wangyum commented on a change in pull request #28642:
URL: https://github.com/apache/spark/pull/28642#discussion_r739098037
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
##########
@@ -1215,6 +1215,15 @@ object InferFiltersFromConstraints extends
Rule[LogicalPlan]
}
}
+ // Whether the result of this expression may be null. For example:
CAST(strCol AS double)
+ // We will infer an IsNotNull expression for this expression to avoid skew
join.
Review comment:
We can infer `IsNotNull(col)` already. For example:
```scala
spark.sql("create table t1 (id string, value int) using parquet")
spark.sql("create table t2 (id int, value int) using parquet")
spark.sql("select * from t1 join t2 on t1.id = t2.id").explain("extended")
```
Before this pr:
```
== Optimized Logical Plan ==
Join Inner, (cast(id#0 as int) = id#2)
:- Filter isnotnull(id#0)
: +- Relation default.t1[id#0,value#1] parquet
+- Filter isnotnull(id#2)
+- Relation default.t2[id#2,value#3] parquet
```
After this pr:
```
== Optimized Logical Plan ==
Join Inner, (cast(id#0 as int) = id#2)
:- Filter (isnotnull(id#0) AND isnotnull(cast(id#0 as int)))
: +- Relation default.t1[id#0,value#1] parquet
+- Filter isnotnull(id#2)
+- Relation default.t2[id#2,value#3] parquet
```
Infer `isnotnull(cast(t1.id as int))` may filter out many strings that can
not be casted to int.
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