[jira] [Updated] (SPARK-26352) join reordering should not change the order of output attributes

["Josh Rosen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-26352?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Josh Rosen updated SPARK-26352:
-------------------------------
    Labels: correctness  (was: )

> join reordering should not change the order of output attributes
> ----------------------------------------------------------------
>
>                 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
>            Assignee: Kris Mok
>            Priority: Major
>              Labels: correctness
>             Fix For: 2.3.3, 2.4.1, 3.0.0
>
>
> 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.



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