issue_comments: 286219858

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https://github.com/pydata/xarray/issues/1301#issuecomment-286219858 https://api.github.com/repos/pydata/xarray/issues/1301 286219858 MDEyOklzc3VlQ29tbWVudDI4NjIxOTg1OA== 1197350 2017-03-13T19:39:15Z 2017-03-13T19:39:15Z MEMBER

There is definitely something funky with these datasets that is causing xarray to go very slow.

This is fast: ```python

%time dsets = [xr.open_dataset(fname) for fname in glob('*.nc')] CPU times: user 1.1 s, sys: 664 ms, total: 1.76 s Wall time: 1.78 s ```

But even just trying to print the repr is slow ```python

%time print(dsets[0]) CPU times: user 3.66 s, sys: 3.49 s, total: 7.15 s Wall time: 7.28 s ```

Maybe some of this has to do with the change at 0.9.0 to allowing index-less dimensions (i.e. coordinates are optional). All of these datasets have such a dimension, e.g. <xarray.Dataset> Dimensions: (obs: 7247697) Coordinates: lon (obs) float64 -124.3 -124.3 ... lat (obs) float64 44.64 44.64 ... time (obs) datetime64[ns] 2014-11-10T00:00:00.011253 ... Dimensions without coordinates: obs Data variables: oxy_calphase (obs) float64 3.293e+04 ... quality_flag (obs) |S2 'ok' 'ok' 'ok' ... ctdbp_no_seawater_conductivity_qc_executed (obs) uint8 29 29 29 29 29 ... ... 

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