issue_comments: 417404832

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https://github.com/pydata/xarray/issues/2314#issuecomment-417404832 https://api.github.com/repos/pydata/xarray/issues/2314 417404832 MDEyOklzc3VlQ29tbWVudDQxNzQwNDgzMg== 1217238 2018-08-30T17:38:40Z 2018-08-30T17:42:00Z MEMBER

I think the explicit chunk() call is the source of your woes here. That creates a bunch of tasks to reshard your data that require loading the entire array into memory. If you're using dask-distributed, I think the large intermediate outputs would get cached to disk but this fails if you're using the simpler multithreaded scheduler.

~~If you drop the line that calls .chunk() and manually index your array to pull out a single time-series before calling map_blocks, does that work properly? e.g., something like merged.isel(x=0, y=0).data.map_blocks(myfunction)~~ (nevermind, this is probably not a great idea)

 
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