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Charlotte Vermandel 2019-11-26 14:23:56 +01:00 committed by Thomas Payet
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@ -4,11 +4,28 @@
[![dependency status](https://deps.rs/repo/github/meilisearch/MeiliSearch/status.svg)](https://deps.rs/repo/github/meilisearch/MeiliSearch) [![dependency status](https://deps.rs/repo/github/meilisearch/MeiliSearch/status.svg)](https://deps.rs/repo/github/meilisearch/MeiliSearch)
[![License](https://img.shields.io/badge/license-MIT-informational)](https://github.com/meilisearch/MeiliSearch/blob/master/LICENSE) [![License](https://img.shields.io/badge/license-MIT-informational)](https://github.com/meilisearch/MeiliSearch/blob/master/LICENSE)
Ultra relevant and instant full-text search API. ⚡ 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. 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 synonyms.
## Features ## What MeiliSearch has to offer
* Search as-you-type experience (answers < 50ms)
* Full-text search
* Typo tolerant (understands typos and spelling mistakes)
* Supports Kanji
* Supports Synonym
* Easy to install, deploy, and maintain
* Whole documents returned
* Highly customizable
* RESTfull API
For more [details about those features, go to our documentation](https://docs.meilisearch.com/introduction/features.html).
[![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)
### In-depth features
- Provides [6 default ranking criteria](https://github.com/meilisearch/MeiliSearch/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilisearch-core/src/criterion/mod.rs#L107-L113) used to [bucket sort](https://en.wikipedia.org/wiki/Bucket_sort) documents - Provides [6 default ranking criteria](https://github.com/meilisearch/MeiliSearch/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilisearch-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/MeiliSearch/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilisearch-core/src/criterion/mod.rs#L24-L33) and can apply them in any custom order - Accepts [custom criteria](https://github.com/meilisearch/MeiliSearch/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilisearch-core/src/criterion/mod.rs#L24-L33) and can apply them in any custom order
@ -22,29 +39,16 @@ MeiliSearch is a powerful, fast, open-source, easy to use and deploy search engi
- Supports [runtime incremental indexing](https://github.com/meilisearch/MeiliSearch/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilisearch-core/src/store/mod.rs#L143-L173) - Supports [runtime incremental indexing](https://github.com/meilisearch/MeiliSearch/blob/dc5c42821e1340e96cb90a3da472264624a26326/meilisearch-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/MeiliSearch/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 glad 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/MeiliSearch/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 ## Quick Start
You can deploy your own instant, relevant and typo-tolerant MeiliSearch search engine by yourself too. You can deploy your instant, relevant, and typo-tolerant MeiliSearch search engine by yourself too.
Something similar to the demo above can be achieve by following these little three steps first. Something similar to the demo above can be achieved by following these little three steps first.
You will need to create your own web front display to make it pretty though. You still need to create your front-end to make it pretty, though.
### Deploy the Server ### 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/> If you have not yet installed Rust and its package manager `cargo`, 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. You can deploy the server on your machine; it listens to HTTP requests on the 8080 port by default.
```bash ```bash
cargo run --release cargo run --release
@ -65,7 +69,7 @@ curl -i -X POST 'http://127.0.0.1:8080/indexes' --data '{ "name": "Movies", "uid
``` ```
Now that the server knows about our brand new index, we can send it data. 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. We provided you a small dataset that is available in the `datasets/` directory.
```bash ```bash
curl -i -X POST 'http://127.0.0.1:8080/indexes/movies/documents' \ curl -i -X POST 'http://127.0.0.1:8080/indexes/movies/documents' \
@ -76,7 +80,7 @@ curl -i -X POST 'http://127.0.0.1:8080/indexes/movies/documents' \
### Search for Documents ### Search for Documents
The search engine is now aware of our documents and can serve those via our HTTP server again. 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. The [`jq` command-line tool](https://stedolan.github.io/jq/) can significantly help you read the server responses.
```bash ```bash
curl 'http://127.0.0.1:8080/indexes/movies/search?q=botman' curl 'http://127.0.0.1:8080/indexes/movies/search?q=botman'
@ -107,14 +111,12 @@ curl 'http://127.0.0.1:8080/indexes/movies/search?q=botman'
} }
``` ```
## Performances ## Performances
With a dataset composed of _100 353_ documents with _352_ attributes each and _3_ of them indexed. 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. 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. Requests are made using [wrk](https://github.com/wg/wrk) and scripted to simulate real users' queries.
``` ```
Running 10s test @ http://localhost:2230 Running 10s test @ http://localhost:2230
@ -127,7 +129,7 @@ Requests/sec: 2806.46
Transfer/sec: 759.17KB 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/> 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. The resulting database was _16 GB_ and search results were between _30 ms_ and _4 seconds_ for short prefix queries.
### Notes ### Notes
@ -135,28 +137,15 @@ The resulting database was _16 GB_ and search results were between _30 ms_ and _
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). 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). We have seen much better performances when [using jemalloc as the global allocator](https://github.com/alexcrichton/jemallocator#documentation).
## Usage and Examples ## How it works
MeiliSearch also provides an example binary that is mostly used for features testing. MeiliSearch 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/MeiliSearch/issues/82) and provides excellent great performances.
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 MeiliSearch. 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. Also, 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.
```bash ## Contributing
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. We will be glad 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/MeiliSearch/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22). It is a good start!
```bash
cargo run --release --example from_file -- \
search example.mdb \
--number-results 4 \
--filter '!adult' \
id popularity adult original_title
```
### Analytic Events ### Analytic Events