issues: 236347050

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id node_id number title user state locked assignee milestone comments created_at updated_at closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
236347050 MDExOlB1bGxSZXF1ZXN0MTI1OTM2ODM1 1457 Feature/benchmark 2443309 closed 0   2415632 16 2017-06-16T00:11:52Z 2017-11-13T04:09:53Z 2017-07-26T16:17:34Z MEMBER   0 pydata/xarray/pulls/1457
  • [x] Closes #1257
  • [x] Tests added / passed
  • [x] Passes git diff upstream/master | flake8 --diff
  • [ ] Fully documented, including whats-new.rst for all changes and api.rst for new API

This is a very bare bones addition of the asv benchmarking tool to xarray. I have added four very rudimentary benchmarks in the dataset_io.py module.

Usage of asv is pretty straightforward but I'll outline the steps for those who want to try this out: cd xarray conda install asv -c conda-forge asv run # this will install some conda environments in ./.asv/envs asv publish # this collates the results asv preview # this will launch a web server so you can visually compare the tests

Before I go any further, I want to get some input from @pydata/xarray on what we want to see in this PR. In previous projects, I have found designing tests after the fact can end up being fairly arbitrary and I want to avoid that if at all possible. I'm guessing that we will want to focus our efforts for now on I/O and dask related performance but how we do that is up for discussion.

cc @shoyer, @rabernat, @MaximilianR, @Zac-HD

 
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