asolimando commented on code in PR #19124:
URL: https://github.com/apache/datafusion/pull/19124#discussion_r2603691395
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datafusion/datasource/src/file_scan_config.rs:
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@@ -591,7 +628,52 @@ impl DataSource for FileScanConfig {
Ok(source.map(|s| Arc::new(s) as _))
}
+ /// Returns the output partitioning for this file scan.
+ ///
+ /// When `partitioned_by_file_group` is true, this returns
`Partitioning::Hash` on
+ /// the Hive partition columns, allowing the optimizer to skip hash
repartitioning
+ /// for aggregates and joins on those columns.
+ ///
+ /// Tradeoffs
+ /// - Benefit: Eliminates `RepartitionExec` and `SortExec` for queries with
+ /// `GROUP BY` or `ORDER BY` on partition columns.
+ /// - Cost: Files are grouped by partition values rather than split by byte
+ /// ranges, which may reduce I/O parallelism when partition sizes are
uneven.
+ /// For simple aggregations without `ORDER BY`, this cost may outweigh
the benefit.
+ ///
+ /// Follow-up Work
+ /// - Idea: Could allow byte-range splitting within partition-aware groups,
+ /// preserving I/O parallelism while maintaining partition semantics.
fn output_partitioning(&self) -> Partitioning {
+ if self.partitioned_by_file_group {
+ let partition_cols = self.table_partition_cols();
+ if !partition_cols.is_empty() {
+ let projected_schema = match self.projected_schema() {
+ Ok(schema) => schema,
+ Err(_) => {
+ return
Partitioning::UnknownPartitioning(self.file_groups.len())
Review Comment:
Nit: I think we should log a message here, defaulting to
`UnknownPartitioning` makes sense but the user might be wondering why that
happened
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