flink：作业不会以更高的taskmanager.heap.mb运行

Question

简单的工作： kafka->flatmap->reduce->map 。

作业运行正常，默认值为taskmanager.heap.mb （512Mb）。 根据文档 ： this value should be as large as possible 。 由于该计算机具有96Gb RAM，因此将其设置为75000（任意值）。

开始工作会出现以下错误：

Caused by: org.apache.flink.runtime.client.JobExecutionException: Job execution failed.   
at org.apache.flink.runtime.jobmanager.JobManager$$anonfun$handleMessage$1$$anonfun$applyOrElse$5.apply$mcV$sp(JobManager.scala:563)   
at org.apache.flink.runtime.jobmanager.JobManager$$anonfun$handleMessage$1$$anonfun$applyOrElse$5.apply(JobManager.scala:509)
at org.apache.flink.runtime.jobmanager.JobManager$$anonfun$handleMessage$1$$anonfun$applyOrElse$5.apply(JobManager.scala:509)
at scala.concurrent.impl.Future$PromiseCompletingRunnable.liftedTree1$1(Future.scala:24)
at scala.concurrent.impl.Future$PromiseCompletingRunnable.run(Future.scala:24)
at akka.dispatch.TaskInvocation.run(AbstractDispatcher.scala:41)
at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:401)
at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)

Caused by: org.apache.flink.runtime.jobmanager.scheduler.NoResourceAvailableException: Not enough free slots available to run the job. You can decrease the operator parallelism or increase the number of slots per TaskManager in the configuration. Task to schedule: < Attempt #0 (Source: Custom Source (1/1)) @ (unassigned) - [SCHEDULED] > with groupID < 95b239d1777b2baf728645df9a1c4232 > in sharing group < SlotSharingGroup [772c9ff1cf0b6cb3a361e3352f75fcee, d4f856f13654f424d7c49d0f00f6ecca, 81bb8c4310faefe32f97ebd6baa4c04f, 95b239d1777b2baf728645df9a1c4232] >. Resources available to scheduler: Number of instances=0, total number of slots=0, available slots=0
at org.apache.flink.runtime.jobmanager.scheduler.Scheduler.scheduleTask(Scheduler.java:255)
at org.apache.flink.runtime.jobmanager.scheduler.Scheduler.scheduleImmediately(Scheduler.java:131)
at org.apache.flink.runtime.executiongraph.Execution.scheduleForExecution(Execution.java:298)
at org.apache.flink.runtime.executiongraph.ExecutionVertex.scheduleForExecution(ExecutionVertex.java:458)
at org.apache.flink.runtime.executiongraph.ExecutionJobVertex.scheduleAll(ExecutionJobVertex.java:322)
at org.apache.flink.runtime.executiongraph.ExecutionGraph.scheduleForExecution(ExecutionGraph.java:686)
at org.apache.flink.runtime.jobmanager.JobManager$$anonfun$org$apache$flink$runtime$jobmanager$JobManager$$submitJob$1.apply$mcV$sp(JobManager.scala:982)
at org.apache.flink.runtime.jobmanager.JobManager$$anonfun$org$apache$flink$runtime$jobmanager$JobManager$$submitJob$1.apply(JobManager.scala:962)
at org.apache.flink.runtime.jobmanager.JobManager$$anonfun$org$apache$flink$runtime$jobmanager$JobManager$$submitJob$1.apply(JobManager.scala:962)
... 8 more

将默认值（512）恢复为该参数，作业运行正常。 在5000时有效->在10000时无效。

我错过了什么？

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编辑：这比我想的要命中。 将值设置为50000并重新提交即可成功。 在每个测试中，群集都会停止并重新启动。

Answer 1

您可能正在经历的是在工人向船长注册之前提交工作。

快速初始化5GB的JVM堆，TaskManager几乎可以立即注册。 对于70GB的堆，JVM需要一段时间来初始化和引导。 因此，工作人员稍后进行注册，并且由于缺少工作人员而无法在提交作业时执行该工作。

这也是为什么您重新提交工作后它就可以工作的原因。

如果以“流式”模式（通过start-cluster-streaming.sh独立）启动集群，则JVM的初始化速度更快，因为这样至少会延迟地初始化Flink的内部内存。