mirror of
https://github.com/meilisearch/MeiliSearch
synced 2024-06-14 00:29:50 +02:00
812 lines
28 KiB
Rust
812 lines
28 KiB
Rust
use std::collections::{BTreeMap, HashSet};
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use std::ops::ControlFlow;
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use std::{fmt, mem};
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use heed::types::ByteSlice;
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use heed::BytesDecode;
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use roaring::RoaringBitmap;
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use crate::error::UserError;
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use crate::facet::FacetType;
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use crate::heed_codec::facet::{
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FacetGroupKeyCodec, FacetGroupValueCodec, FieldDocIdFacetF64Codec, FieldDocIdFacetStringCodec,
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OrderedF64Codec,
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};
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use crate::heed_codec::{ByteSliceRefCodec, StrRefCodec};
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use crate::search::facet::facet_distribution_iter;
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use crate::{FieldId, Index, Result};
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/// The default number of values by facets that will
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/// be fetched from the key-value store.
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pub const DEFAULT_VALUES_PER_FACET: usize = 100;
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/// Threshold on the number of candidates that will make
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/// the system to choose between one algorithm or another.
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const CANDIDATES_THRESHOLD: u64 = 3000;
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pub struct FacetDistribution<'a> {
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facets: Option<HashSet<String>>,
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candidates: Option<RoaringBitmap>,
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max_values_per_facet: usize,
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rtxn: &'a heed::RoTxn<'a>,
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index: &'a Index,
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}
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impl<'a> FacetDistribution<'a> {
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pub fn new(rtxn: &'a heed::RoTxn, index: &'a Index) -> FacetDistribution<'a> {
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FacetDistribution {
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facets: None,
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candidates: None,
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max_values_per_facet: DEFAULT_VALUES_PER_FACET,
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rtxn,
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index,
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}
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}
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pub fn facets<I: IntoIterator<Item = A>, A: AsRef<str>>(&mut self, names: I) -> &mut Self {
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self.facets = Some(names.into_iter().map(|s| s.as_ref().to_string()).collect());
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self
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}
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pub fn max_values_per_facet(&mut self, max: usize) -> &mut Self {
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self.max_values_per_facet = max;
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self
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}
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pub fn candidates(&mut self, candidates: RoaringBitmap) -> &mut Self {
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self.candidates = Some(candidates);
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self
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}
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/// There is a small amount of candidates OR we ask for facet string values so we
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/// decide to iterate over the facet values of each one of them, one by one.
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fn facet_distribution_from_documents(
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&self,
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field_id: FieldId,
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facet_type: FacetType,
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candidates: &RoaringBitmap,
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distribution: &mut BTreeMap<String, u64>,
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) -> heed::Result<()> {
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match facet_type {
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FacetType::Number => {
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let mut key_buffer: Vec<_> = field_id.to_be_bytes().to_vec();
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let distribution_prelength = distribution.len();
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let db = self.index.field_id_docid_facet_f64s;
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for docid in candidates.into_iter() {
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key_buffer.truncate(mem::size_of::<FieldId>());
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key_buffer.extend_from_slice(&docid.to_be_bytes());
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let iter = db
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.remap_key_type::<ByteSlice>()
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.prefix_iter(self.rtxn, &key_buffer)?
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.remap_key_type::<FieldDocIdFacetF64Codec>();
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for result in iter {
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let ((_, _, value), ()) = result?;
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*distribution.entry(value.to_string()).or_insert(0) += 1;
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if distribution.len() - distribution_prelength == self.max_values_per_facet
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{
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break;
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}
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}
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}
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}
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FacetType::String => {
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let mut normalized_distribution = BTreeMap::new();
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let mut key_buffer: Vec<_> = field_id.to_be_bytes().to_vec();
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let db = self.index.field_id_docid_facet_strings;
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'outer: for docid in candidates.into_iter() {
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key_buffer.truncate(mem::size_of::<FieldId>());
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key_buffer.extend_from_slice(&docid.to_be_bytes());
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let iter = db
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.remap_key_type::<ByteSlice>()
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.prefix_iter(self.rtxn, &key_buffer)?
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.remap_key_type::<FieldDocIdFacetStringCodec>();
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for result in iter {
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let ((_, _, normalized_value), original_value) = result?;
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let (_, count) = normalized_distribution
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.entry(normalized_value)
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.or_insert_with(|| (original_value, 0));
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*count += 1;
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if normalized_distribution.len() == self.max_values_per_facet {
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break 'outer;
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}
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}
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}
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let iter = normalized_distribution
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.into_iter()
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.map(|(_normalized, (original, count))| (original.to_string(), count));
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distribution.extend(iter);
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}
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}
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Ok(())
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}
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/// There is too much documents, we use the facet levels to move throught
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/// the facet values, to find the candidates and values associated.
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fn facet_numbers_distribution_from_facet_levels(
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&self,
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field_id: FieldId,
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candidates: &RoaringBitmap,
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distribution: &mut BTreeMap<String, u64>,
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) -> heed::Result<()> {
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facet_distribution_iter::iterate_over_facet_distribution(
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self.rtxn,
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self.index
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.facet_id_f64_docids
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.remap_key_type::<FacetGroupKeyCodec<ByteSliceRefCodec>>(),
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field_id,
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candidates,
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|facet_key, nbr_docids, _| {
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let facet_key = OrderedF64Codec::bytes_decode(facet_key).unwrap();
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distribution.insert(facet_key.to_string(), nbr_docids);
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if distribution.len() == self.max_values_per_facet {
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Ok(ControlFlow::Break(()))
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} else {
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Ok(ControlFlow::Continue(()))
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}
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},
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)
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}
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fn facet_strings_distribution_from_facet_levels(
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&self,
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field_id: FieldId,
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candidates: &RoaringBitmap,
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distribution: &mut BTreeMap<String, u64>,
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) -> heed::Result<()> {
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facet_distribution_iter::iterate_over_facet_distribution(
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self.rtxn,
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self.index
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.facet_id_string_docids
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.remap_key_type::<FacetGroupKeyCodec<ByteSliceRefCodec>>(),
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field_id,
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candidates,
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|facet_key, nbr_docids, any_docid| {
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let facet_key = StrRefCodec::bytes_decode(facet_key).unwrap();
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let key: (FieldId, _, &str) = (field_id, any_docid, facet_key);
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let original_string = self
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.index
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.field_id_docid_facet_strings
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.get(self.rtxn, &key)?
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.unwrap()
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.to_owned();
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distribution.insert(original_string, nbr_docids);
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if distribution.len() == self.max_values_per_facet {
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Ok(ControlFlow::Break(()))
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} else {
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Ok(ControlFlow::Continue(()))
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}
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},
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)
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}
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/// Placeholder search, a.k.a. no candidates were specified. We iterate throught the
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/// facet values one by one and iterate on the facet level 0 for numbers.
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fn facet_values_from_raw_facet_database(
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&self,
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field_id: FieldId,
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) -> heed::Result<BTreeMap<String, u64>> {
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let mut distribution = BTreeMap::new();
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let db = self.index.facet_id_f64_docids;
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let mut prefix = vec![];
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prefix.extend_from_slice(&field_id.to_be_bytes());
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prefix.push(0); // read values from level 0 only
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let iter = db
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.as_polymorph()
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.prefix_iter::<_, ByteSlice, ByteSlice>(self.rtxn, prefix.as_slice())?
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.remap_types::<FacetGroupKeyCodec<OrderedF64Codec>, FacetGroupValueCodec>();
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for result in iter {
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let (key, value) = result?;
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distribution.insert(key.left_bound.to_string(), value.bitmap.len());
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if distribution.len() == self.max_values_per_facet {
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break;
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}
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}
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let iter = self
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.index
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.facet_id_string_docids
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.as_polymorph()
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.prefix_iter::<_, ByteSlice, ByteSlice>(self.rtxn, prefix.as_slice())?
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.remap_types::<FacetGroupKeyCodec<StrRefCodec>, FacetGroupValueCodec>();
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for result in iter {
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let (key, value) = result?;
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let docid = value.bitmap.iter().next().unwrap();
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let key: (FieldId, _, &'a str) = (field_id, docid, key.left_bound);
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let original_string =
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self.index.field_id_docid_facet_strings.get(self.rtxn, &key)?.unwrap().to_owned();
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distribution.insert(original_string, value.bitmap.len());
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if distribution.len() == self.max_values_per_facet {
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break;
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}
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}
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Ok(distribution)
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}
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fn facet_values(&self, field_id: FieldId) -> heed::Result<BTreeMap<String, u64>> {
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use FacetType::{Number, String};
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match self.candidates {
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Some(ref candidates) => {
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// Classic search, candidates were specified, we must return facet values only related
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// to those candidates. We also enter here for facet strings for performance reasons.
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let mut distribution = BTreeMap::new();
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if candidates.len() <= CANDIDATES_THRESHOLD {
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self.facet_distribution_from_documents(
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field_id,
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Number,
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candidates,
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&mut distribution,
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)?;
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self.facet_distribution_from_documents(
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field_id,
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String,
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candidates,
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&mut distribution,
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)?;
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} else {
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self.facet_numbers_distribution_from_facet_levels(
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field_id,
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candidates,
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&mut distribution,
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)?;
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self.facet_strings_distribution_from_facet_levels(
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field_id,
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candidates,
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&mut distribution,
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)?;
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}
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Ok(distribution)
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}
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None => self.facet_values_from_raw_facet_database(field_id),
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}
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}
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pub fn compute_stats(&self) -> Result<BTreeMap<String, (f64, f64)>> {
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let fields_ids_map = self.index.fields_ids_map(self.rtxn)?;
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let filterable_fields = self.index.filterable_fields(self.rtxn)?;
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let candidates = if let Some(candidates) = self.candidates.clone() {
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candidates
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} else {
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return Ok(Default::default());
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};
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let fields = match &self.facets {
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Some(facets) => {
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let invalid_fields: HashSet<_> = facets
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.iter()
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.filter(|facet| !crate::is_faceted(facet, &filterable_fields))
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.collect();
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if !invalid_fields.is_empty() {
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return Err(UserError::InvalidFacetsDistribution {
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invalid_facets_name: invalid_fields.into_iter().cloned().collect(),
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valid_facets_name: filterable_fields.into_iter().collect(),
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}
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.into());
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} else {
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facets.clone()
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}
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}
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None => filterable_fields,
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};
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let mut distribution = BTreeMap::new();
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for (fid, name) in fields_ids_map.iter() {
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if crate::is_faceted(name, &fields) {
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let min_value = if let Some(min_value) = crate::search::criteria::facet_min_value(
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self.index,
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self.rtxn,
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fid,
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candidates.clone(),
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)? {
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min_value
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} else {
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continue;
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};
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let max_value = if let Some(max_value) = crate::search::criteria::facet_max_value(
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self.index,
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self.rtxn,
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fid,
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candidates.clone(),
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)? {
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max_value
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} else {
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continue;
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};
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distribution.insert(name.to_string(), (min_value, max_value));
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}
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}
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Ok(distribution)
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}
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pub fn execute(&self) -> Result<BTreeMap<String, BTreeMap<String, u64>>> {
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let fields_ids_map = self.index.fields_ids_map(self.rtxn)?;
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let filterable_fields = self.index.filterable_fields(self.rtxn)?;
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let fields = match self.facets {
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Some(ref facets) => {
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let invalid_fields: HashSet<_> = facets
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.iter()
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.filter(|facet| !crate::is_faceted(facet, &filterable_fields))
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.collect();
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if !invalid_fields.is_empty() {
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return Err(UserError::InvalidFacetsDistribution {
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invalid_facets_name: invalid_fields.into_iter().cloned().collect(),
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valid_facets_name: filterable_fields.into_iter().collect(),
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}
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.into());
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} else {
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facets.clone()
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}
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}
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None => filterable_fields,
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};
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let mut distribution = BTreeMap::new();
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for (fid, name) in fields_ids_map.iter() {
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if crate::is_faceted(name, &fields) {
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let values = self.facet_values(fid)?;
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distribution.insert(name.to_string(), values);
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}
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}
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Ok(distribution)
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}
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}
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impl fmt::Debug for FacetDistribution<'_> {
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fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
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let FacetDistribution { facets, candidates, max_values_per_facet, rtxn: _, index: _ } =
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self;
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f.debug_struct("FacetDistribution")
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.field("facets", facets)
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.field("candidates", candidates)
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.field("max_values_per_facet", max_values_per_facet)
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.finish()
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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 big_s::S;
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use maplit::hashset;
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use crate::documents::documents_batch_reader_from_objects;
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use crate::index::tests::TempIndex;
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use crate::{milli_snap, FacetDistribution};
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#[test]
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fn few_candidates_few_facet_values() {
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// All the tests here avoid using the code in `facet_distribution_iter` because there aren't
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// enough candidates.
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let mut index = TempIndex::new();
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index.index_documents_config.autogenerate_docids = true;
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index
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.update_settings(|settings| settings.set_filterable_fields(hashset! { S("colour") }))
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.unwrap();
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let documents = documents!([
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{ "colour": "Blue" },
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{ "colour": " blue" },
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{ "colour": "RED" }
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]);
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index.add_documents(documents).unwrap();
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let txn = index.read_txn().unwrap();
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let map = FacetDistribution::new(&txn, &index)
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.facets(std::iter::once("colour"))
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.execute()
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.unwrap();
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milli_snap!(format!("{map:?}"), @r###"{"colour": {"Blue": 2, "RED": 1}}"###);
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let map = FacetDistribution::new(&txn, &index)
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.facets(std::iter::once("colour"))
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.candidates([0, 1, 2].iter().copied().collect())
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.execute()
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.unwrap();
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milli_snap!(format!("{map:?}"), @r###"{"colour": {"Blue": 2, "RED": 1}}"###);
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let map = FacetDistribution::new(&txn, &index)
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.facets(std::iter::once("colour"))
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.candidates([1, 2].iter().copied().collect())
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.execute()
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.unwrap();
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// I think it would be fine if " blue" was "Blue" instead.
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// We just need to get any non-normalised string I think, even if it's not in
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// the candidates
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milli_snap!(format!("{map:?}"), @r###"{"colour": {" blue": 1, "RED": 1}}"###);
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let map = FacetDistribution::new(&txn, &index)
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.facets(std::iter::once("colour"))
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.candidates([2].iter().copied().collect())
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.execute()
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.unwrap();
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milli_snap!(format!("{map:?}"), @r###"{"colour": {"RED": 1}}"###);
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let map = FacetDistribution::new(&txn, &index)
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.facets(std::iter::once("colour"))
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.candidates([0, 1, 2].iter().copied().collect())
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.max_values_per_facet(1)
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.execute()
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.unwrap();
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milli_snap!(format!("{map:?}"), @r###"{"colour": {"Blue": 1}}"###);
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}
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#[test]
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fn many_candidates_few_facet_values() {
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let mut index = TempIndex::new_with_map_size(4096 * 10_000);
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index.index_documents_config.autogenerate_docids = true;
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index
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.update_settings(|settings| settings.set_filterable_fields(hashset! { S("colour") }))
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.unwrap();
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let facet_values = ["Red", "RED", " red ", "Blue", "BLUE"];
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let mut documents = vec![];
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for i in 0..10_000 {
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let document = serde_json::json!({
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"colour": facet_values[i % 5],
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})
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.as_object()
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.unwrap()
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.clone();
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documents.push(document);
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}
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let documents = documents_batch_reader_from_objects(documents);
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index.add_documents(documents).unwrap();
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let txn = index.read_txn().unwrap();
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let map = FacetDistribution::new(&txn, &index)
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.facets(std::iter::once("colour"))
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.execute()
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.unwrap();
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milli_snap!(format!("{map:?}"), @r###"{"colour": {"Blue": 4000, "Red": 6000}}"###);
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let map = FacetDistribution::new(&txn, &index)
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.facets(std::iter::once("colour"))
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.max_values_per_facet(1)
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.execute()
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.unwrap();
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|
milli_snap!(format!("{map:?}"), @r###"{"colour": {"Blue": 4000}}"###);
|
|
|
|
let map = FacetDistribution::new(&txn, &index)
|
|
.facets(std::iter::once("colour"))
|
|
.candidates((0..10_000).collect())
|
|
.execute()
|
|
.unwrap();
|
|
|
|
milli_snap!(format!("{map:?}"), @r###"{"colour": {"Blue": 4000, "Red": 6000}}"###);
|
|
|
|
let map = FacetDistribution::new(&txn, &index)
|
|
.facets(std::iter::once("colour"))
|
|
.candidates((0..5_000).collect())
|
|
.execute()
|
|
.unwrap();
|
|
|
|
milli_snap!(format!("{map:?}"), @r###"{"colour": {"Blue": 2000, "Red": 3000}}"###);
|
|
|
|
let map = FacetDistribution::new(&txn, &index)
|
|
.facets(std::iter::once("colour"))
|
|
.candidates((0..5_000).collect())
|
|
.execute()
|
|
.unwrap();
|
|
|
|
milli_snap!(format!("{map:?}"), @r###"{"colour": {"Blue": 2000, "Red": 3000}}"###);
|
|
|
|
let map = FacetDistribution::new(&txn, &index)
|
|
.facets(std::iter::once("colour"))
|
|
.candidates((0..5_000).collect())
|
|
.max_values_per_facet(1)
|
|
.execute()
|
|
.unwrap();
|
|
|
|
milli_snap!(format!("{map:?}"), @r###"{"colour": {"Blue": 2000}}"###);
|
|
}
|
|
|
|
#[test]
|
|
fn many_candidates_many_facet_values() {
|
|
let mut index = TempIndex::new_with_map_size(4096 * 10_000);
|
|
index.index_documents_config.autogenerate_docids = true;
|
|
|
|
index
|
|
.update_settings(|settings| settings.set_filterable_fields(hashset! { S("colour") }))
|
|
.unwrap();
|
|
|
|
let facet_values = (0..1000).map(|x| format!("{x:x}")).collect::<Vec<_>>();
|
|
|
|
let mut documents = vec![];
|
|
for i in 0..10_000 {
|
|
let document = serde_json::json!({
|
|
"colour": facet_values[i % 1000],
|
|
})
|
|
.as_object()
|
|
.unwrap()
|
|
.clone();
|
|
documents.push(document);
|
|
}
|
|
|
|
let documents = documents_batch_reader_from_objects(documents);
|
|
|
|
index.add_documents(documents).unwrap();
|
|
|
|
let txn = index.read_txn().unwrap();
|
|
|
|
let map = FacetDistribution::new(&txn, &index)
|
|
.facets(std::iter::once("colour"))
|
|
.execute()
|
|
.unwrap();
|
|
|
|
milli_snap!(format!("{map:?}"), "no_candidates", @"ac9229ed5964d893af96a7076e2f8af5");
|
|
|
|
let map = FacetDistribution::new(&txn, &index)
|
|
.facets(std::iter::once("colour"))
|
|
.max_values_per_facet(2)
|
|
.execute()
|
|
.unwrap();
|
|
|
|
milli_snap!(format!("{map:?}"), "no_candidates_with_max_2", @r###"{"colour": {"0": 10, "1": 10}}"###);
|
|
|
|
let map = FacetDistribution::new(&txn, &index)
|
|
.facets(std::iter::once("colour"))
|
|
.candidates((0..10_000).collect())
|
|
.execute()
|
|
.unwrap();
|
|
|
|
milli_snap!(format!("{map:?}"), "candidates_0_10_000", @"ac9229ed5964d893af96a7076e2f8af5");
|
|
|
|
let map = FacetDistribution::new(&txn, &index)
|
|
.facets(std::iter::once("colour"))
|
|
.candidates((0..5_000).collect())
|
|
.execute()
|
|
.unwrap();
|
|
|
|
milli_snap!(format!("{map:?}"), "candidates_0_5_000", @"825f23a4090d05756f46176987b7d992");
|
|
}
|
|
|
|
#[test]
|
|
fn facet_stats() {
|
|
let mut index = TempIndex::new_with_map_size(4096 * 10_000);
|
|
index.index_documents_config.autogenerate_docids = true;
|
|
|
|
index
|
|
.update_settings(|settings| settings.set_filterable_fields(hashset! { S("colour") }))
|
|
.unwrap();
|
|
|
|
let facet_values = (0..1000).collect::<Vec<_>>();
|
|
|
|
let mut documents = vec![];
|
|
for i in 0..1000 {
|
|
let document = serde_json::json!({
|
|
"colour": facet_values[i % 1000],
|
|
})
|
|
.as_object()
|
|
.unwrap()
|
|
.clone();
|
|
documents.push(document);
|
|
}
|
|
|
|
let documents = documents_batch_reader_from_objects(documents);
|
|
|
|
index.add_documents(documents).unwrap();
|
|
|
|
let txn = index.read_txn().unwrap();
|
|
|
|
let map = FacetDistribution::new(&txn, &index)
|
|
.facets(std::iter::once("colour"))
|
|
.compute_stats()
|
|
.unwrap();
|
|
|
|
milli_snap!(format!("{map:?}"), "no_candidates", @"{}");
|
|
|
|
let map = FacetDistribution::new(&txn, &index)
|
|
.facets(std::iter::once("colour"))
|
|
.candidates((0..1000).collect())
|
|
.compute_stats()
|
|
.unwrap();
|
|
|
|
milli_snap!(format!("{map:?}"), "candidates_0_1000", @r###"{"colour": (0.0, 999.0)}"###);
|
|
|
|
let map = FacetDistribution::new(&txn, &index)
|
|
.facets(std::iter::once("colour"))
|
|
.candidates((217..777).collect())
|
|
.compute_stats()
|
|
.unwrap();
|
|
|
|
milli_snap!(format!("{map:?}"), "candidates_217_777", @r###"{"colour": (217.0, 776.0)}"###);
|
|
}
|
|
|
|
#[test]
|
|
fn facet_stats_array() {
|
|
let mut index = TempIndex::new_with_map_size(4096 * 10_000);
|
|
index.index_documents_config.autogenerate_docids = true;
|
|
|
|
index
|
|
.update_settings(|settings| settings.set_filterable_fields(hashset! { S("colour") }))
|
|
.unwrap();
|
|
|
|
let facet_values = (0..1000).collect::<Vec<_>>();
|
|
|
|
let mut documents = vec![];
|
|
for i in 0..1000 {
|
|
let document = serde_json::json!({
|
|
"colour": [facet_values[i % 1000], facet_values[i % 1000] + 1000],
|
|
})
|
|
.as_object()
|
|
.unwrap()
|
|
.clone();
|
|
documents.push(document);
|
|
}
|
|
|
|
let documents = documents_batch_reader_from_objects(documents);
|
|
|
|
index.add_documents(documents).unwrap();
|
|
|
|
let txn = index.read_txn().unwrap();
|
|
|
|
let map = FacetDistribution::new(&txn, &index)
|
|
.facets(std::iter::once("colour"))
|
|
.compute_stats()
|
|
.unwrap();
|
|
|
|
milli_snap!(format!("{map:?}"), "no_candidates", @"{}");
|
|
|
|
let map = FacetDistribution::new(&txn, &index)
|
|
.facets(std::iter::once("colour"))
|
|
.candidates((0..1000).collect())
|
|
.compute_stats()
|
|
.unwrap();
|
|
|
|
milli_snap!(format!("{map:?}"), "candidates_0_1000", @r###"{"colour": (0.0, 1999.0)}"###);
|
|
|
|
let map = FacetDistribution::new(&txn, &index)
|
|
.facets(std::iter::once("colour"))
|
|
.candidates((217..777).collect())
|
|
.compute_stats()
|
|
.unwrap();
|
|
|
|
milli_snap!(format!("{map:?}"), "candidates_217_777", @r###"{"colour": (217.0, 1776.0)}"###);
|
|
}
|
|
|
|
#[test]
|
|
fn facet_stats_mixed_array() {
|
|
let mut index = TempIndex::new_with_map_size(4096 * 10_000);
|
|
index.index_documents_config.autogenerate_docids = true;
|
|
|
|
index
|
|
.update_settings(|settings| settings.set_filterable_fields(hashset! { S("colour") }))
|
|
.unwrap();
|
|
|
|
let facet_values = (0..1000).collect::<Vec<_>>();
|
|
|
|
let mut documents = vec![];
|
|
for i in 0..1000 {
|
|
let document = serde_json::json!({
|
|
"colour": [facet_values[i % 1000], format!("{}", facet_values[i % 1000] + 1000)],
|
|
})
|
|
.as_object()
|
|
.unwrap()
|
|
.clone();
|
|
documents.push(document);
|
|
}
|
|
|
|
let documents = documents_batch_reader_from_objects(documents);
|
|
|
|
index.add_documents(documents).unwrap();
|
|
|
|
let txn = index.read_txn().unwrap();
|
|
|
|
let map = FacetDistribution::new(&txn, &index)
|
|
.facets(std::iter::once("colour"))
|
|
.compute_stats()
|
|
.unwrap();
|
|
|
|
milli_snap!(format!("{map:?}"), "no_candidates", @"{}");
|
|
|
|
let map = FacetDistribution::new(&txn, &index)
|
|
.facets(std::iter::once("colour"))
|
|
.candidates((0..1000).collect())
|
|
.compute_stats()
|
|
.unwrap();
|
|
|
|
milli_snap!(format!("{map:?}"), "candidates_0_1000", @r###"{"colour": (0.0, 999.0)}"###);
|
|
|
|
let map = FacetDistribution::new(&txn, &index)
|
|
.facets(std::iter::once("colour"))
|
|
.candidates((217..777).collect())
|
|
.compute_stats()
|
|
.unwrap();
|
|
|
|
milli_snap!(format!("{map:?}"), "candidates_217_777", @r###"{"colour": (217.0, 776.0)}"###);
|
|
}
|
|
|
|
#[test]
|
|
fn facet_mixed_values() {
|
|
let mut index = TempIndex::new_with_map_size(4096 * 10_000);
|
|
index.index_documents_config.autogenerate_docids = true;
|
|
|
|
index
|
|
.update_settings(|settings| settings.set_filterable_fields(hashset! { S("colour") }))
|
|
.unwrap();
|
|
|
|
let facet_values = (0..1000).collect::<Vec<_>>();
|
|
|
|
let mut documents = vec![];
|
|
for i in 0..1000 {
|
|
let document = if i % 2 == 0 {
|
|
serde_json::json!({
|
|
"colour": [facet_values[i % 1000], facet_values[i % 1000] + 1000],
|
|
})
|
|
} else {
|
|
serde_json::json!({
|
|
"colour": format!("{}", facet_values[i % 1000] + 10000),
|
|
})
|
|
};
|
|
let document = document.as_object().unwrap().clone();
|
|
documents.push(document);
|
|
}
|
|
|
|
let documents = documents_batch_reader_from_objects(documents);
|
|
|
|
index.add_documents(documents).unwrap();
|
|
|
|
let txn = index.read_txn().unwrap();
|
|
|
|
let map = FacetDistribution::new(&txn, &index)
|
|
.facets(std::iter::once("colour"))
|
|
.compute_stats()
|
|
.unwrap();
|
|
|
|
milli_snap!(format!("{map:?}"), "no_candidates", @"{}");
|
|
|
|
let map = FacetDistribution::new(&txn, &index)
|
|
.facets(std::iter::once("colour"))
|
|
.candidates((0..1000).collect())
|
|
.compute_stats()
|
|
.unwrap();
|
|
|
|
milli_snap!(format!("{map:?}"), "candidates_0_1000", @r###"{"colour": (0.0, 1998.0)}"###);
|
|
|
|
let map = FacetDistribution::new(&txn, &index)
|
|
.facets(std::iter::once("colour"))
|
|
.candidates((217..777).collect())
|
|
.compute_stats()
|
|
.unwrap();
|
|
|
|
milli_snap!(format!("{map:?}"), "candidates_217_777", @r###"{"colour": (218.0, 1776.0)}"###);
|
|
}
|
|
}
|