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/2314#issuecomment-417419321,https://api.github.com/repos/pydata/xarray/issues/2314,417419321,MDEyOklzc3VlQ29tbWVudDQxNzQxOTMyMQ==,2489879,2018-08-30T18:22:31Z,2018-08-30T18:22:31Z,NONE,"Thanks for all the suggestions!

An update from when I originally posted this.  Aligning with @shoyer, @darothen and @scottyhq 's comments, we've tested the code using cloud-optimized geoTIF files and regular geoTIFs, and it does perform better with the cloud-optimized form, though still appears to ""over-eagerly"" load more than just what is being worked on.  With the cloud-optimized form, performance is much better when we specify the chunking strategy on the initial open_rasterio and it aligns with the chunk sizes.
e.g.
`rasterlist = [xr.open_rasterio(x, chunks={'x': 256, 'y': 256}) for x in filelist]`
vs.
`rasterlist = [xr.open_rasterio(x, chunks={'x': None, 'y': None}) for x in filelist]`

The result is a larger task graph (and much more time spent developing the task graph) but more cases where we don't run into memory problems. There still appears to be a lot more memory used than I expect, but am actively working on exploring options.

We've also noticed better performance using a k8s Dask cluster distributed across multiple ""independent"" workers as opposed to using a LocalCluster on a single large machine.  As in, with the distributed cluster the ""myfunction"" (__fit__) operation starts happening on chunks well before the entire dataset is loaded, whereas in the LocalCluster it still tends not to begin until all chunks have been loaded in.  Not exactly sure what would cause that...

I'm intrigued by @shoyer 's last suggestion of an ""intermediate"" chunking step.  Will test that and potentially the manual iteration over the tiles.  Thanks for all the suggestions and thoughts!
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