issues: 334366223

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id node_id number title user state locked assignee milestone comments created_at updated_at closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
334366223 MDU6SXNzdWUzMzQzNjYyMjM= 2241 Slow performance with isel on stacked coordinates 1386642 closed 0     4 2018-06-21T07:13:32Z 2020-06-20T20:51:48Z 2020-06-20T20:51:48Z CONTRIBUTOR      

Code Sample

```python

a = xr.DataArray(np.random.rand(64,64,64), dims=list('xyz')).chunk({'x':8, 'y': 8}) b = a.stack(b=['x', 'y']) %timeit b.isel(b=0).load() 3.81 ms ± 24.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) %timeit a.isel(x=0, y=0).load() 822 µs ± 3.68 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) np.allclose(b.isel(b=0).values, a.isel(x=0, y=0).values) True ```

Problem description

I have noticed some pretty significant slow downs when using dask and stacked indices. As you can see in the example above, selecting the point x=0, y=0 takes about 4 times as long when the x and y dimensions are stacked together. This big difference only appears when .load is called. Does this mean it's a dask issue?

Output of xr.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.6.3.final.0 python-bits: 64 OS: Darwin OS-release: 17.5.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: None LOCALE: en_US.UTF-8 xarray: 0.10.7 pandas: 0.22.0 numpy: 1.13.3 scipy: 1.0.0 netCDF4: 1.3.0 h5netcdf: 0.4.2 h5py: 2.7.1 Nio: None zarr: 2.2.0 bottleneck: 1.2.1 cyordereddict: None dask: 0.17.1 distributed: 1.21.1 matplotlib: 2.2.2 cartopy: 0.16.0 seaborn: 0.8.1 setuptools: 39.1.0 pip: 9.0.1 conda: None pytest: 3.5.1 IPython: 6.2.1 sphinx: 1.6.5 
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   completed 13221727 issue

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