Merge pull request #195 from meilisearch/update-readme

Update the README
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Clément Renault 2019-09-19 12:01:09 +02:00 committed by GitHub
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@ -59,35 +59,24 @@ We have seen much better performances when [using jemalloc as the global allocat
## Usage and examples ## Usage and examples
You can try a little part of MeiliDB with the following commands. Currently MeiliDB do not provide an http server but you can run these two examples to try it out.
It creates an index named _movies_ and insert two great Tarantino movies in it.
It creates an index named _movies_ and insert _19 700_ (in batches of _1000_) movies into it.
```bash ```bash
cargo run --release cargo run --release --example create-database -- \
--schema examples/movies/schema-movies.toml \
curl -XPOST 'http://127.0.0.1:8000/movies' \ --update-group-size 1000 \
-d ' movies.mdb \
identifier = "id" examples/movies/movies.csv
[attributes.id]
stored = true
[attributes.title]
stored = true
indexed = true
'
curl -H 'Content-Type: application/json' \
-XPUT 'http://127.0.0.1:8000/movies' \
-d '{ "id": 123, "title": "Inglorious Bastards" }'
curl -H 'Content-Type: application/json' \
-XPUT 'http://127.0.0.1:8000/movies' \
-d '{ "id": 456, "title": "Django Unchained" }'
``` ```
Once the database is initialized you can query it by using the following command: Once this is done, you can query this database using the second binary example.
```bash ```bash
curl -XGET 'http://127.0.0.1:8000/movies/search?q=inglo' cargo run --release --example query-database -- \
movies.mdb \
--fetch-timeout-ms 50 \
-n 4 \
id title overview release_date poster
``` ```