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* add RegexPhraseQuery RegexPhraseQuery supports phrase queries with regex. It supports regex and wildcards. E.g. a query with wildcards: "b* b* wolf" matches "big bad wolf" Slop is supported as well: "b* wolf"~2 matches "big bad wolf" Regex queries may match a lot of terms where we still need to keep track which term hit to load the positions. The phrase query algorithm groups terms by their frequency together in the union to prefilter groups early. This PR comes with some new datastructures: SimpleUnion - A union docset for a list of docsets. It doesn't do any caching and is therefore well suited for datasets with lots of skipping. (phrase search, but intersections in general) LoadedPostings - Like SegmentPostings, but all docs and positions are loaded in memory. SegmentPostings uses 1840 bytes per instance with its caches, which is equivalent to 460 docids. LoadedPostings is used for terms which have less than 100 docs. LoadedPostings is only used to reduce memory consumption. BitSetPostingUnion - Creates a `Posting` that uses the bitset for docid hits and the docsets for positions. The BitSet is the precalculated union of the docsets In the RegexPhraseQuery there is a size limit of 512 docsets per PreAggregatedUnion, before creating a new one. Renamed Union to BufferedUnionScorer Added proptests to test different union types. * cleanup * use Box instead of Vec * use RefCell instead of term_freq(&mut) * remove wildcard mode * move RefCell to outer * clippy
212 lines
6.9 KiB
Rust
212 lines
6.9 KiB
Rust
use std::io;
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use std::sync::Arc;
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use common::BitSet;
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use tantivy_fst::Automaton;
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use super::phrase_prefix_query::prefix_end;
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use crate::index::SegmentReader;
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use crate::postings::TermInfo;
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use crate::query::{BitSetDocSet, ConstScorer, Explanation, Scorer, Weight};
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use crate::schema::{Field, IndexRecordOption};
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use crate::termdict::{TermDictionary, TermStreamer};
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use crate::{DocId, Score, TantivyError};
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/// A weight struct for Fuzzy Term and Regex Queries
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pub struct AutomatonWeight<A> {
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field: Field,
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automaton: Arc<A>,
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// For JSON fields, the term dictionary include terms from all paths.
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// We apply additional filtering based on the given JSON path, when searching within the term
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// dictionary. This prevents terms from unrelated paths from matching the search criteria.
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json_path_bytes: Option<Box<[u8]>>,
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}
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impl<A> AutomatonWeight<A>
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where
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A: Automaton + Send + Sync + 'static,
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A::State: Clone,
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{
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/// Create a new AutomationWeight
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pub fn new<IntoArcA: Into<Arc<A>>>(field: Field, automaton: IntoArcA) -> AutomatonWeight<A> {
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AutomatonWeight {
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field,
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automaton: automaton.into(),
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json_path_bytes: None,
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}
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}
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/// Create a new AutomationWeight for a json path
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pub fn new_for_json_path<IntoArcA: Into<Arc<A>>>(
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field: Field,
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automaton: IntoArcA,
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json_path_bytes: &[u8],
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) -> AutomatonWeight<A> {
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AutomatonWeight {
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field,
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automaton: automaton.into(),
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json_path_bytes: Some(json_path_bytes.to_vec().into_boxed_slice()),
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}
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}
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fn automaton_stream<'a>(
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&'a self,
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term_dict: &'a TermDictionary,
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) -> io::Result<TermStreamer<'a, &'a A>> {
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let automaton: &A = &self.automaton;
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let mut term_stream_builder = term_dict.search(automaton);
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if let Some(json_path_bytes) = &self.json_path_bytes {
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term_stream_builder = term_stream_builder.ge(json_path_bytes);
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if let Some(end) = prefix_end(json_path_bytes) {
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term_stream_builder = term_stream_builder.lt(&end);
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}
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}
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term_stream_builder.into_stream()
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}
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/// Returns the term infos that match the automaton
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pub fn get_match_term_infos(&self, reader: &SegmentReader) -> crate::Result<Vec<TermInfo>> {
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let inverted_index = reader.inverted_index(self.field)?;
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let term_dict = inverted_index.terms();
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let mut term_stream = self.automaton_stream(term_dict)?;
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let mut term_infos = Vec::new();
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while term_stream.advance() {
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term_infos.push(term_stream.value().clone());
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}
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Ok(term_infos)
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}
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}
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impl<A> Weight for AutomatonWeight<A>
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where
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A: Automaton + Send + Sync + 'static,
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A::State: Clone,
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{
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fn scorer(&self, reader: &SegmentReader, boost: Score) -> crate::Result<Box<dyn Scorer>> {
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let max_doc = reader.max_doc();
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let mut doc_bitset = BitSet::with_max_value(max_doc);
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let inverted_index = reader.inverted_index(self.field)?;
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let term_dict = inverted_index.terms();
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let mut term_stream = self.automaton_stream(term_dict)?;
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while term_stream.advance() {
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let term_info = term_stream.value();
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let mut block_segment_postings = inverted_index
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.read_block_postings_from_terminfo(term_info, IndexRecordOption::Basic)?;
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loop {
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let docs = block_segment_postings.docs();
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if docs.is_empty() {
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break;
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}
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for &doc in docs {
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doc_bitset.insert(doc);
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}
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block_segment_postings.advance();
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}
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}
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let doc_bitset = BitSetDocSet::from(doc_bitset);
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let const_scorer = ConstScorer::new(doc_bitset, boost);
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Ok(Box::new(const_scorer))
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}
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fn explain(&self, reader: &SegmentReader, doc: DocId) -> crate::Result<Explanation> {
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let mut scorer = self.scorer(reader, 1.0)?;
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if scorer.seek(doc) == doc {
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Ok(Explanation::new("AutomatonScorer", 1.0))
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} else {
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Err(TantivyError::InvalidArgument(
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"Document does not exist".to_string(),
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))
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}
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}
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}
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#[cfg(test)]
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mod tests {
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use tantivy_fst::Automaton;
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use super::AutomatonWeight;
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use crate::docset::TERMINATED;
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use crate::query::Weight;
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use crate::schema::{Schema, STRING};
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use crate::{Index, IndexWriter};
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fn create_index() -> crate::Result<Index> {
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let mut schema = Schema::builder();
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let title = schema.add_text_field("title", STRING);
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let index = Index::create_in_ram(schema.build());
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let mut index_writer: IndexWriter = index.writer_for_tests()?;
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index_writer.add_document(doc!(title=>"abc"))?;
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index_writer.add_document(doc!(title=>"bcd"))?;
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index_writer.add_document(doc!(title=>"abcd"))?;
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index_writer.commit()?;
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Ok(index)
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}
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#[derive(Clone, Copy)]
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enum State {
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Start,
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NotMatching,
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AfterA,
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}
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struct PrefixedByA;
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impl Automaton for PrefixedByA {
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type State = State;
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fn start(&self) -> Self::State {
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State::Start
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}
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fn is_match(&self, state: &Self::State) -> bool {
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matches!(*state, State::AfterA)
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}
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fn accept(&self, state: &Self::State, byte: u8) -> Self::State {
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match *state {
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State::Start => {
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if byte == b'a' {
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State::AfterA
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} else {
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State::NotMatching
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}
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}
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State::AfterA => State::AfterA,
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State::NotMatching => State::NotMatching,
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}
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}
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}
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#[test]
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fn test_automaton_weight() -> crate::Result<()> {
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let index = create_index()?;
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let field = index.schema().get_field("title").unwrap();
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let automaton_weight = AutomatonWeight::new(field, PrefixedByA);
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let reader = index.reader()?;
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let searcher = reader.searcher();
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let mut scorer = automaton_weight.scorer(searcher.segment_reader(0u32), 1.0)?;
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assert_eq!(scorer.doc(), 0u32);
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assert_eq!(scorer.score(), 1.0);
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assert_eq!(scorer.advance(), 2u32);
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assert_eq!(scorer.doc(), 2u32);
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assert_eq!(scorer.score(), 1.0);
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assert_eq!(scorer.advance(), TERMINATED);
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Ok(())
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}
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#[test]
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fn test_automaton_weight_boost() -> crate::Result<()> {
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let index = create_index()?;
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let field = index.schema().get_field("title").unwrap();
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let automaton_weight = AutomatonWeight::new(field, PrefixedByA);
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let reader = index.reader()?;
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let searcher = reader.searcher();
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let mut scorer = automaton_weight.scorer(searcher.segment_reader(0u32), 1.32)?;
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assert_eq!(scorer.doc(), 0u32);
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assert_eq!(scorer.score(), 1.32);
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Ok(())
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}
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}
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