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https://github.com/meilisearch/meilisearch.git
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Factorize phrase computation
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parent
cbb3b25459
commit
b389be48a0
@ -326,12 +326,28 @@ pub fn resolve_query_tree<'t>(
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}
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}
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Ok(candidates)
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Ok(candidates)
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}
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}
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Phrase(words) => {
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Phrase(words) => resolve_phrase(ctx, &words),
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Or(_, ops) => {
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let mut candidates = RoaringBitmap::new();
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for op in ops {
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let docids = resolve_operation(ctx, op, wdcache)?;
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candidates |= docids;
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}
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Ok(candidates)
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}
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Query(q) => Ok(query_docids(ctx, q, wdcache)?),
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}
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}
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resolve_operation(ctx, query_tree, wdcache)
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}
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pub fn resolve_phrase<'t>(ctx: &'t dyn Context, phrase: &[String]) -> Result<RoaringBitmap> {
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let mut candidates = RoaringBitmap::new();
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let mut candidates = RoaringBitmap::new();
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let mut first_iter = true;
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let mut first_iter = true;
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let winsize = words.len().min(7);
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let winsize = phrase.len().min(7);
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for win in words.windows(winsize) {
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for win in phrase.windows(winsize) {
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// Get all the documents with the matching distance for each word pairs.
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// Get all the documents with the matching distance for each word pairs.
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let mut bitmaps = Vec::with_capacity(winsize.pow(2));
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let mut bitmaps = Vec::with_capacity(winsize.pow(2));
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for (offset, s1) in win.iter().enumerate() {
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for (offset, s1) in win.iter().enumerate() {
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@ -363,20 +379,6 @@ pub fn resolve_query_tree<'t>(
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}
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}
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Ok(candidates)
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Ok(candidates)
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}
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}
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Or(_, ops) => {
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let mut candidates = RoaringBitmap::new();
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for op in ops {
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let docids = resolve_operation(ctx, op, wdcache)?;
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candidates |= docids;
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}
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Ok(candidates)
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}
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Query(q) => Ok(query_docids(ctx, q, wdcache)?),
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}
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}
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resolve_operation(ctx, query_tree, wdcache)
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}
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fn all_word_pair_proximity_docids<T: AsRef<str>, U: AsRef<str>>(
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fn all_word_pair_proximity_docids<T: AsRef<str>, U: AsRef<str>>(
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ctx: &dyn Context,
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ctx: &dyn Context,
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@ -6,8 +6,8 @@ use log::debug;
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use roaring::RoaringBitmap;
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use roaring::RoaringBitmap;
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use super::{
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use super::{
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query_docids, query_pair_proximity_docids, resolve_query_tree, Context, Criterion,
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query_docids, query_pair_proximity_docids, resolve_phrase, resolve_query_tree, Context,
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CriterionParameters, CriterionResult,
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Criterion, CriterionParameters, CriterionResult,
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};
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};
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use crate::search::query_tree::{maximum_proximity, Operation, Query, QueryKind};
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use crate::search::query_tree::{maximum_proximity, Operation, Query, QueryKind};
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use crate::search::{build_dfa, WordDerivationsCache};
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use crate::search::{build_dfa, WordDerivationsCache};
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@ -192,42 +192,9 @@ fn resolve_candidates<'t>(
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let most_right = words
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let most_right = words
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.last()
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.last()
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.map(|w| Query { prefix: false, kind: QueryKind::exact(w.clone()) });
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.map(|w| Query { prefix: false, kind: QueryKind::exact(w.clone()) });
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let mut candidates = RoaringBitmap::new();
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let mut first_iter = true;
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let winsize = words.len().min(7);
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for win in words.windows(winsize) {
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// Get all the documents with the matching distance for each word pairs.
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let mut bitmaps = Vec::with_capacity(winsize.pow(2));
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for (offset, s1) in win.iter().enumerate() {
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for (dist, s2) in win.iter().skip(offset + 1).enumerate() {
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match ctx.word_pair_proximity_docids(s1, s2, dist as u8 + 1)? {
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Some(m) => bitmaps.push(m),
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// If there are no document for this distance, there will be no
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// results for the phrase query.
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None => return Ok(Default::default()),
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}
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}
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}
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// We sort the bitmaps so that we perform the small intersections first, which is faster.
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bitmaps.sort_unstable_by(|a, b| a.len().cmp(&b.len()));
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for bitmap in bitmaps {
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if first_iter {
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candidates = bitmap;
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first_iter = false;
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} else {
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candidates &= bitmap;
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}
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// There will be no match, return early
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if candidates.is_empty() {
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break;
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}
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}
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}
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match (most_left, most_right) {
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match (most_left, most_right) {
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(Some(l), Some(r)) => vec![(l, r, candidates)],
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(Some(l), Some(r)) => vec![(l, r, resolve_phrase(ctx, &words)?)],
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_otherwise => Default::default(),
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_otherwise => Default::default(),
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}
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}
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} else {
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} else {
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