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
342180429,MDU6SXNzdWUzNDIxODA0Mjk=,2298,Making xarray math lazy,1217238,open,0,,,7,2018-07-18T05:18:53Z,2022-04-19T15:38:59Z,,MEMBER,,,,"At SciPy, I had the realization that it would be relatively straightforward to make element-wise math between xarray objects lazy. This would let us support lazy coordinate arrays, a feature that has quite a few use-cases, e.g., for both geoscience and astronomy.

The trick would be to write a lazy array class that holds an element-wise vectorized function and passes indexers on to its arguments. I haven't thought too hard about this yet for vectorized indexing, but it could be quite efficient for outer indexing. I have some prototype code but no tests yet.

The question is how to hook this into xarray operations. In particular, supposing that the inputs to a function do no hold dask arrays:
- Should we try to make *every* element-wise operation with vectorized functions (ufuncs) lazy by default? This might have negative performance implications and would be a little tricky to implement with xarray's current code, since we still implement binary operations like `+` with separate logic from `apply_ufunc`.
- Should we make every element-wise operation that explicitly uses `apply_ufunc()` lazy by default?
- Or should we only make element-wise operations lazy with `apply_ufunc()` if you use some special flag, e.g., `apply_ufunc(..., lazy=True)`?

I am leaning towards the last option for now but would welcome other opinions.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2298/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue