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EDIT: The issue of this post is now separated #2531

I think I have a fix, but wanted to write some failing tests before committing the changes. Doing this I discovered that also DataArray.rolling() does not preserve the chunksizes, apparently depending on the applied method.

```python import pandas as pd import numpy as np import xarray as xr

t = pd.date_range(start='2018-01-01', end='2018-02-01', freq='H') bar = np.sin(np.arange(len(t))) baz = np.cos(np.arange(len(t)))

da_test = xr.DataArray(data=np.stack([bar, baz]), coords={'time': t, 'sensor': ['one', 'two']}, dims=('sensor', 'time'))

print(da_test.chunk({'time': 100}).rolling(time=60).mean().chunks)

print(da_test.chunk({'time': 100}).rolling(time=60).count().chunks) Output for mean: ((2,), (745,)) Output for count: ((2,), (100, 100, 100, 100, 100, 100, 100, 45)) Desired Output: ((2,), (100, 100, 100, 100, 100, 100, 100, 45)) ```

My fix solves my initial problem, but maybe if done correctly it should also solve this bug, too.

Any idea why this depends on whether .mean() or .count() is used?

I have already pushed some WIP changes. Should I already open a PR if though most new test still fail?

@dcherian Okay. A WIP PR will follow, but might take some days.

Nice catch!

For some historical reasons, mean and some reduction method uses bottleneck as default, while count does not.

mean goes through this function https://github.com/pydata/xarray/blob/b622c5e7da928524ef949d9e389f6c7f38644494/xarray/core/dask_array_ops.py#L23

It looks there is another but for this function.

@cchwala Discussion is a lot easier on a PR so go ahead and do that. You can add WIP in the title.

Thanks @fujiisoup for the quick response and the pointers. I will have a look and report back if a PR is within my capabilities or not.

Thanks, @cchwala, for reporting the issue.

It looks that the actual chunks size is ((10, 735), ) not all 10. python In [16]: ds_test.interpolate_na(dim='time', limit=20)['foo'].chunks Out[16]: ((10, 735),) (why does our __repr__ only show the first chunk size?) But it should be ((745, ), ) as you suggested.

The problem would be in https://github.com/pydata/xarray/blob/5940100761478604080523ebb1291ecff90e779e/xarray/core/dask_array_ops.py#L74-L85

This method is desinged to be used for multiplly chunked array, so I didn't care to add a small chunk on the head. Do you mind to look the inside?

The problem seems to occur here

https://github.com/pydata/xarray/blob/5940100761478604080523ebb1291ecff90e779e/xarray/core/missing.py#L368-L376

because of the usage of .construct(). A quick try without it, shows that the chunksize is preserved then.

Hence, .construct() might need a fix for correctly dealing with the chunks of dask.arrays.