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https://issues.apache.org/jira/browse/SPARK-7902?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14619675#comment-14619675
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Apache Spark commented on SPARK-7902:
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User 'davies' has created a pull request for this issue:
https://github.com/apache/spark/pull/7301
> SQL UDF doesn't support UDT in PySpark
> --------------------------------------
>
> Key: SPARK-7902
> URL: https://issues.apache.org/jira/browse/SPARK-7902
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
> Affects Versions: 1.4.0
> Reporter: Xiangrui Meng
> Assignee: Davies Liu
> Priority: Critical
>
> We don't convert Python SQL internal types to Python types in SQL UDF
> execution. This causes problems if the input arguments contain UDTs or the
> return type is a UDT. Right now, the raw SQL types are passed into the Python
> UDF and the return value is not converted to Python SQL types.
> This is the code (from [~rams]) to produce this bug. (Actually, it triggers
> another bug first right now.)
> {code}
> from pyspark.mllib.linalg import SparseVector
> from pyspark.sql.functions import udf
> from pyspark.sql.types import IntegerType
> df = sqlContext.createDataFrame([(SparseVector(2, {0: 0.0}),)], ["features"])
> sz = udf(lambda s: s.size, IntegerType())
> df.select(sz(df.features).alias("sz")).collect()
> {code}
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