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https://issues.apache.org/jira/browse/SPARK-7902?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Davies Liu resolved SPARK-7902.
-------------------------------
Resolution: Fixed
Fix Version/s: 1.5.0
Issue resolved by pull request 7301
[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
> Fix For: 1.5.0
>
>
> 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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