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/6799#issuecomment-1317352980,https://api.github.com/repos/pydata/xarray/issues/6799,1317352980,IC_kwDOAMm_X85OhTYU,3309802,2022-11-16T17:00:04Z,2022-11-16T17:00:04Z,NONE,"> The current code also has the unfortunate side-effect of merging all chunks too

Don't really know what I'm talking about here, but it looks to me like the current dask-interpolation routine [uses `blockwise`](https://github.com/pydata/xarray/blob/df909b444991c3d76210d018e5268de541b8e17b/xarray/core/missing.py#L750-L762). That is, it's trying to simply map a function over each chunk in the array. To get the chunks into a structure where this is correct to do, you have to first merge all the chunks along the interpolation axis.

I would have expected interpolation to use [`map_overlap`](https://docs.dask.org/en/stable/array-overlap.html). You'd add some padding to each chunk, map the interpolation over each chunk (without combining them), then trim off the extra. By using overlap, you don't need to combine all the chunks into one big array first, so the operation can actually be parallel.

FYI, fixing this would probably be a big deal to geospatial people—then you could do array reprojection without GDAL! Unfortunately not something I have time to work on right now, but perhaps someone else would be interested?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1307112340