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  • open_mfdataset in v.0.11.1 is very slow · 2 ✖

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  • CONTRIBUTOR · 2 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
454439392 https://github.com/pydata/xarray/issues/2662#issuecomment-454439392 https://api.github.com/repos/pydata/xarray/issues/2662 MDEyOklzc3VlQ29tbWVudDQ1NDQzOTM5Mg== malmans2 22245117 2019-01-15T15:45:03Z 2019-01-15T15:45:03Z CONTRIBUTOR

I checked PR #2678 with the data that originated the issue and it fixes the problem! 

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   open_mfdataset in v.0.11.1 is very slow 397063221
454086847 https://github.com/pydata/xarray/issues/2662#issuecomment-454086847 https://api.github.com/repos/pydata/xarray/issues/2662 MDEyOklzc3VlQ29tbWVudDQ1NDA4Njg0Nw== malmans2 22245117 2019-01-14T17:20:03Z 2019-01-14T17:20:03Z CONTRIBUTOR

I've created a little script to reproduce the problem. @TomNicholas it looks like datasets are opened correctly. The problem arises when open_mfdatasets calls _auto_combine. Indeed, _auto_combine was introduced in v0.11.1

```python import numpy as np import xarray as xr import os Tsize=100; T = np.arange(Tsize); Xsize=900; X = np.arange(Xsize); Ysize=800; Y = np.arange(Ysize) data = np.random.randn(Tsize, Xsize, Ysize) for i in range(2):

# Create 2 datasets with different variables
dsA = xr.Dataset({'A': xr.DataArray(data, coords={'T': T+i*Tsize}, dims=('T', 'X', 'Y'))})
dsB = xr.Dataset({'B': xr.DataArray(data, coords={'T': T+i*Tsize}, dims=('T', 'X', 'Y'))})

# Save datasets in one folder
dsA.to_netcdf('dsA'+str(i)+'.nc')
dsB.to_netcdf('dsB'+str(i)+'.nc')

# Save datasets in two folders
dirname='rep'+str(i)
os.mkdir(dirname)
dsA.to_netcdf(dirname+'/'+'dsA'+str(i)+'.nc')
dsB.to_netcdf(dirname+'/'+'dsB'+str(i)+'.nc')

```

Fast if netCDFs are stored in one folder:

python %%time ds_1folder = xr.open_mfdataset('*.nc', concat_dim='T')

CPU times: user 49.9 ms, sys: 5.06 ms, total: 55 ms
Wall time: 59.7 ms

Slow if netCDFs are stored in several folders:

python %%time ds_2folders = xr.open_mfdataset('rep*/*.nc', concat_dim='T')

CPU times: user 8.6 s, sys: 5.95 s, total: 14.6 s
Wall time: 10.3 s

Fast if files containing different variables are opened separately, then merged:

python %%time ds_A = xr.open_mfdataset('rep*/dsA*.nc', concat_dim='T') ds_B = xr.open_mfdataset('rep*/dsB*.nc', concat_dim='T') ds_merged = xr.merge([ds_A, ds_B])

CPU times: user 33.8 ms, sys: 3.7 ms, total: 37.5 ms
Wall time: 34.5 ms

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   open_mfdataset in v.0.11.1 is very slow 397063221

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