⚡ Ultra relevant and instant full-text search API 🔍
**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. For more information about features go to [our documentation](https://docs.meilisearch.com/). [](https://crates.meilisearch.com) > Meili helps the Rust community find crates on [crates.meilisearch.com](https://crates.meilisearch.com) ## Features * Search as-you-type experience (answers < 50 milliseconds) * Full-text search * Typo tolerant (understands typos and miss-spelling) * Supports Kanji * Supports Synonym * Easy to install, deploy, and maintain * Whole documents are returned * Highly customizable * RESTful API ## Get started ### Deploy the Server #### Run it using Docker ```bash docker run -it -p 7700:7700 --rm getmeili/meilisearch ``` #### Installing with Homebrew ```bash brew update && brew install meilisearch meilisearch ``` #### Installing with APT ```bash echo "deb [trusted=yes] https://apt.fury.io/meilisearch/ /" > /etc/apt/sources.list.d/fury.list apt update && apt install meilisearch-http meilisearch ``` #### Download the binary ```bash curl -L https://install.meilisearch.com | sh ./meilisearch ``` #### Run it on heroku [](https://heroku.com/deploy?template=https://github.com/meilisearch/MeiliSearch) #### Compile and run it from sources If you have the Rust toolchain already installed on your local system, clone the repository and change it to your working directory. ```bash git clone https://github.com/meilisearch/MeiliSearch.git cd MeiliSearch ``` In the cloned repository, compile MeiliSearch. ```bash cargo run --release ``` ### Create an Index and Upload Some Documents 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. ```bash curl -L 'https://bit.ly/2PAcw9l' -o movies.json ``` MeiliSearch can serve multiple indexes, with different kinds of documents. It is required to create an index before sending documents to it. ```bash curl -i -X POST 'http://127.0.0.1:7700/indexes' --data '{ "name": "Movies", "uid": "movies" }' ``` Now that the server knows about your brand new index, you're ready to send it some data. ```bash curl -i -X POST 'http://127.0.0.1:7700/indexes/movies/documents' \ --header 'content-type: application/json' \ --data-binary @movies.json ``` ### Search for Documents #### In command line The search engine is now aware of your documents and can serve those via an HTTP server. The [`jq` command-line tool](https://stedolan.github.io/jq/) can greatly help you read the server responses. ```bash curl 'http://127.0.0.1:7700/indexes/movies/search?q=botman+robin&limit=2' | jq ``` ```json { "hits": [ { "id": "415", "title": "Batman & Robin", "poster": "https://image.tmdb.org/t/p/w1280/79AYCcxw3kSKbhGpx1LiqaCAbwo.jpg", "overview": "Along with crime-fighting partner Robin and new recruit Batgirl...", "release_date": "1997-06-20", }, { "id": "411736", "title": "Batman: Return of the Caped Crusaders", "poster": "https://image.tmdb.org/t/p/w1280/GW3IyMW5Xgl0cgCN8wu96IlNpD.jpg", "overview": "Adam West and Burt Ward returns to their iconic roles of Batman and Robin...", "release_date": "2016-10-08", } ], "offset": 0, "limit": 2, "processingTimeMs": 1, "query": "botman robin" } ``` #### Use the Web Interface We also deliver an **out-of-the-box web interface** in which you can test MeiliSearch interactively. 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 will allow you to search in the selected index. ### Documentation 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). ### Technical features - 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 - Supports [ranged queries](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/query_builder.rs#L342), useful for paginating results - 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) - Supports [runtime incremental indexing](https://github.com/meilisearch/MeiliSearch/blob/3ea5aa18a209b6973b921542d46a79e1c753c163/meilisearch-core/src/store/mod.rs#L143-L212) ## Performances 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. Requests are made using [wrk](https://github.com/wg/wrk) and scripted to simulate real users' queries. ``` Running 10s test @ http://localhost:2230 2 threads and 25 connections Thread Stats Avg Stdev Max +/- Stdev Latency 9.52ms 7.61ms 99.25ms 84.58% Req/Sec 1.41k 119.11 1.78k 64.50% 28080 requests in 10.01s, 7.42MB read Requests/sec: 2806.46 Transfer/sec: 759.17KB ``` 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.