comphead commented on PR #7256:
URL: 
https://github.com/apache/datafusion-comet/pull/7256#issuecomment-6612993769

   Thanks @andygrove. 3ff09398ec fixes the unpartitioned case instead of 
documenting it, so no issue is needed to track it.
   
   1. The collation fallback no longer depends on trust. A window with a 
collated key always falls back to Spark. When `mayReportFalseOrder` doubts the 
order a window's input reports, `CometExecRule` sorts the input of a window 
that stays in Spark, so Spark's window sees the rows in order and applies the 
collation. The comments on the guard and on the new pass say so. The trust 
check now decides only what the native window can rely on. A window with `ORDER 
BY` but no `PARTITION BY` over a doubted order falls back too, since the native 
window checks no order there.
   2. `compatibility/operators.md` now lists collated keys among the window's 
fallbacks. It said so only for `WindowGroupLimitExec` before, and with this 
change it holds for the window in every case.
   3. `a window that stays in Spark over a nested loop join sorts the join's 
rows again` runs sunchao's unpartitioned fixture, his partitioned one from the 
review before, and the same unpartitioned shape with an integer key. It 
compares the rows with Spark, and it fails without the new pass.
   4. On `rust-test`: since #7311 the upstream run skips it for a pull request 
from a fork, and the fork run behind `CI (fork)` runs it. It passed there on 
`1bb942a33a`.
   
   The nested loop join's own order (#6898) is unchanged. A partitioned native 
window above it still fails with the ordering error, and #6785 fixes that by 
keeping such a join in Spark.
   


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