issue_comments: 463367754

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https://github.com/pydata/xarray/issues/1385#issuecomment-463367754 https://api.github.com/repos/pydata/xarray/issues/1385 463367754 MDEyOklzc3VlQ29tbWVudDQ2MzM2Nzc1NA== 30007270 2019-02-13T20:58:52Z 2019-02-13T20:59:06Z NONE

It seems my issue has to do with the time coordinate:
```

fname = '/work/xrc/AM4_xrc/c192L33_am4p0_cmip6Diag/daily/5yr/atmos.20100101-20141231.sphum.nc' %prun ds = xr.open_mfdataset(fname,drop_variables='time') 7510 function calls (7366 primitive calls) in 0.068 seconds

Ordered by: internal time

ncalls tottime percall cumtime percall filename:lineno(function) 1 0.039 0.039 0.039 0.039 netCDF4_.py:244(_open_netcdf4_group) 3 0.022 0.007 0.022 0.007 {built-in method _operator.getitem} 1 0.001 0.001 0.001 0.001 {built-in method posix.lstat} 125/113 0.000 0.000 0.001 0.000 indexing.py:504(shape) 11 0.000 0.000 0.000 0.000 core.py:137(<genexpr>)

fname = '/work/xrc/AM4_xrc/c192L33_am4p0_cmip6Diag/daily/5yr/atmos.20000101-20041231.sphum.nc' %prun ds = xr.open_mfdataset(fname)

      13143 function calls (12936 primitive calls) in 23.853 seconds

Ordered by: internal time

ncalls tottime percall cumtime percall filename:lineno(function) 6 23.791 3.965 23.791 3.965 {built-in method operator.getitem} 1 0.029 0.029 0.029 0.029 netCDF4.py:244(_open_netcdf4_group) 2 0.023 0.012 0.023 0.012 {cftime._cftime.num2date} 1 0.001 0.001 0.001 0.001 {built-in method posix.lstat} 158/139 0.000 0.000 0.001 0.000 indexing.py:504(shape) ```

Both files are 33 GB. This is using xarray 0.11.3.

I also confirm that nc.MFDataset is much faster (<1s).

Is there any speed-up for the time coordinates possible, given that my data follows a standard calendar? (Short of using drop_variables='time' and then manually adding the time coordinate...)

 
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