QLever is a very fast SPARQL engine, much faster than most existing engines. It can handle graphs with more than hundred billion triples on a single machine with moderate resources. See https://qlever.cs.uni-freiburg.de for more information and many public SPARQL endpoints that use QLever
This project provides a Python script that can control everything that QLever does, in particular, creating SPARQL endpoints for arbitrary RDF datasets. It is supposed to be very easy to use and self-explanatory as you use it. In particular, the tool provides context-sensitive autocompletion of all its commands and options. If you use a container system (like Docker or Podman), you don't even have to download any QLever code, but the script will download the required image for you.
NOTE: There has been a major update on 24.03.2024, which changed some of the Qleverfile variables and command-line options (all for the better, of course). If you encounter any problems, please contact us by opening an issue on https://github.com/ad-freiburg/qlever-control/issues.
Simply do pip install qlever and make sure that the directory where pip
installs the package is in your PATH. Typically, pip will warn you when
that is not the case and tell you what to do.
Create an empty directory, with a name corresponding to the dataset you want to
work with. For the following example, take olympics. Go to that directory
and do the following. After the first call, qlever will tell you how to
activate autocompletion for all its commands and options (it's very easy, but
pip cannot do that automatically).
qlever setup-config olympics # Get Qleverfile (config file) for this dataset
qlever get-data # Download the dataset
qlever index # Build index data structures for this dataset
qlever start # Start a QLever server using that index
qlever example-queries # Launch some example queries
qlever ui # Launch the QLever UI
This will create a SPARQL endpoint for the 120 Years of Olympics dataset. It is a great dataset for getting started because it is small, but not trivial (around 2 million triples), and the downloading and indexing should only take a few seconds.
Each command will also show you the command line it uses. That way you can
learn, on the side, how QLever works internally. If you just want to know the
command line for a particular command, without executing it, you can append
--show like this:
qlever index --show
There are many more commands and options, see qlever --help for general help,
qlever <command> --help for help on a specific command, or just the
autocompletion.
To use QLever with your own dataset, you should also write a Qleverfile, like
in the example above. The easiest way to write a Qleverfile is to get one of
the existing ones (using qlever setup-config ... as explained above) and then
change it according to your needs (the variable names should be self-explanatory).
Pick one for a dataset that is similar to yours and when in doubt, pick olympics.
The (Python) code for the script is in the *.py files in src/qlever. The
preconfigured Qleverfiles are in src/qlever/Qleverfiles.
If you want to make changes to the script, or add new commands, do as follows:
git clone https://github.com/ad-freiburg/qlever-control
cd qlever-control
pip install -e .
Then you can use qlever just as if you had installed it via pip install qlever. Note that you don't have to rerun pip install -e . when you modify
any of the *.py files and not even when you add new commands in
src/qlever/commands. The exceutable created by pip simply links and refers
to the files in your working copy.
If you have bug fixes or new useful features or commands, please open a pull request. If you have questions or suggestions, please open an issue.