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Thomas Payet
2019-11-21 11:18:54 +01:00
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README.md
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@ -1,6 +1,6 @@
# MeiliDB
[![Build Status](https://dev.azure.com/thomas0884/thomas/_apis/build/status/meilisearch.MeiliDB?branchName=master)](https://dev.azure.com/thomas0884/thomas/_build/latest?definitionId=1&branchName=master)
[![Build Status](https://github.com/meilisearch/MeiliDB/workflows/Cargo%20test/badge.svg)](https://dev.azure.com/thomas0884/thomas/_build/latest?definitionId=1&branchName=master)
[![dependency status](https://deps.rs/repo/github/meilisearch/MeiliDB/status.svg)](https://deps.rs/repo/github/meilisearch/MeiliDB)
[![License](https://img.shields.io/badge/license-commons%20clause-lightgrey)](https://commonsclause.com/)
@ -27,7 +27,7 @@ It uses [LMDB](https://en.wikipedia.org/wiki/Lightning_Memory-Mapped_Database) a
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!
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/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)
@ -47,7 +47,6 @@ If you have not installed Rust and its package manager `cargo` yet, go to [the i
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
```
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## 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.
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.
@ -128,7 +127,7 @@ 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/>
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