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 Scala에서 countByWindow를 이용하기(예제) 2018.03.08 990
40 Scala를 이용한 Streaming예제 2018.03.08 895
39 scala application 샘플소스(SparkSession이용) 2018.03.07 1093
38 spark-submit 실행시 "java.lang.OutOfMemoryError: Java heap space"발생시 조치사항 2018.02.01 754
» Could not compute split, block input-0-1517397051800 not found형태의 오류가 발생시 조치방법 2018.02.01 456
36 spark stream처리할때 두개의 client프로그램이 동일한 checkpoint로 접근할때 발생하는 오류 내용 2018.01.16 1225
35 Windows7 64bit 환경에서 Apache Spark 2.2.0 설치하기 2017.07.26 946
34 Spark에서 KafkaUtils.createStream()를 이용하여 이용하여 kafka topic에 접근하여 객채로 저장된 값을 가져오고 처리하는 예제 소스 2017.04.26 459
33 Spark에서 Serializable관련 오류및 조치사항 2017.04.21 5107
32 Caused by: java.lang.ClassNotFoundException: org.apache.spark.Logging 발생시 조치사항 2017.04.19 851
31 streaming작업시 입력된 값에 대한 사본을 만들게 되는데 이것이 실패했을때 발생하는 경고메세지 2017.04.03 824
30 JavaStreamingContext를 이용하여 스트림으로 들어오는 문자열 카운트 소스 2017.03.30 287
29 spark 2.0.0의 api를 이용하는 예제 프로그램 2017.03.15 349
28 It is indirectly referenced from required .class files 오류 발생시 조치방법 2017.03.09 873
27 spark2.0.0에서 hive 2.0.1 table을 읽어 출력하는 예제 소스(HiveContext, SparkSession, SQLContext) 2017.03.09 365
26 spark에서 hive table을 읽어 출력하는 예제 소스 2017.03.09 661
25 spark에서 hive table을 읽어 출력하는 예제 소스 2017.03.09 871
24 spark 2.0.0를 windows에서 실행시 로컬 파일을 읽을때 발생하는 오류 해결 방법 2017.01.12 568
23 spark notebook 0.7.0설치및 설정 2016.11.14 753
22 참고할만한 spark예제를 설명하는 사이트 2016.11.11 576
위로