searchengines-web/MeiliSearch
searchengines-web/MeiliSearch
1
0
Fork 0
mirror of https://github.com/meilisearch/MeiliSearch synced 2024-12-25 22:20:06 +01:00
Code Issues Releases Wiki Activity

Merge pull request #22 from meilisearch/update-readme

Browse Source 
Update the README
This commit is contained in:
Clément Renault 2020-11-02 18:28:16 +01:00 committed by GitHub
commit 87902de010
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 18 additions and 31 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

49
README.md
View File

@ -2,7 +2,7 @@
<img alt="the milli logo" src="public/logo-black.svg"> <img alt="the milli logo" src="public/logo-black.svg">
</p> </p>
<p align="center">A concurrent indexer combined with fast and relevant search algorithms.</p> <p align="center">a concurrent indexer combined with fast and relevant search algorithms</p>
## Introduction ## Introduction
@ -10,46 +10,33 @@ This engine is a prototype, do not use it in production.
This is one of the most advanced search engine I have worked on. This is one of the most advanced search engine I have worked on.
It currently only supports the proximity criterion. It currently only supports the proximity criterion.
### Compile all the binaries ### Compile and Run the server
You can specify the number of threads to use to index documents and many other settings too.
```bash ```bash
cargo build --release --bins cargo run --release -- serve --db my-database.mdb -vvv --indexing-jobs 8
``` ```
## Indexing
It can index mass documents in no much time, I already achieved to index:
- 109m songs (song and artist name) in 21min and take 29GB on disk.
- 12m cities (name, timezone and country ID) in 3min13s and take 3.3GB on disk.
All of that on a 39$/month machine with 4cores.
### Index your documents ### Index your documents
You can feed the engine with your CSV data: It can index a massive amount of documents in not much time, I already achieved to index:
- 115m songs (song and artist name) in ~1h and take 107GB on disk.
- 12m cities (name, timezone and country ID) in 15min and take 10GB on disk.
All of that on a 39$/month machine with 4cores.
You can feed the engine with your CSV (comma-seperated, yes) data like this:
```bash ```bash
./target/release/indexer --db my-data.mmdb ../my-data.csv cat "name,age\nhello,32\nkiki,24\n" | http POST 127.0.0.1:9700/documents content-type:text/csv
``` ```
## Querying Here ids will be automatically generated as UUID v4 if they doesn't exist in some or every documents.
The engine is designed to handle very frequent words like any other word frequency. Note that it also support JSON and JSON streaming, you can send them to the engine by using
This is why you can search for "asia dubai" (the most common timezone) in the countries datasets in no time (59ms) even with 12m documents. the `content-type:application/json` and `content-type:application/x-ndjson` headers respectively.
We haven't modified the algorithm to handle queries that are scattered over multiple attributes, this is an open issue (#4). ### Querying the engine via the website
### Exposing a website to request the database You can query the engine by going to [the HTML page itself](http://127.0.0.1:9700).
Once you've indexed the dataset you will be able to access it with your brwoser.
```bash
./target/release/serve -l 0.0.0.0:8700 --db my-data.mmdb
```
## Gaps
There is many ways to make the engine search for too long and consume too much CPU.
This can for example be achieved by querying the engine for "the best of the do" on the songs and subreddits datasets.
There is plenty of way to improve the algorithms and there is and will be new issues explaining potential improvements.