mirror of
https://github.com/meilisearch/meilisearch.git
synced 2024-11-30 17:14:59 +08:00
203 lines
7.2 KiB
Rust
203 lines
7.2 KiB
Rust
use std::collections::VecDeque;
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use fxhash::FxHashMap;
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use heed::BytesDecode;
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use roaring::{MultiOps, RoaringBitmap};
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use super::interner::Interned;
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use super::query_term::{Phrase, QueryTerm, WordDerivations};
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use super::small_bitmap::SmallBitmap;
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use super::{QueryGraph, QueryNode, SearchContext};
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use crate::{CboRoaringBitmapCodec, Result, RoaringBitmapCodec};
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// TODO: manual performance metrics: access to DB, bitmap deserializations/operations, etc.
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#[derive(Default)]
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pub struct NodeDocIdsCache {
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pub cache: FxHashMap<u16, RoaringBitmap>,
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}
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impl<'search> SearchContext<'search> {
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fn get_node_docids<'cache>(
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&'cache mut self,
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term: &QueryTerm,
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node_idx: u16,
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) -> Result<&'cache RoaringBitmap> {
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if self.node_docids_cache.cache.contains_key(&node_idx) {
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return Ok(&self.node_docids_cache.cache[&node_idx]);
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};
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let docids = match term {
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QueryTerm::Phrase { phrase } => resolve_phrase(self, *phrase)?,
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QueryTerm::Word {
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derivations:
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WordDerivations {
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original,
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zero_typo,
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one_typo,
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two_typos,
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use_prefix_db,
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synonyms,
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split_words,
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},
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} => {
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let mut or_docids = vec![];
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for word in zero_typo.iter().chain(one_typo.iter()).chain(two_typos.iter()).copied()
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{
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if let Some(word_docids) = self.get_word_docids(word)? {
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or_docids.push(word_docids);
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}
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}
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if *use_prefix_db {
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if let Some(prefix_docids) = self.get_prefix_docids(*original)? {
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or_docids.push(prefix_docids);
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}
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}
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let mut docids = or_docids
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.into_iter()
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.map(|slice| RoaringBitmapCodec::bytes_decode(slice).unwrap())
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.collect::<Vec<_>>();
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for synonym in synonyms.iter().copied() {
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// TODO: cache resolve_phrase?
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docids.push(resolve_phrase(self, synonym)?);
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}
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if let Some(split_words) = split_words {
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docids.push(resolve_phrase(self, *split_words)?);
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}
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MultiOps::union(docids)
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}
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};
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let _ = self.node_docids_cache.cache.insert(node_idx, docids);
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let docids = &self.node_docids_cache.cache[&node_idx];
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Ok(docids)
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}
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}
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pub fn resolve_query_graph<'search>(
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ctx: &mut SearchContext<'search>,
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q: &QueryGraph,
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universe: &RoaringBitmap,
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) -> Result<RoaringBitmap> {
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// TODO: there is definitely a faster way to compute this big
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// roaring bitmap expression
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let mut nodes_resolved = SmallBitmap::new(64);
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let mut path_nodes_docids = vec![RoaringBitmap::new(); q.nodes.len()];
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let mut next_nodes_to_visit = VecDeque::new();
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next_nodes_to_visit.push_front(q.root_node);
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while let Some(node) = next_nodes_to_visit.pop_front() {
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let predecessors = &q.edges[node as usize].predecessors;
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if !predecessors.is_subset(&nodes_resolved) {
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next_nodes_to_visit.push_back(node);
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continue;
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}
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// Take union of all predecessors
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let mut predecessors_docids = RoaringBitmap::new();
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for p in predecessors.iter() {
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predecessors_docids |= &path_nodes_docids[p as usize];
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}
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let n = &q.nodes[node as usize];
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let node_docids = match n {
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QueryNode::Term(located_term) => {
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let term = &located_term.value;
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let derivations_docids = ctx.get_node_docids(term, node)?;
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predecessors_docids & derivations_docids
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}
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QueryNode::Deleted => {
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panic!()
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}
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QueryNode::Start => universe.clone(),
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QueryNode::End => {
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return Ok(predecessors_docids);
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}
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};
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nodes_resolved.insert(node);
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path_nodes_docids[node as usize] = node_docids;
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for succ in q.edges[node as usize].successors.iter() {
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if !next_nodes_to_visit.contains(&succ) && !nodes_resolved.contains(succ) {
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next_nodes_to_visit.push_back(succ);
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}
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}
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// This is currently slow but could easily be implemented very efficiently
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for prec in q.edges[node as usize].predecessors.iter() {
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if q.edges[prec as usize].successors.is_subset(&nodes_resolved) {
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path_nodes_docids[prec as usize].clear();
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}
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}
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}
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panic!()
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}
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pub fn resolve_phrase(ctx: &mut SearchContext, phrase: Interned<Phrase>) -> Result<RoaringBitmap> {
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let Phrase { words } = ctx.phrase_interner.get(phrase).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(3);
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if words.is_empty() {
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return Ok(candidates);
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}
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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
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.iter()
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.enumerate()
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.filter_map(|(index, word)| word.as_ref().map(|word| (index, word)))
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{
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for (dist, &s2) in win
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.iter()
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.skip(offset + 1)
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.enumerate()
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.filter_map(|(index, word)| word.as_ref().map(|word| (index, word)))
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{
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if dist == 0 {
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match ctx.get_word_pair_proximity_docids(s1, s2, 1)? {
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Some(m) => bitmaps.push(CboRoaringBitmapCodec::deserialize_from(m)?),
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// If there are no documents for this pair, there will be no
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// results for the phrase query.
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None => return Ok(RoaringBitmap::new()),
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}
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} else {
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let mut bitmap = RoaringBitmap::new();
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for dist in 0..=dist {
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if let Some(m) =
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ctx.get_word_pair_proximity_docids(s1, s2, dist as u8 + 1)?
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{
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bitmap |= CboRoaringBitmapCodec::deserialize_from(m)?;
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}
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}
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if bitmap.is_empty() {
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return Ok(bitmap);
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} else {
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bitmaps.push(bitmap);
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}
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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_key(|a| a.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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Ok(candidates)
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}
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