记flink消费kafka 数据序列化失败

本文探讨了在使用Flink进行Kafka连接时遇到的JobExecutionException,通过定位报错发现是由于Flink和Kafka版本不匹配引发的。解决方案是更新依赖到flink-connector-kafka_2.12的1.11.1版本,确保与Flink版本同步。

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报错如下

Exception in thread "main" org.apache.flink.runtime.client.JobExecutionException: Job execution failed.
	at org.apache.flink.runtime.jobmaster.JobResult.toJobExecutionResult(JobResult.java:147)
	at org.apache.flink.runtime.minicluster.MiniClusterJobClient.lambda$getJobExecutionResult$2(MiniClusterJobClient.java:119)
	at java.util.concurrent.CompletableFuture.uniApply(CompletableFuture.java:602)
	at java.util.concurrent.CompletableFuture$UniApply.tryFire(CompletableFuture.java:577)
	at java.util.concurrent.CompletableFuture.postComplete(CompletableFuture.java:474)
	at java.util.concurrent.CompletableFuture.complete(CompletableFuture.java:1962)
	at org.apache.flink.runtime.rpc.akka.AkkaInvocationHandler.lambda$invokeRpc$0(AkkaInvocationHandler.java:229)
	at java.util.concurrent.CompletableFuture.uniWhenComplete(CompletableFuture.java:760)
	at java.util.concurrent.CompletableFuture$UniWhenComplete.tryFire(CompletableFuture.java:736)
	at java.util.concurrent.CompletableFuture.postComplete(CompletableFuture.java:474)
	at java.util.concurrent.CompletableFuture.complete(CompletableFuture.java:1962)
	at org.apache.flink.runtime.concurrent.FutureUtils$1.onComplete(FutureUtils.java:996)
	at akka.dispatch.OnComplete.internal(Future.scala:264)
	at akka.dispatch.OnComplete.internal(Future.scala:261)
	at akka.dispatch.japi$CallbackBridge.apply(Future.scala:191)
	at akka.dispatch.japi$CallbackBridge.apply(Future.scala:188)
	at scala.concurrent.impl.CallbackRunnable.run$$$capture(Promise.scala:36)
	at scala.concurrent.impl.CallbackRunnable.run(Promise.scala)
	at org.apache.flink.runtime.concurrent.Executors$DirectExecutionContext.execute(Executors.java:74)
	at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:44)
	at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:252)
	at akka.pattern.PromiseActorRef.$bang(AskSupport.scala:572)
	at akka.pattern.PipeToSupport$PipeableFuture$$anonfun$pipeTo$1.applyOrElse(PipeToSupport.scala:22)
	at akka.pattern.PipeToSupport$PipeableFuture$$anonfun$pipeTo$1.applyOrElse(PipeToSupport.scala:21)
	at scala.concurrent.Future$$anonfun$andThen$1.apply(Future.scala:436)
	at scala.concurrent.Future$$anonfun$andThen$1.apply(Future.scala:435)
	at scala.concurrent.impl.CallbackRunnable.run$$$capture(Promise.scala:36)
	at scala.concurrent.impl.CallbackRunnable.run(Promise.scala)
	at akka.dispatch.BatchingExecutor$AbstractBatch.processBatch(BatchingExecutor.scala:55)
	at akka.dispatch.BatchingExecutor$BlockableBatch$$anonfun$run$1.apply$mcV$sp(BatchingExecutor.scala:91)
	at akka.dispatch.BatchingExecutor$BlockableBatch$$anonfun$run$1.apply(BatchingExecutor.scala:91)
	at akka.dispatch.BatchingExecutor$BlockableBatch$$anonfun$run$1.apply(BatchingExecutor.scala:91)
	at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
	at akka.dispatch.BatchingExecutor$BlockableBatch.run(BatchingExecutor.scala:90)
	at akka.dispatch.TaskInvocation.run(AbstractDispatcher.scala:40)
	at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(ForkJoinExecutorConfigurator.scala:44)
	at akka.dispatch.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
	at akka.dispatch.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
	at akka.dispatch.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
	at akka.dispatch.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
Caused by: org.apache.flink.runtime.JobException: Recovery is suppressed by NoRestartBackoffTimeStrategy
	at org.apache.flink.runtime.executiongraph.failover.flip1.ExecutionFailureHandler.handleFailure(ExecutionFailureHandler.java:116)
	at org.apache.flink.runtime.executiongraph.failover.flip1.ExecutionFailureHandler.getFailureHandlingResult(ExecutionFailureHandler.java:78)
	at org.apache.flink.runtime.scheduler.DefaultScheduler.handleTaskFailure(DefaultScheduler.java:224)
	at org.apache.flink.runtime.scheduler.DefaultScheduler.maybeHandleTaskFailure(DefaultScheduler.java:217)
	at org.apache.flink.runtime.scheduler.DefaultScheduler.updateTaskExecutionStateInternal(DefaultScheduler.java:208)
	at org.apache.flink.runtime.scheduler.SchedulerBase.updateTaskExecutionState(SchedulerBase.java:610)
	at org.apache.flink.runtime.scheduler.SchedulerNG.updateTaskExecutionState(SchedulerNG.java:89)
	at org.apache.flink.runtime.jobmaster.JobMaster.updateTaskExecutionState(JobMaster.java:419)
	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.flink.runtime.rpc.akka.AkkaRpcActor.handleRpcInvocation(AkkaRpcActor.java:286)
	at org.apache.flink.runtime.rpc.akka.AkkaRpcActor.handleRpcMessage(AkkaRpcActor.java:201)
	at org.apache.flink.runtime.rpc.akka.FencedAkkaRpcActor.handleRpcMessage(FencedAkkaRpcActor.java:74)
	at org.apache.flink.runtime.rpc.akka.AkkaRpcActor.handleMessage(AkkaRpcActor.java:154)
	at akka.japi.pf.UnitCaseStatement.apply(CaseStatements.scala:26)
	at akka.japi.pf.UnitCaseStatement.apply(CaseStatements.scala:21)
	at scala.PartialFunction$class.applyOrElse(PartialFunction.scala:123)
	at akka.japi.pf.UnitCaseStatement.applyOrElse(CaseStatements.scala:21)
	at scala.PartialFunction$OrElse.applyOrElse(PartialFunction.scala:170)
	at scala.PartialFunction$OrElse.applyOrElse(PartialFunction.scala:171)
	at scala.PartialFunction$OrElse.applyOrElse(PartialFunction.scala:171)
	at akka.actor.Actor$class.aroundReceive$$$capture(Actor.scala:517)
	at akka.actor.Actor$class.aroundReceive(Actor.scala)
	at akka.actor.AbstractActor.aroundReceive(AbstractActor.scala:225)
	at akka.actor.ActorCell.receiveMessage(ActorCell.scala:592)
	at akka.actor.ActorCell.invoke(ActorCell.scala:561)
	at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:258)
	at akka.dispatch.Mailbox.run(Mailbox.scala:225)
	at akka.dispatch.Mailbox.exec(Mailbox.scala:235)
	... 4 more
Caused by: java.lang.NoSuchMethodError: org.apache.flink.api.common.state.OperatorStateStore.getSerializableListState(Ljava/lang/String;)Lorg/apache/flink/api/common/state/ListState;
	at org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase.initializeState(FlinkKafkaConsumerBase.java:858)
	at org.apache.flink.streaming.util.functions.StreamingFunctionUtils.tryRestoreFunction(StreamingFunctionUtils.java:185)
	at org.apache.flink.streaming.util.functions.StreamingFunctionUtils.restoreFunctionState(StreamingFunctionUtils.java:167)
	at org.apache.flink.streaming.api.operators.AbstractUdfStreamOperator.initializeState(AbstractUdfStreamOperator.java:96)
	at org.apache.flink.streaming.api.operators.StreamOperatorStateHandler.initializeOperatorState(StreamOperatorStateHandler.java:107)
	at org.apache.flink.streaming.api.operators.AbstractStreamOperator.initializeState(AbstractStreamOperator.java:264)
	at org.apache.flink.streaming.runtime.tasks.OperatorChain.initializeStateAndOpenOperators(OperatorChain.java:400)
	at org.apache.flink.streaming.runtime.tasks.StreamTask.lambda$beforeInvoke$2(StreamTask.java:507)
	at org.apache.flink.streaming.runtime.tasks.StreamTaskActionExecutor$SynchronizedStreamTaskActionExecutor.runThrowing(StreamTaskActionExecutor.java:92)
	at org.apache.flink.streaming.runtime.tasks.StreamTask.beforeInvoke(StreamTask.java:501)
	at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:531)
	at org.apache.flink.runtime.taskmanager.Task.doRun(Task.java:722)
	at org.apache.flink.runtime.taskmanager.Task.run(Task.java:547)
	at java.lang.Thread.run(Thread.java:748)

在这里插入图片描述
改成如下:问题解决,是版本的问题

  <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-connector-kafka_2.12</artifactId><!-- 与Scala大版本号一致-->
      <version>1.11.1</version><!-- 与Flink版本号一致-->
  </dependency>
要将FlinkKafka消费数据序列化并存入Hive,可以按照以下步骤进行操作: 1. 配置Kafka消费者和Hive连接 首先需要配置FlinkKafka消费者和Hive连接,可以使用Flink提供的Kafka连接器和Hive连接器来实现。具体的配置可以参考Flink官方文档进行设置。 2. 设计反序列化类 根据你从Kafka消费数据格式,需要设计一个反序列化类来将数据转换为Flink中的对象。例如,如果你从Kafka消费的是JSON格式的数据,可以使用Flink提供的JSON反序列化类进行转换。 3. 定义Hive表结构 在Hive中创建一个表来存储反序列化后的数据。你需要定义表的结构,包括列名、列类型和分区等信息。 4. 编写Flink程序 编写一个Flink程序来消费Kafka中的数据,并将数据序列化后存入Hive表中。具体的实现可以参考以下代码示例: ```java DataStream<String> dataStream = env.addSource(new FlinkKafkaConsumer<String>( "topic", new SimpleStringSchema(), properties)); DataStream<MyObject> myObjects = dataStream.map(new MapFunction<String, MyObject>() { @Override public MyObject map(String value) throws Exception { ObjectMapper mapper = new ObjectMapper(); return mapper.readValue(value, MyObject.class); } }); HiveCatalog hiveCatalog = new HiveCatalog("myHiveCatalog", "default", "/path/to/hive/conf"); TableSchema schema = new TableSchema( new String[] {"id", "name", "age"}, new TypeInformation<?>[] {Types.STRING, Types.STRING, Types.INT}); HiveTableSink hiveTableSink = new HiveTableSink( "myDatabase.myTable", schema, hiveCatalog, new Configuration(), "myPartition"); myObjects.addSink(hiveTableSink); ``` 其中,`MyObject`是你从Kafka消费数据序列化后的对象,`hiveCatalog`是Hive连接器的配置信息,`schema`是Hive表的列信息,`hiveTableSink`是Hive表的输出目的地。 5. 运行Flink程序 配置好Flink程序后,就可以运行程序了。程序会从Kafka消费数据,将数据序列化后存入Hive表中。 以上就是将FlinkKafka消费数据序列化存入Hive的步骤和示例代码。
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