issue_comments: 439263419

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https://github.com/pydata/xarray/issues/1385#issuecomment-439263419 https://api.github.com/repos/pydata/xarray/issues/1385 439263419 MDEyOklzc3VlQ29tbWVudDQzOTI2MzQxOQ== 1217238 2018-11-16T02:45:05Z 2018-11-16T02:45:05Z MEMBER

@chuaxr What do you see when you use %prun when opening the dataset? This might point to the bottleneck.

One way to fix this would be to move our call to decode_cf() in open_dataset() to after applying chunking, i.e., to switch up the order of operations on these lines: https://github.com/pydata/xarray/blob/f547ed0b379ef70a3bda5e77f66de95ec2332ddf/xarray/backends/api.py#L270-L296

In practice, is the difference between using xarray's internal lazy array classes for decoding and dask for decoding. I would expect to see small differences in performance between these approaches (especially when actually computing data), but for constructing the computation graph I would expect them to have similar performance. It is puzzling that dask is orders of magnitude faster -- that suggests that something else is going wrong in the normal code path for decode_cf(). It would certainly be good to understand this before trying to apply any fixes.

 
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