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Bigdata, Semantic IoT, Hadoop, NoSQL

Bigdata, Hadoop ecosystem, Semantic IoT등의 프로젝트를 진행중에 습득한 내용을 정리하는 곳입니다.
필요한 분을 위해서 공개하고 있습니다. 문의사항은 gooper@gooper.com로 메일을 보내주세요.


StringBuffer의 값을 toString()을 이용하여 문자열로 변환할때 "java.lang.OutOfMemoryError: Java heap space"가 발생하는데 이것은 StringBuffer.toString()하는 과정에서 값을 복사하는데 이때 heap메모리가 부족해서 발생하는 오류이다.

이때는 spark-submit에서 --driver-memory 5g처럼 지정하는 메모리를 크게 증가시켜서 -Xmx값을 증가시켜준다.


------------------오류내용------------------------

[2018-02-01 10:12:40,253] [internal.Logging$class] [logError(#70)] [ERROR] Task 0 in stage 20.0 failed 1 times; aborting job
[2018-02-01 10:12:40,267] [internal.Logging$class] [logError(#91)] [ERROR] Error running job streaming job 1517447030000 ms.0
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 20.0 failed 1 times, most recent failure: Lost task 0.0 in stage 20.0 (TID 20, localhost, executor driver): java.lang.OutOfMemoryError: Java heap space
        at java.util.Arrays.copyOfRange(Arrays.java:3664)
        at java.lang.StringBuffer.toString(StringBuffer.java:671)
        at com.pineone.icbms.sda.sf.TripleService.sendTripleFileToHalyard(TripleService.java:500)
        at com.pineone.icbms.sda.kafka.onem2m.AvroOneM2MDataSparkSubscribe.sendTriples(AvroOneM2MDataSparkSubscribe.java:296)
        at com.pineone.icbms.sda.kafka.onem2m.AvroOneM2MDataSparkSubscribe.access$100(AvroOneM2MDataSparkSubscribe.java:34)
        at com.pineone.icbms.sda.kafka.onem2m.AvroOneM2MDataSparkSubscribe$ConsumerT.go(AvroOneM2MDataSparkSubscribe.java:202)
        at com.pineone.icbms.sda.kafka.onem2m.AvroOneM2MDataSparkSubscribe$1.call(AvroOneM2MDataSparkSubscribe.java:101)
        at com.pineone.icbms.sda.kafka.onem2m.AvroOneM2MDataSparkSubscribe$1.call(AvroOneM2MDataSparkSubscribe.java:93)
        at org.apache.spark.api.java.JavaPairRDD$$anonfun$toScalaFunction$1.apply(JavaPairRDD.scala:1040)
        at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
        at scala.collection.Iterator$$anon$10.next(Iterator.scala:393)
        at scala.collection.Iterator$class.foreach(Iterator.scala:893)
        at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
        at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
        at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
        at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
        at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
        at scala.collection.AbstractIterator.to(Iterator.scala:1336)
        at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
        at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1336)
        at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
        at scala.collection.AbstractIterator.toArray(Iterator.scala:1336)
        at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$29.apply(RDD.scala:1354)
        at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$29.apply(RDD.scala:1354)
        at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1951)
        at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1951)
        at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
        at org.apache.spark.scheduler.Task.run(Task.scala:99)
        at org.apache.spark.executor.Executor$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$$anonfun$run$1.apply(JobScheduler.scala:256)
        at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
        at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:255)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
        at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.OutOfMemoryError: Java heap space
        at java.util.Arrays.copyOfRange(Arrays.java:3664)
        at java.lang.StringBuffer.toString(StringBuffer.java:671)
        at com.pineone.icbms.sda.sf.TripleService.sendTripleFileToHalyard(TripleService.java:500)
        at com.pineone.icbms.sda.kafka.onem2m.AvroOneM2MDataSparkSubscribe.sendTriples(AvroOneM2MDataSparkSubscribe.java:296)
        at com.pineone.icbms.sda.kafka.onem2m.AvroOneM2MDataSparkSubscribe.access$100(AvroOneM2MDataSparkSubscribe.java:34)
        at com.pineone.icbms.sda.kafka.onem2m.AvroOneM2MDataSparkSubscribe$ConsumerT.go(AvroOneM2MDataSparkSubscribe.java:202)
        at com.pineone.icbms.sda.kafka.onem2m.AvroOneM2MDataSparkSubscribe$1.call(AvroOneM2MDataSparkSubscribe.java:101)
        at com.pineone.icbms.sda.kafka.onem2m.AvroOneM2MDataSparkSubscribe$1.call(AvroOneM2MDataSparkSubscribe.java:93)
        at org.apache.spark.api.java.JavaPairRDD$$anonfun$toScalaFunction$1.apply(JavaPairRDD.scala:1040)
        at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
        at scala.collection.Iterator$$anon$10.next(Iterator.scala:393)
        at scala.collection.Iterator$class.foreach(Iterator.scala:893)
        at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
        at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
        at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
        at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
        at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
        at scala.collection.AbstractIterator.to(Iterator.scala:1336)
        at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
        at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1336)
        at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
        at scala.collection.AbstractIterator.toArray(Iterator.scala:1336)
        at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$29.apply(RDD.scala:1354)
        at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$29.apply(RDD.scala:1354)
        at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1951)
        at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1951)
        at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
        at org.apache.spark.scheduler.Task.run(Task.scala:99)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:322)
        ... 3 more

번호 제목 글쓴이 날짜 조회 수
130 [Impala] alter table구문수행시 "WARNINGS: Impala does not have READ_WRITE access to path 'hdfs://nameservice1/DATA/Temp/DB/source/table01_ccd'" 발생시 조치 gooper 2024.04.26 0
129 [CDP7.1.7, Hive Replication]Hive Replication진행중 "The following columns have types incompatible with the existing columns in their respective positions " 오류 gooper 2023.12.27 7
128 [CDP7.1.7]Oozie job에서 ERROR: Kudu error(s) reported, first error: Timed out: Failed to write batch of 774 ops to tablet 8003f9a064bf4be5890a178439b2ba91가 발생하면서 쿼리가 실패하는 경우 gooper 2024.01.05 7
127 [CDP7.1.7]impala-shell수행시 간헐적으로 "-k requires a valid kerberos ticket but no valid kerberos ticket found." 오류 gooper 2023.11.16 11
126 임시 테이블에서 데이터를 읽어서 partitioned table에 입력하는 impala SQL문 예시 gooper 2023.11.10 16
125 [impala]insert into db명.table명 select a, b from db명.table명 쿼리 수행시 "Memory limit exceeded: Failed to allocate memory for Parquet page index"오류 조치 방법 gooper 2023.05.31 22
124 not leader of this config: current role FOLLOWER 오류 발생시 확인방법 총관리자 2022.01.17 23
123 kudu table와 impala(hive) table정보가 틀어져서 테이블을 읽지 못하는 경우(Error Loading Metadata) 조치방법 gooper 2023.11.10 25
122 [CDP7.1.7]Impala Query의 Memory Spilled 양은 ScratchFileUsedBytes값을 누적해서 구할 수 있다. gooper 2022.07.29 29
121 [Cloudera 6.3.4, Kudu]]Service Monitor에서 사용하는 metric중에 일부를 blacklist로 설정하여 모니터링 정보 수집 제외하는 방법 gooper 2022.07.08 31
120 Failed to write to server: (no server available): 총관리자 2022.01.17 32
119 AnalysisException: Incomplatible return type 'DECIMAL(38,0)' and 'DECIMAL(38,5)' of exprs가 발생시 조치 총관리자 2021.07.26 34
118 spark에서 hive table을 읽어 출력하는 예제 소스 총관리자 2017.03.09 35
117 [TLS/SSL]Kudu Tablet Server설정 총관리자 2022.05.13 35
116 spark에서 hive table을 읽어 출력하는 예제 소스 총관리자 2017.03.09 37
115 [KUDU] kudu tablet server여러가지 원인에 의해서 corrupted상태가 된 경우 복구방법 gooper 2023.03.28 37
114 [CDP7.1.7]impala-shell을 이용하여 kudu table에 insert/update수행시 발생하는 오류(Transport endpoint is not connected (error 107)) 발생시 확인할 내용 gooper 2023.11.30 45
113 spark 온라인 책자링크 (제목 : mastering-apache-spark) 총관리자 2016.05.25 51
112 [hive] hive.tbls테이블의 owner컬럼값은 hadoop.security.auth_to_local에 의해서 filtering된다. 총관리자 2022.04.14 55
111 [Impala jdbc]CDP7.1.7환경에서 java프로그램을 이용하여 kerberized impala cluster에 접근하여 SQL을 수행하는 방법 gooper 2023.08.22 58

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