SPARK 1.6.1:在 DataFrame 上评估分类器时，任务不可序列化 [英] SPARK 1.6.1: Task not serializable when evaluating a classifier on a DataFrame

问题描述

我有一个 DataFrame，我将它映射到 () 的 RDD 中以测试 SVMModel.

I have a DataFrame, I map it into an RDD of () to test an SVMModel.

我使用的是 Zeppelin 和 Spark 1.6.1

I am using Zeppelin, and Spark 1.6.1

这是我的代码:

val loadedSVMModel = SVMModel.load(sc, pathToSvmModel)

// Clear the default threshold.
loadedSVMModel.clearThreshold()

// Compute raw scores on the test set.
val scoreAndLabels = df.select($"features", $"label")
                       .map { case Row(features:Vector, label: Double) =>
                                val score = loadedSVMModel.predict(features)
                                (score,label)
                            }

// Get evaluation metrics.
val metrics = new BinaryClassificationMetrics(scoreAndLabels)
val auROC = metrics.areaUnderROC()

println("Area under ROC = " + auROC)

在执行代码时，我有一个 org.apache.spark.SparkException: Task not serializable; 我很难理解为什么会发生这种情况以及如何修复它.

When executing the code I have a org.apache.spark.SparkException: Task not serializable; and I have a hard time understanding why this is happening and how can I fix it.

• 是不是因为我使用了 Zeppelin?
• 是不是因为原来的DataFrame?

我已经执行了 Spark 编程指南中的 SVM 示例，它工作得很好.所以原因应该与以上几点有关......我猜.

I have executed the SVM example in the Spark Programming Guide, and it worked perfectly. So the reason should be related to one of the points above... I guess.

这是异常堆栈的一些相关元素:

Here is the some relevant elements of the Exception stack:

Caused by: java.io.NotSerializableException: org.apache.spark.sql.Column
Serialization stack:
    - object not serializable (class: org.apache.spark.sql.Column, value: (sum(CASE WHEN (domainIndex = 0) THEN sumOfScores ELSE 0),mode=Complete,isDistinct=false) AS 0#100278)
    - element of array (index: 0)
    - array (class [Lorg.apache.spark.sql.Column;, size 372)

我没有发布完整的异常堆栈，因为 Zeppelin 倾向于显示很长的不相关文本.如果你想让我跳过完整的例外，请告诉我.

I didn't post the full exception stack, because Zeppelin tend to show a very long not relevant text. please let me know if you want me to past the full exception.

其他信息

使用 VectorAssembler() 生成特征向量，如下所示

The feature vectors are generated using a VectorAssembler() as follow

// Prepare vector assemble
val vecAssembler =  new VectorAssembler()
                               .setInputCols(arrayOfIndices)
                               .setOutputCol("features")


// Aggregation expressions
val exprs = arrayOfIndices
                .map(c => sum(when($"domainIndex" === c, $"sumOfScores")
                .otherwise(lit(0))).alias(c))

val df = vecAssembler
           .transform(anotherDF.groupBy($"userID", $"val")
           .agg(exprs.head, exprs.tail: _*))
           .select($"userID", $"features", $"val")
           .withColumn("label", sqlCreateLabelValue($"val"))
           .drop($"val").drop($"userID")

推荐答案

问题的根源实际上与您使用的 DataFrame 甚至与 Zeppelin 无关.更多的是代码组织的问题，再加上同一范围内不可序列化对象的存在.

The source of the problem is actually not related to the DataFrame you use or even directly to Zeppelin. It is more a matter of code organization combined with existence of non-serializable object in the same scope.

由于您使用交互式会话，因此所有对象都定义在同一范围内并成为闭包的一部分.它包括 exprs，它看起来像一个 Seq[Column]，其中 Column 是不可序列化的.

Since you use interactive session all objects are defined in the same scope and become a part of the closure. It includes exprs which looks like a Seq[Column] where Column is not serializable.

操作SQL表达式不是问题，因为exprs只在本地使用，但是当你下拉到RDD操作时就会有问题.exprs 作为闭包的一部分包含在一个表达式中.重现此行为的最简单方法(ColumnName 是 Column 的子类之一)如下所示:

It is not a problem when operate on SQL expressions because exprs are used only locally, but becomes problematic when you drop down to RDD operations. exprs is included as a part of a closure and leads to an expression. The simplest way you can reproduce this behavior (ColumnName is one the subclasses of Column) is something like this:

Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 2.0.0-SNAPSHOT
      /_/

Using Scala version 2.11.8 (OpenJDK 64-Bit Server VM, Java 1.8.0_91)
Type in expressions to have them evaluated.
Type :help for more information.

scala> val df = Seq(1, 2, 3).toDF("x")
df: org.apache.spark.sql.DataFrame = [x: int]

scala> val x = $"x"
x: org.apache.spark.sql.ColumnName = x

scala> def f(x: Any) = 0
f: (x: Any)Int

scala> df.select(x).rdd.map(f _)
org.apache.spark.SparkException: Task not serializable
...
Caused by: java.io.NotSerializableException: org.apache.spark.sql.ColumnName
Serialization stack:
    - object not serializable (class: org.apache.spark.sql.ColumnName, value: x)
...

您可以尝试解决此问题的一种方法是将 exprs 标记为瞬态:

One way you can try to approach this problem is to mark exprs as transient:

@transient val exprs: Seq[Column] = ???

在我们的最小示例中也能正常工作:

which works fine as well in our minimal example:

scala> @transient val x = $"x"
x: org.apache.spark.sql.ColumnName = x

scala> df.select(x).rdd.map(f _)
res1: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[8] at map at <console>:30

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