MeiliSearch/milli/src/search/new/geo_sort.rs

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use std::collections::VecDeque;
use std::iter::FromIterator;
use heed::types::{ByteSlice, Unit};
use heed::{RoPrefix, RoTxn};
use roaring::RoaringBitmap;
use rstar::RTree;
use super::facet_string_values;
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use super::ranking_rules::{RankingRule, RankingRuleOutput, RankingRuleQueryTrait};
use crate::heed_codec::facet::{FieldDocIdFacetCodec, OrderedF64Codec};
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use crate::score_details::{self, ScoreDetails};
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use crate::{
distance_between_two_points, lat_lng_to_xyz, GeoPoint, Index, Result, SearchContext,
SearchLogger,
};
const FID_SIZE: usize = 2;
const DOCID_SIZE: usize = 4;
#[allow(clippy::drop_non_drop)]
fn facet_values_prefix_key(distinct: u16, id: u32) -> [u8; FID_SIZE + DOCID_SIZE] {
concat_arrays::concat_arrays!(distinct.to_be_bytes(), id.to_be_bytes())
}
/// Return an iterator over each number value in the given field of the given document.
fn facet_number_values<'a>(
docid: u32,
field_id: u16,
index: &Index,
txn: &'a RoTxn,
) -> Result<RoPrefix<'a, FieldDocIdFacetCodec<OrderedF64Codec>, Unit>> {
let key = facet_values_prefix_key(field_id, docid);
let iter = index
.field_id_docid_facet_f64s
.remap_key_type::<ByteSlice>()
.prefix_iter(txn, &key)?
.remap_key_type();
Ok(iter)
}
/// Define the strategy used by the geo sort.
/// The paramater represents the cache size, and, in the case of the Dynamic strategy,
/// the point where we move from using the iterative strategy to the rtree.
#[derive(Debug, Clone, Copy)]
pub enum Strategy {
AlwaysIterative(usize),
AlwaysRtree(usize),
Dynamic(usize),
}
impl Default for Strategy {
fn default() -> Self {
Strategy::Dynamic(1000)
}
}
impl Strategy {
pub fn use_rtree(&self, candidates: usize) -> bool {
match self {
Strategy::AlwaysIterative(_) => false,
Strategy::AlwaysRtree(_) => true,
Strategy::Dynamic(i) => candidates >= *i,
}
}
pub fn cache_size(&self) -> usize {
match self {
Strategy::AlwaysIterative(i) | Strategy::AlwaysRtree(i) | Strategy::Dynamic(i) => *i,
}
}
}
pub struct GeoSort<Q: RankingRuleQueryTrait> {
query: Option<Q>,
strategy: Strategy,
ascending: bool,
point: [f64; 2],
field_ids: Option<[u16; 2]>,
rtree: Option<RTree<GeoPoint>>,
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cached_sorted_docids: VecDeque<(u32, [f64; 2])>,
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geo_candidates: RoaringBitmap,
}
impl<Q: RankingRuleQueryTrait> GeoSort<Q> {
pub fn new(
strategy: Strategy,
geo_faceted_docids: RoaringBitmap,
point: [f64; 2],
ascending: bool,
) -> Result<Self> {
Ok(Self {
query: None,
strategy,
ascending,
point,
geo_candidates: geo_faceted_docids,
field_ids: None,
rtree: None,
cached_sorted_docids: VecDeque::new(),
})
}
/// Refill the internal buffer of cached docids based on the strategy.
/// Drop the rtree if we don't need it anymore.
fn fill_buffer(&mut self, ctx: &mut SearchContext) -> Result<()> {
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debug_assert!(self.field_ids.is_some(), "fill_buffer can't be called without the lat&lng");
debug_assert!(self.cached_sorted_docids.is_empty());
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// lazily initialize the rtree if needed by the strategy, and cache it in `self.rtree`
let rtree = if self.strategy.use_rtree(self.geo_candidates.len() as usize) {
if let Some(rtree) = self.rtree.as_ref() {
// get rtree from cache
Some(rtree)
} else {
let rtree = ctx.index.geo_rtree(ctx.txn)?.expect("geo candidates but no rtree");
// insert rtree in cache and returns it.
// Can't use `get_or_insert_with` because getting the rtree from the DB is a fallible operation.
Some(&*self.rtree.insert(rtree))
}
} else {
None
};
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let cache_size = self.strategy.cache_size();
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if let Some(rtree) = rtree {
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if self.ascending {
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let point = lat_lng_to_xyz(&self.point);
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for point in rtree.nearest_neighbor_iter(&point) {
if self.geo_candidates.contains(point.data.0) {
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self.cached_sorted_docids.push_back(point.data);
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if self.cached_sorted_docids.len() >= cache_size {
break;
}
}
}
} else {
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// in the case of the desc geo sort we look for the closest point to the opposite of the queried point
// and we insert the points in reverse order they get reversed when emptying the cache later on
let point = lat_lng_to_xyz(&opposite_of(self.point));
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for point in rtree.nearest_neighbor_iter(&point) {
if self.geo_candidates.contains(point.data.0) {
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self.cached_sorted_docids.push_front(point.data);
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if self.cached_sorted_docids.len() >= cache_size {
break;
}
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}
}
}
} else {
// the iterative version
let [lat, lng] = self.field_ids.unwrap();
let mut documents = self
.geo_candidates
.iter()
.map(|id| -> Result<_> { Ok((id, geo_value(id, lat, lng, ctx.index, ctx.txn)?)) })
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.collect::<Result<Vec<(u32, [f64; 2])>>>()?;
// computing the distance between two points is expensive thus we cache the result
documents
.sort_by_cached_key(|(_, p)| distance_between_two_points(&self.point, p) as usize);
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self.cached_sorted_docids.extend(documents.into_iter());
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};
Ok(())
}
}
/// Extracts the lat and long values from a single document.
///
/// If it is not able to find it in the facet number index it will extract it
/// from the facet string index and parse it as f64 (as the geo extraction behaves).
fn geo_value(
docid: u32,
field_lat: u16,
field_lng: u16,
index: &Index,
rtxn: &RoTxn,
) -> Result<[f64; 2]> {
let extract_geo = |geo_field: u16| -> Result<f64> {
match facet_number_values(docid, geo_field, index, rtxn)?.next() {
Some(Ok(((_, _, geo), ()))) => Ok(geo),
Some(Err(e)) => Err(e.into()),
None => match facet_string_values(docid, geo_field, index, rtxn)?.next() {
Some(Ok((_, geo))) => {
Ok(geo.parse::<f64>().expect("cannot parse geo field as f64"))
}
Some(Err(e)) => Err(e.into()),
None => panic!("A geo faceted document doesn't contain any lat or lng"),
},
}
};
let lat = extract_geo(field_lat)?;
let lng = extract_geo(field_lng)?;
Ok([lat, lng])
}
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impl<'ctx, Q: RankingRuleQueryTrait> RankingRule<'ctx, Q> for GeoSort<Q> {
fn id(&self) -> String {
"geo_sort".to_owned()
}
fn start_iteration(
&mut self,
ctx: &mut SearchContext<'ctx>,
_logger: &mut dyn SearchLogger<Q>,
universe: &RoaringBitmap,
query: &Q,
) -> Result<()> {
assert!(self.query.is_none());
self.query = Some(query.clone());
self.geo_candidates &= universe;
if self.geo_candidates.is_empty() {
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return Ok(());
}
let fid_map = ctx.index.fields_ids_map(ctx.txn)?;
let lat = fid_map.id("_geo.lat").expect("geo candidates but no fid for lat");
let lng = fid_map.id("_geo.lng").expect("geo candidates but no fid for lng");
self.field_ids = Some([lat, lng]);
self.fill_buffer(ctx)?;
Ok(())
}
#[allow(clippy::only_used_in_recursion)]
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fn next_bucket(
&mut self,
ctx: &mut SearchContext<'ctx>,
logger: &mut dyn SearchLogger<Q>,
universe: &RoaringBitmap,
) -> Result<Option<RankingRuleOutput<Q>>> {
let query = self.query.as_ref().unwrap().clone();
self.geo_candidates &= universe;
if self.geo_candidates.is_empty() {
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return Ok(Some(RankingRuleOutput {
query,
candidates: universe.clone(),
score: ScoreDetails::GeoSort(score_details::GeoSort {
target_point: self.point,
ascending: self.ascending,
value: None,
}),
}));
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}
let ascending = self.ascending;
let next = |cache: &mut VecDeque<_>| {
if ascending {
cache.pop_front()
} else {
cache.pop_back()
}
};
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while let Some((id, point)) = next(&mut self.cached_sorted_docids) {
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if self.geo_candidates.contains(id) {
return Ok(Some(RankingRuleOutput {
query,
candidates: RoaringBitmap::from_iter([id]),
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score: ScoreDetails::GeoSort(score_details::GeoSort {
target_point: self.point,
ascending: self.ascending,
value: Some(point),
}),
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}));
}
}
// if we got out of this loop it means we've exhausted our cache.
// we need to refill it and run the function again.
self.fill_buffer(ctx)?;
self.next_bucket(ctx, logger, universe)
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}
fn end_iteration(&mut self, _ctx: &mut SearchContext<'ctx>, _logger: &mut dyn SearchLogger<Q>) {
// we do not reset the rtree here, it could be used in a next iteration
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self.query = None;
self.cached_sorted_docids.clear();
}
}
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/// Compute the antipodal coordinate of `coord`
fn opposite_of(mut coord: [f64; 2]) -> [f64; 2] {
coord[0] *= -1.;
// in the case of x,0 we want to return x,180
if coord[1] > 0. {
coord[1] -= 180.;
} else {
coord[1] += 180.;
}
coord
}