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@ -8,49 +8,47 @@ https://www.rust-lang.org)
A _full-text search database_ using a key-value store internally.
It uses [RocksDB](https://github.com/facebook/rocksdb) like a classic database, to store documents and internal data. The key-value store power allow us to handle updates and queries with small memory and CPU overheads.
It uses [RocksDB](https://github.com/facebook/rocksdb) as a built-in database, to store documents and internal data. The key-value store power allow us to handle updates and queries with small memory and CPU overheads.
You can [read the deep dive](deep-dive.md) if you want more informations on the engine, it describes the whole process of generating updates and handling queries.
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.
We will be proud if you send pull requests to help us grow this project, you can start with [issues tagged "good-first-issue"](https://github.com/Kerollmops/MeiliDB/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) to start !
We will be proud if you submit pull requests. It will help to help to grow this project, you can 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 a good start!
At the moment this is a library only, this means that binaries are not part of this repository but since I'm still nice I have made some examples for you in the `examples/` folder that works with the data located in the `misc/` folder.
At the moment this project is only a library. It means that it's not prividing yet any binaries. To get started, we provided some examples in the `examples/` folder that are made to work with the data located in the `misc/` folder.
In a near future MeiliDB we be a binary like any database: updated and queried using some kind of protocol. It is the final goal, [see the milestones](https://github.com/Kerollmops/MeiliDB/milestones). MeiliDB will just be a bunch of network and protocols functions wrapping the library which itself will be published to https://crates.io, following the same update cycle.
In a near future MeiliDB, we will provide a binary to execute this project as database, so you will be able to update and query it using a protocol. This will be our final goal, [see the milestones](https://github.com/Kerollmops/MeiliDB/milestones). At the end, MeiliDB will be a bunch of network protocols, and wrappers. We will publish the entire project on https://crates.io, following our usual update cycle.
## Performances
_these informations have been made with a version dated of october 2018, we must update them_
_these information are outdated (October 2018) It will be updated soon_
We made some tests on remote machines and found that we can handle with a dataset of near 280k products, on a server that cost 5$/month with 1vCPU and 1GB of ram and on the same index and with a simple query:
We made some tests on remote machines and found that MeiliDB easily handles a dataset of near 280k products, on a $5/month server with a single vCPU and 1GB of RAM, running the same index, with a simple query:
- near 190 users with an average response time of 90ms
- 150 users with an average response time of 70ms
- 100 users with an average response time of 45ms
- near 190 concurrent users with an average response time of 90ms
- 150 concurrent users with an average response time of 70ms
- 100 concurrent users with an average response time of 45ms
Network is mesured, servers are located in amsterdam and tests are made between two different datacenters.
Servers were located in Amsterdam and tests were made between two different locations.
### Notes
The default Rust allocator has recently been [changed to use the system allocator](https://github.com/rust-lang/rust/pull/51241/).
We have seen much better performances when [using jemalloc as the global allocator](https://github.com/alexcrichton/jemallocator#documentation).
## Usage and examples
MeiliDB work with an index like most of the search engines.
MeiliDB runs with an index like most search engines.
So to test the library you can create one by indexing a simple csv file.
```bash
cargo run --release --example create-database -- test.mdb misc/kaggle.csv
```
Once the command finished indexing the database should have been saved under the `test.mdb` folder.
Once the command is executed, the index should be in the `test.mdb` folder.
Now you can easily run the `query-database` example to check what is stored in it.
You are now able to run the `query-database` example, to play with MeiliDB.
```bash
cargo run --release --example query-database -- test.mdb