[ https://issues.apache.org/jira/browse/SPARK-23352?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16355442#comment-16355442 ]
Apache Spark commented on SPARK-23352: -------------------------------------- User 'HyukjinKwon' has created a pull request for this issue: https://github.com/apache/spark/pull/20531 > Explicitly specify supported types in Pandas UDFs > ------------------------------------------------- > > Key: SPARK-23352 > URL: https://issues.apache.org/jira/browse/SPARK-23352 > Project: Spark > Issue Type: Sub-task > Components: PySpark > Affects Versions: 2.3.0 > Reporter: Hyukjin Kwon > Priority: Major > > Currently, we don't support {{BinaryType}} in Pandas UDFs: > {code} > >>> from pyspark.sql.functions import pandas_udf > >>> pudf = pandas_udf(lambda x: x, "binary") > >>> df = spark.createDataFrame([[bytearray("a")]]) > >>> df.select(pudf("_1")).show() > ... > TypeError: Unsupported type in conversion to Arrow: BinaryType > {code} > Also, the grouped aggregate Pandas UDF fail fast on {{ArrayType}} but seems > we can support this case. > We should better clarify it in doc in Pandas UDFs, and fail fast with type > checking ahead, rather than execution time. > Please consider this case: > {code} > pandas_udf(lambda x: x, BinaryType()) # we can fail fast at this stage > because we know the schema ahead > {code} -- 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