Spark工作与Google Dataproc不兼容(Spark job became incompatible with Google Dataproc)
我有这个代码工作好几个月，并在大约2个月前使用Google Dataproc停止工作，虽然我没有改变一行。
我可以用几行重现这个bug，所以我不必发布一大堆代码：
SparkConf sparkConf = new SparkConf().setAppName("test"); JavaSparkContext jsc = new JavaSparkContext(sparkConf); JavaRDD<String> rdd = jsc.parallelize(Arrays.asList("a", "b", "c")); JavaPairRDD<String, String> pairs = rdd.flatMapToPair(value -> Arrays.asList( new Tuple2<>(value, value + "1"), new Tuple2<>(value, value + "2") ) ); pairs.collect().forEach(System.out::println);然后我得到了这个模糊的例外：
WARN org.apache.spark.scheduler.TaskSetManager: Lost task 0.0 in stage 0.0 (TID 0, test-cluster-w-1.c.test-project.internal): java.lang.AbstractMethodError: uk.co.test.CalculateScore$$Lambda$10/1666820030.call(Ljava/lang/Object;)Ljava/util/Iterator; at org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$3$1.apply(JavaRDDLike.scala:142) at org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$3$1.apply(JavaRDDLike.scala:142) at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434) at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440) at scala.collection.Iterator$class.foreach(Iterator.scala:893) at scala.collection.AbstractIterator.foreach(Iterator.scala:1336) at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48) at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310) at scala.collection.AbstractIterator.to(Iterator.scala:1336) at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302) at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1336) at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289) at scala.collection.AbstractIterator.toArray(Iterator.scala:1336) at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:893) at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:893) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1897) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1897) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70) at org.apache.spark.scheduler.Task.run(Task.scala:85) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274) 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) Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 4 times, most recent failure: Lost task 0.3 in stage 0.0 (TID 7, test-cluster-w-0.c.test-project.internal): ExecutorLostFailure (executor 2 exited caused by one of the running tasks) Reason: Container marked as failed: container_1475077182957_0001_01_000005 on host: sun-recommendations-evaluation-w-0.c.test-project.internal. Exit status: 50. Diagnostics: Exception from container-launch. Container id: container_1475077182957_0001_01_000005 Exit code: 50 Stack trace: ExitCodeException exitCode=50: at org.apache.hadoop.util.Shell.runCommand(Shell.java:545) at org.apache.hadoop.util.Shell.run(Shell.java:456) at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:722) at org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor.launchContainer(DefaultContainerExecutor.java:212) at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:302) at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:82) at java.util.concurrent.FutureTask.run(FutureTask.java:266) 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) Container exited with a non-zero exit code 50 Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1450) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1438) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1437) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1437) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811) at scala.Option.foreach(Option.scala:257) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:811) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1659) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1618) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1607) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:632) at org.apache.spark.SparkContext.runJob(SparkContext.scala:1871) at org.apache.spark.SparkContext.runJob(SparkContext.scala:1884) at org.apache.spark.SparkContext.runJob(SparkContext.scala:1897) at org.apache.spark.SparkContext.runJob(SparkContext.scala:1911) at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:893) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) at org.apache.spark.rdd.RDD.withScope(RDD.scala:358) at org.apache.spark.rdd.RDD.collect(RDD.scala:892) at org.apache.spark.api.java.JavaRDDLike$class.collect(JavaRDDLike.scala:360) at org.apache.spark.api.java.AbstractJavaRDDLike.collect(JavaRDDLike.scala:45) at uk.co.test.CalculateScore.main(CalculateScore.java:50) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:729) at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:185) at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:210) at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:124) at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)如果我在本地运行它：
sparkConf.setMaster("local[2]")然后它工作正常并输出：
(a,a1) (a,a2) (b,b1) (b,b2) (c,c1) (c,c2)这些是我的Spark依赖项：
<dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-core_2.10</artifactId> <version>1.6.0</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-mllib_2.10</artifactId> <version>1.6.0</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-streaming_2.10</artifactId> <version>1.6.0</version> </dependency>任何帮助表示赞赏。
I have this code that was working fine for months and stopped working about 2 months ago with Google Dataproc although I hadn't changed a single line.
I could reproduce the bug with just a few lines so I didn't have to post a huge block of code:
SparkConf sparkConf = new SparkConf().setAppName("test"); JavaSparkContext jsc = new JavaSparkContext(sparkConf); JavaRDD<String> rdd = jsc.parallelize(Arrays.asList("a", "b", "c")); JavaPairRDD<String, String> pairs = rdd.flatMapToPair(value -> Arrays.asList( new Tuple2<>(value, value + "1"), new Tuple2<>(value, value + "2") ) ); pairs.collect().forEach(System.out::println);Then I get this obscure Exception:
WARN org.apache.spark.scheduler.TaskSetManager: Lost task 0.0 in stage 0.0 (TID 0, test-cluster-w-1.c.test-project.internal): java.lang.AbstractMethodError: uk.co.test.CalculateScore$$Lambda$10/1666820030.call(Ljava/lang/Object;)Ljava/util/Iterator; at org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$3$1.apply(JavaRDDLike.scala:142) at org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$3$1.apply(JavaRDDLike.scala:142) at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434) at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440) at scala.collection.Iterator$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$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:893) at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:893) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1897) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1897) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70) at org.apache.spark.scheduler.Task.run(Task.scala:85) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 4 times, most recent failure: Lost task 0.3 in stage 0.0 (TID 7, test-cluster-w-0.c.test-project.internal): ExecutorLostFailure (executor 2 exited caused by one of the running tasks) Reason: Container marked as failed: container_1475077182957_0001_01_000005 on host: sun-recommendations-evaluation-w-0.c.test-project.internal. Exit status: 50. Diagnostics: Exception from container-launch. Container id: container_1475077182957_0001_01_000005 Exit code: 50 Stack trace: ExitCodeException exitCode=50: at org.apache.hadoop.util.Shell.runCommand(Shell.java:545) at org.apache.hadoop.util.Shell.run(Shell.java:456) at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:722) at org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor.launchContainer(DefaultContainerExecutor.java:212) at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:302) at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:82) at java.util.concurrent.FutureTask.run(FutureTask.java:266) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) Container exited with a non-zero exit code 50 Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1450) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1438) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1437) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1437) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811) at scala.Option.foreach(Option.scala:257) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:811) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1659) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1618) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1607) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:632) at org.apache.spark.SparkContext.runJob(SparkContext.scala:1871) at org.apache.spark.SparkContext.runJob(SparkContext.scala:1884) at org.apache.spark.SparkContext.runJob(SparkContext.scala:1897) at org.apache.spark.SparkContext.runJob(SparkContext.scala:1911) at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:893) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) at org.apache.spark.rdd.RDD.withScope(RDD.scala:358) at org.apache.spark.rdd.RDD.collect(RDD.scala:892) at org.apache.spark.api.java.JavaRDDLike$class.collect(JavaRDDLike.scala:360) at org.apache.spark.api.java.AbstractJavaRDDLike.collect(JavaRDDLike.scala:45) at uk.co.test.CalculateScore.main(CalculateScore.java:50) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:729) at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:185) at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:210) at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:124) at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)If I run it locally with:
sparkConf.setMaster("local[2]")Then it works fine and outputs:
(a,a1) (a,a2) (b,b1) (b,b2) (c,c1) (c,c2)These are my Spark dependencies:
<dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-core_2.10</artifactId> <version>1.6.0</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-mllib_2.10</artifactId> <version>1.6.0</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-streaming_2.10</artifactId> <version>1.6.0</version> </dependency>Any help is appreciated.
最满意答案
Dataproc使用的默认映像最近已升级到Spark 2.0 / Scala 2.11。 这在8月份发生了变化，可能会解释其中的差异。
此页面详细说明了每个Dataproc映像版本中包含的软件包版本。
使用以下内容更新pom.xml，重新编译并重新运行应用程序可能就足够了
<dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-core_2.11</artifactId> <version>2.0.0</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-mllib_2.11</artifactId> <version>2.0.0</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-streaming_2.11</artifactId> <version>2.0.0</version> </dependency>Spark 2.0的发行说明包含Spark 1.6和2.0之间的更改和删除 。
作为替代方案，您仍然可以使用以下gcloud调用来使用1.0图像轨道：
$ gcloud dataproc clusters create --image-version 1.0 ...使用显式图像轨道时，请记住可以弃用并最终删除主要/次要版本。 可以参考Dataproc图像版本控制策略以获得图像版本的支持时间线。
The default image used by Dataproc was recently upgraded to Spark 2.0 / Scala 2.11. This was changed in August and could potentially explain the difference.
This page details which versions of software packages are included in each Dataproc image release.
It may be sufficient to update your pom.xml with the following, recompile, and re-run your application
<dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-core_2.11</artifactId> <version>2.0.0</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-mllib_2.11</artifactId> <version>2.0.0</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-streaming_2.11</artifactId> <version>2.0.0</version> </dependency>The release notes for Spark 2.0 contain changes and removals between Spark 1.6 and 2.0.
As an alternative, you can still make use of the 1.0 image track using the following gcloud invocation:
$ gcloud dataproc clusters create --image-version 1.0 ...When using an explicit image track, keep in mind that major/minor versions can be deprecated and eventually removed. The Dataproc image versioning policy can be consulted for support timelines for image versions.
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