[jira] [Assigned] (SPARK-22232) Row objects in pyspark created using the `Row(**kwars)` syntax do not get serialized/deserialized properly

["Apache Spark (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-22232?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-22232:
------------------------------------

    Assignee:     (was: Apache Spark)

> Row objects in pyspark created using the `Row(**kwars)` syntax do not get serialized/deserialized properly
> ----------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-22232
>                 URL: https://issues.apache.org/jira/browse/SPARK-22232
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark, SQL
>    Affects Versions: 2.2.0
>            Reporter: Bago Amirbekian
>            Priority: Major
>
> The fields in a Row object created from a dict (ie {{Row(**kwargs)}}) should be accessed by field name, not by position because {{Row.__new__}} sorts the fields alphabetically by name. It seems like this promise is not being honored when these Row objects are shuffled. I've included an example to help reproduce the issue.
> {code:none}
> from pyspark.sql.types import *
> from pyspark.sql import *
> def toRow(i):
>   return Row(a="a", c=3.0, b=2)
> schema = StructType([
>   # Putting fields in alphabetical order masks the issue
>   StructField("a", StringType(),  False),
>   StructField("c", FloatType(), False),
>   StructField("b", IntegerType(), False),
> ])
> rdd = sc.parallelize(range(10)).repartition(2).map(lambda i: toRow(i))
> # As long as we don't shuffle things work fine.
> print rdd.toDF(schema).take(2)
> # If we introduce a shuffle we have issues
> print rdd.repartition(3).toDF(schema).take(2)
> {code}



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