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Remove delete documents files

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Louis Dureuil 2023-10-25 13:38:56 +02:00
parent 762b0b47e6
commit d651b3ef01
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4 changed files with 0 additions and 1600 deletions
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1249
milli/src/update/delete_documents.rs
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349
milli/src/update/facet/delete.rs
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@ -1,349 +0,0 @@
use std::collections::{HashMap, HashSet};
use heed::RwTxn;
use log::debug;
use roaring::RoaringBitmap;
use time::OffsetDateTime;
use super::{FACET_GROUP_SIZE, FACET_MAX_GROUP_SIZE, FACET_MIN_LEVEL_SIZE};
use crate::facet::FacetType;
use crate::heed_codec::facet::{FacetGroupKey, FacetGroupKeyCodec, FacetGroupValueCodec};
use crate::heed_codec::ByteSliceRefCodec;
use crate::update::{FacetsUpdateBulk, FacetsUpdateIncrementalInner};
use crate::{FieldId, Index, Result};
/// A builder used to remove elements from the `facet_id_string_docids` or `facet_id_f64_docids` databases.
///
/// Depending on the number of removed elements and the existing size of the database, we use either
/// a bulk delete method or an incremental delete method.
pub struct FacetsDelete<'i, 'b> {
index: &'i Index,
database: heed::Database<FacetGroupKeyCodec<ByteSliceRefCodec>, FacetGroupValueCodec>,
facet_type: FacetType,
affected_facet_values: HashMap<FieldId, HashSet<Vec<u8>>>,
docids_to_delete: &'b RoaringBitmap,
group_size: u8,
max_group_size: u8,
min_level_size: u8,
}
impl<'i, 'b> FacetsDelete<'i, 'b> {
pub fn new(
index: &'i Index,
facet_type: FacetType,
affected_facet_values: HashMap<FieldId, HashSet<Vec<u8>>>,
docids_to_delete: &'b RoaringBitmap,
) -> Self {
let database = match facet_type {
FacetType::String => index
.facet_id_string_docids
.remap_key_type::<FacetGroupKeyCodec<ByteSliceRefCodec>>(),
FacetType::Number => {
index.facet_id_f64_docids.remap_key_type::<FacetGroupKeyCodec<ByteSliceRefCodec>>()
}
};
Self {
index,
database,
facet_type,
affected_facet_values,
docids_to_delete,
group_size: FACET_GROUP_SIZE,
max_group_size: FACET_MAX_GROUP_SIZE,
min_level_size: FACET_MIN_LEVEL_SIZE,
}
}
pub fn execute(self, wtxn: &mut RwTxn) -> Result<()> {
debug!("Computing and writing the facet values levels docids into LMDB on disk...");
self.index.set_updated_at(wtxn, &OffsetDateTime::now_utc())?;
for (field_id, affected_facet_values) in self.affected_facet_values {
// This is an incorrect condition, since we assume that the length of the database is equal
// to the number of facet values for the given field_id. It means that in some cases, we might
// wrongly choose the incremental indexer over the bulk indexer. But the only case where that could
// really be a performance problem is when we fully delete a large ratio of all facet values for
// each field id. This would almost never happen. Still, to be overly cautious, I have added a
// 2x penalty to the incremental indexer. That is, instead of assuming a 70x worst-case performance
// penalty to the incremental indexer, we assume a 150x worst-case performance penalty instead.
if affected_facet_values.len() >= (self.database.len(wtxn)? / 150) {
// Bulk delete
let mut modified = false;
for facet_value in affected_facet_values {
let key =
FacetGroupKey { field_id, level: 0, left_bound: facet_value.as_slice() };
let mut old = self.database.get(wtxn, &key)?.unwrap();
let previous_len = old.bitmap.len();
old.bitmap -= self.docids_to_delete;
if old.bitmap.is_empty() {
modified = true;
self.database.delete(wtxn, &key)?;
} else if old.bitmap.len() != previous_len {
modified = true;
self.database.put(wtxn, &key, &old)?;
}
}
if modified {
let builder = FacetsUpdateBulk::new_not_updating_level_0(
self.index,
vec![field_id],
self.facet_type,
);
builder.execute(wtxn)?;
}
} else {
// Incremental
let inc = FacetsUpdateIncrementalInner {
db: self.database,
group_size: self.group_size,
min_level_size: self.min_level_size,
max_group_size: self.max_group_size,
};
for facet_value in affected_facet_values {
inc.delete(wtxn, field_id, facet_value.as_slice(), self.docids_to_delete)?;
}
}
}
Ok(())
}
}
#[cfg(test)]
mod tests {
use std::iter::FromIterator;
use big_s::S;
use maplit::hashset;
use rand::seq::SliceRandom;
use rand::SeedableRng;
use roaring::RoaringBitmap;
use crate::db_snap;
use crate::documents::documents_batch_reader_from_objects;
use crate::index::tests::TempIndex;
use crate::update::facet::test_helpers::ordered_string;
use crate::update::{DeleteDocuments, DeletionStrategy};
#[test]
fn delete_mixed_incremental_and_bulk() {
// The point of this test is to create an index populated with documents
// containing different filterable attributes. Then, we delete a bunch of documents
// such that a mix of the incremental and bulk indexer is used (depending on the field id)
let index = TempIndex::new_with_map_size(4096 * 1000 * 100);
index
.update_settings(|settings| {
settings.set_filterable_fields(
hashset! { S("id"), S("label"), S("timestamp"), S("colour") },
);
})
.unwrap();
let mut documents = vec![];
for i in 0..1000 {
documents.push(
serde_json::json! {
{
"id": i,
"label": i / 10,
"colour": i / 100,
"timestamp": i / 2,
}
}
.as_object()
.unwrap()
.clone(),
);
}
let documents = documents_batch_reader_from_objects(documents);
index.add_documents(documents).unwrap();
db_snap!(index, facet_id_f64_docids, 1, @"550cd138d6fe31ccdd42cd5392fbd576");
let mut wtxn = index.env.write_txn().unwrap();
let mut builder = DeleteDocuments::new(&mut wtxn, &index).unwrap();
builder.strategy(DeletionStrategy::AlwaysHard);
builder.delete_documents(&RoaringBitmap::from_iter(0..100));
// by deleting the first 100 documents, we expect that:
// - the "id" part of the DB will be updated in bulk, since #affected_facet_value = 100 which is > database_len / 150 (= 13)
// - the "label" part will be updated incrementally, since #affected_facet_value = 10 which is < 13
// - the "colour" part will also be updated incrementally, since #affected_values = 1 which is < 13
// - the "timestamp" part will be updated in bulk, since #affected_values = 50 which is > 13
// This has to be verified manually by inserting breakpoint/adding print statements to the code when running the test
builder.execute().unwrap();
wtxn.commit().unwrap();
db_snap!(index, soft_deleted_documents_ids, @"[]");
db_snap!(index, facet_id_f64_docids, 2, @"d4d5f14e7f1e1f09b86821a0b6defcc6");
}
// Same test as above but working with string values for the facets
#[test]
fn delete_mixed_incremental_and_bulk_string() {
// The point of this test is to create an index populated with documents
// containing different filterable attributes. Then, we delete a bunch of documents
// such that a mix of the incremental and bulk indexer is used (depending on the field id)
let index = TempIndex::new_with_map_size(4096 * 1000 * 100);
index
.update_settings(|settings| {
settings.set_filterable_fields(
hashset! { S("id"), S("label"), S("timestamp"), S("colour") },
);
})
.unwrap();
let mut documents = vec![];
for i in 0..1000 {
documents.push(
serde_json::json! {
{
"id": i,
"label": ordered_string(i / 10),
"colour": ordered_string(i / 100),
"timestamp": ordered_string(i / 2),
}
}
.as_object()
.unwrap()
.clone(),
);
}
let documents = documents_batch_reader_from_objects(documents);
index.add_documents(documents).unwrap();
// Note that empty strings are not stored in the facet db due to commit 4860fd452965 (comment written on 29 Nov 2022)
db_snap!(index, facet_id_string_docids, 1, @"5fd1bd0724c65a6dc1aafb6db93c7503");
let mut wtxn = index.env.write_txn().unwrap();
let mut builder = DeleteDocuments::new(&mut wtxn, &index).unwrap();
builder.strategy(DeletionStrategy::AlwaysHard);
builder.delete_documents(&RoaringBitmap::from_iter(0..100));
// by deleting the first 100 documents, we expect that:
// - the "id" part of the DB will be updated in bulk, since #affected_facet_value = 100 which is > database_len / 150 (= 13)
// - the "label" part will be updated incrementally, since #affected_facet_value = 10 which is < 13
// - the "colour" part will also be updated incrementally, since #affected_values = 1 which is < 13
// - the "timestamp" part will be updated in bulk, since #affected_values = 50 which is > 13
// This has to be verified manually by inserting breakpoint/adding print statements to the code when running the test
builder.execute().unwrap();
wtxn.commit().unwrap();
db_snap!(index, soft_deleted_documents_ids, @"[]");
db_snap!(index, facet_id_string_docids, 2, @"7f9c00b29e04d58c1821202a5dda0ebc");
}
#[test]
fn delete_almost_all_incrementally_string() {
let index = TempIndex::new_with_map_size(4096 * 1000 * 100);
index
.update_settings(|settings| {
settings.set_filterable_fields(
hashset! { S("id"), S("label"), S("timestamp"), S("colour") },
);
})
.unwrap();
let mut documents = vec![];
for i in 0..1000 {
documents.push(
serde_json::json! {
{
"id": i,
"label": ordered_string(i / 10),
"colour": ordered_string(i / 100),
"timestamp": ordered_string(i / 2),
}
}
.as_object()
.unwrap()
.clone(),
);
}
let documents = documents_batch_reader_from_objects(documents);
index.add_documents(documents).unwrap();
// Note that empty strings are not stored in the facet db due to commit 4860fd452965 (comment written on 29 Nov 2022)
db_snap!(index, facet_id_string_docids, 1, @"5fd1bd0724c65a6dc1aafb6db93c7503");
let mut rng = rand::rngs::SmallRng::from_seed([0; 32]);
let mut docids_to_delete = (0..1000).collect::<Vec<_>>();
docids_to_delete.shuffle(&mut rng);
for docid in docids_to_delete.into_iter().take(990) {
let mut wtxn = index.env.write_txn().unwrap();
let mut builder = DeleteDocuments::new(&mut wtxn, &index).unwrap();
builder.strategy(DeletionStrategy::AlwaysHard);
builder.delete_documents(&RoaringBitmap::from_iter([docid]));
builder.execute().unwrap();
wtxn.commit().unwrap();
}
db_snap!(index, soft_deleted_documents_ids, @"[]");
db_snap!(index, facet_id_string_docids, 2, @"ece56086e76d50e661fb2b58475b9f7d");
}
}
#[allow(unused)]
#[cfg(test)]
mod comparison_bench {
use std::iter::once;
use rand::Rng;
use roaring::RoaringBitmap;
use crate::heed_codec::facet::OrderedF64Codec;
use crate::update::facet::test_helpers::FacetIndex;
// This is a simple test to get an intuition on the relative speed
// of the incremental vs. bulk indexer.
//
// The benchmark shows the worst-case scenario for the incremental indexer, since
// each facet value contains only one document ID.
//
// In that scenario, it appears that the incremental indexer is about 70 times slower than the
// bulk indexer.
// #[test]
fn benchmark_facet_indexing_delete() {
let mut r = rand::thread_rng();
for i in 1..=20 {
let size = 50_000 * i;
let index = FacetIndex::<OrderedF64Codec>::new(4, 8, 5);
let mut txn = index.env.write_txn().unwrap();
let mut elements = Vec::<((u16, f64), RoaringBitmap)>::new();
for i in 0..size {
// field id = 0, left_bound = i, docids = [i]
elements.push(((0, i as f64), once(i).collect()));
}
let timer = std::time::Instant::now();
index.bulk_insert(&mut txn, &[0], elements.iter());
let time_spent = timer.elapsed().as_millis();
println!("bulk {size} : {time_spent}ms");
txn.commit().unwrap();
for nbr_doc in [1, 100, 1000, 10_000] {
let mut txn = index.env.write_txn().unwrap();
let timer = std::time::Instant::now();
//
// delete one document
//
for _ in 0..nbr_doc {
let deleted_u32 = r.gen::<u32>() % size;
let deleted_f64 = deleted_u32 as f64;
index.delete_single_docid(&mut txn, 0, &deleted_f64, deleted_u32)
}
let time_spent = timer.elapsed().as_millis();
println!(" delete {nbr_doc} : {time_spent}ms");
txn.abort().unwrap();
}
}
}
}

1
milli/src/update/facet/mod.rs
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@ -98,7 +98,6 @@ use crate::update::merge_btreeset_string;
use crate::{BEU16StrCodec, Index, Result, BEU16, MAX_FACET_VALUE_LENGTH};
pub mod bulk;
pub mod delete;
pub mod incremental;
/// A builder used to add new elements to the `facet_id_string_docids` or `facet_id_f64_docids` databases.

1
milli/src/update/mod.rs
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@ -22,7 +22,6 @@ pub use self::words_prefixes_fst::WordsPrefixesFst;
mod available_documents_ids;
mod clear_documents;
pub(crate) mod del_add;
mod delete_documents;
pub(crate) mod facet;
mod index_documents;
mod indexer_config;