robreeves opened a new pull request, #46490:
URL: https://github.com/apache/spark/pull/46490
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### What changes were proposed in this pull request?
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This introduces a new debugging method to identify which values are
producing skew in a dataset.
### Why are the changes needed?
Debugging skew join is a pain point. Once the skew is identified from the
metrics (e.g. one partition has much more data), it is cumbersome to figure out
exactly where that skew is coming from. Usually you need to write a manual
aggregation and write the output to the logs. This streamlines the process by
adding a method `inlineColumnsCount` to do this automatically, inferring the
join keys to count.
The current solution still requires a code change. A useful extension to
this would be to add a configuration like `spark.sql.join.countkeyvals=true` so
this feature can be enabled without any code changes. It would have to be
enabled for all joins, but for debugging that is okay. This can be useful for
prod jobs and more generally anytime uploading a code change to test is a
longer iteration cycle.
I would also like to add a SQL hint for pure SQL jobs.
### Does this PR introduce _any_ user-facing change?
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### How was this patch tested?
Manual testing:
```
import org.apache.spark.sql.execution.debug._
val a = spark.range(3)
val b = spark.range(100).withColumn("id2", $"id" % 2).select($"id2")
a.join(b, $"id" === $"id2").inlineColumnsCount().collect
Column value counts after processing 10 rows
id#0L: 0,id2#4L: 0 = 10 (100%)
Column value counts after processing 20 rows
id#0L: 0,id2#4L: 0 = 20 (100%)
Column value counts after processing 30 rows
id#0L: 0,id2#4L: 0 = 30 (100%)
Column value counts after processing 40 rows
id#0L: 0,id2#4L: 0 = 40 (100%)
Column value counts after processing 50 rows
id#0L: 0,id2#4L: 0 = 50 (100%)
Column value counts after processing 60 rows
id#0L: 0,id2#4L: 0 = 50 (83%)
id#0L: 1,id2#4L: 1 = 10 (16%)
Column value counts after processing 70 rows
id#0L: 0,id2#4L: 0 = 50 (71%)
id#0L: 1,id2#4L: 1 = 20 (28%)
Column value counts after processing 80 rows
id#0L: 0,id2#4L: 0 = 50 (62%)
id#0L: 1,id2#4L: 1 = 30 (37%)
Column value counts after processing 90 rows
id#0L: 0,id2#4L: 0 = 50 (55%)
id#0L: 1,id2#4L: 1 = 40 (44%)
Column value counts after processing 100 rows
id#0L: 0,id2#4L: 0 = 50 (50%)
id#0L: 1,id2#4L: 1 = 50 (50%)
```
Here is an example of what the plan looks like:
```
== Parsed Logical Plan ==
DebugInlineColumnsCount
+- Join Inner, (id#17L = id2#21L)
:- Range (0, 3, step=1, splits=Some(12))
+- Project [id2#21L]
+- Project [id#19L, (id#19L % cast(2 as bigint)) AS id2#21L]
+- Range (0, 100, step=1, splits=Some(12))
== Analyzed Logical Plan ==
id: bigint, id2: bigint
DebugInlineColumnsCount [id#17L, id2#21L]
+- Join Inner, (id#17L = id2#21L)
:- Range (0, 3, step=1, splits=Some(12))
+- Project [id2#21L]
+- Project [id#19L, (id#19L % cast(2 as bigint)) AS id2#21L]
+- Range (0, 100, step=1, splits=Some(12))
== Optimized Logical Plan ==
DebugInlineColumnsCount [id#17L, id2#21L]
+- Join Inner, (id#17L = id2#21L)
:- Range (0, 3, step=1, splits=Some(12))
+- Project [(id#19L % 2) AS id2#21L]
+- Filter isnotnull((id#19L % 2))
+- Range (0, 100, step=1, splits=Some(12))
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- DebugInlineColumnsCount [id#17L, id2#21L]
+- SortMergeJoin [id#17L], [id2#21L], Inner
:- Sort [id#17L ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(id#17L, 200), ENSURE_REQUIREMENTS,
[plan_id=128]
: +- Range (0, 3, step=1, splits=12)
+- Sort [id2#21L ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(id2#21L, 200), ENSURE_REQUIREMENTS,
[plan_id=127]
+- Project [(id#19L % 2) AS id2#21L]
+- Filter isnotnull((id#19L % 2))
+- Range (0, 100, step=1, splits=12)
```
### Was this patch authored or co-authored using generative AI tooling?
No
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