issue_comments: 652598966

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https://github.com/pydata/xarray/issues/2937#issuecomment-652598966 https://api.github.com/repos/pydata/xarray/issues/2937 652598966 MDEyOklzc3VlQ29tbWVudDY1MjU5ODk2Ng== 1197350 2020-07-01T19:14:35Z 2020-07-01T19:14:35Z MEMBER

My approach here was to use compression and filters to minimize the on-disk storage. Here is my .zarray for the dataset in question

json { "chunks": [ 1, 13, 4320, 4320 ], "compressor": { "check": -1, "filters": [ { "dist": 1, "id": 3 }, { "id": 33, "preset": 1 } ], "format": 3, "id": "lzma", "preset": null }, "dtype": "|i1", "fill_value": null, "filters": null, "order": "C", "shape": [ 90, 13, 4320, 4320 ], "zarr_format": 2

This does not solve the in-memory problem, but that's a numpy issue.

 
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