Is your feature request related to a problem? Please describe.
as described in SPARK-31973, skipping partial aggregates where data cardinality is high (like group by user_id) sufficiently improves performance.
Describe the solution you'd like
implements partial agg skipping strategy in agg_tables.rs:
- mark an AggExec as skippable. (in spark side, where requiredDistribution is empty)
- process the first N records.
- check the number of input records and the number of aggregated records, if reached threshold, directly outputs all in-memory, spilled and rest records.
Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.
Additional context
Add any other context or screenshots about the feature request here.
Is your feature request related to a problem? Please describe.
as described in SPARK-31973, skipping partial aggregates where data cardinality is high (like
group by user_id) sufficiently improves performance.Describe the solution you'd like
implements partial agg skipping strategy in
agg_tables.rs:Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.
Additional context
Add any other context or screenshots about the feature request here.