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issues: 187873247

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id node_id number title user state locked assignee milestone comments created_at updated_at closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
187873247 MDU6SXNzdWUxODc4NzMyNDc= 1094 Supporting out-of-core computation/indexing for very large indexes 4160723 open 0     5 2016-11-08T00:56:56Z 2021-01-26T20:09:12Z   MEMBER      

(Follow-up of discussion here https://github.com/pydata/xarray/pull/1024#issuecomment-258524115).

xarray + dask.array successfully enable out-of-core computation for very large variables that doesn't fit in memory. One current limitation is that the indexes of a Dataset or DataArray, which rely on pandas.Index, are still fully loaded into memory (it will be soon loaded eagerly after #1024). In many cases this is not a problem, as the sizes of 1-dimensional indexes are usually much smaller than the sizes of n-dimensional variables or coordinates.

However, this may be problematic in some specific cases where we have to deal with very large indexes. As an example, big unstructured meshes often have coordinates (x, y, z) arranged as 1-d arrays of length that equals the number of nodes, which can be very large!! (See, e.g., ugrid conventions).

It would be very nice if xarray could also help for these use cases. Therefore I'm wondering if (and how) out-of-core support can be extended to indexes and indexing.

I've briefly looked at the documentation on dask.dataframe, and a first naive approach I have in mind would be to allow partitioning an index into multiple, contiguous indexes. For label-based indexing, we might for example map indexing.convert_label_indexer to each partition and combine the returned indexers.

My knowledge of dask is very limited, though. So I've no doubt that this suggestion is very simplistic and not very efficient, or that there are better approaches. I'm also certainly missing other issues not directly related to indexing.

Any thoughts?

cc @shoyer @mrocklin

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