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Posted to issues@spark.apache.org by "Jakob Odersky (JIRA)" <ji...@apache.org> on 2016/01/06 23:22:39 UTC
[jira] [Comment Edited] (SPARK-12648) UDF with Option[Double]
throws ClassCastException
[ https://issues.apache.org/jira/browse/SPARK-12648?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15086388#comment-15086388 ]
Jakob Odersky edited comment on SPARK-12648 at 1/6/16 10:22 PM:
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In spark-shell:
{code}
val df = sc.parallelize(List(("a", Some(4D)), ("b", None))).toDF("name", "weight")
df: org.apache.spark.sql.DataFrame = [name: string, weight: double]
{code}
You're getting a DataFrame containing doubles, not optional doubles. Not 100% sure, but I'm guessing that creating a DataFrame from Option types is syntactic sugar to avoid using nulls in client code. Spark then optimizes the option types to nullable or default values.
was (Author: jodersky):
In spark-shell:
{code}
val df = sc.parallelize(List(("a", Some(4D)), ("b", None))).toDF("name", "weight")
df: org.apache.spark.sql.DataFrame = [name: string, weight: double]
{/code}
You're getting a DataFrame containing doubles, not optional doubles. Not 100% sure, but I'm guessing that creating a DataFrame from Option types is syntactic sugar to avoid using nulls in client code. Spark then optimizes the option types to nullable or default values.
> UDF with Option[Double] throws ClassCastException
> -------------------------------------------------
>
> Key: SPARK-12648
> URL: https://issues.apache.org/jira/browse/SPARK-12648
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.6.0
> Reporter: Mikael Valot
>
> I can write an UDF that returns an Option[Double], and the DataFrame's schema is correctly inferred to be a nullable double.
> However I cannot seem to be able to write a UDF that takes an Option as an argument:
> import org.apache.spark.sql.SQLContext
> import org.apache.spark.{SparkContext, SparkConf}
> val conf = new SparkConf().setMaster("local[4]").setAppName("test")
> val sc = new SparkContext(conf)
> val sqlc = new SQLContext(sc)
> import sqlc.implicits._
> val df = sc.parallelize(List(("a", Some(4D)), ("b", None))).toDF("name", "weight")
> import org.apache.spark.sql.functions._
> val addTwo = udf((d: Option[Double]) => d.map(_+2))
> df.withColumn("plusTwo", addTwo(df("weight"))).show()
> =>
> 2016-01-05T14:41:52 Executor task launch worker-0 ERROR org.apache.spark.executor.Executor Exception in task 0.0 in stage 1.0 (TID 1)
> java.lang.ClassCastException: java.lang.Double cannot be cast to scala.Option
> at $line14.$read$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$1.apply(<console>:18) ~[na:na]
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source) ~[na:na]
> at org.apache.spark.sql.execution.Project$$anonfun$1$$anonfun$apply$1.apply(basicOperators.scala:51) ~[spark-sql_2.10-1.6.0.jar:1.6.0]
> at org.apache.spark.sql.execution.Project$$anonfun$1$$anonfun$apply$1.apply(basicOperators.scala:49) ~[spark-sql_2.10-1.6.0.jar:1.6.0]
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) ~[scala-library-2.10.5.jar:na]
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