MCVE Code Sample

```python

Your code here

import numpy as np import xarray as xr

from dask.distributed import Client

temp= xr.DataArray(np.zeros((5000, 50000)),dims=("x","y")).chunk({"y":100, }) temp.rolling(x=100).mean() ```

Expected Output

Problem Description

I was thrilled to find that the new release (both 0.12.2 and 0.12.3) fixed the rolling window issue. However, When I tried, it seems the problem is still there. Previously, the above code runs with bottleneck installed. However, with the new version, with or without bottleneck, it simply gives the memory error as below.

I have tried to use old and new versions of Dask and pandas, but with no much difference. However, the dask Dataframe version of the code (shown below) runs ok. ```python import dask.dataframe as dd import dask.array as da import numpy as np

da_array=da.from_array(np.zeros((5000, 50000)), chunks=(5000,100)) df = dd.from_dask_array(da_array) df.rolling(window=100,axis=0).mean() ``` I have also tried to apply the similar thing on dataset from netcdf files, it simply started consuming very large portion of memory and gives the similar errors.

Any help are appreciated.

/miniconda3/envs/xarray/lib/python3.7/site-packages/xarray/core/merge.py:17: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version PANDAS_TYPES = (pd.Series, pd.DataFrame, pd.Panel) /miniconda3/envs/xarray/lib/python3.7/site-packages/xarray/core/dataarray.py:219: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version elif isinstance(data, pd.Panel): Traceback (most recent call last): File "rolltest.py", line 5, in <module> temp.rolling(x=100).mean() File "/miniconda3/envs/xarray/lib/python3.7/site-packages/xarray/core/rolling.py", line 245, in wrapped_func return self.reduce(func, **kwargs) File "/miniconda3/envs/xarray/lib/python3.7/site-packages/xarray/core/rolling.py", line 217, in reduce result = windows.reduce(func, dim=rolling_dim, **kwargs) File "/miniconda3/envs/xarray/lib/python3.7/site-packages/xarray/core/dataarray.py", line 1636, in reduce var = self.variable.reduce(func, dim, axis, keep_attrs, **kwargs) File "/miniconda3/envs/xarray/lib/python3.7/site-packages/xarray/core/variable.py", line 1369, in reduce input_data = self.data if allow_lazy else self.values File "/miniconda3/envs/xarray/lib/python3.7/site-packages/xarray/core/variable.py", line 392, in values return _as_array_or_item(self._data) File "/miniconda3/envs/xarray/lib/python3.7/site-packages/xarray/core/variable.py", line 213, in _as_array_or_item data = np.asarray(data) File "/miniconda3/envs/xarray/lib/python3.7/site-packages/numpy/core/numeric.py", line 538, in asarray return array(a, dtype, copy=False, order=order) File "/miniconda3/envs/xarray/lib/python3.7/site-packages/dask/array/core.py", line 1047, in __array__ x = self.compute() File "/miniconda3/envs/xarray/lib/python3.7/site-packages/dask/base.py", line 156, in compute (result,) = compute(self, traverse=False, **kwargs) File "/miniconda3/envs/xarray/lib/python3.7/site-packages/dask/base.py", line 399, in compute return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)]) File "/miniconda3/envs/xarray/lib/python3.7/site-packages/dask/base.py", line 399, in <listcomp> return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)]) File "/miniconda3/envs/xarray/lib/python3.7/site-packages/dask/array/core.py", line 828, in finalize return concatenate3(results) File "/miniconda3/envs/xarray/lib/python3.7/site-packages/dask/array/core.py", line 3621, in concatenate3 result = np.empty(shape=shape, dtype=dtype(deepfirst(arrays))) MemoryError

Output of xr.show_versions()

# Paste the output here xr.show_versions() here INSTALLED VERSIONS ------------------ commit: None python: 3.7.3 (default, Mar 27 2019, 22:11:17) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 4.15.0-51-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.4 libnetcdf: 4.6.2 xarray: 0.12.2 pandas: 0.24.2 numpy: 1.16.4 scipy: 1.3.0 netCDF4: 1.5.1.2 pydap: None h5netcdf: 0.7.3 h5py: 2.9.0 Nio: None zarr: 2.3.2 cftime: 1.0.3.4 nc_time_axis: None PseudonetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.2.1 dask: 1.2.2 distributed: 1.28.1 matplotlib: 3.1.0 cartopy: None seaborn: None numbagg: None setuptools: 41.0.0 pip: 19.1.1 conda: 4.7.5 pytest: None IPython: 7.5.0 sphinx: None