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Posted to commits@spark.apache.org by ya...@apache.org on 2020/08/29 21:52:34 UTC
[spark] branch branch-3.0 updated: [SPARK-32693][SQL][3.0] Compare
two dataframes with same schema except nullable property
This is an automated email from the ASF dual-hosted git repository.
yamamuro pushed a commit to branch branch-3.0
in repository https://gitbox.apache.org/repos/asf/spark.git
The following commit(s) were added to refs/heads/branch-3.0 by this push:
new 0dff99b [SPARK-32693][SQL][3.0] Compare two dataframes with same schema except nullable property
0dff99b is described below
commit 0dff99b172e0d85c1855fe7a027931bdaf9b3aa7
Author: Liang-Chi Hsieh <vi...@gmail.com>
AuthorDate: Sun Aug 30 06:50:44 2020 +0900
[SPARK-32693][SQL][3.0] Compare two dataframes with same schema except nullable property
### What changes were proposed in this pull request?
This PR changes key data types check in `HashJoin` to use `sameType`. This backports #29555 to branch-3.0.
### Why are the changes needed?
Looks at the resolving condition of `SetOperation`, it requires only each left data types should be `sameType` as the right ones. Logically the `EqualTo` expression in equi-join, also requires only left data type `sameType` as right data type. Then `HashJoin` requires left keys data type exactly the same as right keys data type, looks not reasonable.
It makes inconsistent results when doing `except` between two dataframes.
If two dataframes don't have nested fields, even their field nullable property different, `HashJoin` passes the key type check because it checks field individually so field nullable property is ignored.
If two dataframes have nested fields like struct, `HashJoin` fails the key type check because now it compare two struct types and nullable property now affects.
### Does this PR introduce _any_ user-facing change?
Yes. Making consistent `except` operation between dataframes.
### How was this patch tested?
Unit test.
Closes #29575 from viirya/SPARK-32693-3.0.
Authored-by: Liang-Chi Hsieh <vi...@gmail.com>
Signed-off-by: Takeshi Yamamuro <ya...@apache.org>
---
.../spark/sql/execution/joins/HashJoin.scala | 7 ++--
.../org/apache/spark/sql/DataFrameJoinSuite.scala | 39 ++++++++++++++++++++++
2 files changed, 44 insertions(+), 2 deletions(-)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
index 305741e..931a75a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
@@ -80,8 +80,11 @@ trait HashJoin {
}
protected lazy val (buildKeys, streamedKeys) = {
- require(leftKeys.map(_.dataType) == rightKeys.map(_.dataType),
- "Join keys from two sides should have same types")
+ require(leftKeys.length == rightKeys.length &&
+ leftKeys.map(_.dataType)
+ .zip(rightKeys.map(_.dataType))
+ .forall(types => types._1.sameType(types._2)),
+ "Join keys from two sides should have same length and types")
val lkeys = bindReferences(HashJoin.rewriteKeyExpr(leftKeys), left.output)
val rkeys = bindReferences(HashJoin.rewriteKeyExpr(rightKeys), right.output)
buildSide match {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala
index 6b772e5..a49f95f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala
@@ -17,6 +17,8 @@
package org.apache.spark.sql
+import scala.collection.JavaConverters._
+
import org.apache.spark.sql.catalyst.TableIdentifier
import org.apache.spark.sql.catalyst.plans.{Inner, InnerLike, LeftOuter, RightOuter}
import org.apache.spark.sql.catalyst.plans.logical.{BROADCAST, Filter, HintInfo, Join, JoinHint, LogicalPlan, Project}
@@ -29,6 +31,7 @@ import org.apache.spark.sql.execution.joins.BroadcastHashJoinExec
import org.apache.spark.sql.functions._
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.test.SharedSparkSession
+import org.apache.spark.sql.types._
class DataFrameJoinSuite extends QueryTest
with SharedSparkSession
@@ -418,4 +421,40 @@ class DataFrameJoinSuite extends QueryTest
}
}
}
+
+ test("SPARK-32693: Compare two dataframes with same schema except nullable property") {
+ val schema1 = StructType(
+ StructField("a", IntegerType, false) ::
+ StructField("b", IntegerType, false) ::
+ StructField("c", IntegerType, false) :: Nil)
+ val rowSeq1: List[Row] = List(Row(10, 1, 1), Row(10, 50, 2))
+ val df1 = spark.createDataFrame(rowSeq1.asJava, schema1)
+
+ val schema2 = StructType(
+ StructField("a", IntegerType) ::
+ StructField("b", IntegerType) ::
+ StructField("c", IntegerType) :: Nil)
+ val rowSeq2: List[Row] = List(Row(10, 1, 1))
+ val df2 = spark.createDataFrame(rowSeq2.asJava, schema2)
+
+ checkAnswer(df1.except(df2), Row(10, 50, 2))
+
+ val schema3 = StructType(
+ StructField("a", IntegerType, false) ::
+ StructField("b", IntegerType, false) ::
+ StructField("c", IntegerType, false) ::
+ StructField("d", schema1, false) :: Nil)
+ val rowSeq3: List[Row] = List(Row(10, 1, 1, Row(10, 1, 1)), Row(10, 50, 2, Row(10, 50, 2)))
+ val df3 = spark.createDataFrame(rowSeq3.asJava, schema3)
+
+ val schema4 = StructType(
+ StructField("a", IntegerType) ::
+ StructField("b", IntegerType) ::
+ StructField("b", IntegerType) ::
+ StructField("d", schema2) :: Nil)
+ val rowSeq4: List[Row] = List(Row(10, 1, 1, Row(10, 1, 1)))
+ val df4 = spark.createDataFrame(rowSeq4.asJava, schema4)
+
+ checkAnswer(df3.except(df4), Row(10, 50, 2, Row(10, 50, 2)))
+ }
}
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