Spark任务不具有滞后窗口功能可序列化

Question

我注意到，如果我使用函数调用map（），我在DataFrame上使用Window函数后，Spark会返回“Task not serializable”Exception这是我的代码：

val hc:org.apache.spark.sql.hive.HiveContext = new org.apache.spark.sql.hive.HiveContext(sc)
import hc.implicits._
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._
def f():String = "test"
case class P(name:String,surname:String)
val lag_result:org.apache.spark.sql.Column = lag($"name",1).over(Window.partitionBy($"surname"))
val lista:List[P] = List(P("N1","S1"),P("N2","S2"),P("N2","S2"))
val data_frame:org.apache.spark.sql.DataFrame = hc.createDataFrame(sc.parallelize(lista))
df.withColumn("lag_result", lag_result).map(x => f)
//df.withColumn("lag_result", lag_result).map{case x => def f():String = "test";f}.collect // This works

这是堆栈跟踪：

org.apache.spark.SparkException：org.apache.spark.util.ClosureCleaner $ .ensureSerializable（ClosureCleaner.scala：304）org.apache.spark.util.ClosureCleaner $ .org $ apache $ spark $ util中无法序列化的任务$ ClosureCleaner $$ clean（ClosureCleaner.scala：294）atg.apache.spark.util.ClosureCleaner $ .clean（ClosureCleaner.scala：122）at org.apache.spark.SparkContext.clean（SparkContext.scala：2055）at at org.apache.spark.rdd.RDD $$ anonfun $ map $ 1.apply（RDD.scala：324）at org.apache.spark.rdd.RDD $$ anonfun $ map $ 1.apply（RDD.scala：323）at at ...以及更多引起：java.io.NotSerializableException：org.apache.spark.sql.Column序列化堆栈： - 对象不可序列化（类：org.apache.spark.sql.Column，值：'lag（name，name， 1，null）windowspecdefinition（surname，UnspecifiedFrame）） - 字段（类：$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC $$ iwC，name：lag_result，type：class org.apache.spark .sql.Column）......等等

Answer 1

lag返回o.a.s.sql.Column，这是不可序列化的。同样的事情适用于WindowSpec。在交互模式下，这些对象可以作为map的闭包的一部分包含在内：

scala> import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.expressions.Window

scala> val df = Seq(("foo", 1), ("bar", 2)).toDF("x", "y")
df: org.apache.spark.sql.DataFrame = [x: string, y: int]

scala> val w = Window.partitionBy("x").orderBy("y")
w: org.apache.spark.sql.expressions.WindowSpec = org.apache.spark.sql.expressions.WindowSpec@307a0097

scala> val lag_y = lag(col("y"), 1).over(w)
lag_y: org.apache.spark.sql.Column = 'lag(y,1,null) windowspecdefinition(x,y ASC,UnspecifiedFrame)

scala> def f(x: Any) = x.toString
f: (x: Any)String

scala> df.select(lag_y).map(f _).first
org.apache.spark.SparkException: Task not serializable
    at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:304)
...
Caused by: java.io.NotSerializableException: org.apache.spark.sql.expressions.WindowSpec
Serialization stack:
    - object not serializable (class: org.apache.spark.sql.expressions.WindowSpec, value: org.apache.spark.sql.expressions.WindowSpec@307a0097)

一个简单的解决方案是将两者标记为瞬态：

scala> @transient val w = Window.partitionBy("x").orderBy("y")
w: org.apache.spark.sql.expressions.WindowSpec = org.apache.spark.sql.expressions.WindowSpec@7dda1470

scala> @transient val lag_y = lag(col("y"), 1).over(w)
lag_y: org.apache.spark.sql.Column = 'lag(y,1,null) windowspecdefinition(x,y ASC,UnspecifiedFrame)

scala> df.select(lag_y).map(f _).first
res1: String = [null]