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  • rabernat · 4 ✖

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  • Xarray to Zarr error (in compress / numcodecs functions) · 4 ✖

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449184669 https://github.com/pydata/xarray/issues/2624#issuecomment-449184669 https://api.github.com/repos/pydata/xarray/issues/2624 MDEyOklzc3VlQ29tbWVudDQ0OTE4NDY2OQ== rabernat 1197350 2018-12-21T00:16:40Z 2018-12-21T00:16:40Z MEMBER

You can also rechunk your dataset after the fact using the chunk method:

Not a good idea in this case. The original 49GB chunks will still exist in the task graph and will have to be computed before the rechunking step.

 
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   Xarray to Zarr error (in compress / numcodecs functions)  393214032
449151325 https://github.com/pydata/xarray/issues/2624#issuecomment-449151325 https://api.github.com/repos/pydata/xarray/issues/2624 MDEyOklzc3VlQ29tbWVudDQ0OTE1MTMyNQ== rabernat 1197350 2018-12-20T22:09:20Z 2018-12-20T22:09:20Z MEMBER

So the key information is this: dask.array<shape=(2920, 32, 361, 720), chunksize=(1460, 32, 361, 720)>

This says that your dask chunk size is 1460 x 32 x 361 x 720 (x 4 bytes for float32 data) = 48573849600 bytes = ~49 GB. So this dataset is probably unusable for any purpose, including serialization (to zarr, netCDF, or any other format supported by xarray.)

Furthermore, the dask chunks will be automatically mapped to zarr chunks by xarray. These zarr chunks would be much too big to be useful. Zarr docs say "at least 1MB". In my example notebook I recommeded 10-100 MB.)

For both zarr and dask, you can think of a chunk as an amount of data that can be comfortably held in memory and passed around the network. (That's where the 10 - 100 MB estimate comes from.) It is also the minimum size of data that can be read from the dataset at once. Even if you only need one single value, the whole chunk needs to be read into memory and decompressed.

I would recommend you chunk along the time dimension. You can accomplish by adding the chunks keyword when opening the dataset python ds = xr.open_mfdataset([f1, f2], chunks={'time': 1})

I imagine that will fix most of your issues.

 
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   Xarray to Zarr error (in compress / numcodecs functions)  393214032
449145011 https://github.com/pydata/xarray/issues/2624#issuecomment-449145011 https://api.github.com/repos/pydata/xarray/issues/2624 MDEyOklzc3VlQ29tbWVudDQ0OTE0NTAxMQ== rabernat 1197350 2018-12-20T21:43:40Z 2018-12-20T21:43:40Z MEMBER

I thought I might try specifying no compression, as supported in Zarr, by adding "compressor = None" as a kwarg in the to_zarr call in xarray, but that is not supported.

The syntax and an example for specifying a compressor is given in the docs here: http://xarray.pydata.org/en/latest/io.html#zarr-compressors-and-filters. It needs to be part of the encoding keyword. But I don't think this will solve your problem.

 
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   Xarray to Zarr error (in compress / numcodecs functions)  393214032
449144275 https://github.com/pydata/xarray/issues/2624#issuecomment-449144275 https://api.github.com/repos/pydata/xarray/issues/2624 MDEyOklzc3VlQ29tbWVudDQ0OTE0NDI3NQ== rabernat 1197350 2018-12-20T21:40:44Z 2018-12-20T21:40:44Z MEMBER

@ktyle - it sounds like your chunks are too big.

Can you post xarray's representation of your dataset before writing it to zarr? Call print(ds) and paste the output here.

p.s. I edited your comment a bit to put the code into code blocks.

 
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   Xarray to Zarr error (in compress / numcodecs functions)  393214032

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