메뉴 건너뛰기

Cloudera, BigData, Semantic IoT, Hadoop, NoSQL

Cloudera CDH/CDP 및 Hadoop EcoSystem, Semantic IoT등의 개발/운영 기술을 정리합니다. gooper@gooper.com로 문의 주세요.


Spark에서 KafkaUtils.createStream을 이용하여 Kafka의 data를 가져올때 StorageLevel을 StorageLevel.MEMORY_ONLY()로 하는 경우 "Could not compute split, block input-0-1517397051800 not found"형태의 오류가 발생하는데 이는 Spark가 메모리 부족 상황이 되면 해당 데이타를 버리기 때문에 문제가 발생한다.

이때는 StorageLevel.MEMORY_ONLY()을 StorageLevel.MEMORY_AND_DISK_SER()로 변경해준다.



-------------소스코드 일부분-----

JavaPairReceiverInputDStream<byte[], byte[]> kafkaStream = KafkaUtils.createStream(jssc,byte[].class, byte[].class, kafka.serializer.DefaultDecoder.class, kafka.serializer.DefaultDecoder.class,
        conf, topic, StorageLevel.MEMORY_AND_DISK_SER());
JavaDStream<byte[]> lines = kafkaStream.map(tuple2 -> tuple2._2());


-----------------------------------오류 메세지------------------

[2018-01-31 20:17:26,404] [internal.Logging$class] [logError(#70)] [ERROR] Task 0 in stage 1020.0 failed 1 times; aborting job
[2018-01-31 20:17:26,404] [internal.Logging$class] [logError(#91)] [ERROR] Error running job streaming job 1517397060000 ms.0
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1020.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1020.0 (TID 1020, localhost, executor driver): java.lang.Exception: Could not compute split, block input-0-1517397051800 not found
        at org.apache.spark.rdd.BlockRDD.compute(BlockRDD.scala:50)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
        at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
        at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
        at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
        at org.apache.spark.scheduler.Task.run(Task.scala:99)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:322)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
        at java.lang.Thread.run(Thread.java:745)

Driver stacktrace:
        at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)
        at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
        at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
        at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
        at scala.Option.foreach(Option.scala:257)
        at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594)
        at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
        at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:1925)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:1938)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:1951)
        at org.apache.spark.rdd.RDD$$anonfun$take$1.apply(RDD.scala:1354)
        at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
        at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
        at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
        at org.apache.spark.rdd.RDD.take(RDD.scala:1327)
        at org.apache.spark.streaming.dstream.DStream$$anonfun$print$2$$anonfun$foreachFunc$3$1.apply(DStream.scala:734)
        at org.apache.spark.streaming.dstream.DStream$$anonfun$print$2$$anonfun$foreachFunc$3$1.apply(DStream.scala:733)
        at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:51)
        at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
        at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
        at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:415)
        at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:50)
        at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50)
        at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50)
        at scala.util.Try$.apply(Try.scala:192)
        at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
        at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:256)
        at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:256)
        at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:256)
        at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
        at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:255)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
        at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.Exception: Could not compute split, block input-0-1517397051800 not found
        at org.apache.spark.rdd.BlockRDD.compute(BlockRDD.scala:50)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
        at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
        at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
        at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
        at org.apache.spark.scheduler.Task.run(Task.scala:99)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:322)
        ... 3 more
[2018-01-31 20:17:26,415] [onem2m.AvroOneM2MDataSparkSubscribe$ConsumerT] [go(#142)] [DEBUG] count data from kafka broker stream in AvroOneM2MDataSparkSubscribe: 39981
[2018-01-31 20:17:29,039] [sf.QueryServiceFactory] [create(#28)] [DEBUG] query gubun : FUSEKISPARQL
[2018-01-31 20:17:29,040] [sf.QueryCommon] [makeFinal(#44)] [DEBUG] Count : 0 , Vals : [] 
[2018-01-31 20:17:29,040] [sf.SparqlFusekiQueryImpl] [runModifySparql(#162)] [DEBUG] runModifySparql() on DatWarehouse server start.................................. 
[2018-01-31 20:17:29,040] [sf.SparqlFusekiQueryImpl] [runModifySparql(#165)] [DEBUG] try (first).................................. 
[2018-01-31 20:17:29,042] [sf.SparqlFusekiQueryImpl] [runModifySparql(#207)] [DEBUG] runModifySparql() on DataWarehouse server end.................................. 
[2018-01-31 20:17:29,042] [sf.SparqlFusekiQueryImpl] [runModifySparql(#212)] [DEBUG] runModifySparql() on DataMart server start.................................. 
[2018-01-31 20:17:29,043] [sf.SparqlFusekiQueryImpl] [runModifySparql(#224)] [DEBUG] runModifySparql() on DataMart server end.................................. 
[2018-01-31 20:17:29,044] [sf.QueryCommon] [makeFinal(#44)] [DEBUG] Count : 0 , Vals : [] 
[2018-01-31 20:17:29,044] [sf.SparqlFusekiQueryImpl] [runModifySparql(#162)] [DEBUG] runModifySparql() on DatWarehouse server start.................................. 
[2018-01-31 20:17:29,044] [sf.SparqlFusekiQueryImpl] [runModifySparql(#165)] [DEBUG] try (first).................................. 
[2018-01-31 20:17:29,045] [sf.SparqlFusekiQueryImpl] [runModifySparql(#207)] [DEBUG] runModifySparql() on DataWarehouse server end.................................. 
[2018-01-31 20:17:29,045] [sf.SparqlFusekiQueryImpl] [runModifySparql(#212)] [DEBUG] runModifySparql() on DataMart server start.................................. 
[2018-01-31 20:17:29,046] [sf.SparqlFusekiQueryImpl] [runModifySparql(#224)] [DEBUG] runModifySparql() on DataMart server end.................................. 
[2018-01-31 20:17:29,047] [sf.QueryCommon] [makeFinal(#44)] [DEBUG] Count : 0 , Vals : [] 
[2018-01-31 20:17:29,047] [sf.SparqlFusekiQueryImpl] [runModifySparql(#212)] [DEBUG] runModifySparql() on DataMart server start.................................. 
[2018-01-31 20:17:29,049] [sf.SparqlFusekiQueryImpl] [runModifySparql(#224)] [DEBUG] runModifySparql() on DataMart server end.................................. 
[2018-01-31 20:17:29,049] [sf.TripleService] [makeTripleFile(#333)] [INFO] makeTripleFile start==========================>
[2018-01-31 20:17:29,049] [sf.TripleService] [makeTripleFile(#334)] [DEBUG] makeTripleFile ========triple_path_file=================>/svc/apps/sda/triples/20180131/AvroOneM2MDataSparkSubscribe_TT20180131T201100S0000000991_WRK20180131T201729.nt
[2018-01-31 20:17:29,170] [sf.TripleService] [makeTripleFile(#346)] [INFO] makeTripleFile end==========================>
[2018-01-31 20:17:29,170] [onem2m.AvroOneM2MDataSparkSubscribe] [sendTriples(#288)] [INFO] Sending triples in com.pineone.icbms.sda.kafka.onem2m.AvroOneM2MDataSparkSubscribe to DW start.......................
[2018-01-31 20:17:29,170] [sf.TripleService] [sendTripleFileToDW(#382)] [INFO] sendTripleFile to DW start==========================>
[2018-01-31 20:17:29,170] [sf.TripleService] [sendTripleFileToDW(#383)] [DEBUG] sendTripleFile ==============triple_path_file============>/svc/apps/sda/triples/20180131/AvroOneM2MDataSparkSubscribe_TT20180131T201100S0000000991_WRK20180131T201729.nt
[2018-01-31 20:17:29,171] [sf.TripleService] [sendTripleFileToDW(#396)] [DEBUG] sendTripleFile ==============args============>/svc/apps/sda/bin/fuseki/bin/s-post http://166.104.112.43:23030/icbms default /svc/apps/sda/triples/20180131/AvroOneM2MDataSparkSubscribe_TT20180131T201100S0000000991_WRK20180131T201729.nt 
[2018-01-31 20:17:29,171] [sf.TripleService] [sendTripleFileToDW(#399)] [DEBUG] try (first).......................
[2018-01-31 20:17:36,950] [util.Utils] [runShell(#737)] [DEBUG] Thread stdMsgT Status : TERMINATED
[2018-01-31 20:17:36,951] [util.Utils] [runShell(#738)] [DEBUG] Thread errMsgT Status : TERMINATED
[2018-01-31 20:17:36,951] [util.Utils] [runShell(#743)] [DEBUG] notTimeOver ==========================>true
[2018-01-31 20:17:36,951] [sf.TripleService] [sendTripleFileToDW(#402)] [DEBUG] resultStr in TripleService.sendTripleFileToDW() == > [, ]
[2018-01-31 20:17:36,951] [sf.TripleService] [sendTripleFileToDW(#433)] [INFO] sendTripleFile to DW  end==========================>
[2018-01-31 20:17:36,951] [onem2m.AvroOneM2MDataSparkSubscribe] [sendTriples(#290)] [INFO] Sending triples in com.pineone.icbms.sda.kafka.onem2m.AvroOneM2MDataSparkSubscribe to DW end.......................
[2018-01-31 20:17:36,951] [onem2m.AvroOneM2MDataSparkSubscribe] [sendTriples(#293)] [INFO] Sending triples in com.pineone.icbms.sda.kafka.onem2m.AvroOneM2MDataSparkSubscribe to Halyard start.......................
[2018-01-31 20:17:36,952] [sf.TripleService] [sendTripleFileToHalyard(#486)] [INFO] sendTripleFile to Halyard  start==========================>
[2018-01-31 20:17:37,294] [sf.QueryServiceFactory] [create(#31)] [DEBUG] query gubun : HALYARDSPARQL
[2018-01-31 20:17:37,317] [sf.SparqlHalyardQueryImpl] [insertByPost(#189)] [DEBUG] ------------------------insertByPost-----start-----------------------
[2018-01-31 20:17:37,317] [sf.SparqlHalyardQueryImpl] [insertByPost(#198)] [DEBUG] ------------------------insertByPost-----end-----------------------

번호 제목 날짜 조회 수
41 Spark에서 Serializable관련 오류및 조치사항 2017.04.21 5028
40 spark-sql실행시 Caused by: java.lang.NumberFormatException: For input string: "0s" 오류발생시 조치사항 2016.06.09 2865
39 VisualVM 1.3.9을 이용한 spark-submit JVM 모니터링을 위한 설정및 spark-submit실행 옵션 2016.10.28 2118
38 Spark 2.1.1 clustering(5대) 설치(YARN기반) 2016.04.22 2050
37 spark stream처리할때 두개의 client프로그램이 동일한 checkpoint로 접근할때 발생하는 오류 내용 2018.01.16 1167
36 "Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources"오류 발생시 조치사항 2016.05.25 1132
35 spark-env.sh에서 사용할 수있는 항목. 2016.05.24 853
34 java.lang.OutOfMemoryError: unable to create new native thread오류 발생지 조치사항 2016.10.17 741
33 Spark 1.6.1 설치후 HA구성 2016.05.24 724
32 Apache Spark와 Drools를 이용한 CEP구현 테스트 2016.07.15 701
31 kafka로 부터 메세지를 stream으로 받아 처리하는 spark샘플소스(spark의 producer와 consumer를 sbt로 컴파일 하고 서버에서 spark-submit하는 방법) 2016.07.13 698
30 spark-submit 실행시 "java.lang.OutOfMemoryError: Java heap space"발생시 조치사항 2018.02.01 631
29 spark-shell실행시 "A read-only user or a user in a read-only database is not permitted to disable read-only mode on a connection."오류가 발생하는 경우 해결방법 2016.05.20 625
28 Scala에서 countByWindow를 이용하기(예제) 2018.03.08 612
27 spark client프로그램 기동시 "Error initializing SparkContext"오류 발생할때 조치사항 2016.05.27 610
26 spark-sql실행시 The specified datastore driver ("com.mysql.jdbc.Driver") was not found in the CLASSPATH오류 발생시 조치사항 2016.06.09 575
25 Windows7 64bit 환경에서 Apache Spark 2.2.0 설치하기 2017.07.26 566
24 It is indirectly referenced from required .class files 오류 발생시 조치방법 2017.03.09 563
23 spark에서 hive table을 읽어 출력하는 예제 소스 2017.03.09 559
22 Caused by: java.lang.ClassNotFoundException: org.apache.spark.Logging 발생시 조치사항 2017.04.19 508
위로