[jira] [Updated] (SPARK-7902) SQL UDF doesn't support UDT in PySpark

["Michael Armbrust (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-7902?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Michael Armbrust updated SPARK-7902:
------------------------------------
    Priority: Critical  (was: Major)

> 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
>            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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