issue_comments: 408860643

@lrntct - this sounds like a reasonable way to use zarr. We routinely do this sort of transcoding and it works reasonable well. Unfortunately something clearly isn't working right in your case. These things can be hard to debug, but we will try to help you.

You might want to start by reviewing the guide I wrote for Pangeo on preparing zarr datasets.

It would also be good to see a bit more detail. You posted a function netcdf2zarr that converts a single netcdf file to a single zarr file. How are you invoking that function? Are you trying to create one zarr store for each netCDF file? How many netCDF files are there? If there are many (e.g. one per timmestep), my recommendation is to create only one zarr store for the whole dataset. Open the netcdf files using open_mfdataset.

If instead you have just one big netCDF file as in the example you posted above, I think I see you problem: you are calling .chunk() after calling open_dataset(), rather calling open_dataset(nc_path, chunks=chunks). This probably means that you are loading the whole dataset in a single task and then re-chunking. That could be the source of the inefficiency.

More ideas: - explicitly specify the chunks (rather than using 'auto') - eliminate the negative number in your chunk sizes - make sure you really need clevel=9

Another useful piece of advice would be to use the dask distributed dashboard to monitor what is happening under the hood. You can do this by running python from dask.distributed import Client client = Client() client In a notebook, this should provide you a link to the scheduler dashboard. Once you call ds.to_zarr(), watch the task stream in the dashboard to see what is happening.

Hopefully these ideas can help you move forward.