Extend windowed aggregate allow-list (collect_set, collect_list, and other natively-supported aggregates) #4766

What is the problem the feature request solves?

The CometWindowExec translator carries a hard-coded allow-list of aggregate functions that may run inside a window:

https://github.com/apache/datafusion-comet/blob/main/spark/src/main/scala/org/apache/spark/sql/comet/CometWindowExec.scala#L100-L141

Today it accepts Count, Min, Max, Sum, Average, First, Last. Anything else hits the catch-all and tags a fallback:

aggregate `collect_set(...)` is not supported for window function

Several aggregates that Comet does support in batch mode are excluded by this list, so queries that wrap them in OVER (...) fall back to Spark even when DataFusion's window machinery could execute them natively. Examples I've hit or expect to hit:

• collect_set / collect_list — fully supported as batch aggregates (Support collect_set built in function #3951, Integrate collect_set to Comet #2525)
• StddevSamp, StddevPop, VarSamp, VarPop
• CorrPearson, Covariance
• BitAnd, BitOr, BitXor
• BloomFilterAggregate

Describe the potential solution

Two paths, in increasing scope:

1. Targeted whitelist additions — add cases to the match in CometWindowExec.scala:100 for each aggregate where DataFusion's window operator can already evaluate it, gated on the relevant data-type checks (the existing Sum/Average/Min/Max arms already follow this pattern). collect_set and collect_list are the cheapest to land first because they have no sliding-frame subtleties when the frame is unbounded.

2. Generalize dispatch — replace the allow-list with a single check that delegates to the same AggSerde path used by the batch aggregate operator, and let DataFusion reject anything it can't run. This stops the gap from re-opening every time a new aggregate is added.

Either way, sliding-frame semantics need consideration for the non-invertible aggregates (collect_*, the variance/covariance family), since DataFusion may have to recompute each frame from scratch. Spark behaves the same way, so correctness shouldn't be at risk — performance may be.

Additional context

Related:

• Enable windowed aggregates and fix correctness issues #4007 (closed) enabled the current window-aggregate allow-list
• AVG(decimal) over a window always falls back to Spark on Spark 4.x (AvgDecimal window branch is dead) #4731 — AVG(decimal) window fallback (same class of gap, different aggregate)
• Support fully-native multi-stage (distinct-combined) collect_list / collect_set #4724 — multi-stage collect_list / collect_set (batch path, not windows)

This issue tracks the window side specifically.