meilisearch/README.md

<p align="center">
  <img src="assets/logo.svg" alt="MeiliSearch" width="200" height="200" />
</p>

<h1 align="center">MeiliSearch</h1>

<h4 align="center">
  <a href="https://www.meilisearch.com">Website</a> | 
  <a href="https://blog.meilisearch.com">Blog</a> | 
  <a href="https://fr.linkedin.com/company/meilisearch">LinkedIn</a> | 
  <a href="https://twitter.com/meilisearch">Twitter</a> | 
  <a href="https://docs.meilisearch.com">Documentation</a> | 
  <a href="https://docs.meilisearch.com/resources/faq.html">FAQ</a>
</h4>

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  <a href="https://github.com/meilisearch/MeiliSearch/actions"><img src="https://github.com/meilisearch/MeiliSearch/workflows/Cargo%20test/badge.svg" alt="Build Status"></a>
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</p>

<p align="center">⚡ Lightning Fast, Ultra Relevant, and Typo-Tolerant Search Engine 🔍</p>

**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/).

<p align="center">
  <a href="https://crates.meilisearch.com"><img src="assets/crates-io-demo.gif" alt="crates.io demo gif" /></a>
</p>

> MeiliSearch 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 characters
* 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 -p 7700:7700 -v $(pwd)/data.ms:/data.ms 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
```

#### 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.

<p align="center">
  <img src="assets/movies-web-demo.gif" alt="Web interface gif" />
</p>

### 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 based languages and Kanji characters
- 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)

## Performance

When processing a dataset composed of 5M books, each with their own titles and authors, MeiliSearch is able to carry out more than 553 req/sec with an average response time of 21 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://1.2.3.4:7700
  2 threads and 10 connections
  Thread Stats   Avg      Stdev     Max   +/- Stdev
    Latency    21.45ms   15.64ms 214.10ms   85.95%
    Req/Sec   256.48     37.66   330.00     69.50%
  5132 requests in 10.05s, 2.31MB read
Requests/sec:    510.46
Transfer/sec:    234.77KB
```

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.

## Contributing

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!

### Analytic Events

Once a day, events are being sent to our Amplitude instance so we can know how many people are using MeiliSearch.<br/>
Only information about the platform on which the server runs is stored. No other information is being sent.<br/>
If this doesn't suit you, you can disable these analytics by using the `MEILI_NO_ANALYTICS` env variable.

## Contact

Feel free to contact us about any questions you may have:
* At [bonjour@meilisearch.com](mailto:bonjour@meilisearch.com): English or French is welcome! 🇬🇧 🇫🇷
* Via the chat box available on every page of [our documentation](https://docs.meilisearch.com/) and on [our landing page](https://www.meilisearch.com/).
* Join our [Slack community](https://slack.meilisearch.com/).
* By opening an issue.

Any suggestion or feedback is highly appreciated. Thank you for your support!