Use partial aggregation schema for spilling to avoid column mismatch in GroupedHashAggregateStream

[kosiew]

Which issue does this PR close?

Closes #13949.

Rationale for this change

When an aggregation operator spills intermediate (partial) state to disk, it needs a schema that includes both the group-by columns and partial-aggregator columns (e.g., partial sums, counts, etc.). Previously, the code used the original input schema for spilling, which does not match the additional columns representing aggregator states. As a result, reading back the spilled data caused a mismatch error:

ArrowError(InvalidArgumentError(
  "number of columns(3) must match number of fields(2) in schema"
))

This PR addresses that by introducing a partial aggregation schema that combines group columns and aggregator state columns, ensuring consistency when spilling and later reading the spilled data.

What changes are included in this PR?

1. A new helper function, build_partial_agg_schema(), creates a partial schema by merging:

• Group-by fields
• Each aggregator’s internal “state fields”

2. The aggregate operator is updated to use this partial schema when spilling or merging spilled data rather than the original (input) schema, which fixes the column mismatch error.

Are these changes tested?

Yes

Are there any user-facing changes?

No

[2010YOUY01]

Thank you. I found the fix easy to follow 😄, and the change makes sense to me.

I have a suggestion to improve test coverage:
Since min/max only has one intermediate aggregate state (partial min/max), we should also test aggregate functions that produce more than one intermediate state, like avg (partial sum and count).
Duplicating the existing test and modifying one of the aggregate functions to avg should be sufficient.

[2010YOUY01]

I suggest to add an assertion here to make sure spilling actually happened for certain test cases. Like:

        let metrics = single_aggregate.metrics();
        // ...and assert some metrics inside like 'spill count' is > 0