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ASF GitHub Bot commented on SPARK-26352: ---------------------------------------- rednaxelafx opened a new pull request #23303: [SPARK-26352][SQL] ReorderJoin should not change the order of columns URL: https://github.com/apache/spark/pull/23303 ## What changes were proposed in this pull request? The optimizer rule `org.apache.spark.sql.catalyst.optimizer.ReorderJoin` performs join reordering on inner joins. This was introduced from SPARK-12032 (https://github.com/apache/spark/pull/10073) in 2015-12. After it had reordered the joins, though, it didn't check whether or not the column order (in terms of the `output` attribute list) is still the same as before. Thus, it's possible to have a mismatch between the reordered column order vs the schema that a DataFrame thinks it has. This can be demonstrated with the example: ```scala spark.sql("create table table_a (x int, y int) using parquet") spark.sql("create table table_b (i int, j int) using parquet") spark.sql("create table table_c (a int, b int) using parquet") val df = spark.sql(""" with df1 as (select * from table_a cross join table_b) select * from df1 join table_c on a = x and b = i """) ``` here's what the DataFrame thinks: ``` scala> df.printSchema root |-- x: integer (nullable = true) |-- y: integer (nullable = true) |-- i: integer (nullable = true) |-- j: integer (nullable = true) |-- a: integer (nullable = true) |-- b: integer (nullable = true) ``` here's what the optimized plan thinks, after join reordering: ``` scala> df.queryExecution.optimizedPlan.output.foreach(a => println(s"|-- ${a.name}: ${a.dataType.typeName}")) |-- x: integer |-- y: integer |-- a: integer |-- b: integer |-- i: integer |-- j: integer ``` If we exclude the `ReorderJoin` rule (using Spark 2.4's optimizer rule exclusion feature), it's back to normal: ``` scala> spark.conf.set("spark.sql.optimizer.excludedRules", "org.apache.spark.sql.catalyst.optimizer.ReorderJoin") scala> val df = spark.sql("with df1 as (select * from table_a cross join table_b) select * from df1 join table_c on a = x and b = i") df: org.apache.spark.sql.DataFrame = [x: int, y: int ... 4 more fields] scala> df.queryExecution.optimizedPlan.output.foreach(a => println(s"|-- ${a.name}: ${a.dataType.typeName}")) |-- x: integer |-- y: integer |-- i: integer |-- j: integer |-- a: integer |-- b: integer ``` Note that this column ordering problem leads to data corruption, and can manifest itself in various symptoms: * Silently corrupting data, if the reordered columns happen to either have matching types or have sufficiently-compatible types (e.g. all fixed length primitive types are considered as "sufficiently compatible" in an `UnsafeRow`), then only the resulting data is going to be wrong but it might not trigger any alarms immediately. Or * Weird Java-level exceptions like `java.lang.NegativeArraySizeException`, or even SIGSEGVs. ## How was this patch tested? Added new unit test in `JoinReorderSuite` and new end-to-end test in `JoinSuite`. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org > ReorderJoin should not change the order of columns > -------------------------------------------------- > > Key: SPARK-26352 > URL: https://issues.apache.org/jira/browse/SPARK-26352 > Project: Spark > Issue Type: Bug > Components: SQL > Affects Versions: 2.3.0, 2.4.0 > Reporter: Kris Mok > Priority: Major > > The optimizer rule {{org.apache.spark.sql.catalyst.optimizer.ReorderJoin}} > performs join reordering on inner joins. This was introduced from SPARK-12032 > in 2015-12. > After it had reordered the joins, though, it didn't check whether or not the > column order (in terms of the {{output}} attribute list) is still the same as > before. Thus, it's possible to have a mismatch between the reordered column > order vs the schema that a DataFrame thinks it has. > This can be demonstrated with the example: > {code:none} > spark.sql("create table table_a (x int, y int) using parquet") > spark.sql("create table table_b (i int, j int) using parquet") > spark.sql("create table table_c (a int, b int) using parquet") > val df = spark.sql("with df1 as (select * from table_a cross join table_b) > select * from df1 join table_c on a = x and b = i") > {code} > here's what the DataFrame thinks: > {code:none} > scala> df.printSchema > root > |-- x: integer (nullable = true) > |-- y: integer (nullable = true) > |-- i: integer (nullable = true) > |-- j: integer (nullable = true) > |-- a: integer (nullable = true) > |-- b: integer (nullable = true) > {code} > here's what the optimized plan thinks, after join reordering: > {code:none} > scala> df.queryExecution.optimizedPlan.output.foreach(a => println(s"|-- > ${a.name}: ${a.dataType.typeName}")) > |-- x: integer > |-- y: integer > |-- a: integer > |-- b: integer > |-- i: integer > |-- j: integer > {code} > If we exclude the {{ReorderJoin}} rule (using Spark 2.4's optimizer rule > exclusion feature), it's back to normal: > {code:none} > scala> spark.conf.set("spark.sql.optimizer.excludedRules", > "org.apache.spark.sql.catalyst.optimizer.ReorderJoin") > scala> val df = spark.sql("with df1 as (select * from table_a cross join > table_b) select * from df1 join table_c on a = x and b = i") > df: org.apache.spark.sql.DataFrame = [x: int, y: int ... 4 more fields] > scala> df.queryExecution.optimizedPlan.output.foreach(a => println(s"|-- > ${a.name}: ${a.dataType.typeName}")) > |-- x: integer > |-- y: integer > |-- i: integer > |-- j: integer > |-- a: integer > |-- b: integer > {code} > Note that this column ordering problem leads to data corruption, and can > manifest itself in various symptoms: > * Silently corrupting data, if the reordered columns happen to either have > matching types or have sufficiently-compatible types (e.g. all fixed length > primitive types are considered as "sufficiently compatible" in an UnsafeRow), > then only the resulting data is going to be wrong but it might not trigger > any alarms immediately. Or > * Weird Java-level exceptions like {{java.lang.NegativeArraySizeException}}, > or even SIGSEGVs. -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org