/fts.rs @@ -1913,31 +1913,6 @@ impl FtsIndexConfig { /// per-block score bound; a block's payload is self-delimiting. const POSTING_BLOCK: usize = 128; -/// Append `v` to `buf` as unsigned LEB128 (VByte). -fn vbyte_put(buf: &mut Vec, mut v: u32) { - while v >= 0x80 { - buf.push((v as u8) | 0x80); - v >>= 7; - } - buf.push(v as u8); -} - -/// Read one unsigned LEB128 value from `buf` at `*pos`, advancing `*pos`. -fn vbyte_get(buf: &[u8], pos: &mut usize) -> u32 { - let mut v = 0u32; - let mut shift = 0u32; - loop { - let b = buf[*pos]; - *pos += 1; - v |= ((b & 0x7f) as u32) << shift; - if b & 0x80 == 0 { - break; - } - shift += 7; - } - v -} - /// Number of docs in block `b` (0-based within a term) of a `doc_count`-long /// posting list. fn block_len(doc_count: u32, b: u32) -> usize { @@ -2040,6 +2015,41 @@ fn unpack_block( } } +/// Random-access read of `n` width-`width` values at logical index `s` from a +/// bit-packed stream whose first value starts at byte `base` of `buf`. Used to +/// decode one document's positions without touching the rest of the block. +fn bitpack_read_at(buf: &[u8], base: usize, s: usize, n: usize, width: u8, out: &mut Vec) { + if n == 0 { + return; + } + if width == 0 { + out.resize(out.len() + n, 0); + return; + } + let w = width as u32; + let mask: u64 = if w >= 32 { + u32::MAX as u64 + } else { + (1u64 << w) - 1 + }; + let start_bit = s * width as usize; + let mut byte = base + start_bit / 8; + let skip = (start_bit % 8) as u32; + let mut acc = (buf[byte] as u64) >> skip; + let mut bits = 8 - skip; + byte += 1; + for _ in 0..n { + while bits < w { + acc |= (buf[byte] as u64) << bits; + byte += 1; + bits += 8; + } + out.push((acc & mask) as u32); + acc >>= w; + bits -= w; + } +} + /// Per-128-doc-block metadata — enough to skip and score-bound a block /// without decoding its payload. #[derive(Clone, Copy)] @@ -2054,6 +2064,8 @@ struct BlockMeta { /// bit width of the packed doc-id deltas (from `first_doc`) and freqs. doc_width: u8, freq_width: u8, + /// bit width of the packed position deltas. + pos_width: u8, } /// Per-term locator into a partition's shared posting buffers. @@ -2122,16 +2134,20 @@ fn build_partition( let freq_width = bit_width(blk_max_freq); let freqs: Vec = chunk.iter().map(|&(_, f, _)| f).collect(); pack_block(&bp, &mut doc_freq_data, &freqs, freq_width); - // positions: VByte count + delta-encoded positions per doc. + // positions: one bit-packed delta stream for the whole block. + // A doc's position count equals its frequency, so no count is + // stored — doc `i`'s slice is found from the freq prefix sum. + let mut pos_deltas: Vec = Vec::new(); for &(d, _, ref positions) in chunk { - vbyte_put(&mut pos_data, positions.len() as u32); let mut prev_p = 0u32; for &p in positions { - vbyte_put(&mut pos_data, p - prev_p); + pos_deltas.push(p - prev_p); prev_p = p; } term_min_dl = term_min_dl.min(docs.num_tokens(d)); } + let pos_width = bit_width(pos_deltas.iter().copied().max().unwrap_or(0)); + bitpack_put(&mut pos_data, &pos_deltas, pos_width); block_meta.push(BlockMeta { first_doc, last_doc, @@ -2139,6 +2155,7 @@ fn build_partition( pos_offset, doc_width, freq_width, + pos_width, }); term_max_freq = term_max_freq.max(blk_max_freq); } @@ -2554,9 +2571,13 @@ struct PostingCursor<'a> { decoded: u32, docs: Vec, freqs: Vec, - /// the block decoded into `positions` (`u32::MAX` = none). - pos_decoded: u32, - positions: Vec>, + /// freq prefix sum of the decoded block (`len == freqs.len() + 1`); the + /// block it was computed for is `prefix_block`. Indexes the position + /// stream for random per-doc access. + prefix: Vec, + prefix_block: u32, + /// scratch for the most recently decoded doc's positions. + pos_scratch: Vec, /// index within the current block. i: usize, /// SIMD bit-(un)packer for full 128-doc blocks. @@ -2573,8 +2594,9 @@ impl<'a> PostingCursor<'a> { decoded: u32::MAX, docs: Vec::new(), freqs: Vec::new(), - pos_decoded: u32::MAX, - positions: Vec::new(), + prefix: Vec::new(), + prefix_block: u32::MAX, + pos_scratch: Vec::new(), i: 0, bp: BitPacker4x::new(), }; @@ -2617,25 +2639,19 @@ impl<'a> PostingCursor<'a> { self.decoded = block; } - fn decode_positions(&mut self, block: u32) { - if self.pos_decoded == block { + /// Ensure `prefix` holds the freq prefix sum of the current block. + fn ensure_prefix(&mut self) { + if self.prefix_block == self.block { return; } - let bm = self.part.block_meta[(self.pref.block_start + block) as usize]; - let n = block_len(self.pref.doc_count, block); - self.positions.clear(); - let mut pos = bm.pos_offset as usize; - for _ in 0..n { - let cnt = vbyte_get(&self.part.pos_data, &mut pos); - let mut v = Vec::with_capacity(cnt as usize); - let mut last = 0u32; - for _ in 0..cnt { - last += vbyte_get(&self.part.pos_data, &mut pos); - v.push(last); - } - self.positions.push(v); + self.prefix.clear(); + self.prefix.push(0); + let mut sum = 0u32; + for &f in &self.freqs { + sum += f; + self.prefix.push(sum); } - self.pos_decoded = block; + self.prefix_block = self.block; } /// Current doc id, or `None` once the list is exhausted. @@ -2657,11 +2673,29 @@ impl<'a> PostingCursor<'a> { self.freqs[self.i] } - /// Positions of the current posting (decoded lazily). + /// Positions of the current posting — decoded on demand for this one + /// document only (random access into the block's position stream). fn positions(&mut self) -> &[u32] { - let block = self.block; - self.decode_positions(block); - &self.positions[self.i] + self.ensure_prefix(); + let bm = self.part.block_meta[(self.pref.block_start + self.block) as usize]; + let s = self.prefix[self.i] as usize; + let n = self.prefix[self.i + 1] as usize - s; + self.pos_scratch.clear(); + bitpack_read_at( + &self.part.pos_data, + bm.pos_offset as usize, + s, + n, + bm.pos_width, + &mut self.pos_scratch, + ); + // un-delta in place. + let mut last = 0u32; + for d in &mut self.pos_scratch { + last += *d; + *d = last; + } + &self.pos_scratch } /// Step to the next posting. @@ -4032,17 +4066,17 @@ mod tests { } #[test] - fn test_vbyte_roundtrip() { - let vals = [0u32, 1, 127, 128, 300, 16_383, 16_384, 70_000, u32::MAX]; + fn test_bitpack_read_at_random_access() { + // Pack a stream, then random-access arbitrary [s, s+n) ranges. + let vals: Vec = (0..200u32).map(|i| (i * 7 + 3) % 1000).collect(); + let width = bit_width(*vals.iter().max().unwrap()); let mut buf = Vec::new(); - for &v in &vals { - vbyte_put(&mut buf, v); - } - let mut pos = 0; - for &v in &vals { - assert_eq!(vbyte_get(&buf, &mut pos), v); + bitpack_put(&mut buf, &vals, width); + for &(s, n) in &[(0usize, 5usize), (1, 1), (63, 10), (130, 70), (199, 1)] { + let mut out = Vec::new(); + bitpack_read_at(&buf, 0, s, n, width, &mut out); + assert_eq!(out, &vals[s..s + n], "s={s} n={n}"); } - assert_eq!(pos, buf.len(), "every byte consumed"); } #[test] From eb8812241d832035ddad5b551bad4b6480db3824 Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Mon, 18 May 2026 01:30:47 -0700 Subject: [PATCH 33/37] fix(bench): pick the freshest FTS bench binary by mtime MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit run_fts_compare.sh selected the bench binary with `sort | tail -1` — a lexical sort by Cargo's build hash. When a dependency change rotates that hash, an older binary can sort last, so the harness silently ran stale code. Now it clears old binaries before building and selects by modification time. --- bench/run_fts_compare.sh | 7 ++++++- rust/lance/benches/mem_wal_fts_bench.rs | 8 +++++++- 2 files changed, 13 insertions(+), 2 deletions(-) diff --git a/bench/run_fts_compare.sh b/bench/run_fts_compare.sh index e06b5c92274..1374127464c 100755 --- a/bench/run_fts_compare.sh +++ b/bench/run_fts_compare.sh @@ -59,9 +59,14 @@ echo "Lucene classpath: $LUCENE_CP" # ---- build the Lance bench ---- echo "=== Building Lance FTS bench ===" +# Drop stale binaries so the freshest build is unambiguous, then pick the +# most-recently-modified one — a lexical sort by build hash can otherwise +# pick a stale binary after a dependency change rotates the hash. +rm -f "$REPO_ROOT"/target/release/deps/mem_wal_fts_bench-* cargo bench -p lance --bench mem_wal_fts_bench --no-run LANCE_BIN="$(find "$REPO_ROOT/target/release/deps" -maxdepth 1 -type f -perm -111 \ - -name 'mem_wal_fts_bench-*' ! -name '*.d' | sort | tail -1)" + -name 'mem_wal_fts_bench-*' ! -name '*.d' -printf '%T@ %p\n' \ + | sort -nr | head -1 | cut -d' ' -f2-)" echo "Lance bench: $LANCE_BIN" # ---- compile the Lucene bench ---- diff --git a/rust/lance/benches/mem_wal_fts_bench.rs b/rust/lance/benches/mem_wal_fts_bench.rs index 9500bc3dba1..0175a0deaa0 100644 --- a/rust/lance/benches/mem_wal_fts_bench.rs +++ b/rust/lance/benches/mem_wal_fts_bench.rs @@ -605,7 +605,13 @@ fn run_bench(args: &BenchArgs) -> Result<()> { } term_lat.sort_by(|a, b| a.partial_cmp(b).unwrap()); phrase_lat.sort_by(|a, b| a.partial_cmp(b).unwrap()); - let pct = |v: &[f64], p: f64| if v.is_empty() { f64::NAN } else { percentile(v, p) }; + let pct = |v: &[f64], p: f64| { + if v.is_empty() { + f64::NAN + } else { + percentile(v, p) + } + }; println!( "result split: term_p50={:.1} term_p95={:.1} ({} q) | phrase_p50={:.1} phrase_p95={:.1} ({} q)", pct(&term_lat, 50.0), From d7da5e5f3bb49e2e7c3fb453cdde95b6b11d8f03 Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Mon, 18 May 2026 17:28:45 -0700 Subject: [PATCH 34/37] chore(bench): adapt fineweb FTS bench to the initialize_mem_wal builder API `initialize_mem_wal` is now a builder (`.maintained_indexes(...).execute()`) rather than taking a `MemWalConfig`; update the benchmark accordingly after rebasing onto main. --- rust/lance/benches/mem_wal_fineweb_fts.rs | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/rust/lance/benches/mem_wal_fineweb_fts.rs b/rust/lance/benches/mem_wal_fineweb_fts.rs index 70a2e9d8926..51f23a0d600 100644 --- a/rust/lance/benches/mem_wal_fineweb_fts.rs +++ b/rust/lance/benches/mem_wal_fineweb_fts.rs @@ -45,7 +45,7 @@ use std::time::{Duration, Instant}; use arrow_array::{Array, ArrayRef, Int64Array, RecordBatch, RecordBatchIterator, StringArray}; use arrow_schema::{DataType, Field, Schema as ArrowSchema}; use futures::TryStreamExt; -use lance::dataset::mem_wal::{DatasetMemWalExt, MemTableScanner, MemWalConfig, ShardWriterConfig}; +use lance::dataset::mem_wal::{DatasetMemWalExt, MemTableScanner, ShardWriterConfig}; use lance::dataset::{Dataset, WriteParams}; use lance::index::DatasetIndexExt; use lance_core::Result; @@ -396,14 +396,13 @@ async fn build_seed_dataset( .await?; } dataset - .initialize_mem_wal(MemWalConfig { - shard_spec: None, - maintained_indexes: if indexed { - vec![FTS_INDEX_NAME.to_string()] - } else { - vec![] - }, + .initialize_mem_wal() + .maintained_indexes(if indexed { + vec![FTS_INDEX_NAME.to_string()] + } else { + vec![] }) + .execute() .await?; Ok(start.elapsed().as_secs_f64()) } From a0c357a7a154595a0a653a71baccb57679da7da9 Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Mon, 18 May 2026 17:48:22 -0700 Subject: [PATCH 35/37] refactor(mem_wal): drop unused lance-index WAND additions MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The FTS MemTable index runs its own block-max WAND over its compressed in-memory posting layout, so the `wand_search` / `WandTerm` entry point added to `lance-index` early in this work is now dead. Revert it and the `wand` re-export — `lance-index` is left with a single one-identifier change: `Scorer` joins `MemBM25Scorer` in the `inverted` re-export so the in-memory index can call the scorer's trait methods. --- rust/lance-index/src/scalar/inverted.rs | 1 - rust/lance-index/src/scalar/inverted/wand.rs | 96 -------------------- 2 files changed, 97 deletions(-) diff --git a/rust/lance-index/src/scalar/inverted.rs b/rust/lance-index/src/scalar/inverted.rs index 399eabd3ff0..fc716b0474a 100644 --- a/rust/lance-index/src/scalar/inverted.rs +++ b/rust/lance-index/src/scalar/inverted.rs @@ -25,7 +25,6 @@ use lance_core::{Result, cache::LanceCache}; pub use lance_tokenizer::Language; pub use scorer::{MemBM25Scorer, Scorer}; pub use tokenizer::*; -pub use wand::{DocCandidate, WandTerm, wand_search}; use crate::scalar::inverted::query::{FtsSearchParams, Tokens}; diff --git a/rust/lance-index/src/scalar/inverted/wand.rs b/rust/lance-index/src/scalar/inverted/wand.rs index 2e2132f5df6..b06c75c0021 100644 --- a/rust/lance-index/src/scalar/inverted/wand.rs +++ b/rust/lance-index/src/scalar/inverted/wand.rs @@ -442,46 +442,6 @@ pub struct DocCandidate { pub doc_length: u32, } -/// One query term for [`wand_search`]: the token text, its id, the token's -/// position in the query (used only by phrase queries), the query-side -/// weight, and the term's posting list. -pub type WandTerm = (String, u32, u32, f32, PostingList); - -/// In-memory block-max WAND search entry point. -/// -/// Runs the same WAND algorithm as the on-disk inverted index query path, -/// but over caller-supplied posting lists. This is the query primitive for -/// the in-memory FTS MemTable index, whose frozen segments hold their -/// postings as [`PostingList::Plain`]. Mirrors `Wand`'s on-disk usage; the -/// row-address mask is a no-op (the in-memory index has no deletions). -pub fn wand_search( - operator: Operator, - terms: Vec, - docs: &DocSet, - scorer: S, - params: &FtsSearchParams, -) -> Result> { - let num_doc = docs.len(); - let postings = terms - .into_iter() - .map(|(token, token_id, position, query_weight, list)| { - PostingIterator::with_query_weight( - token, - token_id, - position, - query_weight, - list, - num_doc, - ) - }); - let mut wand = Wand::new(operator, postings, docs, scorer); - wand.search( - params, - Arc::new(RowAddrMask::default()), - &crate::metrics::NoOpMetricsCollector, - ) -} - struct HeadPosting { // Iterators that are already positioned on or after the next candidate doc. // The heap is ordered by smallest doc id so the top element determines @@ -1695,62 +1655,6 @@ mod tests { assert_eq!(result.len(), 0); // Should not panic } - // Proves the public `wand_search` entry point works over caller-built - // in-memory posting lists — the query primitive for the segmented - // FTS MemTable index. (BM25 ranking itself is covered by `test_wand` - // and the on-disk index tests.) - #[test] - fn test_wand_search_in_memory() { - let mut docs = DocSet::default(); - for i in 0..10u64 { - docs.append(i, 1); - } - // term "a": docs 0,2,4,6,8 — term "b": docs 1,2,5,8 — union of 7. - let term_a = generate_posting_list(vec![0, 2, 4, 6, 8], 1.0, None, false); - let term_b = generate_posting_list(vec![1, 2, 5, 8], 1.0, None, false); - let union: std::collections::HashSet = [0, 1, 2, 4, 5, 6, 8].into_iter().collect(); - let terms: Vec = vec![ - ("a".to_string(), 0, 0, 1.0, term_a), - ("b".to_string(), 1, 1, 1.0, term_b), - ]; - let params = FtsSearchParams::default().with_limit(Some(3)); - let scorer = IndexBM25Scorer::new(std::iter::empty()); - let result = wand_search(Operator::Or, terms, &docs, scorer, ¶ms).unwrap(); - assert_eq!(result.len(), 3, "limit=3 should return 3 candidates"); - for c in &result { - assert!(union.contains(&c.row_id), "id {} not in union", c.row_id); - assert!(!c.freqs.is_empty(), "candidate must record term freqs"); - } - - // Unlimited search returns exactly the union of the two posting lists. - let scorer = IndexBM25Scorer::new(std::iter::empty()); - let all = wand_search( - Operator::Or, - vec![ - ( - "a".to_string(), - 0, - 0, - 1.0, - generate_posting_list(vec![0, 2, 4, 6, 8], 1.0, None, false), - ), - ( - "b".to_string(), - 1, - 1, - 1.0, - generate_posting_list(vec![1, 2, 5, 8], 1.0, None, false), - ), - ], - &docs, - scorer, - &FtsSearchParams::default(), - ) - .unwrap(); - let got: std::collections::HashSet = all.iter().map(|c| c.row_id).collect(); - assert_eq!(got, union, "OR search must return the posting-list union"); - } - #[test] fn test_posting_iterator_next_compressed_partition_point() { let mut docs = DocSet::default(); From d182f3c949e6efb095909e23130f888961a27507 Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Mon, 18 May 2026 18:25:09 -0700 Subject: [PATCH 36/37] chore: sync python/Cargo.lock with the new bitpacking dependency The `lance` crate gained a `bitpacking` dependency; the separate pylance lockfile must list it so CI's `--locked` lint passes. --- python/Cargo.lock | 1 + 1 file changed, 1 insertion(+) diff --git a/python/Cargo.lock b/python/Cargo.lock index c71f0f09b8c..c3d1fe07fa5 100644 --- a/python/Cargo.lock +++ b/python/Cargo.lock @@ -3993,6 +3993,7 @@ dependencies = [ "async_cell", "aws-credential-types", "aws-sdk-dynamodb", + "bitpacking", "byteorder", "bytes", "chrono", From 9f62f87a26e2813c152ea1bc414f8808a40baca4 Mon Sep 17 00:00:00 2001 From: Heng Ge Date: Mon, 18 May 2026 22:21:02 -0700 Subject: [PATCH 37/37] chore(bench): move mem_wal benches under benches/mem_wal and scrub infra references Relocate the FTS bench files out of the top-level bench/ folder into rust/lance/benches/mem_wal/, with an fts/ subfolder for FTS-specific benchmarks and drivers. Replace hard-coded S3 buckets, instance paths, and AWS region exports in the driver scripts and bench doc comments with generic placeholders and tmpdir-based defaults. --- rust/lance/Cargo.toml | 2 ++ .../benches/mem_wal/fts}/LuceneFtsBench.java | Bin .../{ => mem_wal/fts}/mem_wal_fineweb_fts.rs | 5 ++-- .../{ => mem_wal/fts}/mem_wal_fts_bench.rs | 0 .../benches/mem_wal/fts}/run_fineweb_fts.sh | 26 +++++++++++------- .../benches/mem_wal/fts}/run_fts_compare.sh | 26 ++++++++++-------- .../mem_wal}/run_shard_writer_backpressure.sh | 21 ++++++++------ .../mem_wal_shard_writer_backpressure.rs | 3 +- 8 files changed, 49 insertions(+), 34 deletions(-) rename {bench/lucene_fts_bench => rust/lance/benches/mem_wal/fts}/LuceneFtsBench.java (100%) rename rust/lance/benches/{ => mem_wal/fts}/mem_wal_fineweb_fts.rs (99%) rename rust/lance/benches/{ => mem_wal/fts}/mem_wal_fts_bench.rs (100%) rename {bench => rust/lance/benches/mem_wal/fts}/run_fineweb_fts.sh (78%) rename {bench => rust/lance/benches/mem_wal/fts}/run_fts_compare.sh (87%) rename {bench => rust/lance/benches/mem_wal}/run_shard_writer_backpressure.sh (86%) diff --git a/rust/lance/Cargo.toml b/rust/lance/Cargo.toml index 6a31d01dda8..9f055fc9154 100644 --- a/rust/lance/Cargo.toml +++ b/rust/lance/Cargo.toml @@ -239,10 +239,12 @@ harness = false [[bench]] name = "mem_wal_fineweb_fts" +path = "benches/mem_wal/fts/mem_wal_fineweb_fts.rs" harness = false [[bench]] name = "mem_wal_fts_bench" +path = "benches/mem_wal/fts/mem_wal_fts_bench.rs" harness = false [lints] diff --git a/bench/lucene_fts_bench/LuceneFtsBench.java b/rust/lance/benches/mem_wal/fts/LuceneFtsBench.java similarity index 100% rename from bench/lucene_fts_bench/LuceneFtsBench.java rename to rust/lance/benches/mem_wal/fts/LuceneFtsBench.java diff --git a/rust/lance/benches/mem_wal_fineweb_fts.rs b/rust/lance/benches/mem_wal/fts/mem_wal_fineweb_fts.rs similarity index 99% rename from rust/lance/benches/mem_wal_fineweb_fts.rs rename to rust/lance/benches/mem_wal/fts/mem_wal_fineweb_fts.rs index 51f23a0d600..42df3e63fb1 100644 --- a/rust/lance/benches/mem_wal_fineweb_fts.rs +++ b/rust/lance/benches/mem_wal/fts/mem_wal_fineweb_fts.rs @@ -25,13 +25,12 @@ //! Example: //! //! ```bash -//! AWS_DEFAULT_REGION=us-east-1 \ //! cargo bench -p lance --bench mem_wal_fineweb_fts -- \ //! --phase write --mode async_idx \ -//! --uri s3://jack-devland-build/bench/mem-fts-fineweb/run1/w_async_idx_mt100k \ +//! --uri /tmp/mem-fts-fineweb/run1/w_async_idx_mt100k \ //! --seed-rows 1000000 --batch-rows 1000 --calls 1000 \ //! --max-memtable-rows 100000 \ -//! --cache-dir /mnt/data/fineweb --output result.json +//! --cache-dir /tmp/fineweb-cache --output result.json //! ``` #![recursion_limit = "256"] diff --git a/rust/lance/benches/mem_wal_fts_bench.rs b/rust/lance/benches/mem_wal/fts/mem_wal_fts_bench.rs similarity index 100% rename from rust/lance/benches/mem_wal_fts_bench.rs rename to rust/lance/benches/mem_wal/fts/mem_wal_fts_bench.rs diff --git a/bench/run_fineweb_fts.sh b/rust/lance/benches/mem_wal/fts/run_fineweb_fts.sh similarity index 78% rename from bench/run_fineweb_fts.sh rename to rust/lance/benches/mem_wal/fts/run_fineweb_fts.sh index c6dedd96cf9..8956ecb91eb 100755 --- a/bench/run_fineweb_fts.sh +++ b/rust/lance/benches/mem_wal/fts/run_fineweb_fts.sh @@ -8,31 +8,37 @@ # times the FTS queries, flushes, and replays on disk. # # Every config runs as its own process under a `timeout` watchdog, so a -# hang costs one timeout window, not days. result.json is uploaded to S3. +# hang costs one timeout window, not days. When DATASET_PREFIX points at +# object storage, each result.json is also uploaded there. # -# Usage: ./bench/run_fineweb_fts.sh [run_id] +# Usage: rust/lance/benches/mem_wal/fts/run_fineweb_fts.sh [run_id] +# +# Env: +# DATASET_PREFIX scratch dataset location (default /mem-fts-fineweb) +# CACHE_DIR FineWeb shard download cache (default /lance-fineweb-cache) set -uo pipefail -cd "$(dirname "${BASH_SOURCE[0]}")/.." +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd -P)" +REPO_ROOT="$(git -C "$SCRIPT_DIR" rev-parse --show-toplevel)" +cd "$REPO_ROOT" RUN_ID="${1:-$(date -u +%Y%m%dT%H%M%SZ)}" -DATASET_PREFIX="${DATASET_PREFIX:-s3://jack-devland-build/bench/mem-fts-fineweb}" +DATASET_PREFIX="${DATASET_PREFIX:-${TMPDIR:-/tmp}/mem-fts-fineweb}" SEED_ROWS="${SEED_ROWS:-1000000}" BATCH_ROWS="${BATCH_ROWS:-1000}" CALLS="${CALLS:-1000}" -CACHE_DIR="${CACHE_DIR:-/mnt/data/fineweb}" +CACHE_DIR="${CACHE_DIR:-${TMPDIR:-/tmp}/lance-fineweb-cache}" CONFIG_TIMEOUT="${CONFIG_TIMEOUT:-3600}" -export AWS_DEFAULT_REGION="${AWS_DEFAULT_REGION:-us-east-1}" -LOCAL_DIR="bench/results/${RUN_ID}" -mkdir -p "$LOCAL_DIR" +LOCAL_DIR="$REPO_ROOT/target/fineweb-fts-results/${RUN_ID}" +mkdir -p "$LOCAL_DIR" "$CACHE_DIR" -BIN="$(find target/release/deps -maxdepth 1 -type f -perm -111 -name 'mem_wal_fineweb_fts-*' ! -name '*.d' 2>/dev/null | sort | tail -1)" +BIN="$(find target/release/deps -maxdepth 1 -type f -perm -111 -name 'mem_wal_fineweb_fts-*' ! -name '*.d' -printf '%T@ %p\n' 2>/dev/null | sort -nr | head -1 | cut -d' ' -f2-)" if [ -z "$BIN" ]; then echo "building bench binary..." cargo bench -p lance --bench mem_wal_fineweb_fts --no-run - BIN="$(find target/release/deps -maxdepth 1 -type f -perm -111 -name 'mem_wal_fineweb_fts-*' ! -name '*.d' 2>/dev/null | sort | tail -1)" + BIN="$(find target/release/deps -maxdepth 1 -type f -perm -111 -name 'mem_wal_fineweb_fts-*' ! -name '*.d' -printf '%T@ %p\n' 2>/dev/null | sort -nr | head -1 | cut -d' ' -f2-)" fi echo "bench binary: $BIN" echo "run id: $RUN_ID" diff --git a/bench/run_fts_compare.sh b/rust/lance/benches/mem_wal/fts/run_fts_compare.sh similarity index 87% rename from bench/run_fts_compare.sh rename to rust/lance/benches/mem_wal/fts/run_fts_compare.sh index 1374127464c..ab5617762bc 100755 --- a/bench/run_fts_compare.sh +++ b/rust/lance/benches/mem_wal/fts/run_fts_compare.sh @@ -6,30 +6,30 @@ # size, runs each impl in Run A (pre-tokenized) and Run B (native # analyzers), and prints build/query/recall side by side. # -# Usage: ./bench/run_fts_compare.sh [run_id] +# Usage: rust/lance/benches/mem_wal/fts/run_fts_compare.sh [run_id] # # Env: # SIZES doc-count sweep (default "100000 500000 1000000") # K top-k (default 10) # THREADS query threads for the multi-thread QPS run -# LUCENE_DIR Lucene checkout (default ~/oss/lucene) # LUCENE_CP pre-built Lucene classpath; if set, the Lucene build is skipped +# LUCENE_DIR Lucene source checkout — built when LUCENE_CP is unset # JAVA_HOME JDK 25 home; if unset the script searches common locations -# CACHE_DIR FineWeb shard cache (default /mnt/data/fineweb) +# CACHE_DIR FineWeb shard download cache (default /lance-fineweb-cache) set -uo pipefail -cd "$(dirname "${BASH_SOURCE[0]}")/.." -REPO_ROOT="$(pwd -P)" +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd -P)" +REPO_ROOT="$(git -C "$SCRIPT_DIR" rev-parse --show-toplevel)" +cd "$REPO_ROOT" RUN_ID="${1:-fts-compare-$(date -u +%Y%m%dT%H%M%SZ)}" SIZES="${SIZES:-100000 500000 1000000}" K="${K:-10}" THREADS="${THREADS:-$(getconf _NPROCESSORS_ONLN 2>/dev/null || echo 8)}" -LUCENE_DIR="${LUCENE_DIR:-$HOME/oss/lucene}" -CACHE_DIR="${CACHE_DIR:-/mnt/data/fineweb}" -WORK="${WORK:-/tmp/fts_compare/$RUN_ID}" -RESULT_DIR="$REPO_ROOT/bench/results/$RUN_ID" -mkdir -p "$WORK" "$RESULT_DIR" +CACHE_DIR="${CACHE_DIR:-${TMPDIR:-/tmp}/lance-fineweb-cache}" +WORK="${WORK:-${TMPDIR:-/tmp}/fts_compare/$RUN_ID}" +RESULT_DIR="$REPO_ROOT/target/fts-compare-results/$RUN_ID" +mkdir -p "$WORK" "$RESULT_DIR" "$CACHE_DIR" # ---- locate JDK 25 ---- if [ -z "${JAVA_HOME:-}" ]; then @@ -47,6 +47,10 @@ echo "JDK: $($JAVA -version 2>&1 | head -1)" # ---- build Lucene classpath ---- if [ -z "${LUCENE_CP:-}" ]; then + if [ -z "${LUCENE_DIR:-}" ]; then + echo "ERROR: set LUCENE_CP (prebuilt jars) or LUCENE_DIR (source checkout)" >&2 + exit 1 + fi echo "=== Building Lucene jars ($LUCENE_DIR) ===" ( cd "$LUCENE_DIR" && JAVA_HOME="$JAVA_HOME" ./gradlew -q \ :lucene:core:jar :lucene:analysis:common:jar ) || { @@ -72,7 +76,7 @@ echo "Lance bench: $LANCE_BIN" # ---- compile the Lucene bench ---- echo "=== Compiling Lucene FTS bench ===" "$JAVAC" -cp "$LUCENE_CP" -d "$WORK" \ - "$REPO_ROOT/bench/lucene_fts_bench/LuceneFtsBench.java" \ + "$SCRIPT_DIR/LuceneFtsBench.java" \ || { echo "ERROR: javac failed" >&2; exit 1; } mutual_overlap() { # $1=topk file A $2=topk file B $3=k diff --git a/bench/run_shard_writer_backpressure.sh b/rust/lance/benches/mem_wal/run_shard_writer_backpressure.sh similarity index 86% rename from bench/run_shard_writer_backpressure.sh rename to rust/lance/benches/mem_wal/run_shard_writer_backpressure.sh index 15ad60f2382..5d669b46cd7 100755 --- a/bench/run_shard_writer_backpressure.sh +++ b/rust/lance/benches/mem_wal/run_shard_writer_backpressure.sh @@ -6,19 +6,25 @@ # vector and FTS numbers are directly comparable. # # Usage: -# INDEX_TYPE=fts ./bench/run_shard_writer_backpressure.sh [run_id] -# INDEX_TYPE=vector ./bench/run_shard_writer_backpressure.sh [run_id] +# INDEX_TYPE=fts rust/lance/benches/mem_wal/run_shard_writer_backpressure.sh [run_id] +# INDEX_TYPE=vector rust/lance/benches/mem_wal/run_shard_writer_backpressure.sh [run_id] # # Finds the max sustainable async-indexed throughput: the highest paced # target where puts never block (slow>=1s == 0) and the WAL flush queue # does not accumulate (tail queue delta ~0). +# +# Env: +# DATASET_PREFIX scratch dataset location (default /mem-wal-backpressure) set -uo pipefail -cd "$(dirname "${BASH_SOURCE[0]}")/.." + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd -P)" +REPO_ROOT="$(git -C "$SCRIPT_DIR" rev-parse --show-toplevel)" +cd "$REPO_ROOT" INDEX_TYPE="${INDEX_TYPE:-fts}" RUN_ID="${1:-bp-${INDEX_TYPE}-$(date -u +%Y%m%dT%H%M%SZ)}" -DATASET_PREFIX="${DATASET_PREFIX:-s3://jack-devland-build/bench/mem-fts-fineweb}" +DATASET_PREFIX="${DATASET_PREFIX:-${TMPDIR:-/tmp}/mem-wal-backpressure}" # Shared knobs — identical to the HNSW vector backpressure sweep so the # two index types are measured the same way. @@ -34,7 +40,6 @@ MAX_WAL_BUFFER_SIZE="${MAX_WAL_BUFFER_SIZE:-52428800}" # 50 MiB MAX_WAL_FLUSH_INTERVAL_MS="${MAX_WAL_FLUSH_INTERVAL_MS:-0}" SAMPLE_INTERVAL_MS="${SAMPLE_INTERVAL_MS:-1000}" CONFIG_TIMEOUT="${CONFIG_TIMEOUT:-2400}" -export AWS_DEFAULT_REGION="${AWS_DEFAULT_REGION:-us-east-1}" # Paced async-indexed target sweep (rows/s). FTS indexing is heavier than # IVF/PQ so its sustainable ceiling is lower; the vector sweep can push @@ -47,15 +52,15 @@ esac TARGETS="${TARGETS:-$TARGETS_DEFAULT}" CALLS=$(( ROWS / BATCH_ROWS )) -LOCAL_DIR="bench/results/${RUN_ID}" +LOCAL_DIR="$REPO_ROOT/target/mem-wal-backpressure-results/${RUN_ID}" mkdir -p "$LOCAL_DIR" BIN="$(find target/release/deps -maxdepth 1 -type f -perm -111 \ - -name 'mem_wal_shard_writer_backpressure-*' ! -name '*.d' 2>/dev/null | sort | tail -1)" + -name 'mem_wal_shard_writer_backpressure-*' ! -name '*.d' -printf '%T@ %p\n' 2>/dev/null | sort -nr | head -1 | cut -d' ' -f2-)" if [ -z "$BIN" ]; then cargo bench -p lance --bench mem_wal_shard_writer_backpressure --no-run BIN="$(find target/release/deps -maxdepth 1 -type f -perm -111 \ - -name 'mem_wal_shard_writer_backpressure-*' ! -name '*.d' 2>/dev/null | sort | tail -1)" + -name 'mem_wal_shard_writer_backpressure-*' ! -name '*.d' -printf '%T@ %p\n' 2>/dev/null | sort -nr | head -1 | cut -d' ' -f2-)" fi echo "index_type=$INDEX_TYPE bin=$BIN run_id=$RUN_ID rows=$ROWS" diff --git a/rust/lance/benches/mem_wal_shard_writer_backpressure.rs b/rust/lance/benches/mem_wal_shard_writer_backpressure.rs index 1e9be110040..d8e74def4d5 100644 --- a/rust/lance/benches/mem_wal_shard_writer_backpressure.rs +++ b/rust/lance/benches/mem_wal_shard_writer_backpressure.rs @@ -11,9 +11,8 @@ //! Example: //! //! ```bash -//! AWS_DEFAULT_REGION=us-east-1 \ //! cargo bench -p lance --bench mem_wal_shard_writer_backpressure -- \ -//! --uri s3://jack-devland-build/memwal-rust-native \ +//! --uri /tmp/memwal-rust-native \ //! --mode async_idx \ //! --seed-rows 100000 \ //! --batch-rows 1000 \