**MeiliSearch** is a powerful, fast, open-source, easy to use and deploy search engine. Both searching and indexing are highly customizable. Features such as typo-tolerance, filters, and synonyms are provided out-of-the-box.
If you have the Rust toolchain already installed on your local system, clone the repository and change it to your working directory. In the cloned repository, compile MeiliSearch.
Let's create an index! If you need a sample dataset, use [this movie database](https://www.notion.so/meilisearch/A-movies-dataset-to-test-Meili-1cbf7c9cfa4247249c40edfa22d7ca87#b5ae399b81834705ba5420ac70358a65). You can also find it in the `datasets/` directory.
You can access the web interface in your web browser at the root of the server. The default URL is [http://127.0.0.1:7700](http://127.0.0.1:7700). All you need to do is open your web browser and enter MeiliSearch’s address to visit it. This will lead you to a web page with a search bar that allows you to search in a given set of documents.
Now that your MeiliSearch server is up and running, you can learn more about how to tune your search engine in [the documentation](https://docs.meilisearch.com).
MeiliSearch uses [LMDB](https://en.wikipedia.org/wiki/Lightning_Memory-Mapped_Database) as the internal key-value store. The key-value store allows to handle updates and queries with small memory and CPU overheads. The whole ranking system is [data oriented](https://github.com/meilisearch/MeiliSearch/issues/82) and ensures great performances.
You can read [this document](deep-dive.md) if you want to dive deeper into the engine. The whole process of generating updates and handling queries is described in it. Besides, to learn the default rules used for sorting documents, you can take a look at this [typos and ranking rules explanation](typos-ranking-rules.md).
- Provides [6 default ranking criteria](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/criterion/mod.rs#L106-L111) used to [bucket sort](https://en.wikipedia.org/wiki/Bucket_sort) documents
- Accepts [custom criteria](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/criterion/mod.rs#L20-L29) and can apply them in any custom order
- Can [distinct](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/query_builder.rs#L324-L329) and [filter](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/query_builder.rs#L313-L318) returned documents based on context defined rules
- Searches for [concatenated](https://github.com/meilisearch/MeiliSearch/pull/164) and [splitted query words](https://github.com/meilisearch/MeiliSearch/pull/232) to improve the search quality.
- Can store complete documents or only [user schema specified fields](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/datasets/movies/schema.toml)
- The [default tokenizer](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-tokenizer/src/lib.rs) can index Latin and Kanji based languages
- Returns [the matching text areas](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-types/src/lib.rs#L49-L65), useful to highlight matched words in results
- Accepts query time search config like the [searchable attributes](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/query_builder.rs#L331-L336)
When processing a dataset composed of _100 353_ documents with _352_ attributes each and _3_ of them indexed, which means more than _300 000_ fields indexed for _35 million_ stored, MeiliSearch is able to carry out more than _2.8k req/sec_ with an average response time of _9 ms_ on an Intel i7-7700 (8) @ 4.2GHz.
We also indexed a dataset containing about _12 millions_ cities names in _24 minutes_ on a _8 cores_, _64 GB of RAM_, and a _300 GB NMVe_ SSD machine.<br/>
The size of the resulting database reached _16 GB_ and search results were presented between _30 ms_ and _4 seconds_ for short prefix queries.
In Rust 1.32, the allocator has been [changed to use the system allocator](https://blog.rust-lang.org/2019/01/17/Rust-1.32.0.html#jemalloc-is-removed-by-default).
We observed significant performance improvements when [using jemalloc as the global allocator](https://github.com/alexcrichton/jemallocator#documentation).
Hey! We're glad you're thinking about contributing to MeiliSearch! If you think something is missing or could be improved, please open issues and pull requests. If you'd like to help this project grow, we'd love to have you! To start contributing, checking [issues tagged as "good-first-issue"](https://github.com/meilisearch/MeiliSearch/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) is a good start!
