mirror of
https://github.com/meilisearch/MeiliSearch
synced 2024-07-01 16:53:18 +02:00
61 lines
3.3 KiB
Markdown
61 lines
3.3 KiB
Markdown
# MeiliDB
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[](https://travis-ci.org/Kerollmops/MeiliDB)
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[](https://deps.rs/repo/github/Kerollmops/MeiliDB)
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[](https://github.com/Kerollmops/MeiliDB)
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[](
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https://www.rust-lang.org)
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A _full-text search database_ using a key-value store internally.
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It uses [RocksDB](https://github.com/facebook/rocksdb) as the internal key-value store. The key-value store allows us to handle updates and queries with small memory and CPU overheads.
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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.
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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/Kerollmops/MeiliDB/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22). It is a good start!
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The project is only a library yet. It means that there is no binary provided yet. To get started, you can check the examples wich are made to work with the data located in the `misc/` folder.
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MeiliDB will be a binary in a near future so you will be able to use it as a database out-of-the-box. We should be able to query it using a [to-be-defined](https://github.com/Kerollmops/MeiliDB/issues/38) protocol. This is our current goal, [see the milestones](https://github.com/Kerollmops/MeiliDB/milestones). In the end, the binary will be a bunch of network protocols and wrappers around the library - which will also be published on [crates.io](https://crates.io). Both the binary and the library will follow the same update cycle.
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## Performances
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With a database composed of _100 353_ documents with _352_ attributes each and _90_ of them indexed.
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So nearly _9 million_ fields indexed for _35 million_ stored we can handle more than _1.2k req/sec_ on an Intel i7-7700 (8) @ 4.2GHz.
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Requests are made using [wrk](https://github.com/wg/wrk) and scripted to generate real users queries.
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```
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Running 10s test @ http://localhost:2230
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2 threads and 12 connections
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Thread Stats Avg Stdev Max +/- Stdev
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Latency 18.86ms 49.39ms 614.89ms 95.23%
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Req/Sec 620.41 59.53 790.00 65.00%
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12359 requests in 10.00s, 3.26MB read
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Requests/sec: 1235.54
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Transfer/sec: 334.22KB
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```
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### Notes
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The default Rust allocator has recently been [changed to use the system allocator](https://github.com/rust-lang/rust/pull/51241/).
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We have seen much better performances when [using jemalloc as the global allocator](https://github.com/alexcrichton/jemallocator#documentation).
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## Usage and examples
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MeiliDB runs with an index like most search engines.
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So to test the library you can create one by indexing a simple csv file.
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```bash
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cargo run --release --example create-database -- test.mdb misc/kaggle.csv --schema schema-example.toml
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```
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Once the command is executed, the index should be in the `test.mdb` folder. You are now able to run the `query-database` example and play with MeiliDB.
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```bash
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cargo run --release --example query-database -- test.mdb -n 10 id title
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```
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