简单作业：kafka->flatmap->reduce->map。
作业运行正常，taskmanager.heap.mb的默认值为512Mb。根据文件：this value should be as large as possible。由于所讨论的机器有96Gb的RAM，我将其设置为75000（任意值）。
启动作业时出现以下错误：
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）恢复到此参数，作业运行正常。在5000时工作->在10000时不工作。
我错过了什么？
编辑：这比我想象的更成功。将值设置为50000并重新提交会获得成功。在每次测试中，集群都会停止并重新启动。
您可能遇到的情况是在工人在master注册之前提交作业。
5GB JVM堆可以快速初始化，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，工作人员稍后注册，并且由于缺少工作人员，在提交作业时无法执行作业。
这也是为什么一旦你重新提交工作，它就会起作用的原因。
如果您以"流式"模式启动集群（通过start-clusterstreaming.sh独立启动），JVM的初始化速度会更快，因为至少Flink的内部内存会延迟初始化。