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Speed-up the MatchingWords highlighting struct

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This commit is contained in:
Kerollmops 2021-02-24 17:44:35 +01:00
parent 4510bbccca
commit 5af63c74e0
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GPG Key ID: 92ADA4E935E71FA4
5 changed files with 91 additions and 93 deletions
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18
http-ui/src/main.rs
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@ -32,7 +32,7 @@ use meilisearch_tokenizer::{Analyzer, AnalyzerConfig};
use milli::facet::FacetValue;
use milli::update::UpdateIndexingStep::*;
use milli::update::{UpdateBuilder, IndexDocumentsMethod, UpdateFormat};
use milli::{obkv_to_json, Index, UpdateStore, SearchResult, FacetCondition};
use milli::{obkv_to_json, Index, UpdateStore, SearchResult, MatchingWords, FacetCondition};
static GLOBAL_THREAD_POOL: OnceCell<ThreadPool> = OnceCell::new();
@ -132,7 +132,7 @@ impl<'a, A: AsRef<[u8]>> Highlighter<'a, A> {
Self { analyzer }
}
fn highlight_value(&self, value: Value, words_to_highlight: &HashSet<String>) -> Value {
fn highlight_value(&self, value: Value, matching_words: &MatchingWords) -> Value {
match value {
Value::Null => Value::Null,
Value::Bool(boolean) => Value::Bool(boolean),
@ -142,7 +142,7 @@ impl<'a, A: AsRef<[u8]>> Highlighter<'a, A> {
let analyzed = self.analyzer.analyze(&old_string);
for (word, token) in analyzed.reconstruct() {
if token.is_word() {
let to_highlight = words_to_highlight.contains(token.text());
let to_highlight = matching_words.matches(token.text());
if to_highlight { string.push_str("<mark>") }
string.push_str(word);
if to_highlight { string.push_str("</mark>") }
@ -154,12 +154,12 @@ impl<'a, A: AsRef<[u8]>> Highlighter<'a, A> {
},
Value::Array(values) => {
Value::Array(values.into_iter()
.map(|v| self.highlight_value(v, words_to_highlight))
.map(|v| self.highlight_value(v, matching_words))
.collect())
},
Value::Object(object) => {
Value::Object(object.into_iter()
.map(|(k, v)| (k, self.highlight_value(v, words_to_highlight)))
.map(|(k, v)| (k, self.highlight_value(v, matching_words)))
.collect())
},
}
@ -168,14 +168,14 @@ impl<'a, A: AsRef<[u8]>> Highlighter<'a, A> {
fn highlight_record(
&self,
object: &mut Map<String, Value>,
words_to_highlight: &HashSet<String>,
matching_words: &MatchingWords,
attributes_to_highlight: &HashSet<String>,
) {
// TODO do we need to create a string for element that are not and needs to be highlight?
for (key, value) in object.iter_mut() {
if attributes_to_highlight.contains(key) {
let old_value = mem::take(value);
*value = self.highlight_value(old_value, words_to_highlight);
*value = self.highlight_value(old_value, matching_words);
}
}
}
@ -722,7 +722,7 @@ async fn main() -> anyhow::Result<()> {
search.facet_condition(condition);
}
let SearchResult { found_words, candidates, documents_ids } = search.execute().unwrap();
let SearchResult { matching_words, candidates, documents_ids } = search.execute().unwrap();
let number_of_candidates = candidates.len();
let facets = if query.facet_distribution == Some(true) {
@ -748,7 +748,7 @@ async fn main() -> anyhow::Result<()> {
for (_id, obkv) in index.documents(&rtxn, documents_ids).unwrap() {
let mut object = obkv_to_json(&displayed_fields, &fields_ids_map, obkv).unwrap();
if !disable_highlighting {
highlighter.highlight_record(&mut object, &found_words, &attributes_to_highlight);
highlighter.highlight_record(&mut object, &matching_words, &attributes_to_highlight);
}
documents.push(object);

2
milli/src/lib.rs
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@ -28,7 +28,7 @@ pub use self::heed_codec::{BEU32StrCodec, StrStrU8Codec, ObkvCodec};
pub use self::heed_codec::{RoaringBitmapCodec, BoRoaringBitmapCodec, CboRoaringBitmapCodec};
pub use self::heed_codec::{RoaringBitmapLenCodec, BoRoaringBitmapLenCodec, CboRoaringBitmapLenCodec};
pub use self::index::Index;
pub use self::search::{Search, FacetDistribution, FacetCondition, SearchResult};
pub use self::search::{Search, FacetDistribution, FacetCondition, SearchResult, MatchingWords};
pub use self::update_store::UpdateStore;
pub type FastMap4<K, V> = HashMap<K, V, BuildHasherDefault<FxHasher32>>;

2
milli/src/search/criteria/typo.rs
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@ -1,8 +1,8 @@
use std::{borrow::Cow, collections::HashMap, mem::take};
use anyhow::bail;
use roaring::RoaringBitmap;
use log::debug;
use roaring::RoaringBitmap;
use crate::search::query_tree::{maximum_typo, Operation, Query, QueryKind};
use crate::search::word_derivations;

51
milli/src/search/mod.rs
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@ -1,10 +1,9 @@
use std::borrow::Cow;
use std::collections::HashSet;
use std::fmt;
use std::time::Instant;
use fst::{IntoStreamer, Streamer, Set};
use levenshtein_automata::LevenshteinAutomatonBuilder as LevBuilder;
use levenshtein_automata::{DFA, LevenshteinAutomatonBuilder as LevBuilder};
use log::debug;
use meilisearch_tokenizer::{AnalyzerConfig, Analyzer};
use once_cell::sync::Lazy;
@ -14,8 +13,9 @@ use crate::search::criteria::{Criterion, CriterionResult};
use crate::search::criteria::{typo::Typo, words::Words, proximity::Proximity};
use crate::{Index, DocumentId};
pub use self::facet::{FacetCondition, FacetDistribution, FacetNumberOperator, FacetStringOperator};
pub use self::facet::FacetIter;
pub use self::facet::{FacetCondition, FacetDistribution, FacetNumberOperator, FacetStringOperator};
pub use self::query_tree::MatchingWords;
use self::query_tree::QueryTreeBuilder;
// Building these factories is not free.
@ -87,6 +87,11 @@ impl<'a> Search<'a> {
debug!("facet candidates: {:?} took {:.02?}", facet_candidates, before.elapsed());
let matching_words = match query_tree.as_ref() {
Some(query_tree) => MatchingWords::from_query_tree(&query_tree),
None => MatchingWords::default(),
};
// We are testing the typo criteria but there will be more of them soon.
let criteria_ctx = criteria::HeedContext::new(self.rtxn, self.index)?;
let typo_criterion = Typo::initial(&criteria_ctx, query_tree, facet_candidates)?;
@ -128,8 +133,7 @@ impl<'a> Search<'a> {
if limit == 0 { break }
}
let found_words = HashSet::new();
Ok(SearchResult { found_words, candidates: initial_candidates, documents_ids })
Ok(SearchResult { matching_words, candidates: initial_candidates, documents_ids })
}
}
@ -147,26 +151,21 @@ impl fmt::Debug for Search<'_> {
#[derive(Default)]
pub struct SearchResult {
pub found_words: HashSet<String>,
pub matching_words: MatchingWords,
pub candidates: RoaringBitmap,
// TODO those documents ids should be associated with their criteria scores.
pub documents_ids: Vec<DocumentId>,
}
pub fn word_derivations(word: &str, is_prefix: bool, max_typo: u8, fst: &fst::Set<Cow<[u8]>>) -> anyhow::Result<Vec<(String, u8)>> {
let lev = match max_typo {
0 => &LEVDIST0,
1 => &LEVDIST1,
_ => &LEVDIST2,
};
let dfa = if is_prefix {
lev.build_prefix_dfa(&word)
} else {
lev.build_dfa(&word)
};
pub fn word_derivations(
word: &str,
is_prefix: bool,
max_typo: u8,
fst: &fst::Set<Cow<[u8]>>,
) -> anyhow::Result<Vec<(String, u8)>>
{
let mut derived_words = Vec::new();
let dfa = build_dfa(word, max_typo, is_prefix);
let mut stream = fst.search_with_state(&dfa).into_stream();
while let Some((word, state)) = stream.next() {
@ -177,3 +176,17 @@ pub fn word_derivations(word: &str, is_prefix: bool, max_typo: u8, fst: &fst::Se
Ok(derived_words)
}
pub fn build_dfa(word: &str, typos: u8, is_prefix: bool) -> DFA {
let lev = match typos {
0 => &LEVDIST0,
1 => &LEVDIST1,
_ => &LEVDIST2,
};
if is_prefix {
lev.build_prefix_dfa(word)
} else {
lev.build_dfa(word)
}
}

111
milli/src/search/query_tree.rs
View File

@ -1,12 +1,13 @@
use std::borrow::Cow;
use std::collections::BTreeMap;
use std::collections::HashSet;
use std::{fmt, cmp, mem};
use levenshtein_automata::{DFA, Distance};
use meilisearch_tokenizer::{TokenKind, tokenizer::TokenStream};
use roaring::RoaringBitmap;
use slice_group_by::GroupBy;
use crate::Index;
use super::build_dfa;
type IsOptionalWord = bool;
type IsPrefix = bool;
@ -113,6 +114,14 @@ impl QueryKind {
QueryKind::Tolerant { typo, word }
}
pub fn is_tolerant(&self) -> bool {
matches!(self, QueryKind::Tolerant { .. })
}
pub fn is_exact(&self) -> bool {
matches!(self, QueryKind::Exact { .. })
}
pub fn typo(&self) -> u8 {
match self {
QueryKind::Tolerant { typo, .. } => *typo,
@ -275,69 +284,45 @@ fn synonyms(ctx: &impl Context, word: &[&str]) -> heed::Result<Option<Vec<Operat
}
/// The query tree builder is the interface to build a query tree.
#[derive(Default)]
pub struct MatchingWords {
inner: BTreeMap<String, IsPrefix>
dfas: Vec<(DFA, u8)>,
}
impl MatchingWords {
/// List all words which can be considered as a match for the query tree.
pub fn from_query_tree(tree: &Operation, fst: &fst::Set<Cow<[u8]>>) -> Self {
Self { inner: fetch_words(tree, fst).into_iter().collect() }
pub fn from_query_tree(tree: &Operation) -> Self {
Self {
dfas: fetch_queries(tree).into_iter().map(|(w, t, p)| (build_dfa(w, t, p), t)).collect()
}
}
/// Return true if the word match.
pub fn is_match(&self, word: &str) -> bool {
fn first_char(s: &str) -> Option<&str> {
s.chars().next().map(|c| &s[..c.len_utf8()])
}
match first_char(word) {
Some(first) => {
let left = first.to_owned();
let right = word.to_owned();
self.inner.range(left..=right).any(|(w, is_prefix)| *is_prefix || *w == word)
},
None => false
pub fn matches(&self, word: &str) -> bool {
self.dfas.iter().any(|(dfa, typo)| match dfa.eval(word) {
Distance::Exact(t) => t <= *typo,
Distance::AtLeast(_) => false,
})
}
}
}
type FetchedWords = Vec<(String, IsPrefix)>;
/// Lists all words which can be considered as a match for the query tree.
fn fetch_words(tree: &Operation, fst: &fst::Set<Cow<[u8]>>) -> FetchedWords {
fn resolve_branch(tree: &[Operation], fst: &fst::Set<Cow<[u8]>>) -> FetchedWords {
tree.iter().map(|op| resolve_ops(op, fst)).flatten().collect()
}
fn resolve_query(query: &Query, fst: &fst::Set<Cow<[u8]>>) -> FetchedWords {
match query.kind.clone() {
QueryKind::Exact { word, .. } => vec![(word, query.prefix)],
QueryKind::Tolerant { typo, word } => {
if let Ok(words) = super::word_derivations(&word, query.prefix, typo, fst) {
words.into_iter().map(|(w, _)| (w, query.prefix)).collect()
} else {
vec![(word, query.prefix)]
}
}
}
}
fn resolve_ops(tree: &Operation, fst: &fst::Set<Cow<[u8]>>) -> FetchedWords {
fn fetch_queries(tree: &Operation) -> HashSet<(&str, u8, IsPrefix)> {
fn resolve_ops<'a>(tree: &'a Operation, out: &mut HashSet<(&'a str, u8, IsPrefix)>) {
match tree {
Operation::Or(_, ops) | Operation::And(ops) | Operation::Consecutive(ops) => {
resolve_branch(ops.as_slice(), fst)
ops.as_slice().iter().for_each(|op| resolve_ops(op, out));
},
Operation::Query(ops) => {
resolve_query(ops, fst)
Operation::Query(Query { prefix, kind }) => {
let typo = if kind.is_exact() { 0 } else { kind.typo() };
out.insert((kind.word(), typo, *prefix));
},
}
}
let mut words = resolve_ops(tree, fst);
words.sort_unstable();
words.dedup();
words
let mut queries = HashSet::new();
resolve_ops(tree, &mut queries);
queries
}
/// Main function that creates the final query tree from the primitive query.
@ -559,7 +544,7 @@ mod test {
use std::collections::HashMap;
use fst::Set;
use maplit::hashmap;
use maplit::{hashmap, hashset};
use meilisearch_tokenizer::{Analyzer, AnalyzerConfig};
use rand::{Rng, SeedableRng, rngs::StdRng};
@ -970,26 +955,26 @@ mod test {
let context = TestContext::default();
let query_tree = context.build(false, true, tokens).unwrap().unwrap();
let expected = vec![
("city".to_string(), false),
("earth".to_string(), false),
("nature".to_string(), false),
("new".to_string(), false),
("nyc".to_string(), false),
("split".to_string(), false),
("word".to_string(), false),
("word".to_string(), true),
("world".to_string(), true),
("york".to_string(), false),
];
let expected = hashset!{
("word", 0, false),
("nyc", 0, false),
("wordsplit", 2, false),
("wordsplitnycworld", 2, true),
("nature", 0, false),
("new", 0, false),
("city", 0, false),
("world", 1, true),
("york", 0, false),
("split", 0, false),
("nycworld", 1, true),
("earth", 0, false),
("wordsplitnyc", 2, false),
};
let mut keys = context.postings.keys().collect::<Vec<_>>();
keys.sort_unstable();
let set = fst::Set::from_iter(keys).unwrap().map_data(|v| Cow::Owned(v)).unwrap();
let words = fetch_words(&query_tree, &set);
let words = fetch_queries(&query_tree);
assert_eq!(expected, words);
}
}