Comprehensive benchmarking suite
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- Dominant language
- Python
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Description
I think a good "infrastructure" target for the NASA OSS call would be to expand our benchmarking suite (https://pandas.pydata.org/speed/xarray/#/)
AFAIK running these in a useful manner on CI is still unsolved (please correct me if I'm wrong). But we can always run it on an NCAR machine using a cron job.
Thoughts?
cc @scottyhq
A quick survey of work needed (please append):
- indexing & slicing #3382 #2799 #2227
- DataArray construction #4744
- attribute access #4741, #4742
- property access #3514
- reindexing? https://github.com/pydata/xarray/issues/1385#issuecomment-297539517
- alignment #3755, #7738
- assignment #1771
- coarsen #10921
- groupby #659 #7795 #7796
- resample #4498 #7795
- weighted #4482 #3883
- concat #7824, #10866
- merge
- open_dataset, open_mfdataset #1823
- stack / unstack
- apply_ufunc?
- interp #4740 #7843
- reprs #4744
- to_(dask)_dataframe #7844 #7474
Related: #3514
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the linked pandas/xarray benchmark page and inspect the repository's existing benchmarking and CI setup. Use the unchecked targets and referenced issues to define a focused first slice; done should include benchmarks for that slice plus an agreed CI or NCAR cron execution path.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- performance, testing-qa
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100