4476: Make the `/facet-search` route use the `sortFacetValuesBy` setting r=irevoire a=Kerollmops

This PR fixes #4423 by ensuring that the `/facet-search` route uses the `sortFacetValuesBy` setting.

Note for the documentation team (to be moved in the tracking issue): Using the new `sortFacetValuesBy` setting can slow down the facet-search requests as Meilisearch iterates over the whole list of facet values and computes the count of documents on every entry. That is hardly or even impossible to optimize correctly.

### TODO
 - [x] Create a custom HashMap wrapper for the facet `OrderBy` settings.
         This wrapper will return the `OrderBy` setting of the facet, if not defined will use the default `*` one, and if not there either (strange) will fall back on the lexicographic one.
- [x] Create a `ValuesCollection` wrapper that implements the logic for the lexicographic and count order by.
  - [x] Use it when there is no search query.
  - [x] Use it when there is a search query with and without allowed typos.
  - [x] Do not change the original logic, only use a wrapper.
- [x] Add tests

Co-authored-by: Clément Renault <clement@meilisearch.com>
This commit is contained in:
meili-bors[bot] 2024-03-13 14:36:14 +00:00 committed by GitHub
commit abd954755d
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GPG Key ID: B5690EEEBB952194
9 changed files with 449 additions and 276 deletions

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@ -671,27 +671,16 @@ pub fn perform_search(
let sort_facet_values_by =
index.sort_facet_values_by(&rtxn).map_err(milli::Error::from)?;
let default_sort_facet_values_by =
sort_facet_values_by.get("*").copied().unwrap_or_default();
if fields.iter().all(|f| f != "*") {
let fields: Vec<_> = fields
.iter()
.map(|n| {
(
n,
sort_facet_values_by
.get(n)
.copied()
.unwrap_or(default_sort_facet_values_by),
)
})
.collect();
let fields: Vec<_> =
fields.iter().map(|n| (n, sort_facet_values_by.get(n))).collect();
facet_distribution.facets(fields);
}
let distribution = facet_distribution
.candidates(candidates)
.default_order_by(default_sort_facet_values_by)
.default_order_by(sort_facet_values_by.get("*"))
.execute()?;
let stats = facet_distribution.compute_stats()?;
(Some(distribution), Some(stats))

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@ -123,6 +123,28 @@ async fn simple_facet_search_with_max_values() {
assert_eq!(dbg!(response)["facetHits"].as_array().unwrap().len(), 1);
}
#[actix_rt::test]
async fn simple_facet_search_by_count_with_max_values() {
let server = Server::new().await;
let index = server.index("test");
let documents = DOCUMENTS.clone();
index
.update_settings_faceting(
json!({ "maxValuesPerFacet": 1, "sortFacetValuesBy": { "*": "count" } }),
)
.await;
index.update_settings_filterable_attributes(json!(["genres"])).await;
index.add_documents(documents, None).await;
index.wait_task(2).await;
let (response, code) =
index.facet_search(json!({"facetName": "genres", "facetQuery": "a"})).await;
assert_eq!(code, 200, "{}", response);
assert_eq!(dbg!(response)["facetHits"].as_array().unwrap().len(), 1);
}
#[actix_rt::test]
async fn non_filterable_facet_search_error() {
let server = Server::new().await;
@ -157,3 +179,24 @@ async fn facet_search_dont_support_words() {
assert_eq!(code, 200, "{}", response);
assert_eq!(response["facetHits"].as_array().unwrap().len(), 0);
}
#[actix_rt::test]
async fn simple_facet_search_with_sort_by_count() {
let server = Server::new().await;
let index = server.index("test");
let documents = DOCUMENTS.clone();
index.update_settings_faceting(json!({ "sortFacetValuesBy": { "*": "count" } })).await;
index.update_settings_filterable_attributes(json!(["genres"])).await;
index.add_documents(documents, None).await;
index.wait_task(2).await;
let (response, code) =
index.facet_search(json!({"facetName": "genres", "facetQuery": "a"})).await;
assert_eq!(code, 200, "{}", response);
let hits = response["facetHits"].as_array().unwrap();
assert_eq!(hits.len(), 2);
assert_eq!(hits[0], json!({ "value": "Action", "count": 3 }));
assert_eq!(hits[1], json!({ "value": "Adventure", "count": 2 }));
}

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@ -20,13 +20,13 @@ use crate::heed_codec::facet::{
use crate::heed_codec::{
BEU16StrCodec, FstSetCodec, ScriptLanguageCodec, StrBEU16Codec, StrRefCodec,
};
use crate::order_by_map::OrderByMap;
use crate::proximity::ProximityPrecision;
use crate::vector::EmbeddingConfig;
use crate::{
default_criteria, CboRoaringBitmapCodec, Criterion, DocumentId, ExternalDocumentsIds,
FacetDistribution, FieldDistribution, FieldId, FieldIdWordCountCodec, GeoPoint, ObkvCodec,
OrderBy, Result, RoaringBitmapCodec, RoaringBitmapLenCodec, Search, U8StrStrCodec, BEU16,
BEU32, BEU64,
Result, RoaringBitmapCodec, RoaringBitmapLenCodec, Search, U8StrStrCodec, BEU16, BEU32, BEU64,
};
pub const DEFAULT_MIN_WORD_LEN_ONE_TYPO: u8 = 5;
@ -1373,21 +1373,19 @@ impl Index {
self.main.remap_key_type::<Str>().delete(txn, main_key::MAX_VALUES_PER_FACET)
}
pub fn sort_facet_values_by(&self, txn: &RoTxn) -> heed::Result<HashMap<String, OrderBy>> {
let mut orders = self
pub fn sort_facet_values_by(&self, txn: &RoTxn) -> heed::Result<OrderByMap> {
let orders = self
.main
.remap_types::<Str, SerdeJson<HashMap<String, OrderBy>>>()
.remap_types::<Str, SerdeJson<OrderByMap>>()
.get(txn, main_key::SORT_FACET_VALUES_BY)?
.unwrap_or_default();
// Insert the default ordering if it is not already overwritten by the user.
orders.entry("*".to_string()).or_insert(OrderBy::Lexicographic);
Ok(orders)
}
pub(crate) fn put_sort_facet_values_by(
&self,
txn: &mut RwTxn,
val: &HashMap<String, OrderBy>,
val: &OrderByMap,
) -> heed::Result<()> {
self.main.remap_types::<Str, SerdeJson<_>>().put(txn, main_key::SORT_FACET_VALUES_BY, &val)
}

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@ -16,6 +16,7 @@ pub mod facet;
mod fields_ids_map;
pub mod heed_codec;
pub mod index;
pub mod order_by_map;
pub mod prompt;
pub mod proximity;
pub mod score_details;
@ -56,10 +57,10 @@ pub use self::heed_codec::{
UncheckedU8StrStrCodec,
};
pub use self::index::Index;
pub use self::search::facet::{FacetValueHit, SearchForFacetValues};
pub use self::search::{
FacetDistribution, FacetValueHit, Filter, FormatOptions, MatchBounds, MatcherBuilder,
MatchingWords, OrderBy, Search, SearchForFacetValues, SearchResult, TermsMatchingStrategy,
DEFAULT_VALUES_PER_FACET,
FacetDistribution, Filter, FormatOptions, MatchBounds, MatcherBuilder, MatchingWords, OrderBy,
Search, SearchResult, TermsMatchingStrategy, DEFAULT_VALUES_PER_FACET,
};
pub type Result<T> = std::result::Result<T, error::Error>;

57
milli/src/order_by_map.rs Normal file
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@ -0,0 +1,57 @@
use std::collections::{hash_map, HashMap};
use std::iter::FromIterator;
use serde::{Deserialize, Deserializer, Serialize};
use crate::OrderBy;
#[derive(Serialize)]
pub struct OrderByMap(HashMap<String, OrderBy>);
impl OrderByMap {
pub fn get(&self, key: impl AsRef<str>) -> OrderBy {
self.0
.get(key.as_ref())
.copied()
.unwrap_or_else(|| self.0.get("*").copied().unwrap_or_default())
}
pub fn insert(&mut self, key: String, value: OrderBy) -> Option<OrderBy> {
self.0.insert(key, value)
}
}
impl Default for OrderByMap {
fn default() -> Self {
let mut map = HashMap::new();
map.insert("*".to_string(), OrderBy::Lexicographic);
OrderByMap(map)
}
}
impl FromIterator<(String, OrderBy)> for OrderByMap {
fn from_iter<T: IntoIterator<Item = (String, OrderBy)>>(iter: T) -> Self {
OrderByMap(iter.into_iter().collect())
}
}
impl IntoIterator for OrderByMap {
type Item = (String, OrderBy);
type IntoIter = hash_map::IntoIter<String, OrderBy>;
fn into_iter(self) -> Self::IntoIter {
self.0.into_iter()
}
}
impl<'de> Deserialize<'de> for OrderByMap {
fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
where
D: Deserializer<'de>,
{
let mut map = Deserialize::deserialize(deserializer).map(OrderByMap)?;
// Insert the default ordering if it is not already overwritten by the user.
map.0.entry("*".to_string()).or_insert(OrderBy::default());
Ok(map)
}
}

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@ -6,15 +6,18 @@ use roaring::RoaringBitmap;
pub use self::facet_distribution::{FacetDistribution, OrderBy, DEFAULT_VALUES_PER_FACET};
pub use self::filter::{BadGeoError, Filter};
pub use self::search::{FacetValueHit, SearchForFacetValues};
use crate::heed_codec::facet::{FacetGroupKeyCodec, FacetGroupValueCodec, OrderedF64Codec};
use crate::heed_codec::BytesRefCodec;
use crate::{Index, Result};
mod facet_distribution;
mod facet_distribution_iter;
mod facet_range_search;
mod facet_sort_ascending;
mod facet_sort_descending;
mod filter;
mod search;
fn facet_extreme_value<'t>(
mut extreme_it: impl Iterator<Item = heed::Result<(RoaringBitmap, &'t [u8])>> + 't,

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@ -0,0 +1,326 @@
use std::cmp::{Ordering, Reverse};
use std::collections::BinaryHeap;
use std::ops::ControlFlow;
use charabia::normalizer::NormalizerOption;
use charabia::Normalize;
use fst::automaton::{Automaton, Str};
use fst::{IntoStreamer, Streamer};
use roaring::RoaringBitmap;
use tracing::error;
use crate::error::UserError;
use crate::heed_codec::facet::{FacetGroupKey, FacetGroupValue};
use crate::search::build_dfa;
use crate::{DocumentId, FieldId, OrderBy, Result, Search};
/// The maximum number of values per facet returned by the facet search route.
const DEFAULT_MAX_NUMBER_OF_VALUES_PER_FACET: usize = 100;
pub struct SearchForFacetValues<'a> {
query: Option<String>,
facet: String,
search_query: Search<'a>,
max_values: usize,
is_hybrid: bool,
}
impl<'a> SearchForFacetValues<'a> {
pub fn new(
facet: String,
search_query: Search<'a>,
is_hybrid: bool,
) -> SearchForFacetValues<'a> {
SearchForFacetValues {
query: None,
facet,
search_query,
max_values: DEFAULT_MAX_NUMBER_OF_VALUES_PER_FACET,
is_hybrid,
}
}
pub fn query(&mut self, query: impl Into<String>) -> &mut Self {
self.query = Some(query.into());
self
}
pub fn max_values(&mut self, max: usize) -> &mut Self {
self.max_values = max;
self
}
fn one_original_value_of(
&self,
field_id: FieldId,
facet_str: &str,
any_docid: DocumentId,
) -> Result<Option<String>> {
let index = self.search_query.index;
let rtxn = self.search_query.rtxn;
let key: (FieldId, _, &str) = (field_id, any_docid, facet_str);
Ok(index.field_id_docid_facet_strings.get(rtxn, &key)?.map(|v| v.to_owned()))
}
pub fn execute(&self) -> Result<Vec<FacetValueHit>> {
let index = self.search_query.index;
let rtxn = self.search_query.rtxn;
let filterable_fields = index.filterable_fields(rtxn)?;
if !filterable_fields.contains(&self.facet) {
let (valid_fields, hidden_fields) =
index.remove_hidden_fields(rtxn, filterable_fields)?;
return Err(UserError::InvalidFacetSearchFacetName {
field: self.facet.clone(),
valid_fields,
hidden_fields,
}
.into());
}
let fields_ids_map = index.fields_ids_map(rtxn)?;
let fid = match fields_ids_map.id(&self.facet) {
Some(fid) => fid,
// we return an empty list of results when the attribute has been
// set as filterable but no document contains this field (yet).
None => return Ok(Vec::new()),
};
let fst = match self.search_query.index.facet_id_string_fst.get(rtxn, &fid)? {
Some(fst) => fst,
None => return Ok(Vec::new()),
};
let search_candidates = self
.search_query
.execute_for_candidates(self.is_hybrid || self.search_query.vector.is_some())?;
let mut results = match index.sort_facet_values_by(rtxn)?.get(&self.facet) {
OrderBy::Lexicographic => ValuesCollection::by_lexicographic(self.max_values),
OrderBy::Count => ValuesCollection::by_count(self.max_values),
};
match self.query.as_ref() {
Some(query) => {
let options = NormalizerOption { lossy: true, ..Default::default() };
let query = query.normalize(&options);
let query = query.as_ref();
let authorize_typos = self.search_query.index.authorize_typos(rtxn)?;
let field_authorizes_typos =
!self.search_query.index.exact_attributes_ids(rtxn)?.contains(&fid);
if authorize_typos && field_authorizes_typos {
let exact_words_fst = self.search_query.index.exact_words(rtxn)?;
if exact_words_fst.map_or(false, |fst| fst.contains(query)) {
if fst.contains(query) {
self.fetch_original_facets_using_normalized(
fid,
query,
query,
&search_candidates,
&mut results,
)?;
}
} else {
let one_typo = self.search_query.index.min_word_len_one_typo(rtxn)?;
let two_typos = self.search_query.index.min_word_len_two_typos(rtxn)?;
let is_prefix = true;
let automaton = if query.len() < one_typo as usize {
build_dfa(query, 0, is_prefix)
} else if query.len() < two_typos as usize {
build_dfa(query, 1, is_prefix)
} else {
build_dfa(query, 2, is_prefix)
};
let mut stream = fst.search(automaton).into_stream();
while let Some(facet_value) = stream.next() {
let value = std::str::from_utf8(facet_value)?;
if self
.fetch_original_facets_using_normalized(
fid,
value,
query,
&search_candidates,
&mut results,
)?
.is_break()
{
break;
}
}
}
} else {
let automaton = Str::new(query).starts_with();
let mut stream = fst.search(automaton).into_stream();
while let Some(facet_value) = stream.next() {
let value = std::str::from_utf8(facet_value)?;
if self
.fetch_original_facets_using_normalized(
fid,
value,
query,
&search_candidates,
&mut results,
)?
.is_break()
{
break;
}
}
}
}
None => {
let prefix = FacetGroupKey { field_id: fid, level: 0, left_bound: "" };
for result in index.facet_id_string_docids.prefix_iter(rtxn, &prefix)? {
let (FacetGroupKey { left_bound, .. }, FacetGroupValue { bitmap, .. }) =
result?;
let count = search_candidates.intersection_len(&bitmap);
if count != 0 {
let value = self
.one_original_value_of(fid, left_bound, bitmap.min().unwrap())?
.unwrap_or_else(|| left_bound.to_string());
if results.insert(FacetValueHit { value, count }).is_break() {
break;
}
}
}
}
}
Ok(results.into_sorted_vec())
}
fn fetch_original_facets_using_normalized(
&self,
fid: FieldId,
value: &str,
query: &str,
search_candidates: &RoaringBitmap,
results: &mut ValuesCollection,
) -> Result<ControlFlow<()>> {
let index = self.search_query.index;
let rtxn = self.search_query.rtxn;
let database = index.facet_id_normalized_string_strings;
let key = (fid, value);
let original_strings = match database.get(rtxn, &key)? {
Some(original_strings) => original_strings,
None => {
error!("the facet value is missing from the facet database: {key:?}");
return Ok(ControlFlow::Continue(()));
}
};
for original in original_strings {
let key = FacetGroupKey { field_id: fid, level: 0, left_bound: original.as_str() };
let docids = match index.facet_id_string_docids.get(rtxn, &key)? {
Some(FacetGroupValue { bitmap, .. }) => bitmap,
None => {
error!("the facet value is missing from the facet database: {key:?}");
return Ok(ControlFlow::Continue(()));
}
};
let count = search_candidates.intersection_len(&docids);
if count != 0 {
let value = self
.one_original_value_of(fid, &original, docids.min().unwrap())?
.unwrap_or_else(|| query.to_string());
if results.insert(FacetValueHit { value, count }).is_break() {
break;
}
}
}
Ok(ControlFlow::Continue(()))
}
}
#[derive(Debug, Clone, serde::Serialize, PartialEq)]
pub struct FacetValueHit {
/// The original facet value
pub value: String,
/// The number of documents associated to this facet
pub count: u64,
}
impl PartialOrd for FacetValueHit {
fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
Some(self.cmp(other))
}
}
impl Ord for FacetValueHit {
fn cmp(&self, other: &Self) -> Ordering {
self.count.cmp(&other.count).then_with(|| self.value.cmp(&other.value))
}
}
impl Eq for FacetValueHit {}
/// A wrapper type that collects the best facet values by
/// lexicographic or number of associated values.
enum ValuesCollection {
/// Keeps the top values according to the lexicographic order.
Lexicographic { max: usize, content: Vec<FacetValueHit> },
/// Keeps the top values according to the number of values associated to them.
///
/// Note that it is a max heap and we need to move the smallest counts
/// at the top to be able to pop them when we reach the max_values limit.
Count { max: usize, content: BinaryHeap<Reverse<FacetValueHit>> },
}
impl ValuesCollection {
pub fn by_lexicographic(max: usize) -> Self {
ValuesCollection::Lexicographic { max, content: Vec::new() }
}
pub fn by_count(max: usize) -> Self {
ValuesCollection::Count { max, content: BinaryHeap::new() }
}
pub fn insert(&mut self, value: FacetValueHit) -> ControlFlow<()> {
match self {
ValuesCollection::Lexicographic { max, content } => {
if content.len() < *max {
content.push(value);
if content.len() < *max {
return ControlFlow::Continue(());
}
}
ControlFlow::Break(())
}
ValuesCollection::Count { max, content } => {
if content.len() == *max {
// Peeking gives us the worst value in the list as
// this is a max-heap and we reversed it.
let Some(mut peek) = content.peek_mut() else { return ControlFlow::Break(()) };
if peek.0.count <= value.count {
// Replace the current worst value in the heap
// with the new one we received that is better.
*peek = Reverse(value);
}
} else {
content.push(Reverse(value));
}
ControlFlow::Continue(())
}
}
}
/// Returns the list of facet values in descending order of, either,
/// count or lexicographic order of the value depending on the type.
pub fn into_sorted_vec(self) -> Vec<FacetValueHit> {
match self {
ValuesCollection::Lexicographic { content, .. } => content.into_iter().collect(),
ValuesCollection::Count { content, .. } => {
// Convert the heap into a vec of hits by removing the Reverse wrapper.
// Hits are already in the right order as they were reversed and there
// are output in ascending order.
content.into_sorted_vec().into_iter().map(|Reverse(hit)| hit).collect()
}
}
}
}

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@ -1,25 +1,17 @@
use std::fmt;
use std::ops::ControlFlow;
use charabia::normalizer::NormalizerOption;
use charabia::Normalize;
use fst::automaton::{Automaton, Str};
use fst::{IntoStreamer, Streamer};
use levenshtein_automata::{LevenshteinAutomatonBuilder as LevBuilder, DFA};
use once_cell::sync::Lazy;
use roaring::bitmap::RoaringBitmap;
use tracing::error;
pub use self::facet::{FacetDistribution, Filter, OrderBy, DEFAULT_VALUES_PER_FACET};
pub use self::new::matches::{FormatOptions, MatchBounds, MatcherBuilder, MatchingWords};
use self::new::{execute_vector_search, PartialSearchResult};
use crate::error::UserError;
use crate::heed_codec::facet::{FacetGroupKey, FacetGroupValue};
use crate::score_details::{ScoreDetails, ScoringStrategy};
use crate::vector::DistributionShift;
use crate::{
execute_search, filtered_universe, AscDesc, DefaultSearchLogger, DocumentId, FieldId, Index,
Result, SearchContext,
execute_search, filtered_universe, AscDesc, DefaultSearchLogger, DocumentId, Index, Result,
SearchContext,
};
// Building these factories is not free.
@ -27,9 +19,6 @@ static LEVDIST0: Lazy<LevBuilder> = Lazy::new(|| LevBuilder::new(0, true));
static LEVDIST1: Lazy<LevBuilder> = Lazy::new(|| LevBuilder::new(1, true));
static LEVDIST2: Lazy<LevBuilder> = Lazy::new(|| LevBuilder::new(2, true));
/// The maximum number of values per facet returned by the facet search route.
const DEFAULT_MAX_NUMBER_OF_VALUES_PER_FACET: usize = 100;
pub mod facet;
mod fst_utils;
pub mod hybrid;
@ -302,240 +291,6 @@ pub fn build_dfa(word: &str, typos: u8, is_prefix: bool) -> DFA {
}
}
pub struct SearchForFacetValues<'a> {
query: Option<String>,
facet: String,
search_query: Search<'a>,
max_values: usize,
is_hybrid: bool,
}
impl<'a> SearchForFacetValues<'a> {
pub fn new(
facet: String,
search_query: Search<'a>,
is_hybrid: bool,
) -> SearchForFacetValues<'a> {
SearchForFacetValues {
query: None,
facet,
search_query,
max_values: DEFAULT_MAX_NUMBER_OF_VALUES_PER_FACET,
is_hybrid,
}
}
pub fn query(&mut self, query: impl Into<String>) -> &mut Self {
self.query = Some(query.into());
self
}
pub fn max_values(&mut self, max: usize) -> &mut Self {
self.max_values = max;
self
}
fn one_original_value_of(
&self,
field_id: FieldId,
facet_str: &str,
any_docid: DocumentId,
) -> Result<Option<String>> {
let index = self.search_query.index;
let rtxn = self.search_query.rtxn;
let key: (FieldId, _, &str) = (field_id, any_docid, facet_str);
Ok(index.field_id_docid_facet_strings.get(rtxn, &key)?.map(|v| v.to_owned()))
}
pub fn execute(&self) -> Result<Vec<FacetValueHit>> {
let index = self.search_query.index;
let rtxn = self.search_query.rtxn;
let filterable_fields = index.filterable_fields(rtxn)?;
if !filterable_fields.contains(&self.facet) {
let (valid_fields, hidden_fields) =
index.remove_hidden_fields(rtxn, filterable_fields)?;
return Err(UserError::InvalidFacetSearchFacetName {
field: self.facet.clone(),
valid_fields,
hidden_fields,
}
.into());
}
let fields_ids_map = index.fields_ids_map(rtxn)?;
let fid = match fields_ids_map.id(&self.facet) {
Some(fid) => fid,
// we return an empty list of results when the attribute has been
// set as filterable but no document contains this field (yet).
None => return Ok(Vec::new()),
};
let fst = match self.search_query.index.facet_id_string_fst.get(rtxn, &fid)? {
Some(fst) => fst,
None => return Ok(vec![]),
};
let search_candidates = self
.search_query
.execute_for_candidates(self.is_hybrid || self.search_query.vector.is_some())?;
match self.query.as_ref() {
Some(query) => {
let options = NormalizerOption { lossy: true, ..Default::default() };
let query = query.normalize(&options);
let query = query.as_ref();
let authorize_typos = self.search_query.index.authorize_typos(rtxn)?;
let field_authorizes_typos =
!self.search_query.index.exact_attributes_ids(rtxn)?.contains(&fid);
if authorize_typos && field_authorizes_typos {
let exact_words_fst = self.search_query.index.exact_words(rtxn)?;
if exact_words_fst.map_or(false, |fst| fst.contains(query)) {
let mut results = vec![];
if fst.contains(query) {
self.fetch_original_facets_using_normalized(
fid,
query,
query,
&search_candidates,
&mut results,
)?;
}
Ok(results)
} else {
let one_typo = self.search_query.index.min_word_len_one_typo(rtxn)?;
let two_typos = self.search_query.index.min_word_len_two_typos(rtxn)?;
let is_prefix = true;
let automaton = if query.len() < one_typo as usize {
build_dfa(query, 0, is_prefix)
} else if query.len() < two_typos as usize {
build_dfa(query, 1, is_prefix)
} else {
build_dfa(query, 2, is_prefix)
};
let mut stream = fst.search(automaton).into_stream();
let mut results = vec![];
while let Some(facet_value) = stream.next() {
let value = std::str::from_utf8(facet_value)?;
if self
.fetch_original_facets_using_normalized(
fid,
value,
query,
&search_candidates,
&mut results,
)?
.is_break()
{
break;
}
}
Ok(results)
}
} else {
let automaton = Str::new(query).starts_with();
let mut stream = fst.search(automaton).into_stream();
let mut results = vec![];
while let Some(facet_value) = stream.next() {
let value = std::str::from_utf8(facet_value)?;
if self
.fetch_original_facets_using_normalized(
fid,
value,
query,
&search_candidates,
&mut results,
)?
.is_break()
{
break;
}
}
Ok(results)
}
}
None => {
let mut results = vec![];
let prefix = FacetGroupKey { field_id: fid, level: 0, left_bound: "" };
for result in index.facet_id_string_docids.prefix_iter(rtxn, &prefix)? {
let (FacetGroupKey { left_bound, .. }, FacetGroupValue { bitmap, .. }) =
result?;
let count = search_candidates.intersection_len(&bitmap);
if count != 0 {
let value = self
.one_original_value_of(fid, left_bound, bitmap.min().unwrap())?
.unwrap_or_else(|| left_bound.to_string());
results.push(FacetValueHit { value, count });
}
if results.len() >= self.max_values {
break;
}
}
Ok(results)
}
}
}
fn fetch_original_facets_using_normalized(
&self,
fid: FieldId,
value: &str,
query: &str,
search_candidates: &RoaringBitmap,
results: &mut Vec<FacetValueHit>,
) -> Result<ControlFlow<()>> {
let index = self.search_query.index;
let rtxn = self.search_query.rtxn;
let database = index.facet_id_normalized_string_strings;
let key = (fid, value);
let original_strings = match database.get(rtxn, &key)? {
Some(original_strings) => original_strings,
None => {
error!("the facet value is missing from the facet database: {key:?}");
return Ok(ControlFlow::Continue(()));
}
};
for original in original_strings {
let key = FacetGroupKey { field_id: fid, level: 0, left_bound: original.as_str() };
let docids = match index.facet_id_string_docids.get(rtxn, &key)? {
Some(FacetGroupValue { bitmap, .. }) => bitmap,
None => {
error!("the facet value is missing from the facet database: {key:?}");
return Ok(ControlFlow::Continue(()));
}
};
let count = search_candidates.intersection_len(&docids);
if count != 0 {
let value = self
.one_original_value_of(fid, &original, docids.min().unwrap())?
.unwrap_or_else(|| query.to_string());
results.push(FacetValueHit { value, count });
}
if results.len() >= self.max_values {
return Ok(ControlFlow::Break(()));
}
}
Ok(ControlFlow::Continue(()))
}
}
#[derive(Debug, Clone, serde::Serialize, PartialEq)]
pub struct FacetValueHit {
/// The original facet value
pub value: String,
/// The number of documents associated to this facet
pub count: u64,
}
#[cfg(test)]
mod test {
#[allow(unused_imports)]

View File

@ -14,12 +14,13 @@ use super::IndexerConfig;
use crate::criterion::Criterion;
use crate::error::UserError;
use crate::index::{DEFAULT_MIN_WORD_LEN_ONE_TYPO, DEFAULT_MIN_WORD_LEN_TWO_TYPOS};
use crate::order_by_map::OrderByMap;
use crate::proximity::ProximityPrecision;
use crate::update::index_documents::IndexDocumentsMethod;
use crate::update::{IndexDocuments, UpdateIndexingStep};
use crate::vector::settings::{check_set, check_unset, EmbedderSource, EmbeddingSettings};
use crate::vector::{Embedder, EmbeddingConfig, EmbeddingConfigs};
use crate::{FieldsIdsMap, Index, OrderBy, Result};
use crate::{FieldsIdsMap, Index, Result};
#[derive(Debug, Clone, PartialEq, Eq, Copy)]
pub enum Setting<T> {
@ -145,7 +146,7 @@ pub struct Settings<'a, 't, 'i> {
/// Attributes on which typo tolerance is disabled.
exact_attributes: Setting<HashSet<String>>,
max_values_per_facet: Setting<usize>,
sort_facet_values_by: Setting<HashMap<String, OrderBy>>,
sort_facet_values_by: Setting<OrderByMap>,
pagination_max_total_hits: Setting<usize>,
proximity_precision: Setting<ProximityPrecision>,
embedder_settings: Setting<BTreeMap<String, Setting<EmbeddingSettings>>>,
@ -340,7 +341,7 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
self.max_values_per_facet = Setting::Reset;
}
pub fn set_sort_facet_values_by(&mut self, value: HashMap<String, OrderBy>) {
pub fn set_sort_facet_values_by(&mut self, value: OrderByMap) {
self.sort_facet_values_by = Setting::Set(value);
}