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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
291512017 https://github.com/pydata/xarray/issues/1301#issuecomment-291512017 https://api.github.com/repos/pydata/xarray/issues/1301 MDEyOklzc3VlQ29tbWVudDI5MTUxMjAxNw== najascutellatus 1360241 2017-04-04T14:11:08Z 2017-04-04T14:11:08Z NONE

@rabernat This data is computed on demand from the OOI (http://oceanobservatories.org/cyberinfrastructure-technology/). Datasets can be massive and so they seem to be split up in ~500 MB files when data gets too big. That is why obs changes for each file. Would having obs be consistent across all files potentially make open_mfdataset faster?

 
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   open_mfdataset() significantly slower on 0.9.1 vs. 0.8.2 212561278
286212647 https://github.com/pydata/xarray/issues/1301#issuecomment-286212647 https://api.github.com/repos/pydata/xarray/issues/1301 MDEyOklzc3VlQ29tbWVudDI4NjIxMjY0Nw== najascutellatus 1360241 2017-03-13T19:12:13Z 2017-03-13T19:12:13Z NONE

Data: Five files that are approximately 450 MB each.

venv1 dask 0.13.0 py27_0 conda-forge xarray 0.8.2 py27_0 conda-forge 1.51642394066 seconds to load using open_mfdataset

venv2: dask 0.13.0 py27_0 conda-forge xarray 0.9.1 py27_0 conda-forge 279.011202097 seconds to load using open_mfdataset

I ran the same code in the OP on two conda envs with the same version of dask but two different versions of xarray. There was a significant difference in load time between the two conda envs.

I've posted the data on my work site if anyone wants to double check: https://marine.rutgers.edu/~michaesm/netcdf/data/

 
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   open_mfdataset() significantly slower on 0.9.1 vs. 0.8.2 212561278

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