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Distributed Writes with dask

You can use Icechunk in conjunction with Xarray and Dask to perform large-scale distributed writes from a multi-node cluster. However, because of how Icechunk works, it's not possible to use the existing Dask.Array.to_zarr or Xarray.Dataset.to_zarr functions with either the Dask multiprocessing or distributed schedulers. (It is fine with the multithreaded scheduler.)

Instead, Icechunk provides its own specialized functions to make distributed writes with Dask and Xarray. This page explains how to use these specialized functions.

Note

Using Xarray, Dask, and Icechunk requires icechunk>=0.1.0a5, dask>=2024.11.0, and xarray>=2024.11.0.

First let's start a distributed Client and create an IcechunkStore.

# initialize a distributed Client
from distributed import Client

client = Client()

# initialize the icechunk store
from icechunk import IcechunkStore, StorageConfig

storage_config = StorageConfig.filesystem("./icechunk-xarray")
icechunk_store = IcechunkStore.create(storage_config)

Icechunk + Dask

Use icechunk.dask.store_dask to write a Dask array to an Icechunk store. The API follows that of dask.array.store without support for the compute kwarg.

First create a dask array to write:

shape = (100, 100)
dask_chunks = (20, 20)
dask_array = dask.array.random.random(shape, chunks=dask_chunks)

Now create the Zarr array you will write to.

zarr_chunks = (10, 10)
group = zarr.group(store=icechunk_store, overwrite=True)

zarray = group.create_array(
    "array",
    shape=shape,
    chunk_shape=zarr_chunks,
    dtype="f8",
    fill_value=float("nan"),
)

Note that the chunks in the store are a divisor of the dask chunks. This means each individual write task is independent, and will not conflict. It is your responsibility to ensure that such conflicts are avoided.

Now write

import icechunk.dask

icechunk.dask.store_dask(icechunk_store, sources=[dask_array], targets=[zarray])

Finally commit your changes!

icechunk_store.commit("wrote a dask array!")

Icechunk + Dask + Xarray

Simple

The icechunk.xarray.to_icechunk is functionally identical to Xarray's Dataset.to_zarr, including many of the same keyword arguments. Notably the compute kwarg is not supported.

Now roundtrip an xarray dataset

import icechunk.xarray
import xarray as xr

dataset = xr.tutorial.open_dataset("rasm", chunks={"time": 1}).isel(time=slice(24))

icechunk.xarray.to_icechunk(dataset, store=store)

roundtripped = xr.open_zarr(store, consolidated=False)
dataset.identical(roundtripped)

Finally commit your changes!

icechunk_store.commit("wrote an Xarray dataset!")