html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/pull/7494#issuecomment-1458453929,https://api.github.com/repos/pydata/xarray/issues/7494,1458453929,IC_kwDOAMm_X85W7j2p,5179430,2023-03-07T16:22:21Z,2023-03-07T16:22:21Z,CONTRIBUTOR,Thanks @Illviljan and @dcherian for helping to see this through.,"{""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1563270549
https://github.com/pydata/xarray/pull/7494#issuecomment-1411206291,https://api.github.com/repos/pydata/xarray/issues/7494,1411206291,IC_kwDOAMm_X85UHUyT,5179430,2023-01-31T23:17:38Z,2023-01-31T23:17:38Z,CONTRIBUTOR,"@Illviljan I gave your update a quick test, it seems to work well enough and still maintains the performance improvement. It looks fine to me though I guess it looks like you still need to fix this failing mypy stuff now?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1563270549
https://github.com/pydata/xarray/pull/7494#issuecomment-1410253782,https://api.github.com/repos/pydata/xarray/issues/7494,1410253782,IC_kwDOAMm_X85UDsPW,5179430,2023-01-31T12:22:02Z,2023-01-31T12:26:37Z,CONTRIBUTOR,"> Thanks for the PR. However, does that actually make a difference? To me it looks like `_contains_cftime_datetimes` also only considers one element of the array.
> 
> https://github.com/pydata/xarray/blob/b4515582ffc8b7f63632bfccd109d19889d00384/xarray/core/common.py#L1779-L1780

This isn't actually the line of code that's causing the performance bottleneck, it's the access to `var.data`  in the function call that is actually problematic as I explained in the issue thread. You can verify this yourself running this simple example before and after applying the changes in this PR:

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

str_array = np.arange(100000000).astype(str)
ds = xr.DataArray(dims=('x',), data=str_array).to_dataset(name='str_array')
ds = ds.chunk(x=10000)
ds['str_array'] = ds.str_array.astype('O') # Needs to actually be object dtype to show the problem
ds.to_zarr('str_array.zarr')

%time xr.open_zarr('str_array.zarr')
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1563270549