MeiliSearch/README.md

# MeiliDB

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Ultra relevant and instant full-text search API.

MeiliSearch is a powerful, fast, open-source, easy to use and deploy search engine. The search and indexation are fully customizable and handles features like typo-tolerance, filters, and ranking.

## Features

- Provides [6 default ranking criteria](https://github.com/meilisearch/MeiliDB/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilidb-core/src/criterion/mod.rs#L107-L113) used to [bucket sort](https://en.wikipedia.org/wiki/Bucket_sort) documents
- Accepts [custom criteria](https://github.com/meilisearch/MeiliDB/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilidb-core/src/criterion/mod.rs#L24-L33) and can apply them in any custom order
- Support [ranged queries](https://github.com/meilisearch/MeiliDB/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilidb-core/src/query_builder.rs#L283), useful for paginating results
- Can [distinct](https://github.com/meilisearch/MeiliDB/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilidb-core/src/query_builder.rs#L265-L270) and [filter](https://github.com/meilisearch/MeiliDB/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilidb-core/src/query_builder.rs#L246-L259) returned documents based on context defined rules
- Searches for [concatenated](https://github.com/meilisearch/MeiliDB/pull/164) and [splitted query words](https://github.com/meilisearch/MeiliDB/pull/232) to improve the search quality.
- Can store complete documents or only [user schema specified fields](https://github.com/meilisearch/MeiliDB/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilidb-schema/src/lib.rs#L265-L279)
- The [default tokenizer](https://github.com/meilisearch/MeiliDB/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilidb-tokenizer/src/lib.rs) can index latin and kanji based languages
- Returns [the matching text areas](https://github.com/meilisearch/MeiliDB/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilidb-core/src/lib.rs#L66-L88), useful to highlight matched words in results
- Accepts query time search config like the [searchable attributes](https://github.com/meilisearch/MeiliDB/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilidb-core/src/query_builder.rs#L272-L275)
- Supports [runtime incremental indexing](https://github.com/meilisearch/MeiliDB/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilidb-core/src/store/mod.rs#L143-L173)



It uses [LMDB](https://en.wikipedia.org/wiki/Lightning_Memory-Mapped_Database) as the internal key-value store. The key-value store allows us to handle updates and queries with small memory and CPU overheads. The whole ranking system is [data oriented](https://github.com/meilisearch/MeiliDB/issues/82) and provides great performances.

You can [read the deep dive](deep-dive.md) if you want more information on the engine, it describes the whole process of generating updates and handling queries or you can take a look at the [typos and ranking rules](typos-ranking-rules.md) if you want to know the default rules used to sort the documents.

We will be proud if you submit issues and pull requests. You can help to grow this project and start contributing by checking [issues tagged "good-first-issue"](https://github.com/meilisearch/MeiliDB/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22). It is a good start!

[![crates.io demo gif](misc/crates-io-demo.gif)](https://crates.meilisearch.com)

> Meili helps the Rust community find crates on [crates.meilisearch.com](https://crates.meilisearch.com)



## Quick Start

You can deploy your own instant, relevant and typo-tolerant MeiliDB search engine by yourself too.
Something similar to the demo above can be achieve by following these little three steps first.
You will need to create your own web front display to make it pretty though.

### Deploy the Server

If you have not installed Rust and its package manager `cargo` yet, go to [the installation page](https://www.rust-lang.org/tools/install).<br/>
You can deploy the server on your own machine, it will listen to HTTP requests on the 8080 port by default.

```bash
rustup override set nightly
cargo run --release
```

For more logs during the execution, run:
```bash
RUST_LOG=info cargo run --release
```

### Create an Index and Upload Some Documents

MeiliDB can serve multiple indexes, with different kinds of documents,
therefore, it is required to create the index before sending documents to it.

```bash
curl -i -X POST 'http://127.0.0.1:8080/indexes/movies'
```

Now that the server knows about our brand new index, we can send it data.
We provided you a little dataset, it is available in the `datasets/` directory.

```bash
curl -i -X POST 'http://127.0.0.1:8080/indexes/movies/documents' \
  --header 'content-type: application/json' \
  --data @datasets/movies/movies.json
```

### Search for Documents

The search engine is now aware of our documents and can serve those via our HTTP server again.
The [`jq` command line tool](https://stedolan.github.io/jq/) can greatly help you read the server responses.

```bash
curl 'http://127.0.0.1:8080/indexes/movies/search?q=botman'
```

```json
{
  "hits": [
    {
      "id": "29751",
      "title": "Batman Unmasked: The Psychology of the Dark Knight",
      "poster": "https://image.tmdb.org/t/p/w1280/jjHu128XLARc2k4cJrblAvZe0HE.jpg",
      "overview": "Delve into the world of Batman and the vigilante justice tha",
      "release_date": "2008-07-15"
    },
    {
      "id": "471474",
      "title": "Batman: Gotham by Gaslight",
      "poster": "https://image.tmdb.org/t/p/w1280/7souLi5zqQCnpZVghaXv0Wowi0y.jpg",
      "overview": "ve Victorian Age Gotham City, Batman begins his war on crime",
      "release_date": "2018-01-12"
    }
  ],
  "offset": 0,
  "limit": 2,
  "processingTimeMs": 1,
  "query": "botman"
}
```



## Performances

With a dataset composed of _100 353_ documents with _352_ attributes each and _3_ of them indexed.
So more than _300 000_ fields indexed for _35 million_ stored we can handle 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 something like _12 millions_ cities names in _24 minutes_ on a machine with _8 cores_, _64 GB of RAM_ and a _300 GB NMVe_ SSD.<br/>
The resulting database was _16 GB_ and search results were between _30 ms_ and _4 seconds_ for short prefix queries.

### Notes

With 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 have seen much better performances when [using jemalloc as the global allocator](https://github.com/alexcrichton/jemallocator#documentation).

## Usage and Examples

MeiliDB also provides an example binary that is mostly used for features testing.
Notice that the example binary is faster to index data as it does read direct CSV files and not JSON HTTP payloads.

The _index_ subcommand has been made to create an index and inject documents into it. Using the command line below, the index will be named _movies_ and the _19 700_ movies of the `datasets/` will be injected in MeiliDB.

```bash
cargo run --release --example from_file -- \
    index example.mdb datasets/movies/movies.csv \
    --schema datasets/movies/schema.toml
```

Once the first command is done, you can query the freshly created _movies_ index using the _search_ subcomand. In this example we filtered the dataset to only show _non-adult_ movies using the non-definitive `!adult` syntax filter.

```bash
cargo run --release --example from_file -- \
    search example.mdb \
    --number-results 4 \
    --filter '!adult' \
    id popularity adult original_title
```