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https://issues.apache.org/jira/browse/SPARK-34771?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17331375#comment-17331375
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Apache Spark commented on SPARK-34771:
--------------------------------------

User 'sadhen' has created a pull request for this issue:
https://github.com/apache/spark/pull/32327

> Support UDT for Pandas/Spark conversion with Arrow support Enabled
> ------------------------------------------------------------------
>
>                 Key: SPARK-34771
>                 URL: https://issues.apache.org/jira/browse/SPARK-34771
>             Project: Spark
>          Issue Type: Sub-task
>          Components: PySpark
>    Affects Versions: 3.0.2, 3.1.1
>            Reporter: Darcy Shen
>            Priority: Major
>
> {code:python}
> spark.conf.set("spark.sql.execution.arrow.enabled", "true")
> from pyspark.testing.sqlutils  import ExamplePoint
> import pandas as pd
> pdf = pd.DataFrame({'point': pd.Series([ExamplePoint(1, 1), ExamplePoint(2, 
> 2)])})
> df = spark.createDataFrame(pdf)
> df.toPandas()
> {code}
> with `spark.sql.execution.arrow.enabled` = false, the above snippet works 
> fine without WARNINGS.
> with `spark.sql.execution.arrow.enabled` = true, the above snippet works fine 
> with WARNINGS. Because of Unsupported type in conversion, the Arrow 
> optimization is actually turned off. 
> Detailed steps to reproduce:
> {code:python}
> $ bin/pyspark
> Python 3.8.8 (default, Feb 24 2021, 13:46:16)
> [Clang 10.0.0 ] :: Anaconda, Inc. on darwin
> Type "help", "copyright", "credits" or "license" for more information.
> Using Spark's default log4j profile: 
> org/apache/spark/log4j-defaults.properties
> Setting default log level to "WARN".
> To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use 
> setLogLevel(newLevel).
> 21/03/17 23:13:27 WARN NativeCodeLoader: Unable to load native-hadoop library 
> for your platform... using builtin-java classes where applicable
> Welcome to
>       ____              __
>      / __/__  ___ _____/ /__
>     _\ \/ _ \/ _ `/ __/  '_/
>    /__ / .__/\_,_/_/ /_/\_\   version 3.2.0-SNAPSHOT
>       /_/
> Using Python version 3.8.8 (default, Feb 24 2021 13:46:16)
> Spark context Web UI available at http://172.30.0.226:4040
> Spark context available as 'sc' (master = local[*], app id = 
> local-1615994008526).
> SparkSession available as 'spark'.
> >>> spark.conf.set("spark.sql.execution.arrow.enabled", "true")
> 21/03/17 23:13:31 WARN SQLConf: The SQL config 
> 'spark.sql.execution.arrow.enabled' has been deprecated in Spark v3.0 and may 
> be removed in the future. Use 'spark.sql.execution.arrow.pyspark.enabled' 
> instead of it.
> >>> from pyspark.testing.sqlutils  import ExamplePoint
> >>> import pandas as pd
> >>> pdf = pd.DataFrame({'point': pd.Series([ExamplePoint(1, 1), 
> >>> ExamplePoint(2, 2)])})
> >>> df = spark.createDataFrame(pdf)
> /Users/da/github/apache/spark/python/pyspark/sql/pandas/conversion.py:332: 
> UserWarning: createDataFrame attempted Arrow optimization because 
> 'spark.sql.execution.arrow.pyspark.enabled' is set to true; however, failed 
> by the reason below:
>   Could not convert (1,1) with type ExamplePoint: did not recognize Python 
> value type when inferring an Arrow data type
> Attempting non-optimization as 
> 'spark.sql.execution.arrow.pyspark.fallback.enabled' is set to true.
>   warnings.warn(msg)
> >>>
> >>> df.show()
> +----------+
> |     point|
> +----------+
> |(0.0, 0.0)|
> |(0.0, 0.0)|
> +----------+
> >>> df.schema
> StructType(List(StructField(point,ExamplePointUDT,true)))
> >>> df.toPandas()
> /Users/da/github/apache/spark/python/pyspark/sql/pandas/conversion.py:87: 
> UserWarning: toPandas attempted Arrow optimization because 
> 'spark.sql.execution.arrow.pyspark.enabled' is set to true; however, failed 
> by the reason below:
>   Unsupported type in conversion to Arrow: ExamplePointUDT
> Attempting non-optimization as 
> 'spark.sql.execution.arrow.pyspark.fallback.enabled' is set to true.
>   warnings.warn(msg)
>        point
> 0  (0.0,0.0)
> 1  (0.0,0.0)
> {code}



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