# bikiDATA
## Developer-friendly Queries over RDF triples
Using this tool you can hit the ground running, and make some useful queries over RDF data using simple JSON calls.
### Getting started TL;DR
You have a n-triple file called `myfile.nt`.
Install bikidata by saying `pip install bikidata`
Import it into bikidata with: `python -m bikidata myfile.nt`
And now, in a python prompt, you can query things, for example:
```python
import bikidata
r = bikidata.query({
"filters": [
{"p":"fts",
"o":"something"
}
]
})
```
or
```python
r = bikidata.query({
"filters": [
{ "p":"id",
"o":"<http://example.com/id/123>"
}
]
})
```
or
```python
r = bikidata.query({
"filters": [
{"p":"fts",
"o":"something"
},
{ "op":"not",
"p":"<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>",
"o":"<https://swapi.co/vocabulary/Species>"
}
]
})
```
For more examples, see the file: [examples.ipynb](examples.ipynb)
# Redis support
When querying non-trivial datasets of a few million triples, or handling many concurrent users, we do not want to open a new database connection for each query, and cache the results in memory.
By using [Redis](https://redis.io/) we can scale the number of bikidata workers horizontally, and share the cache between them.
The `query_async` function can now be awaited in an async context. This function places the query in a Redis queue, and awaits the result.
To use this, you need to have a Redis server running, and install the `redis` python package:
```bash
pip install redis
```
And then you need to run the bikidata worker in a separate terminal:
```bash
python -m bikidata worker
```
It is possible to run multiple workers, and they will share the load.
Then, in your code, you can await the `query_async` function in stead of the regular `query` function:
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