[jira] [Commented] (SPARK-50839) Spark Connect createDataFrame behavior does not match with classic Spark

["Hyukjin Kwon (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-50839?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17913531#comment-17913531 ] 

Hyukjin Kwon commented on SPARK-50839:
--------------------------------------

Actually I think we should just leave this behavior as is ... maybe we had to allow this in Spark Classic too.. Spark Connect is already out, and I want to avoid breaking changes. Let's leave as is for now.

> Spark Connect createDataFrame behavior does not match with classic Spark
> ------------------------------------------------------------------------
>
>                 Key: SPARK-50839
>                 URL: https://issues.apache.org/jira/browse/SPARK-50839
>             Project: Spark
>          Issue Type: Bug
>          Components: Connect, PySpark
>    Affects Versions: 4.0.0
>            Reporter: Harsh Motwani
>            Priority: Major
>
> Classic Spark and Spark Connect behave differently when `List(Integer)` is passed as the first argument instead of `List(Row)` or `List(Tuple)`. I personally agree with the classic Spark behavior and Spark Connect should fail in this case as well.
> In classic Spark shell (PySpark):
> {code:java}
> >>> spark.createDataFrame([1], "v int").show()
> Traceback (most recent call last):
>   File "<stdin>", line 1, in <module>
> ...
> pyspark.errors.exceptions.base.PySparkTypeError: [FIELD_DATA_TYPE_UNACCEPTABLE] StructType([StructField('v', IntegerType(), True)]) can not accept object 1 in type <class 'int'>.
> {code}
> In Spark Connect shell (PySpark):
> {code:java}
> >>> spark.createDataFrame([1], "v int").show()
> 25/01/15 14:05:01 WARN CheckAllocator: More than one DefaultAllocationManager on classpath. Choosing first found
> +---+
> | v|
> +---+
> | 1|
> +---+
> {code}



--
This message was sent by Atlassian Jira
(v8.20.10#820010)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org