bors[bot] e1612fcb01
Merge #712
712: Fix bulk facet indexing bug r=Kerollmops a=loiclec

# Pull Request

## Related issue
Fixes (partially, until merged into meilisearch) https://github.com/meilisearch/meilisearch/issues/3165

## What does this PR do?
Fixes a bug where indexing certain numbers of filterable attribute values in bulk led to corrupted facet databases. This was due to a lossy integer conversion which would ultimately prevent entire levels of the facet database to be written into LMDB.

More specifically, this change was made:
```diff
      - if cur_writer_len as u8 >= self.min_level_size {
      + if cur_writer_len >= self.min_level_size as usize {
```
I also checked other comparisons to `min_level_size` and other conversions such as `x as u8` in this part of the codebase.



Co-authored-by: Loïc Lecrenier <loic.lecrenier@me.com>
2022-11-30 16:51:48 +00:00
2022-11-29 10:11:38 +07:00
2022-11-30 16:51:48 +00:00
2021-06-16 18:33:33 +02:00
2022-10-26 13:47:46 +02:00
2021-06-16 18:33:33 +02:00
2022-11-04 08:58:12 +09:00
2022-11-15 16:16:40 +01:00
2022-02-15 15:52:50 +01:00

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 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.

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