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  • rabernat · 2 ✖

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  • We need a fast path for open_mfdataset · 2 ✖

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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
531813935 https://github.com/pydata/xarray/issues/1823#issuecomment-531813935 https://api.github.com/repos/pydata/xarray/issues/1823 MDEyOklzc3VlQ29tbWVudDUzMTgxMzkzNQ== rabernat 1197350 2019-09-16T14:53:57Z 2019-09-16T14:53:57Z MEMBER

Is this issue really closed?!?

🎉🎂🏆🥇

 
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   We need a fast path for open_mfdataset 288184220
489101053 https://github.com/pydata/xarray/issues/1823#issuecomment-489101053 https://api.github.com/repos/pydata/xarray/issues/1823 MDEyOklzc3VlQ29tbWVudDQ4OTEwMTA1Mw== rabernat 1197350 2019-05-03T13:47:12Z 2019-05-03T13:47:12Z MEMBER

So I think it is quite important to consider this issue together with #2697. An xml specification called NCML already exists which tells software how to put together multiple netCDF files into a single virtual netcdf. We should leverage this existing spec as much as possible.

A realistic use case for me is that I have, say 1000 files of high-res model output, each with large coordinate variables, all generated from the same model run. If we want to for for which we know a priori that certain coordinates (dimension coordinates or otherwise) are identical, we could save a lot of disk reads (the slow part of open_mfdataset) by never reading those coordinates at all. Enabling this would require a pretty low-level change in xarray. For example, we couldn't even rely on open_dataset in its current form to open files, because open_dataset eagerly loads all dimension coordinates into indexes. One way forward might be to create a new Store class.

For a catalog of tricks I use to optimize opening these sorts of big, complex, multi-file datasets (e.g. CMIP), check out https://github.com/pangeo-data/esgf2xarray/blob/master/esgf2zarr/aggregate.py

 
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   We need a fast path for open_mfdataset 288184220

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