Using this tool you can hit the ground running, and make some useful queries over RDF data using simple JSON calls.

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:

import bikidata

r = bikidata.query({
    "filters": [
        {"p":"fts",
         "o":"something"
        }
    ]
})

or

r = bikidata.query({
    "filters": [
        { "p":"id",
          "o":"<http://example.com/id/123>"
        }
    ]
})

or

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

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 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:

pip install redis

And then you need to run the bikidata worker in a separate terminal:

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: