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https://github.com/meilisearch/MeiliSearch
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Move content of readme for WordPrefixPairProximityDocids into the code
This commit is contained in:
Loïc Lecrenier
parent
220921628b
commit
ea4a96761c
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@ -1,16 +1,161 @@
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use std::collections::{HashMap, HashSet};
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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 the form (`word`, `prefix`, `proximity`) and the values are roaring bitmaps of the documents which contain `word` followed by another word starting with `prefix` at a distance of `proximity`.
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The prefixes present in this database are only those that correspond to many different words in the documents.
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## How is it created/updated? (simplified version)
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To compute it, we have access to (mainly) two inputs:
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* a list of sorted prefixes, such as:
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```
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c
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ca
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cat
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d
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do
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dog
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```
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Note that only prefixes which correspond to more than a certain number of 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), associated with a roaring bitmap, such as:
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```
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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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```
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I illustrate a simplified version of the algorithm to create the word-prefix-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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```
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word1 : good
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word2 : dog
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proximity: 3
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```
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2. **Inner loop:** Then, we iterate over all the prefixes of `word2` that are in the list of sorted prefixes. And we insert the key (`prefix`, `proximity`) and the value (`docids`) to a sorted map which we call the “batch”. For example, at the end of the first inner loop, we may have:
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```
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Outer loop 1:
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------------------------------
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word1 : good
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word2 : dog
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proximity: 3
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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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]
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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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```
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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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docids : docids2
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prefixes: [d, do, dog]
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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 of the elements inserted in the second iteration of the outer loop appear *before* elements from the first iteration.
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4. And a third:
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```
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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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]
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```
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Notice that there were some conflicts which were resolved by merging the conflicting values together.
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5. On the fourth iteration of the outer loop, we have:
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```
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Outer loop 4:
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------------------------------
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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`, 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 any element in the batch.
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Therefore, we know that we can insert every element from the batch into the database before proceeding any further. This operation is called “flushing the batch”. Flushing the batch should also be done whenever `word1` is different than the previous `word1`.
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6. **Flushing the batch:** to flush the batch, we look at the `word1` and iterate over the elements of the batch in sorted order:
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```
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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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docids : [docids2, docids3]
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```
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We then merge the array of `docids` (of type `Vec<Vec<u8>>`) using `merge_cbo_roaring_bitmap` in order to get a single byte vector representing a roaring bitmap of all the document ids where `word1` is followed by `prefix` at a distance of `proximity`.
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Once we have done that, we insert (`word1`, `prefix`, `proximity`) -> `merged_docids` into the database.
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7. That's it! ... except...
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## How is it created/updated (continued)
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I lied a little bit about the input data. In reality, we get two sets of the inputs described above, which come from different places:
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* For the list of sorted prefixes, we have:
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* `new_prefixes`, which are all the prefixes that were not present in the database before the insertion of the new documents
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* `common_prefixes` which are the prefixes that are present both in the database and in the newly added documents
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* For the list of word pairs and proximities, we have:
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* `new_word_pairs`, which is the list of word pairs and their proximities present in the newly added documents
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* `word_pairs_db`, which is the list of word pairs from the database. **This list includes all elements in `new_word_pairs`** since `new_word_pairs` was added to the database prior to calling the `WordPrefixPairProximityDocIds::execute` function.
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To update the prefix database correctly, we call the algorithm described earlier first on (`common_prefixes`, `new_word_pairs`) and then on (`new_prefixes`, `word_pairs_db`). Thus:
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1. For all the word pairs that were already present in the DB, we insert them again with the `new_prefixes`. Calling the algorithm on them with the `common_prefixes` would not result in any new data.
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3. For all the new word pairs, we insert them twice: first with the `common_prefixes`, and then, because they are part of `word_pairs_db`, with the `new_prefixes`.
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Note, also, that since we read data from the database when iterating over `word_pairs_db`, we cannot insert the computed word-prefix-pair-proximity-docids from the batch directly into the database (we would have a concurrent reader and writer). Therefore, when calling the algorithm on (`new_prefixes`, `word_pairs_db`), we insert the computed ((`word`, `prefix`, `proximity`), `docids`) elements in an intermediary grenad Writer instead of the DB. At the end of the outer loop, we finally read from the grenad and insert its elements in the database.
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*/
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use crate::update::index_documents::{
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create_writer, merge_cbo_roaring_bitmaps, CursorClonableMmap,
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};
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use crate::{CboRoaringBitmapCodec, Index, Result, UncheckedStrStrU8Codec};
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use grenad::CompressionType;
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use heed::types::ByteSlice;
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use heed::BytesDecode;
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use log::debug;
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use slice_group_by::GroupBy;
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use crate::update::index_documents::{
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create_sorter, merge_cbo_roaring_bitmaps, sorter_into_lmdb_database, valid_lmdb_key,
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CursorClonableMmap, MergeFn,
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};
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use crate::{Index, Result, StrStrU8Codec};
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use std::borrow::Cow;
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use std::collections::HashSet;
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use std::io::BufReader;
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pub struct WordPrefixPairProximityDocids<'t, 'u, 'i> {
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wtxn: &'t mut heed::RwTxn<'i, 'u>,
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@ -62,94 +207,104 @@ impl<'t, 'u, 'i> WordPrefixPairProximityDocids<'t, 'u, 'i> {
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}
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#[logging_timer::time("WordPrefixPairProximityDocids::{}")]
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pub fn execute(
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self,
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pub fn execute<'a>(
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mut self,
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new_word_pair_proximity_docids: grenad::Reader<CursorClonableMmap>,
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new_prefix_fst_words: &[String],
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common_prefix_fst_words: &[&[String]],
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new_prefix_fst_words: &'a [String],
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common_prefix_fst_words: &[&'a [String]],
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del_prefix_fst_words: &HashSet<Vec<u8>>,
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) -> Result<()> {
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debug!("Computing and writing the word prefix pair proximity docids into LMDB on disk...");
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let new_prefix_fst_words: Vec<_> =
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new_prefix_fst_words.linear_group_by_key(|x| x.chars().nth(0).unwrap()).collect();
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// This is an optimisation, to reuse allocations between loop iterations
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let mut allocations = Allocations::default();
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let mut new_wppd_iter = new_word_pair_proximity_docids.into_cursor()?;
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let mut word_prefix_pair_proximity_docids_sorter = create_sorter(
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merge_cbo_roaring_bitmaps,
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self.chunk_compression_type,
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self.chunk_compression_level,
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self.max_nb_chunks,
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self.max_memory,
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// Make a prefix trie from the common prefixes that are shorter than self.max_prefix_length
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let prefixes = PrefixTrieNode::from_sorted_prefixes(
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common_prefix_fst_words
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.into_iter()
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.map(|s| s.into_iter())
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.flatten()
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.map(|s| s.as_str())
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.filter(|s| s.len() <= self.max_prefix_length),
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);
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if !common_prefix_fst_words.is_empty() {
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// We compute the prefix docids associated with the common prefixes between
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// the old and new word prefix fst.
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let mut buffer = Vec::new();
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let mut current_prefixes: Option<&&[String]> = None;
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let mut prefixes_cache = HashMap::new();
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while let Some((key, data)) = new_wppd_iter.move_on_next()? {
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let (w1, w2, prox) =
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StrStrU8Codec::bytes_decode(key).ok_or(heed::Error::Decoding)?;
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if prox > self.max_proximity {
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continue;
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}
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insert_current_prefix_data_in_sorter(
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&mut buffer,
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&mut current_prefixes,
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&mut prefixes_cache,
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&mut word_prefix_pair_proximity_docids_sorter,
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common_prefix_fst_words,
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self.max_prefix_length,
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w1,
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w2,
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prox,
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data,
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)?;
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}
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write_prefixes_in_sorter(
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&mut prefixes_cache,
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&mut word_prefix_pair_proximity_docids_sorter,
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// If the prefix trie is not empty, then we can iterate over all new
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// word pairs to look for new (word1, common_prefix, proximity) elements
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// to insert in the DB
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if !prefixes.is_empty() {
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let mut cursor = new_word_pair_proximity_docids.into_cursor()?;
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// This is the core of the algorithm
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execute_on_word_pairs_and_prefixes(
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// the first two arguments tell how to iterate over the new word pairs
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&mut cursor,
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|cursor| {
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if let Some((key, value)) = cursor.move_on_next()? {
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let (word1, word2, proximity) = UncheckedStrStrU8Codec::bytes_decode(key)
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.ok_or(heed::Error::Decoding)?;
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Ok(Some(((word1, word2, proximity), value)))
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} else {
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Ok(None)
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}
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},
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&prefixes,
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&mut allocations,
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self.max_proximity,
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// and this argument tells what to do with each new key (word1, prefix, proximity) and value (roaring bitmap)
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|key, value| {
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insert_into_database(
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&mut self.wtxn,
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*self.index.word_prefix_pair_proximity_docids.as_polymorph(),
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key,
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value,
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)
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},
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)?;
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}
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if !new_prefix_fst_words.is_empty() {
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// We compute the prefix docids associated with the newly added prefixes
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// in the new word prefix fst.
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// Now we do the same thing with the new prefixes and all word pairs in the DB
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let prefixes = PrefixTrieNode::from_sorted_prefixes(
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new_prefix_fst_words
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.into_iter()
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.map(|s| s.as_str())
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.filter(|s| s.len() <= self.max_prefix_length),
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);
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if !prefixes.is_empty() {
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let mut db_iter = self
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.index
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.word_pair_proximity_docids
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.remap_key_type::<UncheckedStrStrU8Codec>()
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.remap_data_type::<ByteSlice>()
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.iter(self.wtxn)?;
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let mut buffer = Vec::new();
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let mut current_prefixes: Option<&&[String]> = None;
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let mut prefixes_cache = HashMap::new();
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while let Some(((w1, w2, prox), data)) = db_iter.next().transpose()? {
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if prox > self.max_proximity {
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continue;
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}
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// Since we read the DB, we can't write to it directly, so we add each new (word1, prefix, proximity)
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// element in an intermediary grenad
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let mut writer = create_writer(
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self.chunk_compression_type,
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self.chunk_compression_level,
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tempfile::tempfile()?,
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);
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insert_current_prefix_data_in_sorter(
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&mut buffer,
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&mut current_prefixes,
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&mut prefixes_cache,
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&mut word_prefix_pair_proximity_docids_sorter,
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&new_prefix_fst_words,
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self.max_prefix_length,
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w1,
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w2,
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prox,
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data,
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)?;
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}
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execute_on_word_pairs_and_prefixes(
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&mut db_iter,
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|db_iter| db_iter.next().transpose().map_err(|e| e.into()),
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&prefixes,
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&mut allocations,
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self.max_proximity,
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|key, value| writer.insert(key, value).map_err(|e| e.into()),
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)?;
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drop(db_iter);
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write_prefixes_in_sorter(
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&mut prefixes_cache,
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&mut word_prefix_pair_proximity_docids_sorter,
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// and then we write the grenad into the DB
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// Since the grenad contains only new prefixes, we know in advance that none
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// of its elements already exist in the DB, thus there is no need to specify
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// how to merge conflicting elements
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write_into_lmdb_database_without_merging(
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self.wtxn,
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*self.index.word_prefix_pair_proximity_docids.as_polymorph(),
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writer,
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)?;
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}
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@ -169,84 +324,359 @@ impl<'t, 'u, 'i> WordPrefixPairProximityDocids<'t, 'u, 'i> {
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}
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}
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// We finally write and merge the new word prefix pair proximity docids
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// in the LMDB database.
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sorter_into_lmdb_database(
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self.wtxn,
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*self.index.word_prefix_pair_proximity_docids.as_polymorph(),
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word_prefix_pair_proximity_docids_sorter,
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merge_cbo_roaring_bitmaps,
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)?;
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Ok(())
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}
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}
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/// This is the core of the algorithm to initialise the Word Prefix Pair Proximity Docids database.
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///
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/// Its main arguments are:
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/// 1. a sorted iterator over ((word1, word2, proximity), docids) elements
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/// 2. a prefix trie
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/// 3. a closure to describe how to handle the new computed (word1, prefix, proximity) elements
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///
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/// For more information about what this function does, read the module documentation.
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fn execute_on_word_pairs_and_prefixes<Iter>(
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iter: &mut Iter,
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mut next_word_pair_proximity: impl for<'a> FnMut(
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&'a mut Iter,
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) -> Result<
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Option<((&'a [u8], &'a [u8], u8), &'a [u8])>,
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>,
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prefixes: &PrefixTrieNode,
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allocations: &mut Allocations,
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max_proximity: u8,
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mut insert: impl for<'a> FnMut(&'a [u8], &'a [u8]) -> Result<()>,
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) -> Result<()> {
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let mut batch = PrefixAndProximityBatch::default();
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let mut prev_word2_start = 0;
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let mut prefix_search_start = PrefixTrieNodeSearchStart(0);
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let mut empty_prefixes = false;
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let mut prefix_buffer = allocations.take_byte_vector();
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let mut merge_buffer = allocations.take_byte_vector();
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while let Some(((word1, word2, proximity), data)) = next_word_pair_proximity(iter)? {
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if proximity > max_proximity {
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continue;
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};
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let word2_start_different_than_prev = word2[0] != prev_word2_start;
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if empty_prefixes && !word2_start_different_than_prev {
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continue;
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}
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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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batch.flush(allocations, &mut merge_buffer, &mut insert)?;
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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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}
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if word2_start_different_than_prev {
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// word2_start_different_than_prev == true
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prev_word2_start = word2[0];
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}
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empty_prefixes = !prefixes.set_search_start(word2, &mut prefix_search_start);
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}
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if !empty_prefixes {
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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 mut value = allocations.take_byte_vector();
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value.extend_from_slice(&data);
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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);
|
||||
batch.insert(&prefix_buffer, value, allocations);
|
||||
prefix_buffer.truncate(prefix_len);
|
||||
},
|
||||
);
|
||||
prefix_buffer.clear();
|
||||
}
|
||||
}
|
||||
batch.flush(allocations, &mut merge_buffer, &mut insert)?;
|
||||
Ok(())
|
||||
}
|
||||
/**
|
||||
A map structure whose keys are (prefix, proximity) and whose values are vectors of bitstrings (serialized roaring bitmaps).
|
||||
The keys are sorted and conflicts are resolved by merging the vectors of bitstrings together.
|
||||
|
||||
It is used to ensure that all ((word1, prefix, proximity), 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
|
||||
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
|
||||
*/
|
||||
#[derive(Default)]
|
||||
struct PrefixAndProximityBatch {
|
||||
word1: Vec<u8>,
|
||||
batch: Vec<(Vec<u8>, Vec<Cow<'static, [u8]>>)>,
|
||||
}
|
||||
|
||||
impl PrefixAndProximityBatch {
|
||||
/// Insert the new key and value into the batch
|
||||
fn insert(&mut self, new_key: &[u8], new_value: Vec<u8>, allocations: &mut Allocations) {
|
||||
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) => {
|
||||
let mut key = allocations.take_byte_vector();
|
||||
key.extend_from_slice(new_key);
|
||||
let mut mergeable_data = allocations.take_mergeable_data_vector();
|
||||
mergeable_data.push(Cow::Owned(new_value));
|
||||
self.batch.insert(position, (key, mergeable_data));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// 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.
|
||||
fn flush(
|
||||
&mut self,
|
||||
allocations: &mut Allocations,
|
||||
merge_buffer: &mut Vec<u8>,
|
||||
insert: &mut impl for<'buffer> FnMut(&'buffer [u8], &'buffer [u8]) -> Result<()>,
|
||||
) -> Result<()> {
|
||||
let PrefixAndProximityBatch { word1, batch } = self;
|
||||
if batch.is_empty() {
|
||||
return Ok(());
|
||||
}
|
||||
merge_buffer.clear();
|
||||
|
||||
let mut buffer = allocations.take_byte_vector();
|
||||
buffer.extend_from_slice(word1);
|
||||
buffer.push(0);
|
||||
|
||||
for (key, mergeable_data) in batch.drain(..) {
|
||||
buffer.truncate(word1.len() + 1);
|
||||
buffer.extend_from_slice(key.as_slice());
|
||||
|
||||
let data = if mergeable_data.len() > 1 {
|
||||
CboRoaringBitmapCodec::merge_into(&mergeable_data, merge_buffer)?;
|
||||
merge_buffer.as_slice()
|
||||
} else {
|
||||
&mergeable_data[0]
|
||||
};
|
||||
insert(buffer.as_slice(), data)?;
|
||||
merge_buffer.clear();
|
||||
allocations.reclaim_byte_vector(key);
|
||||
allocations.reclaim_mergeable_data_vector(mergeable_data);
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
fn write_prefixes_in_sorter(
|
||||
prefixes: &mut HashMap<Vec<u8>, Vec<Vec<u8>>>,
|
||||
sorter: &mut grenad::Sorter<MergeFn>,
|
||||
// This is adapted from `sorter_into_lmdb_database`
|
||||
fn insert_into_database(
|
||||
wtxn: &mut heed::RwTxn,
|
||||
database: heed::PolyDatabase,
|
||||
new_key: &[u8],
|
||||
new_value: &[u8],
|
||||
) -> Result<()> {
|
||||
for (key, data_slices) in prefixes.drain() {
|
||||
for data in data_slices {
|
||||
if valid_lmdb_key(&key) {
|
||||
sorter.insert(&key, data)?;
|
||||
}
|
||||
let mut iter = database.prefix_iter_mut::<_, ByteSlice, ByteSlice>(wtxn, new_key)?;
|
||||
match iter.next().transpose()? {
|
||||
Some((key, old_val)) if new_key == key => {
|
||||
let val =
|
||||
merge_cbo_roaring_bitmaps(key, &[Cow::Borrowed(old_val), Cow::Borrowed(new_value)])
|
||||
.map_err(|_| {
|
||||
// TODO just wrap this error?
|
||||
crate::error::InternalError::IndexingMergingKeys {
|
||||
process: "get-put-merge",
|
||||
}
|
||||
})?;
|
||||
// safety: we don't keep references from inside the LMDB database.
|
||||
unsafe { iter.put_current(key, &val)? };
|
||||
}
|
||||
_ => {
|
||||
drop(iter);
|
||||
database.put::<_, ByteSlice, ByteSlice>(wtxn, new_key, new_value)?;
|
||||
}
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Computes the current prefix based on the previous and the currently iterated value
|
||||
/// i.e. w1, w2, prox. It also makes sure to follow the `max_prefix_length` setting.
|
||||
///
|
||||
/// Uses the current prefixes values to insert the associated data i.e. RoaringBitmap,
|
||||
/// into the sorter that will, later, be inserted in the LMDB database.
|
||||
fn insert_current_prefix_data_in_sorter<'a>(
|
||||
buffer: &mut Vec<u8>,
|
||||
current_prefixes: &mut Option<&'a &'a [String]>,
|
||||
prefixes_cache: &mut HashMap<Vec<u8>, Vec<Vec<u8>>>,
|
||||
word_prefix_pair_proximity_docids_sorter: &mut grenad::Sorter<MergeFn>,
|
||||
prefix_fst_keys: &'a [&'a [std::string::String]],
|
||||
max_prefix_length: usize,
|
||||
w1: &str,
|
||||
w2: &str,
|
||||
prox: u8,
|
||||
data: &[u8],
|
||||
// This is adapted from `sorter_into_lmdb_database` and `write_into_lmdb_database`,
|
||||
// but it uses `append` if the database is empty, and it assumes that the values in the
|
||||
// writer don't conflict with values in the database.
|
||||
pub fn write_into_lmdb_database_without_merging(
|
||||
wtxn: &mut heed::RwTxn,
|
||||
database: heed::PolyDatabase,
|
||||
writer: grenad::Writer<std::fs::File>,
|
||||
) -> Result<()> {
|
||||
*current_prefixes = match current_prefixes.take() {
|
||||
Some(prefixes) if w2.starts_with(&prefixes[0]) => Some(prefixes),
|
||||
_otherwise => {
|
||||
write_prefixes_in_sorter(prefixes_cache, word_prefix_pair_proximity_docids_sorter)?;
|
||||
prefix_fst_keys.iter().find(|prefixes| w2.starts_with(&prefixes[0]))
|
||||
let file = writer.into_inner()?;
|
||||
let reader = grenad::Reader::new(BufReader::new(file))?;
|
||||
if database.is_empty(wtxn)? {
|
||||
let mut out_iter = database.iter_mut::<_, ByteSlice, ByteSlice>(wtxn)?;
|
||||
let mut cursor = reader.into_cursor()?;
|
||||
while let Some((k, v)) = cursor.move_on_next()? {
|
||||
// safety: we don't keep references from inside the LMDB database.
|
||||
unsafe { out_iter.append(k, v)? };
|
||||
}
|
||||
};
|
||||
} else {
|
||||
let mut cursor = reader.into_cursor()?;
|
||||
while let Some((k, v)) = cursor.move_on_next()? {
|
||||
database.put::<_, ByteSlice, ByteSlice>(wtxn, k, v)?;
|
||||
}
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
if let Some(prefixes) = current_prefixes {
|
||||
buffer.clear();
|
||||
buffer.extend_from_slice(w1.as_bytes());
|
||||
buffer.push(0);
|
||||
for prefix in prefixes.iter() {
|
||||
if prefix.len() <= max_prefix_length && w2.starts_with(prefix) {
|
||||
buffer.truncate(w1.len() + 1);
|
||||
buffer.extend_from_slice(prefix.as_bytes());
|
||||
buffer.push(prox);
|
||||
struct Allocations {
|
||||
byte_vectors: Vec<Vec<u8>>,
|
||||
mergeable_data_vectors: Vec<Vec<Cow<'static, [u8]>>>,
|
||||
}
|
||||
impl Default for Allocations {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
byte_vectors: Vec::with_capacity(65_536),
|
||||
mergeable_data_vectors: Vec::with_capacity(4096),
|
||||
}
|
||||
}
|
||||
}
|
||||
impl Allocations {
|
||||
fn take_byte_vector(&mut self) -> Vec<u8> {
|
||||
self.byte_vectors.pop().unwrap_or_else(|| Vec::with_capacity(16))
|
||||
}
|
||||
fn take_mergeable_data_vector(&mut self) -> Vec<Cow<'static, [u8]>> {
|
||||
self.mergeable_data_vectors.pop().unwrap_or_else(|| Vec::with_capacity(8))
|
||||
}
|
||||
|
||||
match prefixes_cache.get_mut(buffer.as_slice()) {
|
||||
Some(value) => value.push(data.to_owned()),
|
||||
None => {
|
||||
prefixes_cache.insert(buffer.clone(), vec![data.to_owned()]);
|
||||
}
|
||||
fn reclaim_byte_vector(&mut self, mut data: Vec<u8>) {
|
||||
data.clear();
|
||||
self.byte_vectors.push(data);
|
||||
}
|
||||
fn reclaim_mergeable_data_vector(&mut self, mut data: Vec<Cow<'static, [u8]>>) {
|
||||
data.clear();
|
||||
self.mergeable_data_vectors.push(data);
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Default, Debug)]
|
||||
struct PrefixTrieNode {
|
||||
children: Vec<(PrefixTrieNode, u8)>,
|
||||
is_end_node: bool,
|
||||
}
|
||||
|
||||
#[derive(Debug)]
|
||||
struct PrefixTrieNodeSearchStart(usize);
|
||||
|
||||
impl PrefixTrieNode {
|
||||
fn is_empty(&self) -> bool {
|
||||
self.children.is_empty()
|
||||
}
|
||||
|
||||
/// Returns false if the trie does not contain a prefix of the given word.
|
||||
/// Returns true if the trie *may* contain a prefix of the given word.
|
||||
///
|
||||
/// Moves the search start to the first node equal to the first letter of the word,
|
||||
/// or to 0 otherwise.
|
||||
fn set_search_start(&self, word: &[u8], search_start: &mut PrefixTrieNodeSearchStart) -> bool {
|
||||
let byte = word[0];
|
||||
if self.children[search_start.0].1 == byte {
|
||||
return true;
|
||||
} else {
|
||||
match self.children[search_start.0..].binary_search_by_key(&byte, |x| x.1) {
|
||||
Ok(position) => {
|
||||
search_start.0 += position;
|
||||
true
|
||||
}
|
||||
Err(_) => {
|
||||
search_start.0 = 0;
|
||||
false
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Ok(())
|
||||
fn from_sorted_prefixes<'a>(prefixes: impl Iterator<Item = &'a str>) -> Self {
|
||||
let mut node = PrefixTrieNode::default();
|
||||
for prefix in prefixes {
|
||||
node.insert_sorted_prefix(prefix.as_bytes().into_iter());
|
||||
}
|
||||
node
|
||||
}
|
||||
fn insert_sorted_prefix(&mut self, mut prefix: std::slice::Iter<u8>) {
|
||||
if let Some(&c) = prefix.next() {
|
||||
if let Some((node, byte)) = self.children.last_mut() {
|
||||
if *byte == c {
|
||||
node.insert_sorted_prefix(prefix);
|
||||
return;
|
||||
}
|
||||
}
|
||||
let mut new_node = PrefixTrieNode::default();
|
||||
new_node.insert_sorted_prefix(prefix);
|
||||
self.children.push((new_node, c));
|
||||
} else {
|
||||
self.is_end_node = true;
|
||||
}
|
||||
}
|
||||
fn for_each_prefix_of(
|
||||
&self,
|
||||
word: &[u8],
|
||||
buffer: &mut Vec<u8>,
|
||||
search_start: &PrefixTrieNodeSearchStart,
|
||||
mut do_fn: impl FnMut(&mut Vec<u8>),
|
||||
) {
|
||||
let first_byte = word[0];
|
||||
let mut cur_node = self;
|
||||
buffer.push(first_byte);
|
||||
if let Some((child_node, c)) =
|
||||
cur_node.children[search_start.0..].iter().find(|(_, c)| *c >= first_byte)
|
||||
{
|
||||
if *c == first_byte {
|
||||
cur_node = child_node;
|
||||
if cur_node.is_end_node {
|
||||
do_fn(buffer);
|
||||
}
|
||||
for &byte in &word[1..] {
|
||||
buffer.push(byte);
|
||||
if let Some((child_node, c)) =
|
||||
cur_node.children.iter().find(|(_, c)| *c >= byte)
|
||||
{
|
||||
if *c == byte {
|
||||
cur_node = child_node;
|
||||
if cur_node.is_end_node {
|
||||
do_fn(buffer);
|
||||
}
|
||||
} else {
|
||||
break;
|
||||
}
|
||||
} else {
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
// fn print(&self, buffer: &mut String, ident: usize) {
|
||||
// let mut spaces = String::new();
|
||||
// for _ in 0..ident {
|
||||
// spaces.push(' ')
|
||||
// }
|
||||
// for (child, c) in &self.children {
|
||||
// buffer.push(char::from_u32(*c as u32).unwrap());
|
||||
// println!("{spaces}{buffer}:");
|
||||
// child.print(buffer, ident + 4);
|
||||
// buffer.pop();
|
||||
// }
|
||||
// }
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use roaring::RoaringBitmap;
|
||||
|
||||
use crate::{CboRoaringBitmapCodec, StrStrU8Codec};
|
||||
|
||||
use super::*;
|
||||
|
||||
use std::io::Cursor;
|
||||
|
||||
use crate::db_snap;
|
||||
|
|
@ -328,4 +758,181 @@ mod tests {
|
|||
|
||||
db_snap!(index, word_prefix_pair_proximity_docids, "update");
|
||||
}
|
||||
|
||||
fn check_prefixes(
|
||||
trie: &PrefixTrieNode,
|
||||
search_start: &PrefixTrieNodeSearchStart,
|
||||
word: &str,
|
||||
expected_prefixes: &[&str],
|
||||
) {
|
||||
let mut actual_prefixes = vec![];
|
||||
trie.for_each_prefix_of(word.as_bytes(), &mut Vec::new(), &search_start, |x| {
|
||||
let s = String::from_utf8(x.to_owned()).unwrap();
|
||||
actual_prefixes.push(s);
|
||||
});
|
||||
assert_eq!(actual_prefixes, expected_prefixes);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_trie() {
|
||||
let trie = PrefixTrieNode::from_sorted_prefixes(IntoIterator::into_iter([
|
||||
"1", "19", "2", "a", "ab", "ac", "ad", "al", "am", "an", "ap", "ar", "as", "at", "au",
|
||||
"b", "ba", "bar", "be", "bi", "bl", "bla", "bo", "br", "bra", "bri", "bro", "bu", "c",
|
||||
"ca", "car", "ce", "ch", "cha", "che", "chi", "ci", "cl", "cla", "co", "col", "com",
|
||||
"comp", "con", "cons", "cont", "cor", "cou", "cr", "cu", "d", "da", "de", "dec", "des",
|
||||
"di", "dis", "do", "dr", "du", "e", "el", "em", "en", "es", "ev", "ex", "exp", "f",
|
||||
"fa", "fe", "fi", "fl", "fo", "for", "fr", "fra", "fre", "fu", "g", "ga", "ge", "gi",
|
||||
"gl", "go", "gr", "gra", "gu", "h", "ha", "har", "he", "hea", "hi", "ho", "hu", "i",
|
||||
"im", "imp", "in", "ind", "ins", "int", "inte", "j", "ja", "je", "jo", "ju", "k", "ka",
|
||||
"ke", "ki", "ko", "l", "la", "le", "li", "lo", "lu", "m", "ma", "mal", "man", "mar",
|
||||
"mat", "mc", "me", "mi", "min", "mis", "mo", "mon", "mor", "mu", "n", "na", "ne", "ni",
|
||||
"no", "o", "or", "ou", "ov", "ove", "over", "p", "pa", "par", "pe", "per", "ph", "pi",
|
||||
"pl", "po", "pr", "pre", "pro", "pu", "q", "qu", "r", "ra", "re", "rec", "rep", "res",
|
||||
"ri", "ro", "ru", "s", "sa", "san", "sc", "sch", "se", "sh", "sha", "shi", "sho", "si",
|
||||
"sk", "sl", "sn", "so", "sp", "st", "sta", "ste", "sto", "str", "su", "sup", "sw", "t",
|
||||
"ta", "te", "th", "ti", "to", "tr", "tra", "tri", "tu", "u", "un", "v", "va", "ve",
|
||||
"vi", "vo", "w", "wa", "we", "wh", "wi", "wo", "y", "yo", "z",
|
||||
]));
|
||||
|
||||
let mut search_start = PrefixTrieNodeSearchStart(0);
|
||||
|
||||
let is_empty = !trie.set_search_start("affair".as_bytes(), &mut search_start);
|
||||
assert!(!is_empty);
|
||||
assert_eq!(search_start.0, 2);
|
||||
|
||||
check_prefixes(&trie, &search_start, "affair", &["a"]);
|
||||
check_prefixes(&trie, &search_start, "shampoo", &["s", "sh", "sha"]);
|
||||
|
||||
let is_empty = !trie.set_search_start("unique".as_bytes(), &mut search_start);
|
||||
assert!(!is_empty);
|
||||
assert_eq!(trie.children[search_start.0].1, b'u');
|
||||
|
||||
check_prefixes(&trie, &search_start, "unique", &["u", "un"]);
|
||||
|
||||
// NOTE: this should fail, because the search start is already beyong 'a'
|
||||
let is_empty = trie.set_search_start("abba".as_bytes(), &mut search_start);
|
||||
assert!(!is_empty);
|
||||
// search start is reset
|
||||
assert_eq!(search_start.0, 0);
|
||||
|
||||
let trie = PrefixTrieNode::from_sorted_prefixes(IntoIterator::into_iter([
|
||||
"arb", "arbre", "cat", "catto",
|
||||
]));
|
||||
check_prefixes(&trie, &search_start, "arbres", &["arb", "arbre"]);
|
||||
check_prefixes(&trie, &search_start, "cattos", &["cat", "catto"]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_execute_on_word_pairs_and_prefixes() {
|
||||
let prefixes = PrefixTrieNode::from_sorted_prefixes(IntoIterator::into_iter([
|
||||
"arb", "arbre", "cat", "catto",
|
||||
]));
|
||||
|
||||
let mut serialised_bitmap123 = vec![];
|
||||
let mut bitmap123 = RoaringBitmap::new();
|
||||
bitmap123.insert(1);
|
||||
bitmap123.insert(2);
|
||||
bitmap123.insert(3);
|
||||
CboRoaringBitmapCodec::serialize_into(&bitmap123, &mut serialised_bitmap123);
|
||||
|
||||
let mut serialised_bitmap456 = vec![];
|
||||
let mut bitmap456 = RoaringBitmap::new();
|
||||
bitmap456.insert(4);
|
||||
bitmap456.insert(5);
|
||||
bitmap456.insert(6);
|
||||
CboRoaringBitmapCodec::serialize_into(&bitmap456, &mut serialised_bitmap456);
|
||||
|
||||
let mut serialised_bitmap789 = vec![];
|
||||
let mut bitmap789 = RoaringBitmap::new();
|
||||
bitmap789.insert(7);
|
||||
bitmap789.insert(8);
|
||||
bitmap789.insert(9);
|
||||
CboRoaringBitmapCodec::serialize_into(&bitmap789, &mut serialised_bitmap789);
|
||||
|
||||
let mut serialised_bitmap_ranges = vec![];
|
||||
let mut bitmap_ranges = RoaringBitmap::new();
|
||||
bitmap_ranges.insert_range(63_000..65_000);
|
||||
bitmap_ranges.insert_range(123_000..128_000);
|
||||
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),
|
||||
];
|
||||
|
||||
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)),
|
||||
];
|
||||
|
||||
let mut result = vec![];
|
||||
|
||||
let mut allocations = Allocations::default();
|
||||
let mut iter =
|
||||
IntoIterator::into_iter(word_pairs).map(|((word1, word2, proximity), data)| {
|
||||
((word1.as_bytes(), word2.as_bytes(), proximity), data.as_slice())
|
||||
});
|
||||
execute_on_word_pairs_and_prefixes(
|
||||
&mut iter,
|
||||
|iter| Ok(iter.next()),
|
||||
&prefixes,
|
||||
&mut allocations,
|
||||
2,
|
||||
|k, v| {
|
||||
let (word1, prefix, proximity) = StrStrU8Codec::bytes_decode(k).unwrap();
|
||||
let bitmap = CboRoaringBitmapCodec::bytes_decode(v).unwrap();
|
||||
result.push(((word1.to_owned(), prefix.to_owned(), proximity.to_owned()), bitmap));
|
||||
Ok(())
|
||||
},
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
for (x, y) in result.into_iter().zip(IntoIterator::into_iter(expected_result)) {
|
||||
let ((actual_word1, actual_prefix, actual_proximity), actual_bitmap) = x;
|
||||
let ((expected_word1, expected_prefix, expected_proximity), expected_bitmap) = y;
|
||||
|
||||
assert_eq!(actual_word1, expected_word1);
|
||||
assert_eq!(actual_prefix, expected_prefix);
|
||||
assert_eq!(actual_proximity, expected_proximity);
|
||||
assert_eq!(actual_bitmap, expected_bitmap);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,709 +0,0 @@
|
|||
use crate::update::index_documents::{
|
||||
create_writer, merge_cbo_roaring_bitmaps, CursorClonableMmap,
|
||||
};
|
||||
use crate::{CboRoaringBitmapCodec, Index, Result, UncheckedStrStrU8Codec};
|
||||
use grenad::CompressionType;
|
||||
use heed::types::ByteSlice;
|
||||
use heed::BytesDecode;
|
||||
use log::debug;
|
||||
use std::borrow::Cow;
|
||||
use std::collections::HashSet;
|
||||
use std::io::BufReader;
|
||||
|
||||
pub struct WordPrefixPairProximityDocids<'t, 'u, 'i> {
|
||||
wtxn: &'t mut heed::RwTxn<'i, 'u>,
|
||||
index: &'i Index,
|
||||
pub(crate) chunk_compression_type: CompressionType,
|
||||
pub(crate) chunk_compression_level: Option<u32>,
|
||||
pub(crate) max_nb_chunks: Option<usize>,
|
||||
pub(crate) max_memory: Option<usize>,
|
||||
max_proximity: u8,
|
||||
max_prefix_length: usize,
|
||||
}
|
||||
|
||||
impl<'t, 'u, 'i> WordPrefixPairProximityDocids<'t, 'u, 'i> {
|
||||
pub fn new(
|
||||
wtxn: &'t mut heed::RwTxn<'i, 'u>,
|
||||
index: &'i Index,
|
||||
) -> WordPrefixPairProximityDocids<'t, 'u, 'i> {
|
||||
WordPrefixPairProximityDocids {
|
||||
wtxn,
|
||||
index,
|
||||
chunk_compression_type: CompressionType::None,
|
||||
chunk_compression_level: None,
|
||||
max_nb_chunks: None,
|
||||
max_memory: None,
|
||||
max_proximity: 4,
|
||||
max_prefix_length: 2,
|
||||
}
|
||||
}
|
||||
|
||||
/// Set the maximum proximity required to make a prefix be part of the words prefixes
|
||||
/// database. If two words are too far from the threshold the associated documents will
|
||||
/// not be part of the prefix database.
|
||||
///
|
||||
/// Default value is 4. This value must be lower or equal than 7 and will be clamped
|
||||
/// to this bound otherwise.
|
||||
pub fn max_proximity(&mut self, value: u8) -> &mut Self {
|
||||
self.max_proximity = value.max(7);
|
||||
self
|
||||
}
|
||||
|
||||
/// Set the maximum length the prefix of a word pair is allowed to have to be part of the words
|
||||
/// prefixes database. If the prefix length is higher than the threshold, the associated documents
|
||||
/// will not be part of the prefix database.
|
||||
///
|
||||
/// Default value is 2.
|
||||
pub fn max_prefix_length(&mut self, value: usize) -> &mut Self {
|
||||
self.max_prefix_length = value;
|
||||
self
|
||||
}
|
||||
|
||||
#[logging_timer::time("WordPrefixPairProximityDocids::{}")]
|
||||
pub fn execute<'a>(
|
||||
mut self,
|
||||
new_word_pair_proximity_docids: grenad::Reader<CursorClonableMmap>,
|
||||
new_prefix_fst_words: &'a [String],
|
||||
common_prefix_fst_words: &[&'a [String]],
|
||||
del_prefix_fst_words: &HashSet<Vec<u8>>,
|
||||
) -> Result<()> {
|
||||
debug!("Computing and writing the word prefix pair proximity docids into LMDB on disk...");
|
||||
|
||||
// This is an optimisation, to reuse allocations between loop iterations
|
||||
let mut allocations = Allocations::default();
|
||||
|
||||
// Make a prefix trie from the common prefixes that are shorter than self.max_prefix_length
|
||||
let prefixes = PrefixTrieNode::from_sorted_prefixes(
|
||||
common_prefix_fst_words
|
||||
.into_iter()
|
||||
.map(|s| s.into_iter())
|
||||
.flatten()
|
||||
.map(|s| s.as_str())
|
||||
.filter(|s| s.len() <= self.max_prefix_length),
|
||||
);
|
||||
|
||||
// If the prefix trie is not empty, then we can iterate over all new
|
||||
// word pairs to look for new (word1, common_prefix, proximity) elements
|
||||
// to insert in the DB
|
||||
if !prefixes.is_empty() {
|
||||
let mut cursor = new_word_pair_proximity_docids.into_cursor()?;
|
||||
// This is the core of the algorithm
|
||||
execute_on_word_pairs_and_prefixes(
|
||||
// the first two arguments tell how to iterate over the new word pairs
|
||||
&mut cursor,
|
||||
|cursor| {
|
||||
if let Some((key, value)) = cursor.move_on_next()? {
|
||||
let (word1, word2, proximity) = UncheckedStrStrU8Codec::bytes_decode(key)
|
||||
.ok_or(heed::Error::Decoding)?;
|
||||
Ok(Some(((word1, word2, proximity), value)))
|
||||
} else {
|
||||
Ok(None)
|
||||
}
|
||||
},
|
||||
&prefixes,
|
||||
&mut allocations,
|
||||
self.max_proximity,
|
||||
// and this argument tells what to do with each new key (word1, prefix, proximity) and value (roaring bitmap)
|
||||
|key, value| {
|
||||
insert_into_database(
|
||||
&mut self.wtxn,
|
||||
*self.index.word_prefix_pair_proximity_docids.as_polymorph(),
|
||||
key,
|
||||
value,
|
||||
)
|
||||
},
|
||||
)?;
|
||||
}
|
||||
|
||||
// Now we do the same thing with the new prefixes and all word pairs in the DB
|
||||
|
||||
let prefixes = PrefixTrieNode::from_sorted_prefixes(
|
||||
new_prefix_fst_words
|
||||
.into_iter()
|
||||
.map(|s| s.as_str())
|
||||
.filter(|s| s.len() <= self.max_prefix_length),
|
||||
);
|
||||
|
||||
if !prefixes.is_empty() {
|
||||
let mut db_iter = self
|
||||
.index
|
||||
.word_pair_proximity_docids
|
||||
.remap_key_type::<UncheckedStrStrU8Codec>()
|
||||
.remap_data_type::<ByteSlice>()
|
||||
.iter(self.wtxn)?;
|
||||
|
||||
// Since we read the DB, we can't write to it directly, so we add each new (word1, prefix, proximity)
|
||||
// element in an intermediary grenad
|
||||
let mut writer = create_writer(
|
||||
self.chunk_compression_type,
|
||||
self.chunk_compression_level,
|
||||
tempfile::tempfile()?,
|
||||
);
|
||||
|
||||
execute_on_word_pairs_and_prefixes(
|
||||
&mut db_iter,
|
||||
|db_iter| db_iter.next().transpose().map_err(|e| e.into()),
|
||||
&prefixes,
|
||||
&mut allocations,
|
||||
self.max_proximity,
|
||||
|key, value| writer.insert(key, value).map_err(|e| e.into()),
|
||||
)?;
|
||||
drop(db_iter);
|
||||
|
||||
// and then we write the grenad into the DB
|
||||
// Since the grenad contains only new prefixes, we know in advance that none
|
||||
// of its elements already exist in the DB, thus there is no need to specify
|
||||
// how to merge conflicting elements
|
||||
write_into_lmdb_database_without_merging(
|
||||
self.wtxn,
|
||||
*self.index.word_prefix_pair_proximity_docids.as_polymorph(),
|
||||
writer,
|
||||
)?;
|
||||
}
|
||||
|
||||
// All of the word prefix pairs in the database that have a w2
|
||||
// that is contained in the `suppr_pw` set must be removed as well.
|
||||
if !del_prefix_fst_words.is_empty() {
|
||||
let mut iter = self
|
||||
.index
|
||||
.word_prefix_pair_proximity_docids
|
||||
.remap_data_type::<ByteSlice>()
|
||||
.iter_mut(self.wtxn)?;
|
||||
while let Some(((_, w2, _), _)) = iter.next().transpose()? {
|
||||
if del_prefix_fst_words.contains(w2.as_bytes()) {
|
||||
// Delete this entry as the w2 prefix is no more in the words prefix fst.
|
||||
unsafe { iter.del_current()? };
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
/// This is the core of the algorithm to initialise the Word Prefix Pair Proximity Docids database.
|
||||
///
|
||||
/// Its main arguments are:
|
||||
/// 1. a sorted iterator over ((word1, word2, proximity), docids) elements
|
||||
/// 2. a prefix trie
|
||||
/// 3. a closure to describe how to handle the new computed (word1, prefix, proximity) elements
|
||||
///
|
||||
/// For more information about the
|
||||
fn execute_on_word_pairs_and_prefixes<Iter>(
|
||||
iter: &mut Iter,
|
||||
mut next_word_pair_proximity: impl for<'a> FnMut(
|
||||
&'a mut Iter,
|
||||
) -> Result<
|
||||
Option<((&'a [u8], &'a [u8], u8), &'a [u8])>,
|
||||
>,
|
||||
prefixes: &PrefixTrieNode,
|
||||
allocations: &mut Allocations,
|
||||
max_proximity: u8,
|
||||
mut insert: impl for<'a> FnMut(&'a [u8], &'a [u8]) -> Result<()>,
|
||||
) -> Result<()> {
|
||||
let mut batch = PrefixAndProximityBatch::default();
|
||||
let mut prev_word2_start = 0;
|
||||
|
||||
let mut prefix_search_start = PrefixTrieNodeSearchStart(0);
|
||||
let mut empty_prefixes = false;
|
||||
|
||||
let mut prefix_buffer = allocations.take_byte_vector();
|
||||
let mut merge_buffer = allocations.take_byte_vector();
|
||||
|
||||
while let Some(((word1, word2, proximity), data)) = next_word_pair_proximity(iter)? {
|
||||
if proximity > max_proximity {
|
||||
continue;
|
||||
};
|
||||
let word2_start_different_than_prev = word2[0] != prev_word2_start;
|
||||
if empty_prefixes && !word2_start_different_than_prev {
|
||||
continue;
|
||||
}
|
||||
let word1_different_than_prev = word1 != batch.word1;
|
||||
if word1_different_than_prev || word2_start_different_than_prev {
|
||||
batch.flush(allocations, &mut merge_buffer, &mut insert)?;
|
||||
if word1_different_than_prev {
|
||||
prefix_search_start.0 = 0;
|
||||
batch.word1.clear();
|
||||
batch.word1.extend_from_slice(word1);
|
||||
}
|
||||
if word2_start_different_than_prev {
|
||||
// word2_start_different_than_prev == true
|
||||
prev_word2_start = word2[0];
|
||||
}
|
||||
empty_prefixes = !prefixes.set_search_start(word2, &mut prefix_search_start);
|
||||
}
|
||||
|
||||
if !empty_prefixes {
|
||||
prefixes.for_each_prefix_of(
|
||||
word2,
|
||||
&mut prefix_buffer,
|
||||
&prefix_search_start,
|
||||
|prefix_buffer| {
|
||||
let mut value = allocations.take_byte_vector();
|
||||
value.extend_from_slice(&data);
|
||||
let prefix_len = prefix_buffer.len();
|
||||
prefix_buffer.push(0);
|
||||
prefix_buffer.push(proximity);
|
||||
batch.insert(&prefix_buffer, value, allocations);
|
||||
prefix_buffer.truncate(prefix_len);
|
||||
},
|
||||
);
|
||||
prefix_buffer.clear();
|
||||
}
|
||||
}
|
||||
batch.flush(allocations, &mut merge_buffer, &mut insert)?;
|
||||
Ok(())
|
||||
}
|
||||
/**
|
||||
A map structure whose keys are (prefix, proximity) and whose values are vectors of bitstrings (serialized roaring bitmaps).
|
||||
The keys are sorted and conflicts are resolved by merging the vectors of bitstrings together.
|
||||
|
||||
It is used to ensure that all ((word1, prefix, proximity), 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
|
||||
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
|
||||
*/
|
||||
#[derive(Default)]
|
||||
struct PrefixAndProximityBatch {
|
||||
word1: Vec<u8>,
|
||||
batch: Vec<(Vec<u8>, Vec<Cow<'static, [u8]>>)>,
|
||||
}
|
||||
|
||||
impl PrefixAndProximityBatch {
|
||||
/// Insert the new key and value into the batch
|
||||
fn insert(&mut self, new_key: &[u8], new_value: Vec<u8>, allocations: &mut Allocations) {
|
||||
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) => {
|
||||
let mut key = allocations.take_byte_vector();
|
||||
key.extend_from_slice(new_key);
|
||||
let mut mergeable_data = allocations.take_mergeable_data_vector();
|
||||
mergeable_data.push(Cow::Owned(new_value));
|
||||
self.batch.insert(position, (key, mergeable_data));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// 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.
|
||||
fn flush(
|
||||
&mut self,
|
||||
allocations: &mut Allocations,
|
||||
merge_buffer: &mut Vec<u8>,
|
||||
insert: &mut impl for<'buffer> FnMut(&'buffer [u8], &'buffer [u8]) -> Result<()>,
|
||||
) -> Result<()> {
|
||||
let PrefixAndProximityBatch { word1, batch } = self;
|
||||
if batch.is_empty() {
|
||||
return Ok(());
|
||||
}
|
||||
merge_buffer.clear();
|
||||
|
||||
let mut buffer = allocations.take_byte_vector();
|
||||
buffer.extend_from_slice(word1);
|
||||
buffer.push(0);
|
||||
|
||||
for (key, mergeable_data) in batch.drain(..) {
|
||||
buffer.truncate(word1.len() + 1);
|
||||
buffer.extend_from_slice(key.as_slice());
|
||||
|
||||
let data = if mergeable_data.len() > 1 {
|
||||
CboRoaringBitmapCodec::merge_into(&mergeable_data, merge_buffer)?;
|
||||
merge_buffer.as_slice()
|
||||
} else {
|
||||
&mergeable_data[0]
|
||||
};
|
||||
insert(buffer.as_slice(), data)?;
|
||||
merge_buffer.clear();
|
||||
allocations.reclaim_byte_vector(key);
|
||||
allocations.reclaim_mergeable_data_vector(mergeable_data);
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
// This is adapted from `sorter_into_lmdb_database`
|
||||
fn insert_into_database(
|
||||
wtxn: &mut heed::RwTxn,
|
||||
database: heed::PolyDatabase,
|
||||
new_key: &[u8],
|
||||
new_value: &[u8],
|
||||
) -> Result<()> {
|
||||
let mut iter = database.prefix_iter_mut::<_, ByteSlice, ByteSlice>(wtxn, new_key)?;
|
||||
match iter.next().transpose()? {
|
||||
Some((key, old_val)) if new_key == key => {
|
||||
let val =
|
||||
merge_cbo_roaring_bitmaps(key, &[Cow::Borrowed(old_val), Cow::Borrowed(new_value)])
|
||||
.map_err(|_| {
|
||||
// TODO just wrap this error?
|
||||
crate::error::InternalError::IndexingMergingKeys {
|
||||
process: "get-put-merge",
|
||||
}
|
||||
})?;
|
||||
// safety: we don't keep references from inside the LMDB database.
|
||||
unsafe { iter.put_current(key, &val)? };
|
||||
}
|
||||
_ => {
|
||||
drop(iter);
|
||||
database.put::<_, ByteSlice, ByteSlice>(wtxn, new_key, new_value)?;
|
||||
}
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
// This is adapted from `sorter_into_lmdb_database` and `write_into_lmdb_database`,
|
||||
// but it uses `append` if the database is empty, and it assumes that the values in the
|
||||
// writer don't conflict with values in the database.
|
||||
pub fn write_into_lmdb_database_without_merging(
|
||||
wtxn: &mut heed::RwTxn,
|
||||
database: heed::PolyDatabase,
|
||||
writer: grenad::Writer<std::fs::File>,
|
||||
) -> Result<()> {
|
||||
let file = writer.into_inner()?;
|
||||
let reader = grenad::Reader::new(BufReader::new(file))?;
|
||||
if database.is_empty(wtxn)? {
|
||||
let mut out_iter = database.iter_mut::<_, ByteSlice, ByteSlice>(wtxn)?;
|
||||
let mut cursor = reader.into_cursor()?;
|
||||
while let Some((k, v)) = cursor.move_on_next()? {
|
||||
// safety: we don't keep references from inside the LMDB database.
|
||||
unsafe { out_iter.append(k, v)? };
|
||||
}
|
||||
} else {
|
||||
let mut cursor = reader.into_cursor()?;
|
||||
while let Some((k, v)) = cursor.move_on_next()? {
|
||||
database.put::<_, ByteSlice, ByteSlice>(wtxn, k, v)?;
|
||||
}
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
struct Allocations {
|
||||
byte_vectors: Vec<Vec<u8>>,
|
||||
mergeable_data_vectors: Vec<Vec<Cow<'static, [u8]>>>,
|
||||
}
|
||||
impl Default for Allocations {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
byte_vectors: Vec::with_capacity(65_536),
|
||||
mergeable_data_vectors: Vec::with_capacity(4096),
|
||||
}
|
||||
}
|
||||
}
|
||||
impl Allocations {
|
||||
fn take_byte_vector(&mut self) -> Vec<u8> {
|
||||
self.byte_vectors.pop().unwrap_or_else(|| Vec::with_capacity(16))
|
||||
}
|
||||
fn take_mergeable_data_vector(&mut self) -> Vec<Cow<'static, [u8]>> {
|
||||
self.mergeable_data_vectors.pop().unwrap_or_else(|| Vec::with_capacity(8))
|
||||
}
|
||||
|
||||
fn reclaim_byte_vector(&mut self, mut data: Vec<u8>) {
|
||||
data.clear();
|
||||
self.byte_vectors.push(data);
|
||||
}
|
||||
fn reclaim_mergeable_data_vector(&mut self, mut data: Vec<Cow<'static, [u8]>>) {
|
||||
data.clear();
|
||||
self.mergeable_data_vectors.push(data);
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Default, Debug)]
|
||||
struct PrefixTrieNode {
|
||||
children: Vec<(PrefixTrieNode, u8)>,
|
||||
is_end_node: bool,
|
||||
}
|
||||
|
||||
#[derive(Debug)]
|
||||
struct PrefixTrieNodeSearchStart(usize);
|
||||
|
||||
impl PrefixTrieNode {
|
||||
fn is_empty(&self) -> bool {
|
||||
self.children.is_empty()
|
||||
}
|
||||
|
||||
/// Returns false if the trie does not contain a prefix of the given word.
|
||||
/// Returns true if the trie *may* contain a prefix of the given word.
|
||||
///
|
||||
/// Moves the search start to the first node equal to the first letter of the word,
|
||||
/// or to 0 otherwise.
|
||||
fn set_search_start(&self, word: &[u8], search_start: &mut PrefixTrieNodeSearchStart) -> bool {
|
||||
let byte = word[0];
|
||||
if self.children[search_start.0].1 == byte {
|
||||
return true;
|
||||
} else {
|
||||
match self.children[search_start.0..].binary_search_by_key(&byte, |x| x.1) {
|
||||
Ok(position) => {
|
||||
search_start.0 += position;
|
||||
true
|
||||
}
|
||||
Err(_) => {
|
||||
search_start.0 = 0;
|
||||
false
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fn from_sorted_prefixes<'a>(prefixes: impl Iterator<Item = &'a str>) -> Self {
|
||||
let mut node = PrefixTrieNode::default();
|
||||
for prefix in prefixes {
|
||||
node.insert_sorted_prefix(prefix.as_bytes().into_iter());
|
||||
}
|
||||
node
|
||||
}
|
||||
fn insert_sorted_prefix(&mut self, mut prefix: std::slice::Iter<u8>) {
|
||||
if let Some(&c) = prefix.next() {
|
||||
if let Some((node, byte)) = self.children.last_mut() {
|
||||
if *byte == c {
|
||||
node.insert_sorted_prefix(prefix);
|
||||
return;
|
||||
}
|
||||
}
|
||||
let mut new_node = PrefixTrieNode::default();
|
||||
new_node.insert_sorted_prefix(prefix);
|
||||
self.children.push((new_node, c));
|
||||
} else {
|
||||
self.is_end_node = true;
|
||||
}
|
||||
}
|
||||
fn for_each_prefix_of(
|
||||
&self,
|
||||
word: &[u8],
|
||||
buffer: &mut Vec<u8>,
|
||||
search_start: &PrefixTrieNodeSearchStart,
|
||||
mut do_fn: impl FnMut(&mut Vec<u8>),
|
||||
) {
|
||||
let first_byte = word[0];
|
||||
let mut cur_node = self;
|
||||
buffer.push(first_byte);
|
||||
if let Some((child_node, c)) =
|
||||
cur_node.children[search_start.0..].iter().find(|(_, c)| *c >= first_byte)
|
||||
{
|
||||
if *c == first_byte {
|
||||
cur_node = child_node;
|
||||
if cur_node.is_end_node {
|
||||
do_fn(buffer);
|
||||
}
|
||||
for &byte in &word[1..] {
|
||||
buffer.push(byte);
|
||||
if let Some((child_node, c)) =
|
||||
cur_node.children.iter().find(|(_, c)| *c >= byte)
|
||||
{
|
||||
if *c == byte {
|
||||
cur_node = child_node;
|
||||
if cur_node.is_end_node {
|
||||
do_fn(buffer);
|
||||
}
|
||||
} else {
|
||||
break;
|
||||
}
|
||||
} else {
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
// fn print(&self, buffer: &mut String, ident: usize) {
|
||||
// let mut spaces = String::new();
|
||||
// for _ in 0..ident {
|
||||
// spaces.push(' ')
|
||||
// }
|
||||
// for (child, c) in &self.children {
|
||||
// buffer.push(char::from_u32(*c as u32).unwrap());
|
||||
// println!("{spaces}{buffer}:");
|
||||
// child.print(buffer, ident + 4);
|
||||
// buffer.pop();
|
||||
// }
|
||||
// }
|
||||
}
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use roaring::RoaringBitmap;
|
||||
|
||||
use crate::{CboRoaringBitmapCodec, StrStrU8Codec};
|
||||
|
||||
use super::*;
|
||||
|
||||
fn check_prefixes(
|
||||
trie: &PrefixTrieNode,
|
||||
search_start: &PrefixTrieNodeSearchStart,
|
||||
word: &str,
|
||||
expected_prefixes: &[&str],
|
||||
) {
|
||||
let mut actual_prefixes = vec![];
|
||||
trie.for_each_prefix_of(word.as_bytes(), &mut Vec::new(), &search_start, |x| {
|
||||
let s = String::from_utf8(x.to_owned()).unwrap();
|
||||
actual_prefixes.push(s);
|
||||
});
|
||||
assert_eq!(actual_prefixes, expected_prefixes);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_trie() {
|
||||
let trie = PrefixTrieNode::from_sorted_prefixes(IntoIterator::into_iter([
|
||||
"1", "19", "2", "a", "ab", "ac", "ad", "al", "am", "an", "ap", "ar", "as", "at", "au",
|
||||
"b", "ba", "bar", "be", "bi", "bl", "bla", "bo", "br", "bra", "bri", "bro", "bu", "c",
|
||||
"ca", "car", "ce", "ch", "cha", "che", "chi", "ci", "cl", "cla", "co", "col", "com",
|
||||
"comp", "con", "cons", "cont", "cor", "cou", "cr", "cu", "d", "da", "de", "dec", "des",
|
||||
"di", "dis", "do", "dr", "du", "e", "el", "em", "en", "es", "ev", "ex", "exp", "f",
|
||||
"fa", "fe", "fi", "fl", "fo", "for", "fr", "fra", "fre", "fu", "g", "ga", "ge", "gi",
|
||||
"gl", "go", "gr", "gra", "gu", "h", "ha", "har", "he", "hea", "hi", "ho", "hu", "i",
|
||||
"im", "imp", "in", "ind", "ins", "int", "inte", "j", "ja", "je", "jo", "ju", "k", "ka",
|
||||
"ke", "ki", "ko", "l", "la", "le", "li", "lo", "lu", "m", "ma", "mal", "man", "mar",
|
||||
"mat", "mc", "me", "mi", "min", "mis", "mo", "mon", "mor", "mu", "n", "na", "ne", "ni",
|
||||
"no", "o", "or", "ou", "ov", "ove", "over", "p", "pa", "par", "pe", "per", "ph", "pi",
|
||||
"pl", "po", "pr", "pre", "pro", "pu", "q", "qu", "r", "ra", "re", "rec", "rep", "res",
|
||||
"ri", "ro", "ru", "s", "sa", "san", "sc", "sch", "se", "sh", "sha", "shi", "sho", "si",
|
||||
"sk", "sl", "sn", "so", "sp", "st", "sta", "ste", "sto", "str", "su", "sup", "sw", "t",
|
||||
"ta", "te", "th", "ti", "to", "tr", "tra", "tri", "tu", "u", "un", "v", "va", "ve",
|
||||
"vi", "vo", "w", "wa", "we", "wh", "wi", "wo", "y", "yo", "z",
|
||||
]));
|
||||
|
||||
let mut search_start = PrefixTrieNodeSearchStart(0);
|
||||
|
||||
let is_empty = !trie.set_search_start("affair".as_bytes(), &mut search_start);
|
||||
assert!(!is_empty);
|
||||
assert_eq!(search_start.0, 2);
|
||||
|
||||
check_prefixes(&trie, &search_start, "affair", &["a"]);
|
||||
check_prefixes(&trie, &search_start, "shampoo", &["s", "sh", "sha"]);
|
||||
|
||||
let is_empty = !trie.set_search_start("unique".as_bytes(), &mut search_start);
|
||||
assert!(!is_empty);
|
||||
assert_eq!(trie.children[search_start.0].1, b'u');
|
||||
|
||||
check_prefixes(&trie, &search_start, "unique", &["u", "un"]);
|
||||
|
||||
// NOTE: this should fail, because the search start is already beyong 'a'
|
||||
let is_empty = trie.set_search_start("abba".as_bytes(), &mut search_start);
|
||||
assert!(!is_empty);
|
||||
// search start is reset
|
||||
assert_eq!(search_start.0, 0);
|
||||
|
||||
let trie = PrefixTrieNode::from_sorted_prefixes(IntoIterator::into_iter([
|
||||
"arb", "arbre", "cat", "catto",
|
||||
]));
|
||||
check_prefixes(&trie, &search_start, "arbres", &["arb", "arbre"]);
|
||||
check_prefixes(&trie, &search_start, "cattos", &["cat", "catto"]);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_execute_on_word_pairs_and_prefixes() {
|
||||
let prefixes = PrefixTrieNode::from_sorted_prefixes(IntoIterator::into_iter([
|
||||
"arb", "arbre", "cat", "catto",
|
||||
]));
|
||||
|
||||
let mut serialised_bitmap123 = vec![];
|
||||
let mut bitmap123 = RoaringBitmap::new();
|
||||
bitmap123.insert(1);
|
||||
bitmap123.insert(2);
|
||||
bitmap123.insert(3);
|
||||
CboRoaringBitmapCodec::serialize_into(&bitmap123, &mut serialised_bitmap123);
|
||||
|
||||
let mut serialised_bitmap456 = vec![];
|
||||
let mut bitmap456 = RoaringBitmap::new();
|
||||
bitmap456.insert(4);
|
||||
bitmap456.insert(5);
|
||||
bitmap456.insert(6);
|
||||
CboRoaringBitmapCodec::serialize_into(&bitmap456, &mut serialised_bitmap456);
|
||||
|
||||
let mut serialised_bitmap789 = vec![];
|
||||
let mut bitmap789 = RoaringBitmap::new();
|
||||
bitmap789.insert(7);
|
||||
bitmap789.insert(8);
|
||||
bitmap789.insert(9);
|
||||
CboRoaringBitmapCodec::serialize_into(&bitmap789, &mut serialised_bitmap789);
|
||||
|
||||
let mut serialised_bitmap_ranges = vec![];
|
||||
let mut bitmap_ranges = RoaringBitmap::new();
|
||||
bitmap_ranges.insert_range(63_000..65_000);
|
||||
bitmap_ranges.insert_range(123_000..128_000);
|
||||
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),
|
||||
];
|
||||
|
||||
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)),
|
||||
];
|
||||
|
||||
let mut result = vec![];
|
||||
|
||||
let mut allocations = Allocations::default();
|
||||
let mut iter =
|
||||
IntoIterator::into_iter(word_pairs).map(|((word1, word2, proximity), data)| {
|
||||
((word1.as_bytes(), word2.as_bytes(), proximity), data.as_slice())
|
||||
});
|
||||
execute_on_word_pairs_and_prefixes(
|
||||
&mut iter,
|
||||
|iter| Ok(iter.next()),
|
||||
&prefixes,
|
||||
&mut allocations,
|
||||
2,
|
||||
|k, v| {
|
||||
let (word1, prefix, proximity) = StrStrU8Codec::bytes_decode(k).unwrap();
|
||||
let bitmap = CboRoaringBitmapCodec::bytes_decode(v).unwrap();
|
||||
result.push(((word1.to_owned(), prefix.to_owned(), proximity.to_owned()), bitmap));
|
||||
Ok(())
|
||||
},
|
||||
)
|
||||
.unwrap();
|
||||
|
||||
for (x, y) in result.into_iter().zip(IntoIterator::into_iter(expected_result)) {
|
||||
let ((actual_word1, actual_prefix, actual_proximity), actual_bitmap) = x;
|
||||
let ((expected_word1, expected_prefix, expected_proximity), expected_bitmap) = y;
|
||||
|
||||
assert_eq!(actual_word1, expected_word1);
|
||||
assert_eq!(actual_prefix, expected_prefix);
|
||||
assert_eq!(actual_proximity, expected_proximity);
|
||||
assert_eq!(actual_bitmap, expected_bitmap);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1,144 +0,0 @@
|
|||
## What is WordPrefixPairProximityDocids?
|
||||
The word-prefix-pair-proximity-docids database is a database whose keys are of the form (`word`, `prefix`, `proximity`) and the values are roaring bitmaps of the documents which contain `word` followed by another word starting with `prefix` at a distance of `proximity`.
|
||||
|
||||
The prefixes present in this database are only those that correspond to many different words in the documents.
|
||||
|
||||
## How is it created/updated? (simplified version)
|
||||
To compute it, we have access to (mainly) two inputs:
|
||||
|
||||
* a list of sorted prefixes, such as:
|
||||
```
|
||||
c
|
||||
ca
|
||||
cat
|
||||
d
|
||||
do
|
||||
dog
|
||||
```
|
||||
Note that only prefixes which correspond to more than a certain number of different words from the database are included in this list.
|
||||
|
||||
* a sorted list of word pairs and the distance between them (i.e. proximity), associated with a roaring bitmap, such as:
|
||||
```
|
||||
good dog 3 -> docids1: [2, 5, 6]
|
||||
good doggo 1 -> docids2: [8]
|
||||
good dogma 1 -> docids3: [7, 19, 20]
|
||||
good ghost 2 -> docids4: [1]
|
||||
horror cathedral 4 -> docids5: [1, 2]
|
||||
```
|
||||
|
||||
I illustrate a simplified version of the algorithm to create the word-prefix-pair-proximity database below:
|
||||
|
||||
1. **Outer loop:** First, we iterate over each word pair and its proximity:
|
||||
```
|
||||
word1 : good
|
||||
word2 : dog
|
||||
proximity: 3
|
||||
```
|
||||
2. **Inner loop:** Then, we iterate over all the prefixes of `word2` that are in the list of sorted prefixes. And we insert the key (`prefix`, `proximity`) and the value (`docids`) to a sorted map which we call the “batch”. For example, at the end of the first inner loop, we may have:
|
||||
```
|
||||
Outer loop 1:
|
||||
------------------------------
|
||||
word1 : good
|
||||
word2 : dog
|
||||
proximity: 3
|
||||
docids : docids1
|
||||
|
||||
prefixes: [d, do, dog]
|
||||
|
||||
batch: [
|
||||
(d, 3) -> [docids1]
|
||||
(do, 3) -> [docids1]
|
||||
(dog, 3) -> [docids1]
|
||||
]
|
||||
```
|
||||
3. For illustration purpose, let's run through a second iteration of the outer loop:
|
||||
```
|
||||
Outer loop 2:
|
||||
------------------------------
|
||||
word1 : good
|
||||
word2 : doggo
|
||||
proximity: 1
|
||||
docids : docids2
|
||||
|
||||
prefixes: [d, do, dog]
|
||||
|
||||
batch: [
|
||||
(d, 1) -> [docids2]
|
||||
(d, 3) -> [docids1]
|
||||
(do, 1) -> [docids2]
|
||||
(do, 3) -> [docids1]
|
||||
(dog, 1) -> [docids2]
|
||||
(dog, 3) -> [docids1]
|
||||
]
|
||||
```
|
||||
Notice that the batch had to re-order some (`prefix`, `proximity`) keys: some of the elements inserted in the second iteration of the outer loop appear *before* elements from the first iteration.
|
||||
|
||||
4. And a third:
|
||||
```
|
||||
Outer loop 3:
|
||||
------------------------------
|
||||
word1 : good
|
||||
word2 : dogma
|
||||
proximity: 1
|
||||
docids : docids3
|
||||
|
||||
prefixes: [d, do, dog]
|
||||
|
||||
batch: [
|
||||
(d, 1) -> [docids2, docids3]
|
||||
(d, 3) -> [docids1]
|
||||
(do, 1) -> [docids2, docids3]
|
||||
(do, 3) -> [docids1]
|
||||
(dog, 1) -> [docids2, docids3]
|
||||
(dog, 3) -> [docids1]
|
||||
]
|
||||
```
|
||||
Notice that there were some conflicts which were resolved by merging the conflicting values together.
|
||||
|
||||
5. On the fourth iteration of the outer loop, we have:
|
||||
```
|
||||
Outer loop 4:
|
||||
------------------------------
|
||||
word1 : good
|
||||
word2 : ghost
|
||||
proximity: 2
|
||||
```
|
||||
Because `word2` begins with a different letter than the previous `word2`, we know that:
|
||||
1. All the prefixes of `word2` are greater than the prefixes of the previous word2
|
||||
2. And therefore, every instance of (`word2`, `prefix`) will be greater than any element in the batch.
|
||||
Therefore, we know that we can insert every element from the batch into the database before proceeding any further. This operation is called “flushing the batch”. Flushing the batch should also be done whenever `word1` is different than the previous `word1`.
|
||||
|
||||
6. **Flushing the batch:** to flush the batch, we look at the `word1` and iterate over the elements of the batch in sorted order:
|
||||
```
|
||||
Flushing Batch loop 1:
|
||||
------------------------------
|
||||
word1 : good
|
||||
word2 : d
|
||||
proximity: 1
|
||||
docids : [docids2, docids3]
|
||||
```
|
||||
We then merge the array of `docids` (of type `Vec<Vec<u8>>`) using `merge_cbo_roaring_bitmap` in order to get a single byte vector representing a roaring bitmap of all the document ids where `word1` is followed by `prefix` at a distance of `proximity`.
|
||||
Once we have done that, we insert (`word1`, `prefix`, `proximity`) -> `merged_docids` into the database.
|
||||
|
||||
7. That's it! ... except...
|
||||
|
||||
## How is it created/updated (continued)
|
||||
|
||||
I lied a little bit about the input data. In reality, we get two sets of the inputs described above, which come from different places:
|
||||
|
||||
* For the list of sorted prefixes, we have:
|
||||
* `new_prefixes`, which are all the prefixes that were not present in the database before the insertion of the new documents
|
||||
* `common_prefixes` which are the prefixes that are present both in the database and in the newly added documents
|
||||
|
||||
* For the list of word pairs and proximities, we have:
|
||||
* `new_word_pairs`, which is the list of word pairs and their proximities present in the newly added documents
|
||||
* `word_pairs_db`, which is the list of word pairs from the database. **This list includes all elements in `new_word_pairs`** since `new_word_pairs` was added to the database prior to calling the `WordPrefixPairProximityDocIds::execute` function.
|
||||
|
||||
To update the prefix database correctly, we call the algorithm described earlier first on (`common_prefixes`, `new_word_pairs`) and then on (`new_prefixes`, `word_pairs_db`). Thus:
|
||||
|
||||
1. For all the word pairs that were already present in the DB, we insert them again with the `new_prefixes`. Calling the algorithm on them with the `common_prefixes` would not result in any new data.
|
||||
3. For all the new word pairs, we insert them twice: first with the `common_prefixes`, and then, because they are part of `word_pairs_db`, with the `new_prefixes`.
|
||||
|
||||
Note, also, that since we read data from the database when iterating over `word_pairs_db`, we cannot insert the computed word-prefix-pair-proximity-docids from the batch directly into the database (we would have a concurrent reader and writer). Therefore, when calling the algorithm on (`new_prefixes`, `word_pairs_db`), we insert the computed ((`word`, `prefix`, `proximity`), `docids`) elements in an intermediary grenad Writer instead of the DB. At the end of the outer loop, we finally read from the grenad and insert its elements in the database.
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user