MeiliSearch/milli/src/search/facet/facet_distribution.rs

260 lines
9.5 KiB
Rust

use std::collections::{BTreeMap, HashSet};
use std::ops::Bound::Unbounded;
use std::{fmt, mem};
use heed::types::ByteSlice;
use roaring::RoaringBitmap;
use crate::error::UserError;
use crate::facet::FacetType;
use crate::heed_codec::facet::{
FacetStringLevelZeroCodec, FieldDocIdFacetF64Codec, FieldDocIdFacetStringCodec,
};
use crate::search::facet::{FacetNumberIter, FacetNumberRange, FacetStringIter};
use crate::{FieldId, Index, Result};
/// Threshold on the number of candidates that will make
/// the system to choose between one algorithm or another.
const CANDIDATES_THRESHOLD: u64 = 3000;
pub struct FacetDistribution<'a> {
facets: Option<HashSet<String>>,
candidates: Option<RoaringBitmap>,
rtxn: &'a heed::RoTxn<'a>,
index: &'a Index,
}
impl<'a> FacetDistribution<'a> {
pub fn new(rtxn: &'a heed::RoTxn, index: &'a Index) -> FacetDistribution<'a> {
FacetDistribution { facets: None, candidates: None, rtxn, index }
}
pub fn facets<I: IntoIterator<Item = A>, A: AsRef<str>>(&mut self, names: I) -> &mut Self {
self.facets = Some(names.into_iter().map(|s| s.as_ref().to_string()).collect());
self
}
pub fn candidates(&mut self, candidates: RoaringBitmap) -> &mut Self {
self.candidates = Some(candidates);
self
}
/// There is a small amount of candidates OR we ask for facet string values so we
/// decide to iterate over the facet values of each one of them, one by one.
fn facet_distribution_from_documents(
&self,
field_id: FieldId,
facet_type: FacetType,
candidates: &RoaringBitmap,
distribution: &mut BTreeMap<String, u64>,
) -> heed::Result<()> {
match facet_type {
FacetType::Number => {
let mut key_buffer: Vec<_> = field_id.to_be_bytes().iter().copied().collect();
let db = self.index.field_id_docid_facet_f64s;
for docid in candidates.into_iter() {
key_buffer.truncate(mem::size_of::<FieldId>());
key_buffer.extend_from_slice(&docid.to_be_bytes());
let iter = db
.remap_key_type::<ByteSlice>()
.prefix_iter(self.rtxn, &key_buffer)?
.remap_key_type::<FieldDocIdFacetF64Codec>();
for result in iter {
let ((_, _, value), ()) = result?;
*distribution.entry(value.to_string()).or_insert(0) += 1;
}
}
}
FacetType::String => {
let mut normalized_distribution = BTreeMap::new();
let mut key_buffer: Vec<_> = field_id.to_be_bytes().iter().copied().collect();
let db = self.index.field_id_docid_facet_strings;
for docid in candidates.into_iter() {
key_buffer.truncate(mem::size_of::<FieldId>());
key_buffer.extend_from_slice(&docid.to_be_bytes());
let iter = db
.remap_key_type::<ByteSlice>()
.prefix_iter(self.rtxn, &key_buffer)?
.remap_key_type::<FieldDocIdFacetStringCodec>();
for result in iter {
let ((_, _, normalized_value), original_value) = result?;
let (_, count) = normalized_distribution
.entry(normalized_value)
.or_insert_with(|| (original_value, 0));
*count += 1;
}
}
let iter = normalized_distribution
.into_iter()
.map(|(_normalized, (original, count))| (original.to_string(), count));
distribution.extend(iter);
}
}
Ok(())
}
/// There is too much documents, we use the facet levels to move throught
/// the facet values, to find the candidates and values associated.
fn facet_numbers_distribution_from_facet_levels(
&self,
field_id: FieldId,
candidates: &RoaringBitmap,
distribution: &mut BTreeMap<String, u64>,
) -> heed::Result<()> {
let iter =
FacetNumberIter::new_non_reducing(self.rtxn, self.index, field_id, candidates.clone())?;
for result in iter {
let (value, mut docids) = result?;
docids &= candidates;
if !docids.is_empty() {
distribution.insert(value.to_string(), docids.len());
}
}
Ok(())
}
fn facet_strings_distribution_from_facet_levels(
&self,
field_id: FieldId,
candidates: &RoaringBitmap,
distribution: &mut BTreeMap<String, u64>,
) -> heed::Result<()> {
let iter =
FacetStringIter::new_non_reducing(self.rtxn, self.index, field_id, candidates.clone())?;
for result in iter {
let (_normalized, original, mut docids) = result?;
docids &= candidates;
if !docids.is_empty() {
distribution.insert(original.to_string(), docids.len());
}
}
Ok(())
}
/// Placeholder search, a.k.a. no candidates were specified. We iterate throught the
/// facet values one by one and iterate on the facet level 0 for numbers.
fn facet_values_from_raw_facet_database(
&self,
field_id: FieldId,
) -> heed::Result<BTreeMap<String, u64>> {
let mut distribution = BTreeMap::new();
let db = self.index.facet_id_f64_docids;
let range = FacetNumberRange::new(self.rtxn, db, field_id, 0, Unbounded, Unbounded)?;
for result in range {
let ((_, _, value, _), docids) = result?;
distribution.insert(value.to_string(), docids.len());
}
let iter = self
.index
.facet_id_string_docids
.remap_key_type::<ByteSlice>()
.prefix_iter(self.rtxn, &field_id.to_be_bytes())?
.remap_key_type::<FacetStringLevelZeroCodec>();
let mut normalized_distribution = BTreeMap::new();
for result in iter {
let ((_, normalized_value), (original_value, docids)) = result?;
normalized_distribution.insert(normalized_value, (original_value, docids.len()));
}
let iter = normalized_distribution
.into_iter()
.map(|(_normalized, (original, count))| (original.to_string(), count));
distribution.extend(iter);
Ok(distribution)
}
fn facet_values(&self, field_id: FieldId) -> heed::Result<BTreeMap<String, u64>> {
use FacetType::{Number, String};
match self.candidates {
Some(ref candidates) => {
// Classic search, candidates were specified, we must return facet values only related
// to those candidates. We also enter here for facet strings for performance reasons.
let mut distribution = BTreeMap::new();
if candidates.len() <= CANDIDATES_THRESHOLD {
self.facet_distribution_from_documents(
field_id,
Number,
candidates,
&mut distribution,
)?;
self.facet_distribution_from_documents(
field_id,
String,
candidates,
&mut distribution,
)?;
} else {
self.facet_numbers_distribution_from_facet_levels(
field_id,
candidates,
&mut distribution,
)?;
self.facet_strings_distribution_from_facet_levels(
field_id,
candidates,
&mut distribution,
)?;
}
Ok(distribution)
}
None => self.facet_values_from_raw_facet_database(field_id),
}
}
pub fn execute(&self) -> Result<BTreeMap<String, BTreeMap<String, u64>>> {
let fields_ids_map = self.index.fields_ids_map(self.rtxn)?;
let filterable_fields = self.index.filterable_fields(self.rtxn)?;
let fields = match self.facets {
Some(ref facets) => {
let invalid_fields: HashSet<_> = facets.difference(&filterable_fields).collect();
if !invalid_fields.is_empty() {
return Err(UserError::InvalidFacetsDistribution {
invalid_facets_name: invalid_fields.into_iter().cloned().collect(),
}
.into());
} else {
facets.clone()
}
}
None => filterable_fields,
};
let mut distribution = BTreeMap::new();
for name in fields {
if let Some(fid) = fields_ids_map.id(&name) {
let values = self.facet_values(fid)?;
distribution.insert(name, values);
}
}
Ok(distribution)
}
}
impl fmt::Debug for FacetDistribution<'_> {
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
let FacetDistribution { facets, candidates, rtxn: _, index: _ } = self;
f.debug_struct("FacetDistribution")
.field("facets", facets)
.field("candidates", candidates)
.finish()
}
}