Scala spark，listbuffer为空_Scala_Apache Spark_Listbuffer

Scala spark，listbuffer为空

scala apache-spark

Scala spark，listbuffer为空,scala,apache-spark,listbuffer,Scala,Apache Spark,Listbuffer,在注释1中的这段代码中，listbuffer项的长度显示正确，但在第2条注释中，代码从不执行。为什么会发生这种情况 val conf = new SparkConf().setAppName("app").setMaster("local") val sc = new SparkContext(conf) var wktReader: WKTReader = new WKTReader(); val dataSet = sc.textFile("dataSet.txt") val item

在注释1中的这段代码中，listbuffer项的长度显示正确，但在第2条注释中，代码从不执行。为什么会发生这种情况

val conf = new SparkConf().setAppName("app").setMaster("local")
val sc = new SparkContext(conf)

var wktReader: WKTReader = new WKTReader(); 
val dataSet = sc.textFile("dataSet.txt")

val items = new ListBuffer[String]() 
dataSet.foreach { e =>
  items += e
  println("len = " + items.length) //1. here length is ok
}

println("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
items.foreach { x => print(x)} //2. this code doesn't be executed

日志如下：

16/11/20 01:16:52 INFO Utils: Successfully started service 'SparkUI' on port 4040.
    16/11/20 01:16:52 INFO SparkUI: Bound SparkUI to 0.0.0.0, and started at http://192.168.56.1:4040
    16/11/20 01:16:53 INFO Executor: Starting executor ID driver on host localhost
    16/11/20 01:16:53 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 58608.
    16/11/20 01:16:53 INFO NettyBlockTransferService: Server created on 192.168.56.1:58608
    16/11/20 01:16:53 INFO BlockManagerMaster: Registering BlockManager BlockManagerId(driver, 192.168.56.1, 58608)
    16/11/20 01:16:53 INFO BlockManagerMasterEndpoint: Registering block manager 192.168.56.1:58608 with 347.1 MB RAM, BlockManagerId(driver, 192.168.56.1, 58608)
    16/11/20 01:16:53 INFO BlockManagerMaster: Registered BlockManager BlockManagerId(driver, 192.168.56.1, 58608)
    Starting app
    16/11/20 01:16:57 INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 139.6 KB, free 347.0 MB)
    16/11/20 01:16:58 INFO MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 15.9 KB, free 346.9 MB)
    16/11/20 01:16:58 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on 192.168.56.1:58608 (size: 15.9 KB, free: 347.1 MB)
    16/11/20 01:16:58 INFO SparkContext: Created broadcast 0 from textFile at main.scala:25
    16/11/20 01:16:58 INFO FileInputFormat: Total input paths to process : 1
    16/11/20 01:16:58 INFO SparkContext: Starting job: foreach at main.scala:28
    16/11/20 01:16:58 INFO DAGScheduler: Got job 0 (foreach at main.scala:28) with 1 output partitions
    16/11/20 01:16:58 INFO DAGScheduler: Final stage: ResultStage 0 (foreach at main.scala:28)
    16/11/20 01:16:58 INFO DAGScheduler: Parents of final stage: List()
    16/11/20 01:16:58 INFO DAGScheduler: Missing parents: List()
    16/11/20 01:16:58 INFO DAGScheduler: Submitting ResultStage 0 (dataSet.txt MapPartitionsRDD[1] at textFile at main.scala:25), which has no missing parents
    16/11/20 01:16:58 INFO MemoryStore: Block broadcast_1 stored as values in memory (estimated size 3.3 KB, free 346.9 MB)
    16/11/20 01:16:58 INFO MemoryStore: Block broadcast_1_piece0 stored as bytes in memory (estimated size 2034.0 B, free 346.9 MB)
    16/11/20 01:16:58 INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on 192.168.56.1:58608 (size: 2034.0 B, free: 347.1 MB)
    16/11/20 01:16:58 INFO SparkContext: Created broadcast 1 from broadcast at DAGScheduler.scala:1012
    16/11/20 01:16:59 INFO DAGScheduler: Submitting 1 missing tasks from ResultStage 0 (dataSet.txt MapPartitionsRDD[1] at textFile at main.scala:25)
    16/11/20 01:16:59 INFO TaskSchedulerImpl: Adding task set 0.0 with 1 tasks
    16/11/20 01:16:59 INFO TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, localhost, partition 0, PROCESS_LOCAL, 5427 bytes)
    16/11/20 01:16:59 INFO Executor: Running task 0.0 in stage 0.0 (TID 0)
    16/11/20 01:16:59 INFO HadoopRDD: Input split: file:/D:/dataSet.txt:0+291
    16/11/20 01:16:59 INFO deprecation: mapred.tip.id is deprecated. Instead, use mapreduce.task.id
    16/11/20 01:16:59 INFO deprecation: mapred.task.id is deprecated. Instead, use mapreduce.task.attempt.id
    16/11/20 01:16:59 INFO deprecation: mapred.task.is.map is deprecated. Instead, use mapreduce.task.ismap
    16/11/20 01:16:59 INFO deprecation: mapred.task.partition is deprecated. Instead, use mapreduce.task.partition
    16/11/20 01:16:59 INFO deprecation: mapred.job.id is deprecated. Instead, use mapreduce.job.id
    len = 1
    len = 2
    len = 3
    len = 4
    len = 5
    len = 6
    len = 7
    16/11/20 01:16:59 INFO Executor: Finished task 0.0 in stage 0.0 (TID 0). 989 bytes result sent to driver
    16/11/20 01:16:59 INFO TaskSetManager: Finished task 0.0 in stage 0.0 (TID 0) in 417 ms on localhost (1/1)
    16/11/20 01:16:59 INFO TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool 
    16/11/20 01:16:59 INFO DAGScheduler: ResultStage 0 (foreach at main.scala:28) finished in 0,456 s
    16/11/20 01:16:59 INFO DAGScheduler: Job 0 finished: foreach at main.scala:28, took 0,795126 s
    !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
    16/11/20 01:16:59 INFO SparkContext: Invoking stop() from shutdown hook
    16/11/20 01:16:59 INFO SparkUI: Stopped Spark web UI at http://192.168.56.1:4040
    16/11/20 01:16:59 INFO MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped!
    16/11/20 01:16:59 INFO MemoryStore: MemoryStore cleared
    16/11/20 01:16:59 INFO BlockManager: BlockManager stopped
    16/11/20 01:16:59 INFO BlockManagerMaster: BlockManagerMaster stopped
    16/11/20 01:16:59 INFO OutputCommitCoordinator$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped!
    16/11/20 01:16:59 INFO SparkContext: Successfully stopped SparkContext
    16/11/20 01:16:59 INFO ShutdownHookManager: Shutdown hook called
    16/11/20 01:16:59 INFO ShutdownHookManager: Deleting directory

Apache Spark不提供共享内存，因此：

dataSet.foreach { e =>
  items += e
  println("len = " + items.length) //1. here length is ok
}

您可以在相应的执行器上修改

项的本地副本。驱动程序上定义的原始项目列表未修改。因此：
items.foreach { x => print(x) }

执行，但没有要打印的内容
请查收
虽然此处建议使用，但您可以使用
Apache Spark不提供共享内存，因此：
dataSet.foreach { e =>
  items += e
  println("len = " + items.length) //1. here length is ok
}

您可以在相应的执行器上修改项的本地副本。驱动程序上定义的原始项目列表未修改。因此：
items.foreach { x => print(x) }

执行，但没有要打印的内容
请查收
虽然此处建议使用，但您可以使用
Spark在Executer中运行并返回结果。上面的代码不能按预期工作。如果需要添加来自foreach
的元素，则需要在驱动程序中收集数据并添加到当前\u集
。但是当你有大数据时，收集数据是个坏主意
val items = new ListBuffer[String]()

val rdd = spark.sparkContext.parallelize(1 to 10, 4)
rdd.collect().foreach(data => items += data.toString())
println(items)

输出：
ListBuffer(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)

Spark在Executer中运行并返回结果。上面的代码不能按预期工作。如果需要添加来自foreach
的元素，则需要在驱动程序中收集数据并添加到当前\u集
。但是当你有大数据时，收集数据是个坏主意
val items = new ListBuffer[String]()

val rdd = spark.sparkContext.parallelize(1 to 10, 4)
rdd.collect().foreach(data => items += data.toString())
println(items)

输出：
ListBuffer(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)

@Kangaroo:另外，如果您在本地运行，它应该打印输出，如果您在集群上运行，它不会打印值，因为您在转换和操作中编写的任何逻辑在我的问题n中的不同机器[完全不同的JVM]上运行，我收到了一个有效的答案：@kangaroo:另外，如果你在本地运行这个，它应该打印输出，如果你在集群上运行这个，它不会打印值，因为在我的问题中，无论你在转换和操作中编写的逻辑是在不同的机器[完全不同的JVM]上运行的，我得到了一个有效的答案：