Justin Uang created SPARK-12157:
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Summary: 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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