Matthew Livesey created SPARK-15251:
---------------------------------------
Summary: Cannot apply PythonUDF to aggregated column
Key: SPARK-15251
URL: https://issues.apache.org/jira/browse/SPARK-15251
Project: Spark
Issue Type: Bug
Components: PySpark
Affects Versions: 1.6.1
Reporter: Matthew Livesey
In scala it is possible to define a UDF an apply it to an aggregated value in
an expression, for example:
{code}
def timesTwo(x: Int): Int = x * 2
sqlContext.udf.register("timesTwo", timesTwo _)
sqlContext.sql("SELECT timesTwo(Sum(x)) t FROM my_data").show()
case class Data(x: Int, y: String)
val data = List(Data(1, "a"), Data(2, "b"))
val rdd = sc.parallelize(data)
val df = rdd.toDF
df.registerTempTable("my_data")
sqlContext.sql("SELECT timesTwo(Sum(x)) t FROM my_data").show()
+---+
| t|
+---+
| 6|
+---+
{code}
Performing the same computation in pyspark:
{code}
def timesTwo(x):
return x * 2
sqlContext.udf.register("timesTwo", timesTwo)
data = [(1, 'a'), (2, 'b')]
rdd = sc.parallelize(data)
df = sqlContext.createDataFrame(rdd, ["x", "y"])
df.registerTempTable("my_data")
sqlContext.sql("SELECT timesTwo(Sum(x)) t FROM my_data").show()
{code}
Gives the following:
{code}
AnalysisException: u"expression 'pythonUDF' is neither present in the group by,
nor is it an aggregate function. Add to group by or wrap in first() (or
first_value) if you don't care which value you get.;"
{code}
Using a lambda rather than a named function gives the same error.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
