What happened?

Hello,

I'm using the Dataset.where function to select data based on some fields values and it takes way to much time! The dask dashboard seems to show some tasks repeating themselves many times.

The provided example uses a 1D array for which the selection could be done with Dataset.sel but with our real usecase we make selections on 2D variables.

This problem seems to have appeared with the 2022.6.0 xarray release, the 2022.3.0 is working as expected.

What did you expect to happen?

Using the 2022.3 release, this selection takes 1.37 seconds. Using the 2022.6.0 up to the 2023.2.0 (the one from yesterday), this selection takes 8.47 seconds.

This example is a very simple and small one, with real data and use case we simply cannot use this function anymore.

Minimal Complete Verifiable Example

```Python import dask.array as da import distributed as dist import xarray as xr

client = dist.Client()

Using small chunks emphasis the problem

ds = xr.Dataset( {"field": xr.DataArray(data=da.empty(shape=10000, chunks=10), dims=("x"))} ) sel = ds["field"] > 0

ds.where(sel, drop=True) ```

MVCE confirmation

• [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• [X] Complete example — the example is self-contained, including all data and the text of any traceback.
• [X] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• [X] New issue — a search of GitHub Issues suggests this is not a duplicate.

Relevant log output

No response

Anything else we need to know?

No response

Environment

Problematic version

INSTALLED VERSIONS ------------------ commit: None python: 3.10.9 | packaged by conda-forge | (main, Feb 2 2023, 20:20:04) [GCC 11.3.0] python-bits: 64 OS: Linux OS-release: 5.15.0-58-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: fr_FR.UTF-8 LOCALE: ('fr_FR', 'UTF-8') libhdf5: 1.12.2 libnetcdf: 4.8.1 xarray: 2023.2.0 pandas: 1.5.3 numpy: 1.23.5 scipy: 1.8.1 netCDF4: 1.6.2 pydap: None h5netcdf: 1.1.0 h5py: 3.8.0 Nio: None zarr: 2.13.6 cftime: 1.6.2 nc_time_axis: None PseudoNetCDF: None rasterio: 1.3.4 cfgrib: 0.9.10.3 iris: None bottleneck: None dask: 2023.1.1 distributed: 2023.1.1 matplotlib: 3.6.3 cartopy: 0.21.1 seaborn: None numbagg: None fsspec: 2023.1.0 cupy: None pint: 0.20.1 sparse: None flox: None numpy_groupies: None setuptools: 67.1.0 pip: 23.0 conda: 22.11.1 pytest: 7.2.1 mypy: None IPython: 8.7.0 sphinx: 5.3.0

Working version

INSTALLED VERSIONS ------------------ commit: None python: 3.10.9 | packaged by conda-forge | (main, Feb 2 2023, 20:20:04) [GCC 11.3.0] python-bits: 64 OS: Linux OS-release: 5.15.0-58-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: fr_FR.UTF-8 LOCALE: ('fr_FR', 'UTF-8') libhdf5: 1.12.2 libnetcdf: 4.8.1 xarray: 2022.3.0 pandas: 1.5.3 numpy: 1.23.5 scipy: 1.8.1 netCDF4: 1.6.2 pydap: None h5netcdf: 1.1.0 h5py: 3.8.0 Nio: None zarr: 2.13.6 cftime: 1.6.2 nc_time_axis: None PseudoNetCDF: None rasterio: 1.3.4 cfgrib: 0.9.10.3 iris: None bottleneck: None dask: 2023.1.1 distributed: 2023.1.1 matplotlib: 3.6.3 cartopy: 0.21.1 seaborn: None numbagg: None fsspec: 2023.1.0 cupy: None pint: 0.20.1 sparse: None setuptools: 67.1.0 pip: 23.0 conda: 22.11.1 pytest: 7.2.1 IPython: 8.7.0 sphinx: 5.3.0