delta/python-packages/gitdb.git/gitdb/test/performance, branch 0.6.2

Applied autopep8

2015-01-04T10:21:36+00:00

autopep8 -v -j 8 --max-line-length 120 --in-place --recursive

Improved decompression test to scan the entire git repository, instead of just packs

2015-01-01T17:26:12+00:00

This should make it easier to assert the issue is truly fixed now

[skip ci]

Added integrity test for loose objects to search large datasets for

2015-01-01T15:01:35+00:00

the issue described in https://github.com/gitpython-developers/GitPython/issues/220

See test notes for proper usage, it all depends on a useful dataset with high entropy

Increased initial size of decompressed data to obtain loose object header information

2015-01-01T12:47:19+00:00

This appears to fix https://github.com/gitpython-developers/GitPython/issues/220 , in this particular case.
Nonetheless, we might just have gotten lucky here, and the actual issue is not yet solved and can thus re-occour.
It would certainly be best to churn through plenty of loose objects to assure this truly works now. Maybe the pack could be recompressed as loose objects
to get a sufficiently large data set

Added requirements.txt for pip, and optimized test-suite performance on travis.

2014-11-14T10:29:18+00:00

With a bit of luck, this one will just work now.

Performance tests are now part of the test-suite.

2014-11-14T08:43:19+00:00

By default, a small repository will be tested, which doesn't take that long actually (~20s)
Additionally, that way we enforce correctness tests, which didn't run by default previously.
As we are handling data here, we must be sure that it's handled correctly, thus the tests should
run.

Fully removed all async dependencies

2014-11-13T12:31:32+00:00

Fixed all applicable lint issues

2014-11-13T11:17:01+00:00

tabs to spaces

2014-02-09T19:51:43+00:00

Optimized test_pack_streaming not to cache the objects anymore. Instead an iterator is provided which does the job. Previously it would easily use 750 MB of ram to keep all the associated objects, more than 350k. Still a lot of memory for just 350k objects, but its python after all

2011-07-05T14:40:54+00:00