meilisearch/milli/src/search/new/resolve_query_graph.rs

198 lines
7.1 KiB
Rust
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use super::interner::Interned;
use super::query_term::{Phrase, QueryTerm, WordDerivations};
use super::{QueryGraph, QueryNode, SearchContext};
use crate::{CboRoaringBitmapCodec, Result, RoaringBitmapCodec};
use fxhash::FxHashMap;
use heed::BytesDecode;
use roaring::{MultiOps, RoaringBitmap};
use std::collections::VecDeque;
// TODO: manual performance metrics: access to DB, bitmap deserializations/operations, etc.
#[derive(Default)]
pub struct NodeDocIdsCache {
pub cache: FxHashMap<u32, RoaringBitmap>,
}
impl<'search> SearchContext<'search> {
fn get_node_docids<'cache>(
&'cache mut self,
term: &QueryTerm,
node_idx: u32,
) -> Result<&'cache RoaringBitmap> {
if self.node_docids_cache.cache.contains_key(&node_idx) {
return Ok(&self.node_docids_cache.cache[&node_idx]);
};
let docids = match term {
QueryTerm::Phrase { phrase } => resolve_phrase(self, *phrase)?,
QueryTerm::Word {
derivations:
WordDerivations {
original,
zero_typo,
one_typo,
two_typos,
use_prefix_db,
synonyms,
split_words,
},
} => {
let mut or_docids = vec![];
for word in zero_typo.iter().chain(one_typo.iter()).chain(two_typos.iter()).copied()
{
if let Some(word_docids) = self.get_word_docids(word)? {
or_docids.push(word_docids);
}
}
if *use_prefix_db {
if let Some(prefix_docids) = self.get_prefix_docids(*original)? {
or_docids.push(prefix_docids);
}
}
let mut docids = or_docids
.into_iter()
.map(|slice| RoaringBitmapCodec::bytes_decode(slice).unwrap())
.collect::<Vec<_>>();
for synonym in synonyms.iter().copied() {
// TODO: cache resolve_phrase?
docids.push(resolve_phrase(self, synonym)?);
}
if let Some(split_words) = split_words {
docids.push(resolve_phrase(self, *split_words)?);
}
MultiOps::union(docids)
}
};
let _ = self.node_docids_cache.cache.insert(node_idx, docids);
let docids = &self.node_docids_cache.cache[&node_idx];
Ok(docids)
}
}
pub fn resolve_query_graph<'search>(
ctx: &mut SearchContext<'search>,
q: &QueryGraph,
universe: &RoaringBitmap,
) -> Result<RoaringBitmap> {
// TODO: there is definitely a faster way to compute this big
// roaring bitmap expression
let mut nodes_resolved = RoaringBitmap::new();
let mut path_nodes_docids = vec![RoaringBitmap::new(); q.nodes.len()];
let mut next_nodes_to_visit = VecDeque::new();
next_nodes_to_visit.push_front(q.root_node);
while let Some(node) = next_nodes_to_visit.pop_front() {
let predecessors = &q.edges[node as usize].predecessors;
if !predecessors.is_subset(&nodes_resolved) {
next_nodes_to_visit.push_back(node);
continue;
}
// Take union of all predecessors
let predecessors_iter = predecessors.iter().map(|p| &path_nodes_docids[p as usize]);
let predecessors_docids = MultiOps::union(predecessors_iter);
let n = &q.nodes[node as usize];
let node_docids = match n {
QueryNode::Term(located_term) => {
let term = &located_term.value;
let derivations_docids = ctx.get_node_docids(term, node)?;
predecessors_docids & derivations_docids
}
QueryNode::Deleted => {
panic!()
}
QueryNode::Start => universe.clone(),
QueryNode::End => {
return Ok(predecessors_docids);
}
};
nodes_resolved.insert(node);
path_nodes_docids[node as usize] = node_docids;
for succ in q.edges[node as usize].successors.iter() {
if !next_nodes_to_visit.contains(&succ) && !nodes_resolved.contains(succ) {
next_nodes_to_visit.push_back(succ);
}
}
2023-02-21 20:57:34 +08:00
// This is currently slow but could easily be implemented very efficiently
for prec in q.edges[node as usize].predecessors.iter() {
if q.edges[prec as usize].successors.is_subset(&nodes_resolved) {
path_nodes_docids[prec as usize].clear();
}
}
}
panic!()
}
pub fn resolve_phrase(ctx: &mut SearchContext, phrase: Interned<Phrase>) -> Result<RoaringBitmap> {
let Phrase { words } = ctx.phrase_interner.get(phrase).clone();
let mut candidates = RoaringBitmap::new();
let mut first_iter = true;
let winsize = words.len().min(3);
if words.is_empty() {
return Ok(candidates);
}
for win in words.windows(winsize) {
// Get all the documents with the matching distance for each word pairs.
let mut bitmaps = Vec::with_capacity(winsize.pow(2));
for (offset, &s1) in win
.iter()
.enumerate()
.filter_map(|(index, word)| word.as_ref().map(|word| (index, word)))
{
for (dist, &s2) in win
.iter()
.skip(offset + 1)
.enumerate()
.filter_map(|(index, word)| word.as_ref().map(|word| (index, word)))
{
if dist == 0 {
match ctx.get_word_pair_proximity_docids(s1, s2, 1)? {
Some(m) => bitmaps.push(CboRoaringBitmapCodec::deserialize_from(m)?),
// If there are no documents for this pair, there will be no
// results for the phrase query.
None => return Ok(RoaringBitmap::new()),
}
} else {
let mut bitmap = RoaringBitmap::new();
for dist in 0..=dist {
if let Some(m) =
ctx.get_word_pair_proximity_docids(s1, s2, dist as u8 + 1)?
{
bitmap |= CboRoaringBitmapCodec::deserialize_from(m)?;
}
}
if bitmap.is_empty() {
return Ok(bitmap);
} else {
bitmaps.push(bitmap);
}
}
}
}
// We sort the bitmaps so that we perform the small intersections first, which is faster.
bitmaps.sort_unstable_by_key(|a| a.len());
for bitmap in bitmaps {
if first_iter {
candidates = bitmap;
first_iter = false;
} else {
candidates &= bitmap;
}
// There will be no match, return early
if candidates.is_empty() {
break;
}
}
}
Ok(candidates)
}