issue_comments: 573196874

This data as json

html_url issue_url id node_id user created_at updated_at author_association body reactions performed_via_github_app issue
https://github.com/pydata/xarray/issues/3668#issuecomment-573196874 https://api.github.com/repos/pydata/xarray/issues/3668 573196874 MDEyOklzc3VlQ29tbWVudDU3MzE5Njg3NA== 2443309 2020-01-10T20:40:14Z 2020-01-10T20:40:14Z MEMBER

The scenario you are describing--trying to open a file that is not accessible at all from the client--is certainly not something we ever considered when designing this. It is a miracle to me that it does work with netCDF.

True. I think its fair to say that the behavior you are enjoying (accessing data that the client cannot see) is the exception, not the rule. I expect there are many places in our backends that will not support this functionality at present.

The motivation for implementing the parallel feature was simply to shard the fileIO time when opening large collections (>10k) of netcdf files.

Ironically, this dask issue also popped up and has some significant overlap here: https://github.com/dask/dask/issues/5769

In both of these cases, the desire is for the worker to open the file (or zarr dataset), construct the underlying dask arrays, and return the meta object. This requires the object to be fully pickle-able and for any references to be maintained. It is possible, as indicated by your traceback, that the zarr backend is trying to reference the zgroup file and its not there. The logical place to start would be to look into why we can't pickle xarray datasets that come from zarr stores. 

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   546562676