c3f4835e8e
733: Avoid a prefix-related worst-case scenario in the proximity criterion r=loiclec a=loiclec # Pull Request ## Related issue Somewhat fixes (until merged into meilisearch) https://github.com/meilisearch/meilisearch/issues/3118 ## What does this PR do? When a query ends with a word and a prefix, such as: ``` word pr ``` Then we first determine whether `pre` *could possibly* be in the proximity prefix database before querying it. There are then three possibilities: 1. `pr` is not in any prefix cache because it is not the prefix of many words. We don't query the proximity prefix database. Instead, we list all the word derivations of `pre` through the FST and query the regular proximity databases. 2. `pr` is in the prefix cache but cannot be found in the proximity prefix databases. **In this case, we partially disable the proximity ranking rule for the pair `word pre`.** This is done as follows: 1. Only find the documents where `word` is in proximity to `pre` **exactly** (no derivations) 2. Otherwise, assume that their proximity in all the documents in which they coexist is >= 8 3. `pr` is in the prefix cache and can be found in the proximity prefix databases. In this case we simply query the proximity prefix databases. Note that if a prefix is longer than 2 bytes, then it cannot be in the proximity prefix databases. Also, proximities larger than 4 are not present in these databases either. Therefore, the impact on relevancy is: 1. For common prefixes of one or two letters: we no longer distinguish between proximities from 4 to 8 2. For common prefixes of more than two letters: we no longer distinguish between any proximities 3. For uncommon prefixes: nothing changes Regarding (1), it means that these two documents would be considered equally relevant according to the proximity rule for the query `heard pr` (IF `pr` is the prefix of more than 200 words in the dataset): ```json [ { "text": "I heard there is a faster proximity criterion" }, { "text": "I heard there is a faster but less relevant proximity criterion" } ] ``` Regarding (2), it means that two documents would be considered equally relevant according to the proximity rule for the query "faster pro": ```json [ { "text": "I heard there is a faster but less relevant proximity criterion" } { "text": "I heard there is a faster proximity criterion" }, ] ``` But the following document would be considered more relevant than the two documents above: ```json { "text": "I heard there is a faster swimmer who is competing in the pro section of the competition " } ``` Note, however, that this change of behaviour only occurs when using the set-based version of the proximity criterion. In cases where there are fewer than 1000 candidate documents when the proximity criterion is called, this PR does not change anything. --- ## Performance I couldn't use the existing search benchmarks to measure the impact of the PR, but I did some manual tests with the `songs` benchmark dataset. ``` 1. 10x 'a': - 640ms ⟹ 630ms = no significant difference 2. 10x 'b': - set-based: 4.47s ⟹ 7.42 = bad, ~2x regression - dynamic: 1s ⟹ 870 ms = no significant difference 3. 'Someone I l': - set-based: 250ms ⟹ 12 ms = very good, x20 speedup - dynamic: 21ms ⟹ 11 ms = good, x2 speedup 4. 'billie e': - set-based: 623ms ⟹ 2ms = very good, x300 speedup - dynamic: ~4ms ⟹ 4ms = no difference 5. 'billie ei': - set-based: 57ms ⟹ 20ms = good, ~2x speedup - dynamic: ~4ms ⟹ ~2ms. = no significant difference 6. 'i am getting o' - set-based: 300ms ⟹ 60ms = very good, 5x speedup - dynamic: 30ms ⟹ 6ms = very good, 5x speedup 7. 'prologue 1 a 1: - set-based: 3.36s ⟹ 120ms = very good, 30x speedup - dynamic: 200ms ⟹ 30ms = very good, 6x speedup 8. 'prologue 1 a 10': - set-based: 590ms ⟹ 18ms = very good, 30x speedup - dynamic: 82ms ⟹ 35ms = good, ~2x speedup ``` Performance is often significantly better, but there is also one regression in the set-based implementation with the query `b b b b b b b b b b`. Co-authored-by: Loïc Lecrenier <loic.lecrenier@me.com> |
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assets | ||
benchmarks | ||
cli | ||
filter-parser | ||
flatten-serde-json | ||
json-depth-checker | ||
milli | ||
script | ||
.gitignore | ||
.rustfmt.toml | ||
bors.toml | ||
Cargo.toml | ||
CONTRIBUTING.md | ||
LICENSE | ||
README.md |
a concurrent indexer combined with fast and relevant search algorithms
Introduction
This repository contains the core engine used in Meilisearch.
It contains a library that can manage one and only one index. Meilisearch manages the multi-index itself. Milli is unable to store updates in a store: it is the job of something else above and this is why it is only able to process one update at a time.
This repository contains crates to quickly debug the engine:
- There are benchmarks located in the
benchmarks
crate. - The
cli
crate is a simple command-line interface that helps run flamegraph on top of it. - The
filter-parser
crate contains the parser for the Meilisearch filter syntax. - The
flatten-serde-json
crate contains the library that flattens serde-jsonValue
objects like Elasticsearch does. - The
json-depth-checker
crate is used to indicate if a JSON must be flattened.
How to use it?
Milli is a library that does search things, it must be embedded in a program.
You can compute the documentation of it by using cargo doc --open
.
Here is an example usage of the library where we insert documents into the engine and search for one of them right after.
let path = tempfile::tempdir().unwrap();
let mut options = EnvOpenOptions::new();
options.map_size(10 * 1024 * 1024); // 10 MB
let index = Index::new(options, &path).unwrap();
let mut wtxn = index.write_txn().unwrap();
let content = documents!([
{
"id": 2,
"title": "Prideand Prejudice",
"author": "Jane Austin",
"genre": "romance",
"price$": "3.5$",
},
{
"id": 456,
"title": "Le Petit Prince",
"author": "Antoine de Saint-Exupéry",
"genre": "adventure",
"price$": "10.0$",
},
{
"id": 1,
"title": "Wonderland",
"author": "Lewis Carroll",
"genre": "fantasy",
"price$": "25.99$",
},
{
"id": 4,
"title": "Harry Potter ing fantasy\0lood Prince",
"author": "J. K. Rowling",
"genre": "fantasy\0",
},
]);
let config = IndexerConfig::default();
let indexing_config = IndexDocumentsConfig::default();
let mut builder =
IndexDocuments::new(&mut wtxn, &index, &config, indexing_config.clone(), |_| ())
.unwrap();
builder.add_documents(content).unwrap();
builder.execute().unwrap();
wtxn.commit().unwrap();
// You can search in the index now!
let mut rtxn = index.read_txn().unwrap();
let mut search = Search::new(&rtxn, &index);
search.query("horry");
search.limit(10);
let result = search.execute().unwrap();
assert_eq!(result.documents_ids.len(), 1);
Contributing
We're glad you're thinking about contributing to this repository! Feel free to pick an issue, and to ask any question you need. Some points might not be clear and we are available to help you!
Also, we recommend following the CONTRIBUTING.md to create your PR.