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  • xarray · 2 ✖
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
1038531231 PR_kwDOAMm_X84tzEEk 5906 Avoid accessing slow .data in unstack TomAugspurger 1312546 closed 0     4 2021-10-28T13:39:36Z 2021-10-29T15:29:39Z 2021-10-29T15:14:43Z MEMBER   0 pydata/xarray/pulls/5906
  • [x] Closes https://github.com/pydata/xarray/issues/5902
  • [x] Passes pre-commit run --all-files
  • [x] User visible changes (including notable bug fixes) are documented in whats-new.rst
  • [x] New functions/methods are listed in api.rst
 
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     xarray 13221727 pull
1037894157 I_kwDOAMm_X8493QIN 5902 Slow performance of `DataArray.unstack()` from checking `variable.data` TomAugspurger 1312546 closed 0     4 2021-10-27T21:54:48Z 2021-10-29T15:21:24Z 2021-10-29T15:21:24Z MEMBER      

What happened:

Calling DataArray.unstack() spends time allocating an object-dtype NumPy array from values of the pandas MultiIndex.

What you expected to happen:

Faster unstack.

Minimal Complete Verifiable Example:

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

t = pd.date_range("2000", periods=2) x = np.arange(1000) y = np.arange(1000) component = np.arange(4)

idx = pd.MultiIndex.from_product([t, y, x], names=["time", "y", "x"])

data = np.random.uniform(size=(len(idx), len(component))) arr = xr.DataArray( data, coords={"pixel": xr.DataArray(idx, name="pixel", dims="pixel"), "component": xr.DataArray(component, name="component", dims="component")}, dims=("pixel", "component") )

%time _ = arr.unstack() CPU times: user 6.33 s, sys: 295 ms, total: 6.62 s Wall time: 6.62 s ```

Anything else we need to know?:

For this example, >99% of the time is spent at on this line: https://github.com/pydata/xarray/blob/df7646182b17d829fe9b2199aebf649ddb2ed480/xarray/core/dataset.py#L4162, specifically on the call to v.data for the pixel array, which is a pandas MultiIndex.

Just going by the comments, it does seem like accessing v.data is necessary to perform the check. I'm wonder if we could make is_duck_dask_array a bit smarter, to avoid unnecessarily allocating data?

Alternatively, if that's too difficult, perhaps we could add a flag to unstack to disable those checks and just take the "slow" path. In my actual use-case, the slow _unstack_full_reindex is necessary since I have large Dask Arrays. But even then, the unstack completes in less than 3s, while I was getting OOM killed on the v.data checks.

Environment:

Output of <tt>xr.show_versions()</tt> ``` INSTALLED VERSIONS ------------------ commit: None python: 3.8.12 | packaged by conda-forge | (default, Sep 29 2021, 19:52:28) [GCC 9.4.0] python-bits: 64 OS: Linux OS-release: 5.4.0-1040-azure machine: x86_64 processor: x86_64 byteorder: little LC_ALL: C.UTF-8 LANG: C.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.12.1 libnetcdf: 4.8.1 xarray: 0.19.0 pandas: 1.3.3 numpy: 1.20.0 scipy: 1.7.1 netCDF4: 1.5.7 pydap: installed h5netcdf: 0.11.0 h5py: 3.4.0 Nio: None zarr: 2.10.1 cftime: 1.5.1 nc_time_axis: 1.3.1 PseudoNetCDF: None rasterio: 1.2.9 cfgrib: 0.9.9.0 iris: None bottleneck: 1.3.2 dask: 2021.08.1 distributed: 2021.08.1 matplotlib: 3.4.3 cartopy: 0.20.0 seaborn: 0.11.2 numbagg: None pint: 0.17 setuptools: 58.0.4 pip: 20.3.4 conda: None pytest: None IPython: 7.28.0 sphinx: None ``` 
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   completed xarray 13221727 issue

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