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
2148245262,PR_kwDOAMm_X85nmmqX,8777,Return a dataclass from Grouper.factorize,2448579,closed,0,,,0,2024-02-22T05:41:29Z,2024-04-03T21:08:25Z,2024-03-15T04:47:30Z,MEMBER,,0,pydata/xarray/pulls/8777,"Toward #8510, builds on #8776","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8777/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
2148164557,PR_kwDOAMm_X85nmU5w,8775,[skip-ci] NamedArray: Add lazy indexing array refactoring plan,2448579,closed,0,,,0,2024-02-22T04:25:49Z,2024-04-03T21:08:21Z,2024-02-23T22:20:09Z,MEMBER,,0,pydata/xarray/pulls/8775,"This adds a proposal for decoupling the lazy indexing array machinery, indexing adapter machinery, and Variable's setitem and getitem methods, so that the latter can be migrated to NamedArray.

cc @andersy005 ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8775/reactions"", ""total_count"": 2, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 2, ""eyes"": 0}",,,13221727,pull
2148242023,PR_kwDOAMm_X85nml9d,8776,Refactor Grouper objects,2448579,closed,0,,,0,2024-02-22T05:38:09Z,2024-03-07T21:50:07Z,2024-03-07T21:50:04Z,MEMBER,,0,pydata/xarray/pulls/8776,"Some refactoring towards the Grouper refactor described in #8510 

1. Rename to Resampler from ResampleGrouper
2. Refactor to a single ""ResolvedGrouper"" object that encapsulates the underling Grouper/Resampler object: UniqueGrouper, BinGrouper, or TimeResampler.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8776/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
2149485914,I_kwDOAMm_X86AHo1a,8778,"Stricter defaults for concat, combine, open_mfdataset",2448579,open,0,,,2,2024-02-22T16:43:38Z,2024-02-23T04:17:40Z,,MEMBER,,,,"### Is your feature request related to a problem?

The defaults for `concat` are excessively permissive: `data_vars=""all"", coords=""different"", compat=""no_conflicts"", join=""outer""`. This [comment](https://github.com/pydata/xarray/issues/1385#issuecomment-1958761334) illustrates why this can be hard to predict or understand: a seemingly unrelated option `decode_cf` controls whether a variable is in `data_vars` or `coords`, and can result in wildly different concatenation behaviour.

1. This always concatenates data_vars along `concat_dim` even if they did not have that dimension to begin with.
2. If the same coordinate var exists in different datasets/files, they will be sequentially compared for equality to decide whether they get concatenated.
3. The outer join (applied along all dimensions that are not `concat_dim`) can result in very large datasets due to small floating points differences in the indexes, and also questionable behaviour with staggered grid datasets.
4. ""no_conflicts"" basically picks the first not-NaN value after aligning all datasets, but is quite slow (we should be using `duck_array_ops.nanfirst` here I think).

While ""convenient"" this really just makes the default experience quite bad with hard-to-understand slowdowns. 


### Describe the solution you'd like

I propose we migrate to `data_vars=""minimal"", coords=""minimal"", join=""exact"", compat=""override""`. This should
1. only concatenate `data_vars` and `coords` variables when they already have `concat_dim`.
2. For any variables that do not have `concat_dim`, it will blindly pick them from the first file.
3. `join=""exact""` will prevent ballooning of dimension sizes due to floating point inequalities.
4. These options will totally avoid any data reads unless explicitly requested by the user.


Unfortunately, this has a pretty big blast radius so we'd need a long deprecation cycle.



### Describe alternatives you've considered

_No response_

### Additional context

xref https://github.com/pydata/xarray/issues/4824
xref https://github.com/pydata/xarray/issues/1385
xref https://github.com/pydata/xarray/issues/8231
xref https://github.com/pydata/xarray/issues/5381
xref https://github.com/pydata/xarray/issues/2064
xref https://github.com/pydata/xarray/issues/2217
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