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  • jhamman · 4 ✖

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  • Feature/benchmark · 4 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
318468605 https://github.com/pydata/xarray/pull/1457#issuecomment-318468605 https://api.github.com/repos/pydata/xarray/issues/1457 MDEyOklzc3VlQ29tbWVudDMxODQ2ODYwNQ== jhamman 2443309 2017-07-27T19:54:01Z 2017-07-27T19:54:01Z MEMBER

Yes! Thanks @wesm and @TomAugspurger.

 
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   Feature/benchmark 236347050
317091662 https://github.com/pydata/xarray/pull/1457#issuecomment-317091662 https://api.github.com/repos/pydata/xarray/issues/1457 MDEyOklzc3VlQ29tbWVudDMxNzA5MTY2Mg== jhamman 2443309 2017-07-21T19:27:49Z 2017-07-21T19:27:49Z MEMBER

Thanks @TomAugspurger - see https://github.com/TomAugspurger/asv-runner/issues/1.

All, I added a series of multi-file benchmarks. I think for a first PR, this is ready to fly and we can add more benchmarks as needed. 

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   Feature/benchmark 236347050
315220704 https://github.com/pydata/xarray/pull/1457#issuecomment-315220704 https://api.github.com/repos/pydata/xarray/issues/1457 MDEyOklzc3VlQ29tbWVudDMxNTIyMDcwNA== jhamman 2443309 2017-07-13T22:37:02Z 2017-07-13T22:37:02Z MEMBER

@rabernat - do you have any thoughts on this?

@pydata/xarray - I'm trying to decide if this is worth spending any more time on. What sort of coverage would we want before we merge this first PR? 

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   Feature/benchmark 236347050
308935684 https://github.com/pydata/xarray/pull/1457#issuecomment-308935684 https://api.github.com/repos/pydata/xarray/issues/1457 MDEyOklzc3VlQ29tbWVudDMwODkzNTY4NA== jhamman 2443309 2017-06-16T05:20:24Z 2017-06-16T05:20:24Z MEMBER

Keep the comments coming! I think we can distinguish between benchmarking for regressions and benchmarking for development and introspection.

The former will require some thought as to what machines we want to rely on and how to achieve consistency throughout the development track. It sounds like there are a number of options that we could pursue toward those ends.

The latter use of benchmarking is useful on a single machine with only a few commits of history. For the four benchmarks in my sample dataset_io.py, we get the following interesting results (for one environment): --[ 0.00%] Benchmarking conda-py2.7-bottleneck-dask-netcdf4-numpy-pandas-scipy ---[ 3.12%] Running dataset_io.IOSingleNetCDF.time_load_dataset_netcdf4 134.34ms ---[ 6.25%] Running dataset_io.IOSingleNetCDF.time_load_dataset_scipy 82.60ms ---[ 9.38%] Running dataset_io.IOSingleNetCDF.time_write_dataset_netcdf4 57.71ms ---[ 12.50%] Running dataset_io.IOSingleNetCDF.time_write_dataset_scipy 267.29ms

So the relative performance is useful information in deciding how to use and/or develop xarray. (Granted the exact factors will change depending on machine/architecture/dataset). 

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   Feature/benchmark 236347050

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