星火：任务不序列化的UDF对数据帧

Question

我得到org.apache.spark.SparkException: Task not serializable当我尝试执行对星火1.4.1如下：

import java.sql.{Date, Timestamp} 
import java.text.SimpleDateFormat 

object ConversionUtils { 
    val iso8601 = new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSX") 

    def tsUTC(s: String): Timestamp = new Timestamp(iso8601.parse(s).getTime) 

    val castTS = udf[Timestamp, String](tsUTC _) 
} 

val df = frame.withColumn("ts", ConversionUtils.castTS(frame("ts_str"))) 
df.first 

这里，frame是DataFrame一个HiveContext内的生活。该数据框没有任何问题。

我有类似的UDF整数，他们工作没有任何问题。但是，带时间戳的那个似乎会造成问题。根据documentation,java.sql.TimeStamp执行Serializable，所以这不是问题。对于SimpleDateFormat也是如此，可以看到here。

这使我相信这是造成问题的UDF。但是，我不确定是什么以及如何解决它。

跟踪的相关章节：

Caused by: java.io.NotSerializableException: ... 
Serialization stack: 
     - object not serializable (class: ..., value: ...$ConversionUtils$@63ed11dd) 
     - field (class: ...$ConversionUtils$$anonfun$3, name: $outer, type: class ...$ConversionUtils$) 
     - object (class ...$ConversionUtils$$anonfun$3, <function1>) 
     - field (class: org.apache.spark.sql.catalyst.expressions.ScalaUdf$$anonfun$2, name: func$2, type: interface scala.Function1) 
     - object (class org.apache.spark.sql.catalyst.expressions.ScalaUdf$$anonfun$2, <function1>) 
     - field (class: org.apache.spark.sql.catalyst.expressions.ScalaUdf, name: f, type: interface scala.Function1) 
     - object (class org.apache.spark.sql.catalyst.expressions.ScalaUdf, scalaUDF(ts_str#2683)) 
     - field (class: org.apache.spark.sql.catalyst.expressions.Alias, name: child, type: class org.apache.spark.sql.catalyst.expressions.Expression) 
     - object (class org.apache.spark.sql.catalyst.expressions.Alias, scalaUDF(ts_str#2683) AS ts#7146) 
     - element of array (index: 35) 
     - array (class [Ljava.lang.Object;, size 36) 
     - field (class: scala.collection.mutable.ArrayBuffer, name: array, type: class [Ljava.lang.Object;) 
     - object (class scala.collection.mutable.ArrayBuffer, 

Answer 1

尝试：

object ConversionUtils extends Serializable { 
    ... 
}