ATTENTION: développement arrêté en 2017 !Informations complémentaires (EN)
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This chapter discusses ways to tune Obnam performance for various situations. It covers the various options that can affect CPU and memory consumption, as well as ways to experiment to find a good set of values.
See http://obnam.org/faq/tuning/ (page no longer available) for a start.
Running Obnam under the Python profiler
A profiler is a program that measures where another program spends its time. This can be very useful for finding out why the other program is slow.
Obnam can easily be run under the Python profiler. You need to have the profiler installed. Check with your operating system or Python installation how to achieve that. To see if you have it installed, run the following command on the command line:
python -c 'import cProfile'
If this outputs nothing, all is well. If it outputs an error such as the following, you have not got the profiler installed:
Traceback (most recent call last): File "<string>", line 1, in <module> ImportError: No module named cProfiler
Once you have the profiler installed, run Obnam like this:
OBNAM_PROFILE=backup.prof obnam backup
This will cause the profiling data to be written to the file
backup.prof. You can do this for any Obnam command, and write it to
The profiling data is in binary form. Obnam comes with a little helper program to transform it to a human-readable form:
obnam-viewprof backup.prof | less
If you run the above command, you'll see that the humans to whom this is readable are programmers and circus clowns. If you can understand the output, great! If not, it's still useful to send that to the Obnam developers to report a performance problem.