MeiliSearch/crates/milli/src/search/facet/facet_distribution.rs
2025-04-01 11:26:34 +02:00

890 lines
31 KiB
Rust

use std::collections::{BTreeMap, BTreeSet, HashMap};
use std::fmt::Display;
use std::ops::ControlFlow;
use std::{fmt, mem};
use heed::types::Bytes;
use heed::BytesDecode;
use indexmap::IndexMap;
use roaring::RoaringBitmap;
use serde::{Deserialize, Serialize};
use crate::attribute_patterns::match_field_legacy;
use crate::facet::FacetType;
use crate::filterable_attributes_rules::{filtered_matching_patterns, matching_features};
use crate::heed_codec::facet::{
FacetGroupKeyCodec, FieldDocIdFacetF64Codec, FieldDocIdFacetStringCodec, OrderedF64Codec,
};
use crate::heed_codec::{BytesRefCodec, StrRefCodec};
use crate::search::facet::facet_distribution_iter::{
count_iterate_over_facet_distribution, lexicographically_iterate_over_facet_distribution,
};
use crate::{Error, FieldId, FilterableAttributesRule, Index, PatternMatch, Result, UserError};
/// The default number of values by facets that will
/// be fetched from the key-value store.
pub const DEFAULT_VALUES_PER_FACET: usize = 100;
/// Threshold on the number of candidates that will make
/// the system to choose between one algorithm or another.
const CANDIDATES_THRESHOLD: u64 = 3000;
/// How should we fetch the facets?
#[derive(Debug, Default, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum OrderBy {
/// By lexicographic order...
#[default]
Lexicographic,
/// Or by number of docids in common?
Count,
}
impl Display for OrderBy {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
match self {
OrderBy::Lexicographic => f.write_str("alphabetically"),
OrderBy::Count => f.write_str("by count"),
}
}
}
pub struct FacetDistribution<'a> {
facets: Option<HashMap<String, OrderBy>>,
candidates: Option<RoaringBitmap>,
max_values_per_facet: usize,
default_order_by: OrderBy,
rtxn: &'a heed::RoTxn<'a>,
index: &'a Index,
}
impl<'a> FacetDistribution<'a> {
pub fn new(rtxn: &'a heed::RoTxn<'a>, index: &'a Index) -> FacetDistribution<'a> {
FacetDistribution {
facets: None,
candidates: None,
max_values_per_facet: DEFAULT_VALUES_PER_FACET,
default_order_by: OrderBy::default(),
rtxn,
index,
}
}
pub fn facets<I: IntoIterator<Item = (A, OrderBy)>, A: AsRef<str>>(
&mut self,
names_ordered_by: I,
) -> &mut Self {
self.facets = Some(
names_ordered_by
.into_iter()
.map(|(name, order_by)| (name.as_ref().to_string(), order_by))
.collect(),
);
self
}
pub fn max_values_per_facet(&mut self, max: usize) -> &mut Self {
self.max_values_per_facet = max;
self
}
pub fn default_order_by(&mut self, order_by: OrderBy) -> &mut Self {
self.default_order_by = order_by;
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 IndexMap<String, u64>,
) -> heed::Result<()> {
match facet_type {
FacetType::Number => {
let mut lexicographic_distribution = BTreeMap::new();
let mut key_buffer: Vec<_> = field_id.to_be_bytes().to_vec();
let db = self.index.field_id_docid_facet_f64s;
for docid in candidates {
key_buffer.truncate(mem::size_of::<FieldId>());
key_buffer.extend_from_slice(&docid.to_be_bytes());
let iter = db
.remap_key_type::<Bytes>()
.prefix_iter(self.rtxn, &key_buffer)?
.remap_key_type::<FieldDocIdFacetF64Codec>();
for result in iter {
let ((_, _, value), ()) = result?;
*lexicographic_distribution.entry(value.to_string()).or_insert(0) += 1;
}
}
distribution.extend(
lexicographic_distribution
.into_iter()
.take(self.max_values_per_facet.saturating_sub(distribution.len())),
);
}
FacetType::String => {
let mut normalized_distribution = BTreeMap::new();
let mut key_buffer: Vec<_> = field_id.to_be_bytes().to_vec();
let db = self.index.field_id_docid_facet_strings;
for docid in candidates {
key_buffer.truncate(mem::size_of::<FieldId>());
key_buffer.extend_from_slice(&docid.to_be_bytes());
let iter = db
.remap_key_type::<Bytes>()
.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;
// we'd like to break here if we have enough facet values, but we are collecting them by increasing docid,
// so higher ranked facets could be in later docids
}
}
let iter = normalized_distribution
.into_iter()
.take(self.max_values_per_facet.saturating_sub(distribution.len()))
.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,
order_by: OrderBy,
distribution: &mut IndexMap<String, u64>,
) -> heed::Result<()> {
let search_function = match order_by {
OrderBy::Lexicographic => lexicographically_iterate_over_facet_distribution,
OrderBy::Count => count_iterate_over_facet_distribution,
};
search_function(
self.rtxn,
self.index.facet_id_f64_docids.remap_key_type::<FacetGroupKeyCodec<BytesRefCodec>>(),
field_id,
candidates,
|facet_key, nbr_docids, _| {
let facet_key = OrderedF64Codec::bytes_decode(facet_key).unwrap();
distribution.insert(facet_key.to_string(), nbr_docids);
if distribution.len() == self.max_values_per_facet {
Ok(ControlFlow::Break(()))
} else {
Ok(ControlFlow::Continue(()))
}
},
)
}
fn facet_strings_distribution_from_facet_levels(
&self,
field_id: FieldId,
candidates: &RoaringBitmap,
order_by: OrderBy,
distribution: &mut IndexMap<String, u64>,
) -> heed::Result<()> {
let search_function = match order_by {
OrderBy::Lexicographic => lexicographically_iterate_over_facet_distribution,
OrderBy::Count => count_iterate_over_facet_distribution,
};
search_function(
self.rtxn,
self.index.facet_id_string_docids.remap_key_type::<FacetGroupKeyCodec<BytesRefCodec>>(),
field_id,
candidates,
|facet_key, nbr_docids, any_docid| {
let facet_key = StrRefCodec::bytes_decode(facet_key).unwrap();
let key: (FieldId, _, &str) = (field_id, any_docid, facet_key);
let optional_original_string =
self.index.field_id_docid_facet_strings.get(self.rtxn, &key)?;
let original_string = match optional_original_string {
Some(original_string) => original_string.to_owned(),
None => {
tracing::error!(
"Missing original facet string. Using the normalized facet {} instead",
facet_key
);
facet_key.to_string()
}
};
distribution.insert(original_string, nbr_docids);
if distribution.len() == self.max_values_per_facet {
Ok(ControlFlow::Break(()))
} else {
Ok(ControlFlow::Continue(()))
}
},
)
}
fn facet_values(
&self,
field_id: FieldId,
order_by: OrderBy,
) -> heed::Result<IndexMap<String, u64>> {
use FacetType::{Number, String};
let mut distribution = IndexMap::new();
match (order_by, &self.candidates) {
(OrderBy::Lexicographic, Some(cnd)) if cnd.len() <= CANDIDATES_THRESHOLD => {
// 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.
self.facet_distribution_from_documents(field_id, Number, cnd, &mut distribution)?;
self.facet_distribution_from_documents(field_id, String, cnd, &mut distribution)?;
}
_ => {
let universe;
let candidates = match &self.candidates {
Some(cnd) => cnd,
None => {
universe = self.index.documents_ids(self.rtxn)?;
&universe
}
};
self.facet_numbers_distribution_from_facet_levels(
field_id,
candidates,
order_by,
&mut distribution,
)?;
self.facet_strings_distribution_from_facet_levels(
field_id,
candidates,
order_by,
&mut distribution,
)?;
}
};
Ok(distribution)
}
pub fn compute_stats(&self) -> Result<BTreeMap<String, (f64, f64)>> {
let candidates = if let Some(candidates) = self.candidates.clone() {
candidates
} else {
return Ok(Default::default());
};
let fields_ids_map = self.index.fields_ids_map(self.rtxn)?;
let filterable_attributes_rules = self.index.filterable_attributes_rules(self.rtxn)?;
self.check_faceted_fields(&filterable_attributes_rules)?;
let mut distribution = BTreeMap::new();
for (fid, name) in fields_ids_map.iter() {
if self.select_field(name, &filterable_attributes_rules) {
let min_value = if let Some(min_value) = crate::search::facet::facet_min_value(
self.index,
self.rtxn,
fid,
candidates.clone(),
)? {
min_value
} else {
continue;
};
let max_value = if let Some(max_value) = crate::search::facet::facet_max_value(
self.index,
self.rtxn,
fid,
candidates.clone(),
)? {
max_value
} else {
continue;
};
distribution.insert(name.to_string(), (min_value, max_value));
}
}
Ok(distribution)
}
pub fn execute(&self) -> Result<BTreeMap<String, IndexMap<String, u64>>> {
let fields_ids_map = self.index.fields_ids_map(self.rtxn)?;
let filterable_attributes_rules = self.index.filterable_attributes_rules(self.rtxn)?;
self.check_faceted_fields(&filterable_attributes_rules)?;
let mut distribution = BTreeMap::new();
for (fid, name) in fields_ids_map.iter() {
if self.select_field(name, &filterable_attributes_rules) {
let order_by = self
.facets
.as_ref()
.and_then(|facets| facets.get(name).copied())
.unwrap_or(self.default_order_by);
let values = self.facet_values(fid, order_by)?;
distribution.insert(name.to_string(), values);
}
}
Ok(distribution)
}
/// Select a field if it is filterable and in the facets.
fn select_field(
&self,
name: &str,
filterable_attributes_rules: &[FilterableAttributesRule],
) -> bool {
// If the field is not filterable, we don't want to compute the facet distribution.
if !matching_features(name, filterable_attributes_rules)
.is_some_and(|(_, features)| features.is_filterable())
{
return false;
}
match &self.facets {
Some(facets) => {
// The list of facets provided by the user is a legacy pattern ("dog.age" must be selected with "dog").
facets.keys().any(|key| match_field_legacy(key, name) == PatternMatch::Match)
}
None => true,
}
}
/// Check if the fields in the facets are valid filterable fields.
fn check_faceted_fields(
&self,
filterable_attributes_rules: &[FilterableAttributesRule],
) -> Result<()> {
let mut invalid_facets = BTreeSet::new();
let mut matching_rule_indices = HashMap::new();
if let Some(facets) = &self.facets {
for field in facets.keys() {
let matched_rule = matching_features(field, filterable_attributes_rules);
let is_filterable = matched_rule.is_some_and(|(_, f)| f.is_filterable());
if !is_filterable {
invalid_facets.insert(field.to_string());
// If the field matched a rule but that rule doesn't enable filtering,
// store the rule index for better error messages
if let Some((rule_index, _)) = matched_rule {
matching_rule_indices.insert(field.to_string(), rule_index);
}
}
}
}
if !invalid_facets.is_empty() {
let valid_patterns =
filtered_matching_patterns(filterable_attributes_rules, &|features| {
features.is_filterable()
})
.into_iter()
.map(String::from)
.collect();
return Err(Error::UserError(UserError::InvalidFacetsDistribution {
invalid_facets_name: invalid_facets,
valid_patterns,
matching_rule_indices,
}));
}
Ok(())
}
}
impl fmt::Debug for FacetDistribution<'_> {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
let FacetDistribution {
facets,
candidates,
max_values_per_facet,
default_order_by,
rtxn: _,
index: _,
} = self;
f.debug_struct("FacetDistribution")
.field("facets", facets)
.field("candidates", candidates)
.field("max_values_per_facet", max_values_per_facet)
.field("default_order_by", default_order_by)
.finish()
}
}
#[cfg(test)]
mod tests {
use std::iter;
use big_s::S;
use crate::documents::mmap_from_objects;
use crate::index::tests::TempIndex;
use crate::{milli_snap, FacetDistribution, FilterableAttributesRule, OrderBy};
#[test]
fn few_candidates_few_facet_values() {
// All the tests here avoid using the code in `facet_distribution_iter` because there aren't
// enough candidates.
let index = TempIndex::new();
index
.update_settings(|settings| {
settings.set_filterable_fields(vec![FilterableAttributesRule::Field(S("colour"))])
})
.unwrap();
let documents = documents!([
{ "id": 0, "colour": "Blue" },
{ "id": 1, "colour": " blue" },
{ "id": 2, "colour": "RED" }
]);
index.add_documents(documents).unwrap();
let txn = index.read_txn().unwrap();
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.execute()
.unwrap();
milli_snap!(format!("{map:?}"), @r###"{"colour": {"Blue": 2, "RED": 1}}"###);
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.candidates([0, 1, 2].iter().copied().collect())
.execute()
.unwrap();
milli_snap!(format!("{map:?}"), @r###"{"colour": {"Blue": 2, "RED": 1}}"###);
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.candidates([1, 2].iter().copied().collect())
.execute()
.unwrap();
// I think it would be fine if " blue" was "Blue" instead.
// We just need to get any non-normalised string I think, even if it's not in
// the candidates
milli_snap!(format!("{map:?}"), @r###"{"colour": {" blue": 1, "RED": 1}}"###);
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.candidates([2].iter().copied().collect())
.execute()
.unwrap();
milli_snap!(format!("{map:?}"), @r###"{"colour": {"RED": 1}}"###);
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.candidates([0, 1, 2].iter().copied().collect())
.max_values_per_facet(1)
.execute()
.unwrap();
milli_snap!(format!("{map:?}"), @r###"{"colour": {"Blue": 2}}"###);
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::Count)))
.candidates([0, 1, 2].iter().copied().collect())
.max_values_per_facet(1)
.execute()
.unwrap();
milli_snap!(format!("{map:?}"), @r###"{"colour": {"Blue": 2}}"###);
}
#[test]
fn many_candidates_few_facet_values() {
let index = TempIndex::new_with_map_size(4096 * 10_000);
index
.update_settings(|settings| {
settings.set_filterable_fields(vec![FilterableAttributesRule::Field(S("colour"))])
})
.unwrap();
let facet_values = ["Red", "RED", " red ", "Blue", "BLUE"];
let mut documents = vec![];
for i in 0..10_000 {
let document = serde_json::json!({
"id": i,
"colour": facet_values[i % 5],
})
.as_object()
.unwrap()
.clone();
documents.push(document);
}
let documents = mmap_from_objects(documents);
index.add_documents(documents).unwrap();
let txn = index.read_txn().unwrap();
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.execute()
.unwrap();
milli_snap!(format!("{map:?}"), @r###"{"colour": {"Blue": 4000, "Red": 6000}}"###);
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.max_values_per_facet(1)
.execute()
.unwrap();
milli_snap!(format!("{map:?}"), @r###"{"colour": {"Blue": 4000}}"###);
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.candidates((0..10_000).collect())
.execute()
.unwrap();
milli_snap!(format!("{map:?}"), @r###"{"colour": {"Blue": 4000, "Red": 6000}}"###);
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.candidates((0..5_000).collect())
.execute()
.unwrap();
milli_snap!(format!("{map:?}"), @r###"{"colour": {"Blue": 2000, "Red": 3000}}"###);
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.candidates((0..5_000).collect())
.execute()
.unwrap();
milli_snap!(format!("{map:?}"), @r###"{"colour": {"Blue": 2000, "Red": 3000}}"###);
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.candidates((0..5_000).collect())
.max_values_per_facet(1)
.execute()
.unwrap();
milli_snap!(format!("{map:?}"), @r###"{"colour": {"Blue": 2000}}"###);
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::Count)))
.candidates((0..5_000).collect())
.max_values_per_facet(1)
.execute()
.unwrap();
milli_snap!(format!("{map:?}"), @r###"{"colour": {"Red": 3000}}"###);
}
#[test]
fn many_candidates_many_facet_values() {
let index = TempIndex::new_with_map_size(4096 * 10_000);
index
.update_settings(|settings| {
settings.set_filterable_fields(vec![FilterableAttributesRule::Field(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!({
"id": i,
"colour": facet_values[i % 1000],
})
.as_object()
.unwrap()
.clone();
documents.push(document);
}
let documents = mmap_from_objects(documents);
index.add_documents(documents).unwrap();
let txn = index.read_txn().unwrap();
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.execute()
.unwrap();
milli_snap!(format!("{map:?}"), "no_candidates", @"ac9229ed5964d893af96a7076e2f8af5");
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.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(iter::once(("colour", OrderBy::default())))
.candidates((0..10_000).collect())
.execute()
.unwrap();
milli_snap!(format!("{map:?}"), "candidates_0_10_000", @"ac9229ed5964d893af96a7076e2f8af5");
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.candidates((0..5_000).collect())
.execute()
.unwrap();
milli_snap!(format!("{map:?}"), "candidates_0_5_000", @"825f23a4090d05756f46176987b7d992");
}
#[test]
fn facet_stats() {
let index = TempIndex::new_with_map_size(4096 * 10_000);
index
.update_settings(|settings| {
settings.set_filterable_fields(vec![FilterableAttributesRule::Field(S("colour"))])
})
.unwrap();
let facet_values = (0..1000).collect::<Vec<_>>();
let mut documents = vec![];
for i in 0..1000 {
let document = serde_json::json!({
"id": i,
"colour": facet_values[i % 1000],
})
.as_object()
.unwrap()
.clone();
documents.push(document);
}
let documents = mmap_from_objects(documents);
index.add_documents(documents).unwrap();
let txn = index.read_txn().unwrap();
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.compute_stats()
.unwrap();
milli_snap!(format!("{map:?}"), "no_candidates", @"{}");
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.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(iter::once(("colour", OrderBy::default())))
.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 index = TempIndex::new_with_map_size(4096 * 10_000);
index
.update_settings(|settings| {
settings.set_filterable_fields(vec![FilterableAttributesRule::Field(S("colour"))])
})
.unwrap();
let facet_values = (0..1000).collect::<Vec<_>>();
let mut documents = vec![];
for i in 0..1000 {
let document = serde_json::json!({
"id": i,
"colour": [facet_values[i % 1000], facet_values[i % 1000] + 1000],
})
.as_object()
.unwrap()
.clone();
documents.push(document);
}
let documents = mmap_from_objects(documents);
index.add_documents(documents).unwrap();
let txn = index.read_txn().unwrap();
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.compute_stats()
.unwrap();
milli_snap!(format!("{map:?}"), "no_candidates", @"{}");
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.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(iter::once(("colour", OrderBy::default())))
.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 index = TempIndex::new_with_map_size(4096 * 10_000);
index
.update_settings(|settings| {
settings.set_filterable_fields(vec![FilterableAttributesRule::Field(S("colour"))])
})
.unwrap();
let facet_values = (0..1000).collect::<Vec<_>>();
let mut documents = vec![];
for i in 0..1000 {
let document = serde_json::json!({
"id": i,
"colour": [facet_values[i % 1000], format!("{}", facet_values[i % 1000] + 1000)],
})
.as_object()
.unwrap()
.clone();
documents.push(document);
}
let documents = mmap_from_objects(documents);
index.add_documents(documents).unwrap();
let txn = index.read_txn().unwrap();
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.compute_stats()
.unwrap();
milli_snap!(format!("{map:?}"), "no_candidates", @"{}");
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.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(iter::once(("colour", OrderBy::default())))
.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 index = TempIndex::new_with_map_size(4096 * 10_000);
index
.update_settings(|settings| {
settings.set_filterable_fields(vec![FilterableAttributesRule::Field(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!({
"id": i,
"colour": [facet_values[i % 1000], facet_values[i % 1000] + 1000],
})
} else {
serde_json::json!({
"id": i,
"colour": format!("{}", facet_values[i % 1000] + 10000),
})
};
let document = document.as_object().unwrap().clone();
documents.push(document);
}
let documents = mmap_from_objects(documents);
index.add_documents(documents).unwrap();
let txn = index.read_txn().unwrap();
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.compute_stats()
.unwrap();
milli_snap!(format!("{map:?}"), "no_candidates", @"{}");
let map = FacetDistribution::new(&txn, &index)
.facets(iter::once(("colour", OrderBy::default())))
.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(iter::once(("colour", OrderBy::default())))
.candidates((217..777).collect())
.compute_stats()
.unwrap();
milli_snap!(format!("{map:?}"), "candidates_217_777", @r###"{"colour": (218.0, 1776.0)}"###);
}
}