What is the best way to intercept a collection of NetCDF files for a Zarr dataset

Question

What is the best way to intercept a collection of NetCDF files for a Zarr dataset

I am trying to intercept a collection of NetCDF files and create a Zarr dataset on AWS S3. I have 168 original NetCDF4 classic files with dimension arrays time: 1, y: 3840, x: 4608marked as chunks={'time':1, 'y':768, 'x':922}.

I want to write this result to Zarr, and I want to optimize for time series extraction, so include more time records in your chunks. I decided that I would use xarray to help do this work, since I have many processors available to use Dask, and xarray has both xr.open_mfdataset, and ds.to_zarr.

At first I tried to rewrite to chunks={'time':24, 'y':768, 'x':922}to match the NetCDF4 input block in xand y, but when I tried to write Zarr, he complained because he needed the same block sizes in xboth and yallowing for uneven size in the last fragment in size time(and unfortunately , in measurement x, the total size of 4608 is not a multiple of the size of block 922.

So, I tried chunks={'time':168, 'y':384, 'x':288}, and it started working, and went on very fast for several minutes, then it became slower and slower. Finally, after 50 minutes, the cluster died using

4072 distributed.core - INFO - Event loop was unresponsive in Worker for 1.41s.  This is often caused by long-running GIL-holding functions or moving large chunks of data. This can cause timeouts and instability.
4073 slurmstepd: error: Step 3294889.0 exceeded memory limit (25346188 > 25165824), being killed

Here is the code I'm using:

from dask.distributed import Client

import pandas as pd
import xarray as xr
import s3fs
import zarr

client = Client(scheduler_file='/home/rsignell/scheduler.json')
client

root = '/lustre/projects/hazards/cmgp/woodshole/rsignell/nwm/forcing_short_range/' 

bucket_endpoint='https://s3.us-west-1.amazonaws.com/'

f_zarr = 'rsignell/nwm/test_week4'

dates = pd.date_range(start='2018-04-01T00:00', end='2018-04-07T23:00', freq='H')

urls = ['{}{}/nwm.t{}z.short_range.forcing.f001.conus.nc'.format(root,a.strftime('%Y%m%d'),a.strftime('%H')) for a in dates]

ds = xr.open_mfdataset(urls, concat_dim='time', chunks={'time':1, 'y':768, 'x':922})
ds = ds.drop(['ProjectionCoordinateSystem','time_bounds'])
ds = ds.chunk(chunks={'time':168, 'y':384, 'x':288}).persist()
ds

production

<xarray.Dataset>
Dimensions:         (reference_time: 168, time: 168, x: 4608, y: 3840)
Coordinates:
  * reference_time  (reference_time) datetime64[ns] 2018-04-01 ...
  * x               (x) float64 -2.304e+06 -2.303e+06 -2.302e+06 -2.301e+06 ...
  * y               (y) float64 -1.92e+06 -1.919e+06 -1.918e+06 -1.917e+06 ...
  * time            (time) datetime64[ns] 2018-04-01T01:00:00 ...
Data variables:
    T2D             (time, y, x) float64 dask.array<shape=(168, 3840, 4608), chunksize=(168, 384, 288)>
    LWDOWN          (time, y, x) float64 dask.array<shape=(168, 3840, 4608), chunksize=(168, 384, 288)>
    Q2D             (time, y, x) float64 dask.array<shape=(168, 3840, 4608), chunksize=(168, 384, 288)>
    U2D             (time, y, x) float64 dask.array<shape=(168, 3840, 4608), chunksize=(168, 384, 288)>
    V2D             (time, y, x) float64 dask.array<shape=(168, 3840, 4608), chunksize=(168, 384, 288)>
    PSFC            (time, y, x) float64 dask.array<shape=(168, 3840, 4608), chunksize=(168, 384, 288)>
    RAINRATE        (time, y, x) float32 dask.array<shape=(168, 3840, 4608), chunksize=(168, 384, 288)>
    SWDOWN          (time, y, x) float64 dask.array<shape=(168, 3840, 4608), chunksize=(168, 384, 288)>

Then i call

fs = s3fs.S3FileSystem(anon=False, client_kwargs=dict(endpoint_url=bucket_endpoint))
d = s3fs.S3Map(f_zarr, s3=fs)

compressor = zarr.Blosc(cname='zstd', clevel=3, shuffle=2)
encoding = {vname: {'compressor': compressor} for vname in ds.data_vars}

delayed_store = ds.to_zarr(store=d, mode='w', encoding=encoding, compute=False)
persist_store = delayed_store.persist(retries=100)