运行 Windows 8.1、Java 1.8、Scala 2.10.5、Spark 1.4.1、Scala IDE (Eclipse 4.4)、Ipython 3.0.0 和 Jupyter Scala .
我对 Scala 和 Spark 比较陌生,我遇到了一个问题,即某些 RDD 命令(例如collect和first)返回“任务不可序列化”错误。对我来说不寻常的是,我在使用 Scala 内核或 Scala IDE 的 Ipython 笔记本中看到了该错误。但是,当我直接在 Spark-Shell 中运行代码时,我没有收到此错误。
我想设置这两个环境,以便在 shell 之外进行更高级的代码评估。我在解决此类问题和确定要查找的内容方面缺乏专业知识;如果您可以提供有关如何开始解决此类问题的指导,我们将不胜感激。
代码:
val logFile = "s3n://[key:[key secret]@mortar-example-data/airline-data"
val sample = sc.parallelize(sc.textFile(logFile).take(100).map(line => line.replace("'","").replace("\"","")).map(line => line.substring(0,line.length()-1)))
val header = sample.first
val data = sample.filter(_!= header)
data.take(1)
data.count
data.collect
堆栈跟踪
org.apache.spark.SparkException: Task not serializable
org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:315)
org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:305)
org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:132)
org.apache.spark.SparkContext.clean(SparkContext.scala:1893)
org.apache.spark.rdd.RDD$$anonfun$filter$1.apply(RDD.scala:311)
org.apache.spark.rdd.RDD$$anonfun$filter$1.apply(RDD.scala:310)
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:147)
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:108)
org.apache.spark.rdd.RDD.withScope(RDD.scala:286)
org.apache.spark.rdd.RDD.filter(RDD.scala:310)
cmd49$$user$$anonfun$4.apply(Main.scala:188)
cmd49$$user$$anonfun$4.apply(Main.scala:187)
java.io.NotSerializableException: org.apache.spark.SparkConf
Serialization stack:
- object not serializable (class: org.apache.spark.SparkConf, value: org.apache.spark.SparkConf@5976e363)
- field (class: cmd12$$user, name: conf, type: class org.apache.spark.SparkConf)
- object (class cmd12$$user, cmd12$$user@39a7edac)
- field (class: cmd49, name: $ref$cmd12, type: class cmd12$$user)
- object (class cmd49, cmd49@3c2a0c4f)
- field (class: cmd49$$user, name: $outer, type: class cmd49)
- object (class cmd49$$user, cmd49$$user@774ea026)
- field (class: cmd49$$user$$anonfun$4, name: $outer, type: class cmd49$$user)
- object (class cmd49$$user$$anonfun$4, <function0>)
- field (class: cmd49$$user$$anonfun$4$$anonfun$apply$3, name: $outer, type: class cmd49$$user$$anonfun$4)
- object (class cmd49$$user$$anonfun$4$$anonfun$apply$3, <function1>)
org.apache.spark.serializer.SerializationDebugger$.improveException(SerializationDebugger.scala:40)
org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:47)
org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:81)
org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:312)
org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:305)
org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:132)
org.apache.spark.SparkContext.clean(SparkContext.scala:1893)
org.apache.spark.rdd.RDD$$anonfun$filter$1.apply(RDD.scala:311)
org.apache.spark.rdd.RDD$$anonfun$filter$1.apply(RDD.scala:310)
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:147)
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:108)
org.apache.spark.rdd.RDD.withScope(RDD.scala:286)
org.apache.spark.rdd.RDD.filter(RDD.scala:310)
cmd49$$user$$anonfun$4.apply(Main.scala:188)
cmd49$$user$$anonfun$4.apply(Main.scala:187)
最佳答案
@Ashalynd 关于 sc.textFile 已经创建和 RDD 的事实是正确的。在这种情况下你不需要 sc.parallelize 。 documentation here
因此考虑到您的示例,这就是您需要执行的操作:
// Read your data from S3
val logFile = "s3n://[key:[key secret]@mortar-example-data/airline-data"
val rawRDD = sc.textFile(logFile)
// Fetch the header
val header = rawRDD.first
// Filter on the header than map to clean the line
val sample = rawRDD.filter(!_.contains(header)).map {
line => line.replaceAll("['\"]","").substring(0,line.length()-1)
}.takeSample(false,100,12L) // takeSample returns a fixed-size sampled subset of this RDD in an array
最好使用 takeSample
函数:
def takeSample(withReplacement: Boolean, num: Int, seed: Long = Utils.random.nextLong): Array[T]
withReplacement : whether sampling is done with replacement
num : size of the returned sample
seed : seed for the random number generator
注1:示例是一个Array[String],因此如果您希望将其转换为RDD,您可以使用parallelize
函数,如下所示:
val sampleRDD = sc.parallelize(sample.toSeq)
注2:如果您希望直接从 rawRDD.filter(...).map(...)
获取示例 RDD,您可以使用返回 RDD[T] 的 sample
函数。不过,您需要指定所需数据的一小部分,而不是具体数字。
关于java - Scala Spark 配置/环境故障排除,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/32661786/