[jira] [Commented] (SPARK-12157) Support numpy types as return values of Python UDFs

["Joseph K. Bradley (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-12157?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15043544#comment-15043544 ] 

Joseph K. Bradley commented on SPARK-12157:
-------------------------------------------

To confirm: Is this just for scalar types?

I'm asking since, for numpy arrays, it might make sense to create UDTs for them.

> Support numpy types as return values of Python UDFs
> ---------------------------------------------------
>
>                 Key: SPARK-12157
>                 URL: https://issues.apache.org/jira/browse/SPARK-12157
>             Project: Spark
>          Issue Type: Improvement
>          Components: PySpark, SQL
>    Affects Versions: 1.5.2
>            Reporter: Justin Uang
>
> Currently, if I have a python UDF
> {code}
> import pyspark.sql.types as T
> import pyspark.sql.functions as F
> from pyspark.sql import Row
> import numpy as np
> argmax = F.udf(lambda x: np.argmax(x), T.IntegerType())
> df = sqlContext.createDataFrame([Row(array=[1,2,3])])
> df.select(argmax("array")).count()
> {code}
> I get an exception that is fairly opaque:
> {code}
> Caused by: net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for numpy.dtype)
>         at net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
>         at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:701)
>         at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:171)
>         at net.razorvine.pickle.Unpickler.load(Unpickler.java:85)
>         at net.razorvine.pickle.Unpickler.loads(Unpickler.java:98)
>         at org.apache.spark.sql.execution.BatchPythonEvaluation$$anonfun$doExecute$1$$anonfun$apply$3.apply(python.scala:404)
>         at org.apache.spark.sql.execution.BatchPythonEvaluation$$anonfun$doExecute$1$$anonfun$apply$3.apply(python.scala:403)
> {code}
> Numpy types like np.int and np.float64 should automatically be cast to the proper dtypes.



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