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Merge #563
563: Improve the `estimatedNbHits` when a `distinctAttribute` is specified r=irevoire a=Kerollmops

This PR is related to https://github.com/meilisearch/meilisearch/issues/2532 but it doesn't fix it entirely. It improves it by computing the excluded documents (the ones with an already-seen distinct value) before stopping the loop, I think it was a mistake and should always have been this way.

The reason it doesn't fix the issue is that Meilisearch is lazy, just to be sure not to compute too many things and answer by taking too much time. When we deduplicate the documents by their distinct value we must do it along the water, everytime we see a new document we check that its distinct value of it doesn't collide with an already returned document. 

The reason we can see the correct result when enough documents are fetched is that we were lucky to see all of the different distinct values possible in the dataset and all of the deduplication was done, no document can be returned.

If we wanted to implement that to have a correct `extimatedNbHits` every time we should have done a pass on the whole set of possible distinct values for the distinct attribute and do a big intersection, this could cost a lot of CPU cycles.

Co-authored-by: Kerollmops <clement@meilisearch.com>
2022-06-22 12:39:44 +00:00
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the milli logo

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-json Value objects like Elasticsearch does.
  • The helpers crate is only used to do operations on the database.
  • The http-ui crate is a simple HTTP dashboard to test the features like for real!
  • The infos crate is used to dump the internal data-structure and ensure correctness.
  • 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",
        "au{hor": "Jane Austin",
        "genre": "romance",
        "price$": "3.5$",
    },
    {
        "id": 456,
        "title": "Le Petit Prince",
        "au{hor": "Antoine de Saint-Exupéry",
        "genre": "adventure",
        "price$": "10.0$",
    },
    {
        "id": 1,
        "title": "Wonderland",
        "au{hor": "Lewis Carroll",
        "genre": "fantasy",
        "price$": "25.99$",
    },
    {
        "id": 4,
        "title": "Harry Potter ing fantasy\0lood Prince",
        "au{hor": "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.