mirror of
https://github.com/meilisearch/meilisearch.git
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Change encoding of word_pair_proximity DB to (proximity, word1, word2)
Same for word_prefix_pair_proximity
This commit is contained in:
Loïc Lecrenier
Loïc Lecrenier
parent
19b2326f3d
commit
bdeb47305e
@ -7,12 +7,11 @@ impl<'a> heed::BytesDecode<'a> for StrStrU8Codec {
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type DItem = (&'a str, &'a str, u8);
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fn bytes_decode(bytes: &'a [u8]) -> Option<Self::DItem> {
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let (n, bytes) = bytes.split_last()?;
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let (n, bytes) = bytes.split_first()?;
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let s1_end = bytes.iter().position(|b| *b == 0)?;
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let (s1_bytes, rest) = bytes.split_at(s1_end);
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let rest = &rest[1..];
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let s2_bytes = &rest[1..];
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let s1 = str::from_utf8(s1_bytes).ok()?;
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let (_, s2_bytes) = rest.split_last()?;
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let s2 = str::from_utf8(s2_bytes).ok()?;
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Some((s1, s2, *n))
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}
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@ -22,12 +21,11 @@ impl<'a> heed::BytesEncode<'a> for StrStrU8Codec {
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type EItem = (&'a str, &'a str, u8);
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fn bytes_encode((s1, s2, n): &Self::EItem) -> Option<Cow<[u8]>> {
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let mut bytes = Vec::with_capacity(s1.len() + s2.len() + 1 + 1);
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let mut bytes = Vec::with_capacity(s1.len() + s2.len() + 1);
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bytes.push(*n);
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bytes.extend_from_slice(s1.as_bytes());
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bytes.push(0);
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bytes.extend_from_slice(s2.as_bytes());
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bytes.push(0);
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bytes.push(*n);
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Some(Cow::Owned(bytes))
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}
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}
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@ -37,11 +35,10 @@ impl<'a> heed::BytesDecode<'a> for UncheckedStrStrU8Codec {
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type DItem = (&'a [u8], &'a [u8], u8);
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fn bytes_decode(bytes: &'a [u8]) -> Option<Self::DItem> {
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let (n, bytes) = bytes.split_last()?;
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let (n, bytes) = bytes.split_first()?;
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let s1_end = bytes.iter().position(|b| *b == 0)?;
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let (s1_bytes, rest) = bytes.split_at(s1_end);
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let rest = &rest[1..];
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let (_, s2_bytes) = rest.split_last()?;
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let s2_bytes = &rest[1..];
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Some((s1_bytes, s2_bytes, *n))
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}
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}
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@ -50,12 +47,11 @@ impl<'a> heed::BytesEncode<'a> for UncheckedStrStrU8Codec {
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type EItem = (&'a [u8], &'a [u8], u8);
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fn bytes_encode((s1, s2, n): &Self::EItem) -> Option<Cow<[u8]>> {
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let mut bytes = Vec::with_capacity(s1.len() + s2.len() + 1 + 1);
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let mut bytes = Vec::with_capacity(s1.len() + s2.len() + 1);
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bytes.push(*n);
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bytes.extend_from_slice(s1);
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bytes.push(0);
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bytes.extend_from_slice(s2);
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bytes.push(0);
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bytes.push(*n);
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Some(Cow::Owned(bytes))
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}
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}
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@ -194,7 +194,7 @@ pub fn snap_word_prefix_pair_proximity_docids(index: &Index) -> String {
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(word1, prefix, proximity),
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b,
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)| {
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&format!("{word1:<16} {prefix:<4} {proximity:<2} {}", display_bitmap(&b))
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&format!("{proximity:<2} {word1:<16} {prefix:<4} {}", display_bitmap(&b))
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});
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snap
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}
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@ -151,11 +151,10 @@ fn document_word_positions_into_sorter<'b>(
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let mut key_buffer = Vec::new();
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for ((w1, w2), prox) in word_pair_proximity {
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key_buffer.clear();
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key_buffer.push(prox as u8);
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key_buffer.extend_from_slice(w1.as_bytes());
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key_buffer.push(0);
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key_buffer.extend_from_slice(w2.as_bytes());
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key_buffer.push(0);
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key_buffer.push(prox as u8);
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word_pair_proximity_docids_sorter.insert(&key_buffer, &document_id.to_ne_bytes())?;
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}
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@ -1,46 +1,46 @@
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---
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source: milli/src/update/word_prefix_pair_proximity_docids.rs
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---
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5 a 1 [101, ]
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5 a 2 [101, ]
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5 b 4 [101, ]
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5 be 4 [101, ]
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am a 3 [101, ]
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amazing a 1 [100, ]
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amazing a 2 [100, ]
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amazing a 3 [100, ]
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amazing b 2 [100, ]
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amazing be 2 [100, ]
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an a 1 [100, ]
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an a 2 [100, ]
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an b 3 [100, ]
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an be 3 [100, ]
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and a 2 [100, ]
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and a 3 [100, ]
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and a 4 [100, ]
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and b 1 [100, ]
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and be 1 [100, ]
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at a 1 [100, ]
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at a 2 [100, 101, ]
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at a 3 [100, ]
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at b 3 [101, ]
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at b 4 [100, ]
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at be 3 [101, ]
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at be 4 [100, ]
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beautiful a 2 [100, ]
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beautiful a 3 [100, ]
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beautiful a 4 [100, ]
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bell a 2 [101, ]
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bell a 4 [101, ]
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house a 3 [100, ]
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house a 4 [100, ]
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house b 2 [100, ]
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house be 2 [100, ]
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rings a 1 [101, ]
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rings a 3 [101, ]
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rings b 2 [101, ]
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rings be 2 [101, ]
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the a 3 [101, ]
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the b 1 [101, ]
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the be 1 [101, ]
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1 5 a [101, ]
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1 amazing a [100, ]
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1 an a [100, ]
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1 and b [100, ]
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1 and be [100, ]
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1 at a [100, ]
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1 rings a [101, ]
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1 the b [101, ]
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1 the be [101, ]
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2 5 a [101, ]
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2 amazing a [100, ]
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2 amazing b [100, ]
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2 amazing be [100, ]
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2 an a [100, ]
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2 and a [100, ]
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2 at a [100, 101, ]
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2 beautiful a [100, ]
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2 bell a [101, ]
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2 house b [100, ]
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2 house be [100, ]
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2 rings b [101, ]
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2 rings be [101, ]
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3 am a [101, ]
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3 amazing a [100, ]
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3 an b [100, ]
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3 an be [100, ]
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3 and a [100, ]
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3 at a [100, ]
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3 at b [101, ]
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3 at be [101, ]
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3 beautiful a [100, ]
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3 house a [100, ]
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3 rings a [101, ]
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3 the a [101, ]
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4 5 b [101, ]
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4 5 be [101, ]
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4 and a [100, ]
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4 at b [100, ]
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4 at be [100, ]
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4 beautiful a [100, ]
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4 bell a [101, ]
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4 house a [100, ]
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@ -1,4 +1,4 @@
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---
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source: milli/src/update/word_prefix_pair_proximity_docids.rs
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---
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5ed4bf83317b10962a55ade353427bdd
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fb88e49fd666886731b62baef8f44995
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@ -1,7 +1,7 @@
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/*!
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## What is WordPrefixPairProximityDocids?
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The word-prefix-pair-proximity-docids database is a database whose keys are of
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the form (`word`, `prefix`, `proximity`) and the values are roaring bitmaps of
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the form `(proximity, word, prefix)` and the values are roaring bitmaps of
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the documents which contain `word` followed by another word starting with
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`prefix` at a distance of `proximity`.
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@ -23,127 +23,100 @@ dog
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Note that only prefixes which correspond to more than a certain number of
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different words from the database are included in this list.
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* a sorted list of word pairs and the distance between them (i.e. proximity),
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* associated with a roaring bitmap, such as:
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* a sorted list of proximities and word pairs (the proximity is the distance between the two words),
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associated with a roaring bitmap, such as:
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```text
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good dog 3 -> docids1: [2, 5, 6]
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good doggo 1 -> docids2: [8]
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good dogma 1 -> docids3: [7, 19, 20]
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good ghost 2 -> docids4: [1]
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horror cathedral 4 -> docids5: [1, 2]
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1 good doggo -> docids1: [8]
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1 good door -> docids2: [7, 19, 20]
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1 good ghost -> docids3: [1]
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2 good dog -> docids4: [2, 5, 6]
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2 horror cathedral -> docids5: [1, 2]
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```
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I illustrate a simplified version of the algorithm to create the word-prefix
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pair-proximity database below:
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1. **Outer loop:** First, we iterate over each word pair and its proximity:
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1. **Outer loop:** First, we iterate over each proximity and word pair:
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```text
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proximity: 1
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word1 : good
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word2 : dog
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proximity: 3
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word2 : doggo
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```
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2. **Inner loop:** Then, we iterate over all the prefixes of `word2` that are
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in the list of sorted prefixes. And we insert the key (`prefix`, `proximity`)
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in the list of sorted prefixes. And we insert the key `prefix`
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and the value (`docids`) to a sorted map which we call the “batch”. For example,
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at the end of the first inner loop, we may have:
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```text
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Outer loop 1:
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------------------------------
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proximity: 1
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word1 : good
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word2 : dog
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proximity: 3
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word2 : doggo
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docids : docids1
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prefixes: [d, do, dog]
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batch: [
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(d, 3) -> [docids1]
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(do, 3) -> [docids1]
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(dog, 3) -> [docids1]
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d, -> [docids1]
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do -> [docids1]
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dog -> [docids1]
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]
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```
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3. For illustration purpose, let's run through a second iteration of the outer loop:
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```text
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Outer loop 2:
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------------------------------
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word1 : good
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word2 : doggo
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proximity: 1
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word1 : good
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word2 : door
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docids : docids2
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prefixes: [d, do, dog]
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prefixes: [d, do, doo]
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batch: [
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(d, 1) -> [docids2]
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(d, 3) -> [docids1]
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(do, 1) -> [docids2]
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(do, 3) -> [docids1]
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(dog, 1) -> [docids2]
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(dog, 3) -> [docids1]
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]
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```
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Notice that the batch had to re-order some (`prefix`, `proximity`) keys: some
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of the elements inserted in the second iteration of the outer loop appear
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*before* elements from the first iteration.
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4. And a third:
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```text
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Outer loop 3:
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------------------------------
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word1 : good
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word2 : dogma
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proximity: 1
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docids : docids3
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prefixes: [d, do, dog]
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batch: [
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(d, 1) -> [docids2, docids3]
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(d, 3) -> [docids1]
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(do, 1) -> [docids2, docids3]
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(do, 3) -> [docids1]
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(dog, 1) -> [docids2, docids3]
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(dog, 3) -> [docids1]
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d -> [docids1, docids2]
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do -> [docids1, docids2]
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dog -> [docids1]
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doo -> [docids2]
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]
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```
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Notice that there were some conflicts which were resolved by merging the
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conflicting values together.
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conflicting values together. Also, an additional prefix was added at the
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end of the batch.
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5. On the fourth iteration of the outer loop, we have:
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4. On the third iteration of the outer loop, we have:
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```text
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Outer loop 4:
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------------------------------
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proximity: 1
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word1 : good
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word2 : ghost
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proximity: 2
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```
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Because `word2` begins with a different letter than the previous `word2`,
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we know that:
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1. All the prefixes of `word2` are greater than the prefixes of the previous word2
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2. And therefore, every instance of (`word2`, `prefix`) will be greater than
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any element in the batch.
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we know that all the prefixes of `word2` are greater than the prefixes of the previous word2
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Therefore, we know that we can insert every element from the batch into the
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database before proceeding any further. This operation is called
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“flushing the batch”. Flushing the batch should also be done whenever `word1`
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is different than the previous `word1`.
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“flushing the batch”. Flushing the batch should also be done whenever:
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* `proximity` is different than the previous `proximity`.
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* `word1` is different than the previous `word1`.
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* `word2` starts with a different letter than the previous word2
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6. **Flushing the batch:** to flush the batch, we look at the `word1` and
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iterate over the elements of the batch in sorted order:
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6. **Flushing the batch:** to flush the batch, we iterate over its elements:
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```text
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Flushing Batch loop 1:
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------------------------------
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word1 : good
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word2 : d
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proximity : 1
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word1 : good
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prefix : d
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docids : [docids2, docids3]
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```
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We then merge the array of `docids` (of type `Vec<Vec<u8>>`) using
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`merge_cbo_roaring_bitmap` in order to get a single byte vector representing a
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roaring bitmap of all the document ids where `word1` is followed by `prefix`
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at a distance of `proximity`.
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Once we have done that, we insert (`word1`, `prefix`, `proximity`) -> `merged_docids`
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Once we have done that, we insert `(proximity, word1, prefix) -> merged_docids`
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into the database.
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7. That's it! ... except...
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@ -184,8 +157,8 @@ Note, also, that since we read data from the database when iterating over
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`word_pairs_db`, we cannot insert the computed word-prefix-pair-proximity-
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docids from the batch directly into the database (we would have a concurrent
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reader and writer). Therefore, when calling the algorithm on
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(`new_prefixes`, `word_pairs_db`), we insert the computed
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((`word`, `prefix`, `proximity`), `docids`) elements in an intermediary grenad
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`(new_prefixes, word_pairs_db)`, we insert the computed
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`((proximity, word, prefix), docids)` elements in an intermediary grenad
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Writer instead of the DB. At the end of the outer loop, we finally read from
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the grenad and insert its elements in the database.
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@ -406,7 +379,7 @@ fn execute_on_word_pairs_and_prefixes<I>(
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while let Some(((word1, word2, proximity), data)) = next_word_pair_proximity(iter)? {
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// skip this iteration if the proximity is over the threshold
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if proximity > max_proximity {
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continue;
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break;
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};
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let word2_start_different_than_prev = word2[0] != prev_word2_start;
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// if there were no potential prefixes for the previous word2 based on its first letter,
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@ -416,16 +389,21 @@ fn execute_on_word_pairs_and_prefixes<I>(
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continue;
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}
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// if word1 is different than the previous word1 OR if the start of word2 is different
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// than the previous start of word2, then we'll need to flush the batch
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// if the proximity is different to the previous one, OR
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// if word1 is different than the previous word1, OR
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// if the start of word2 is different than the previous start of word2,
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// THEN we'll need to flush the batch
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let prox_different_than_prev = proximity != batch.proximity;
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let word1_different_than_prev = word1 != batch.word1;
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if word1_different_than_prev || word2_start_different_than_prev {
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if prox_different_than_prev || word1_different_than_prev || word2_start_different_than_prev
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{
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batch.flush(&mut merge_buffer, &mut insert)?;
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// don't forget to reset the value of batch.word1 and prev_word2_start
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if word1_different_than_prev {
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prefix_search_start.0 = 0;
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batch.word1.clear();
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batch.word1.extend_from_slice(word1);
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batch.proximity = proximity;
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}
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if word2_start_different_than_prev {
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// word2_start_different_than_prev == true
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@ -437,74 +415,70 @@ fn execute_on_word_pairs_and_prefixes<I>(
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if !empty_prefixes {
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// All conditions are satisfied, we can now insert each new prefix of word2 into the batch
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prefix_buffer.clear();
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prefixes.for_each_prefix_of(
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word2,
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&mut prefix_buffer,
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&prefix_search_start,
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|prefix_buffer| {
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let prefix_len = prefix_buffer.len();
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prefix_buffer.push(0);
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prefix_buffer.push(proximity);
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batch.insert(&prefix_buffer, data.to_vec());
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prefix_buffer.truncate(prefix_len);
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},
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);
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prefix_buffer.clear();
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}
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}
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batch.flush(&mut merge_buffer, &mut insert)?;
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Ok(())
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}
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/**
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A map structure whose keys are (prefix, proximity) and whose values are vectors of bitstrings (serialized roaring bitmaps).
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A map structure whose keys are prefixes and whose values are vectors of bitstrings (serialized roaring bitmaps).
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The keys are sorted and conflicts are resolved by merging the vectors of bitstrings together.
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It is used to ensure that all ((word1, prefix, proximity), docids) are inserted into the database in sorted order and efficiently.
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It is used to ensure that all ((proximity, word1, prefix), docids) are inserted into the database in sorted order and efficiently.
|
||||
|
||||
The batch is flushed as often as possible, when we are sure that every (word1, prefix, proximity) key derived from its content
|
||||
The batch is flushed as often as possible, when we are sure that every (proximity, word1, prefix) key derived from its content
|
||||
can be inserted into the database in sorted order. When it is flushed, it calls a user-provided closure with the following arguments:
|
||||
- key : (word1, prefix, proximity) as bytes
|
||||
- value : merged roaring bitmaps from all values associated with (prefix, proximity) in the batch, serialised to bytes
|
||||
- key : (proximity, word1, prefix) as bytes
|
||||
- value : merged roaring bitmaps from all values associated with prefix in the batch, serialised to bytes
|
||||
*/
|
||||
#[derive(Default)]
|
||||
struct PrefixAndProximityBatch {
|
||||
proximity: u8,
|
||||
word1: Vec<u8>,
|
||||
batch: Vec<(Vec<u8>, Vec<Cow<'static, [u8]>>)>,
|
||||
}
|
||||
|
||||
impl PrefixAndProximityBatch {
|
||||
/// Insert the new key and value into the batch
|
||||
///
|
||||
/// The key must either exist in the batch or be greater than all existing keys
|
||||
fn insert(&mut self, new_key: &[u8], new_value: Vec<u8>) {
|
||||
match self.batch.binary_search_by_key(&new_key, |(k, _)| k.as_slice()) {
|
||||
Ok(position) => {
|
||||
self.batch[position].1.push(Cow::Owned(new_value));
|
||||
}
|
||||
Err(position) => {
|
||||
self.batch.insert(position, (new_key.to_vec(), vec![Cow::Owned(new_value)]));
|
||||
}
|
||||
match self.batch.iter_mut().find(|el| el.0 == new_key) {
|
||||
Some((_prefix, docids)) => docids.push(Cow::Owned(new_value)),
|
||||
None => self.batch.push((new_key.to_vec(), vec![Cow::Owned(new_value)])),
|
||||
}
|
||||
}
|
||||
|
||||
/// Empties the batch, calling `insert` on each element.
|
||||
///
|
||||
/// The key given to `insert` is `(word1, prefix, proximity)` and the value is the associated merged roaring bitmap.
|
||||
/// The key given to `insert` is `(proximity, word1, prefix)` and the value is the associated merged roaring bitmap.
|
||||
fn flush(
|
||||
&mut self,
|
||||
merge_buffer: &mut Vec<u8>,
|
||||
insert: &mut impl for<'buffer> FnMut(&'buffer [u8], &'buffer [u8]) -> Result<()>,
|
||||
) -> Result<()> {
|
||||
let PrefixAndProximityBatch { word1, batch } = self;
|
||||
let PrefixAndProximityBatch { proximity, word1, batch } = self;
|
||||
if batch.is_empty() {
|
||||
return Ok(());
|
||||
}
|
||||
merge_buffer.clear();
|
||||
|
||||
let mut buffer = Vec::with_capacity(word1.len() + 1 + 6 + 1);
|
||||
let mut buffer = Vec::with_capacity(word1.len() + 1 + 6);
|
||||
buffer.push(*proximity);
|
||||
buffer.extend_from_slice(word1);
|
||||
buffer.push(0);
|
||||
|
||||
for (key, mergeable_data) in batch.drain(..) {
|
||||
buffer.truncate(word1.len() + 1);
|
||||
buffer.truncate(1 + word1.len() + 1);
|
||||
buffer.extend_from_slice(key.as_slice());
|
||||
|
||||
let data = if mergeable_data.len() > 1 {
|
||||
@ -884,51 +858,33 @@ mod tests {
|
||||
CboRoaringBitmapCodec::serialize_into(&bitmap_ranges, &mut serialised_bitmap_ranges);
|
||||
|
||||
let word_pairs = [
|
||||
// 1, 3: (healthy arb 2) and (healthy arbre 2) with (bitmap123 | bitmap456)
|
||||
(("healthy", "arbre", 2), &serialised_bitmap123),
|
||||
// not inserted because 3 > max_proximity
|
||||
(("healthy", "arbre", 3), &serialised_bitmap456),
|
||||
// 0, 2: (healthy arb 1) and (healthy arbre 1) with (bitmap123)
|
||||
(("healthy", "arbres", 1), &serialised_bitmap123),
|
||||
// 1, 3:
|
||||
(("healthy", "arbres", 2), &serialised_bitmap456),
|
||||
// not be inserted because 3 > max_proximity
|
||||
(("healthy", "arbres", 3), &serialised_bitmap789),
|
||||
// not inserted because no prefixes for boat
|
||||
(("healthy", "boat", 1), &serialised_bitmap123),
|
||||
// not inserted because no prefixes for ca
|
||||
(("healthy", "ca", 1), &serialised_bitmap123),
|
||||
// 4: (healthy cat 1) with (bitmap456 + bitmap123)
|
||||
(("healthy", "cats", 1), &serialised_bitmap456),
|
||||
// 5: (healthy cat 2) with (bitmap789 + bitmap_ranges)
|
||||
(("healthy", "cats", 2), &serialised_bitmap789),
|
||||
// 4 + 6: (healthy catto 1) with (bitmap123)
|
||||
(("healthy", "cattos", 1), &serialised_bitmap123),
|
||||
// 5 + 7: (healthy catto 2) with (bitmap_ranges)
|
||||
(("healthy", "cattos", 2), &serialised_bitmap_ranges),
|
||||
// 8: (jittery cat 1) with (bitmap123 | bitmap456 | bitmap789 | bitmap_ranges)
|
||||
(("jittery", "cat", 1), &serialised_bitmap123),
|
||||
// 8:
|
||||
(("jittery", "cata", 1), &serialised_bitmap456),
|
||||
// 8:
|
||||
(("jittery", "catb", 1), &serialised_bitmap789),
|
||||
// 8:
|
||||
(("jittery", "catc", 1), &serialised_bitmap_ranges),
|
||||
(("healthy", "arbre", 2), &serialised_bitmap123),
|
||||
(("healthy", "arbres", 2), &serialised_bitmap456),
|
||||
(("healthy", "cats", 2), &serialised_bitmap789),
|
||||
(("healthy", "cattos", 2), &serialised_bitmap_ranges),
|
||||
(("healthy", "arbre", 3), &serialised_bitmap456),
|
||||
(("healthy", "arbres", 3), &serialised_bitmap789),
|
||||
];
|
||||
|
||||
let expected_result = [
|
||||
// first batch:
|
||||
(("healthy", "arb", 1), bitmap123.clone()),
|
||||
(("healthy", "arb", 2), &bitmap123 | &bitmap456),
|
||||
(("healthy", "arbre", 1), bitmap123.clone()),
|
||||
(("healthy", "arbre", 2), &bitmap123 | &bitmap456),
|
||||
// second batch:
|
||||
(("healthy", "cat", 1), &bitmap456 | &bitmap123),
|
||||
(("healthy", "cat", 2), &bitmap789 | &bitmap_ranges),
|
||||
(("healthy", "catto", 1), bitmap123.clone()),
|
||||
(("healthy", "catto", 2), bitmap_ranges.clone()),
|
||||
// third batch
|
||||
(("jittery", "cat", 1), (&bitmap123 | &bitmap456 | &bitmap789 | &bitmap_ranges)),
|
||||
(("healthy", "arb", 2), &bitmap123 | &bitmap456),
|
||||
(("healthy", "arbre", 2), &bitmap123 | &bitmap456),
|
||||
(("healthy", "cat", 2), &bitmap789 | &bitmap_ranges),
|
||||
(("healthy", "catto", 2), bitmap_ranges.clone()),
|
||||
];
|
||||
|
||||
let mut result = vec![];
|
||||
|
||||
Loading…
Reference in New Issue
Block a user