mustafasrepo commented on code in PR #4691:
URL: https://github.com/apache/arrow-datafusion/pull/4691#discussion_r1095492409
##########
datafusion/core/tests/sql/window.rs:
##########
@@ -1748,17 +1748,20 @@ async fn test_window_partition_by_order_by() ->
Result<()> {
let msg = format!("Creating logical plan for '{}'", sql);
let dataframe = ctx.sql(sql).await.expect(&msg);
- let physical_plan = dataframe.create_physical_plan().await.unwrap();
+ let physical_plan = dataframe.create_physical_plan().await?;
let formatted = displayable(physical_plan.as_ref()).indent().to_string();
- // Only 1 SortExec was added
let expected = {
vec![
- "ProjectionExec: expr=[SUM(aggregate_test_100.c4) PARTITION BY
[aggregate_test_100.c1, aggregate_test_100.c2] ORDER BY [aggregate_test_100.c2
ASC NULLS LAST] ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING@0 as
SUM(aggregate_test_100.c4), COUNT(UInt8(1)) PARTITION BY
[aggregate_test_100.c1] ORDER BY [aggregate_test_100.c2 ASC NULLS LAST] ROWS
BETWEEN 1 PRECEDING AND 1 FOLLOWING@1 as COUNT(UInt8(1))]",
- " WindowAggExec: wdw=[SUM(aggregate_test_100.c4): Ok(Field {
name: \"SUM(aggregate_test_100.c4)\", data_type: Int64, nullable: true,
dict_id: 0, dict_is_ordered: false, metadata: {} }), frame: WindowFrame {
units: Rows, start_bound: Preceding(UInt64(1)), end_bound: Following(UInt64(1))
}, COUNT(UInt8(1)): Ok(Field { name: \"COUNT(UInt8(1))\", data_type: Int64,
nullable: true, dict_id: 0, dict_is_ordered: false, metadata: {} }), frame:
WindowFrame { units: Rows, start_bound: Preceding(UInt64(1)), end_bound:
Following(UInt64(1)) }]",
- " SortExec: [c1@0 ASC NULLS LAST,c2@1 ASC NULLS LAST]",
+ "ProjectionExec: expr=[SUM(aggregate_test_100.c4) PARTITION BY
[aggregate_test_100.c1, aggregate_test_100.c2] ORDER BY [aggregate_test_100.c2
ASC NULLS LAST] ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING@1 as
SUM(aggregate_test_100.c4), COUNT(UInt8(1)) PARTITION BY
[aggregate_test_100.c1] ORDER BY [aggregate_test_100.c2 ASC NULLS LAST] ROWS
BETWEEN 1 PRECEDING AND 1 FOLLOWING@0 as COUNT(UInt8(1))]",
+ " WindowAggExec: wdw=[COUNT(UInt8(1)): Ok(Field { name:
\"COUNT(UInt8(1))\", data_type: Int64, nullable: true, dict_id: 0,
dict_is_ordered: false, metadata: {} }), frame: WindowFrame { units: Rows,
start_bound: Preceding(UInt64(1)), end_bound: Following(UInt64(1)) }]",
+ " SortExec: [c1@1 ASC NULLS LAST,c2@2 ASC NULLS LAST]",
" CoalesceBatchesExec: target_batch_size=4096",
- " RepartitionExec: partitioning=Hash([Column { name:
\"c1\", index: 0 }], 2)",
- " RepartitionExec: partitioning=RoundRobinBatch(2)",
+ " RepartitionExec: partitioning=Hash([Column { name:
\"c1\", index: 1 }], 2)",
+ " WindowAggExec: wdw=[SUM(aggregate_test_100.c4):
Ok(Field { name: \"SUM(aggregate_test_100.c4)\", data_type: Int64, nullable:
true, dict_id: 0, dict_is_ordered: false, metadata: {} }), frame: WindowFrame {
units: Rows, start_bound: Preceding(UInt64(1)), end_bound: Following(UInt64(1))
}]",
+ " SortExec: [c1@0 ASC NULLS LAST,c2@1 ASC NULLS LAST]",
Review Comment:
The reason is that ordering requirement by `ORDER BY` clause gives the
required ordering for `WindowAggExec` to produce desired result. However,
`PARTITION BY` ordering is only used to determine partition boundaries. Hence
as long as we know that `PARTITION BY` columns are ordered (ordering direction
is not important). We can calculate partition boundaries. This differentiation
between `PARTITION BY` columns and `ORDER BY` columns enables us to produce
better plans. With this differation we can produce more optimized plans for
queries like following
```sql
SELECT
c9, SUM(c9) OVER(ORDER BY c1, c9 DESC ROWS BETWEEN 1 PRECEDING AND 5
FOLLOWING) as sum1,
SUM(c9) OVER(PARTITION BY c1 ORDER BY c9 DESC ROWS BETWEEN 1 PRECEDING
AND 5 FOLLOWING) as sum2
FROM aggregate_test_100
LIMIT 5
```
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