使用rdd.collect（）获取序列文件内容时出错，其中rdd.foreach（println）没有得到spark中的错误

时间：2017-02-14 12:44:26

标签： scala apache-spark

我正在尝试将一些数据保存到序列文件中，并再次读取相同的数据并尝试将其打印为一个小练习。当我使用rdd.foreach（println）时我能够看到数据，但是当我执行rdd.collect（）时会出现错误。两种情况究竟会发生什么。在第二种情况下，错误是“对象不可序列化”。我在Spark REPL中运行这些命令。我的火花版本是1.5.1

保存序列文件： - 

val data = sc.parallelize(List(("Hadoop", 1), ("Spark", 2), ("Kafka", 3)))
data.saveAsSequenceFile("file:///home/user/outputfiles/seqFileDemo1")

=================

序列文件读取： - 

import org.apache.hadoop.io.{Text, IntWritable} 
val data1 = sc.sequenceFile("file:///home/user/outputfiles/seqFileDemo1", classOf[Text], classOf[IntWritable])
data1.foreach(println)

输出：

("Hadoop", 1), 
("Spark", 2), 
("Kafka", 3)




scala> data1.collect()

输出：

17/02/14 16:56:33 INFO SparkContext: Starting job: collect at <console>:19
17/02/14 16:56:33 INFO DAGScheduler: Got job 5 (collect at <console>:19) with 2 output partitions
17/02/14 16:56:33 INFO DAGScheduler: Final stage: ResultStage 5(collect at <console>:19)
17/02/14 16:56:33 INFO DAGScheduler: Parents of final stage: List()
17/02/14 16:56:33 INFO DAGScheduler: Missing parents: List()
17/02/14 16:56:33 INFO DAGScheduler: Submitting ResultStage 5 (file:///home/srini/Desktop/SeqFileData/ HadoopRDD[3] at sequenceFile at <console>:16), which has no missing parents
17/02/14 16:56:33 INFO MemoryStore: ensureFreeSpace(2440) called with curMem=694040, maxMem=556038881
17/02/14 16:56:33 INFO MemoryStore: Block broadcast_7 stored as values in memory (estimated size 2.4 KB, free 529.6 MB)
17/02/14 16:56:33 INFO MemoryStore: ensureFreeSpace(1448) called with curMem=696480, maxMem=556038881
17/02/14 16:56:33 INFO MemoryStore: Block broadcast_7_piece0 stored as bytes in memory (estimated size 1448.0 B, free 529.6 MB)
17/02/14 16:56:33 INFO BlockManagerInfo: Added broadcast_7_piece0 in memory on localhost:60094 (size: 1448.0 B, free: 530.2 MB)
17/02/14 16:56:33 INFO SparkContext: Created broadcast 7 from broadcast at DAGScheduler.scala:861
17/02/14 16:56:33 INFO DAGScheduler: Submitting 2 missing tasks from ResultStage 5 (file:///home/srini/Desktop/SeqFileData/ HadoopRDD[3] at sequenceFile at <console>:16)
17/02/14 16:56:33 INFO TaskSchedulerImpl: Adding task set 5.0 with 2 tasks
17/02/14 16:56:33 INFO TaskSetManager: Starting task 0.0 in stage 5.0 (TID 10, localhost, PROCESS_LOCAL, 2156 bytes)
17/02/14 16:56:33 INFO TaskSetManager: Starting task 1.0 in stage 5.0 (TID 11, localhost, PROCESS_LOCAL, 2156 bytes)
17/02/14 16:56:33 INFO Executor: Running task 1.0 in stage 5.0 (TID 11)
17/02/14 16:56:33 INFO Executor: Running task 0.0 in stage 5.0 (TID 10)
17/02/14 16:56:33 INFO HadoopRDD: Input split: file:/home/srini/Desktop/SeqFileData/part-00000:0+104
17/02/14 16:56:33 ERROR Executor: Exception in task 1.0 in stage 5.0 (TID 11)
java.io.NotSerializableException: org.apache.hadoop.io.Text
Serialization stack:
    - object not serializable (class: org.apache.hadoop.io.Text, value: Hadoop)
    - field (class: scala.Tuple2, name: _1, type: class java.lang.Object)
    - object (class scala.Tuple2, (Hadoop,1))
    - element of array (index: 0)
    - array (class [Lscala.Tuple2;, size 1)
    at org.apache.spark.serializer.SerializationDebugger$.improveException(SerializationDebugger.scala:40)
    at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:47)
    at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:84)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:240)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
    at java.lang.Thread.run(Thread.java:744)
17/02/14 16:56:33 INFO HadoopRDD: Input split: file:/home/srini/Desktop/SeqFileData/part-00001:0+121
17/02/14 16:56:33 ERROR Executor: Exception in task 0.0 in stage 5.0 (TID 10)
java.io.NotSerializableException: org.apache.hadoop.io.Text
Serialization stack:
    - object not serializable (class: org.apache.hadoop.io.Text, value: Kafka)
    - field (class: scala.Tuple2, name: _1, type: class java.lang.Object)
    - object (class scala.Tuple2, (Kafka,3))
    - element of array (index: 0)
    - array (class [Lscala.Tuple2;, size 2)
    at org.apache.spark.serializer.SerializationDebugger$.improveException(SerializationDebugger.scala:40)
    at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:47)
    at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:84)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:240)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
    at java.lang.Thread.run(Thread.java:744)
17/02/14 16:56:33 ERROR TaskSetManager: Task 1.0 in stage 5.0 (TID 11) had a not serializable result: org.apache.hadoop.io.Text
Serialization stack:
    - object not serializable (class: org.apache.hadoop.io.Text, value: Hadoop)
    - field (class: scala.Tuple2, name: _1, type: class java.lang.Object)
    - object (class scala.Tuple2, (Hadoop,1))
    - element of array (index: 0)
    - array (class [Lscala.Tuple2;, size 1); not retrying
17/02/14 16:56:33 INFO TaskSchedulerImpl: Cancelling stage 5
17/02/14 16:56:33 INFO TaskSchedulerImpl: Stage 5 was cancelled
17/02/14 16:56:33 INFO DAGScheduler: ResultStage 5 (collect at <console>:19) failed in 0.022 s
17/02/14 16:56:33 INFO DAGScheduler: Job 5 failed: collect at <console>:19, took 0.083701 s
17/02/14 16:56:33 ERROR TaskSetManager: Task 0.0 in stage 5.0 (TID 10) had a not serializable result: org.apache.hadoop.io.Text
Serialization stack:
    - object not serializable (class: org.apache.hadoop.io.Text, value: Kafka)
    - field (class: scala.Tuple2, name: _1, type: class java.lang.Object)
    - object (class scala.Tuple2, (Kafka,3))
    - element of array (index: 0)
    - array (class [Lscala.Tuple2;, size 2); not retrying
17/02/14 16:56:33 INFO TaskSchedulerImpl: Removed TaskSet 5.0, whose tasks have all completed, from pool 
org.apache.spark.SparkException: Job aborted due to stage failure: Task 1.0 in stage 5.0 (TID 11) had a not serializable result: org.apache.hadoop.io.Text
Serialization stack:
    - object not serializable (class: org.apache.hadoop.io.Text, value: Hadoop)
    - field (class: scala.Tuple2, name: _1, type: class java.lang.Object)
    - object (class scala.Tuple2, (Hadoop,1))
    - element of array (index: 0)
    - array (class [Lscala.Tuple2;, size 1)
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1283)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1271)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1270)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1270)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:697)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:697)
    at scala.Option.foreach(Option.scala:236)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:697)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1496)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1458)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1447)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:567)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1822)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1835)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1848)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1919)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:905)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:147)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:108)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:306)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:904)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:19)
    at $iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:24)
    at $iwC$$iwC$$iwC$$iwC.<init>(<console>:26)
    at $iwC$$iwC$$iwC.<init>(<console>:28)
    at $iwC$$iwC.<init>(<console>:30)
    at $iwC.<init>(<console>:32)
    at <init>(<console>:34)
    at .<init>(<console>:38)
    at .<clinit>(<console>)
    at .<init>(<console>:7)
    at .<clinit>(<console>)
    at $print(<console>)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)
    at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1340)
    at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840)
    at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871)
    at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819)
    at org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:857)
    at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:902)
    at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:814)
    at org.apache.spark.repl.SparkILoop.processLine$1(SparkILoop.scala:657)
    at org.apache.spark.repl.SparkILoop.innerLoop$1(SparkILoop.scala:665)
    at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$loop(SparkILoop.scala:670)
    at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply$mcZ$sp(SparkILoop.scala:997)
    at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:945)
    at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:945)
    at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135)
    at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$process(SparkILoop.scala:945)
    at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:1059)
    at org.apache.spark.repl.Main$.main(Main.scala:31)
    at org.apache.spark.repl.Main.main(Main.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:672)
    at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180)
    at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
    at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:120)
    at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

1 个答案:

答案 0 :(得分：3)

问题是add_filter( 'ninja_forms_render_default_value', 'my_ninja_forms_pre_populate', 10, 3 ); function my_ninja_forms_pre_populate( $default_value, $field_type, $field_settings ){ if( 'field_key' == $field_settings[ 'key' ] ){ $default_value = 2; } return $default_value; } 和Text不可序列化。当您调用IntWritable时，它会尝试序列化所有数据并将其发送回驱动程序。为了实现这一目标，您首先需要将collect()和Text分别提取到IntWritable和String，这些可以序列化：

Int
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