Important: Use the python-qlever branch of this repository. The main branch
only works with bash on certain Linux systems and is no longer maintained.
This is a very small repository. Its main contents is a script qlever
that can control everything that QLever does. The script is supposed to be very
easy to use and pretty much self-explanatory as you use it. If you use Docker, you
don't even have to download any QLever code (Docker will pull anything it needs)
and the script is all you need.
We recommend that you have a directory qlever for all things QLever on your machine,
with subdirectories for the different components, in particular: qlever-control (this
repository), qlever-indices (with a subfolder for each of your datasets), and qlever-code
(only needed if you want to compile the QLever binaries on your machine instead of using
Docker).
Make sure that the qlever-control directory (which contains the qlever script) is in
your PATH. If you have compiled QLever binaries, the directory qlever-code/build
(which contains these binaries) should also be in your PATH. Note that Docker is easier
to use, but typically 10 - 20% slower compared to using the binaries directly.
Create an empty directory as a subdirectory of qlever-indices (see the previous section),
go there, and call
eval "$(qlever setup-autocompletion)"
qlever setup-config olympics
The first line will enable autocompletion for the qlever script, which is very useful. You can also
put that line in your .bashrc (or similar file if you are using another shell). The second
line will create a Qleverfile preconfigured for the
120 Years of Olympics dataset, which is a great
dataset to get started because it is small. To see the list of all available configs, type
qlever help or just qlever. A dataset that is more interesting and larger, but can still
be downloaded and indexed in a matter of minutes is dblp. Have a look at the Qleverfile and see
whether the entries make sense (most of them are self-explanatory).
Now you can download the data, build an index for it (which QLever then uses to answer queries efficiently), start the server, and launch a test query as follows:
qlever get-data
qlever index
qlever start
qlever test-query
Each command will not only execute the respective action, but it will also show you the exact command line it uses. That way you can learn, on the side, how QLever works internally. If you just want to know the command used for a particular action, but not execute it, you can append "show" like this:
qlever index show
You can also perform a sequence of actions with a single call, for example:
qlever stop remove-index index start
There are many more actions. If you have enabled the autocompletion as described above,
you can just type qlever and then TAB and you will get a list of all the available
actions.