问题描述
今天搭建好flink集群,并使用如下命令提交任务,报了异常。
我的命令是:
[root@tuge1 flink-1.10.1]# ./bin/flink run -m yarn-cluster -ynm ryj -c vip.shuai7boy.flink.checkpoint.TestSavepoints /data/flinkdata/MyFlinkObj-1.0-SNAPSHOT-jar-with-dependencies.jar
提交后,开始查看Web UI是能正常显示的,但是一直处于请求资源的状态。
如下所示:
然后等一会,这个界面就挂掉了,跳转到如下界面:
然后控制台报了如下错误:
------------------------------------------------------------ The program finished with the following exception: org.apache.flink.client.program.ProgramInvocationException: The main method caused an error: org.apache.flink.client.program.ProgramInvocationException: Job failed (JobID: 12b759f143190ee08d831f2fabb4c3f2) at org.apache.flink.client.program.PackagedProgram.callMainMethod(PackagedProgram.java:335) at org.apache.flink.client.program.PackagedProgram.invokeInteractiveModeForExecution(PackagedProgram.java:205) at org.apache.flink.client.ClientUtils.executeProgram(ClientUtils.java:138) at org.apache.flink.client.cli.CliFrontend.executeProgram(CliFrontend.java:662) at org.apache.flink.client.cli.CliFrontend.run(CliFrontend.java:210) at org.apache.flink.client.cli.CliFrontend.parseParameters(CliFrontend.java:893) at org.apache.flink.client.cli.CliFrontend.lambda$main$10(CliFrontend.java:966) at java.security.AccessController.doPrivileged(Native Method) at javax.security.auth.Subject.doAs(Subject.java:422) at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1692) at org.apache.flink.runtime.security.HadoopSecurityContext.runSecured(HadoopSecurityContext.java:41) at org.apache.flink.client.cli.CliFrontend.main(CliFrontend.java:966) Caused by: java.util.concurrent.ExecutionException: org.apache.flink.client.program.ProgramInvocationException: Job failed (JobID: 12b759f143190ee08d831f2fabb4c3f2) at java.util.concurrent.CompletableFuture.reportGet(CompletableFuture.java:357) at java.util.concurrent.CompletableFuture.get(CompletableFuture.java:1895) at org.apache.flink.streaming.api.environment.StreamContextEnvironment.execute(StreamContextEnvironment.java:83) at org.apache.flink.streaming.api.environment.StreamExecutionEnvironment.execute(StreamExecutionEnvironment.java:1620) at org.apache.flink.streaming.api.environment.StreamExecutionEnvironment.execute(StreamExecutionEnvironment.java:1602) at org.apache.flink.streaming.api.scala.StreamExecutionEnvironment.execute(StreamExecutionEnvironment.scala:667) at vip.shuai7boy.flink.checkpoint.TestSavepoints$.main(TestSavepoints.scala:30) at vip.shuai7boy.flink.checkpoint.TestSavepoints.main(TestSavepoints.scala) 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.client.program.PackagedProgram.callMainMethod(PackagedProgram.java:321) ... 11 more Caused by: org.apache.flink.client.program.ProgramInvocationException: Job failed (JobID: 12b759f143190ee08d831f2fabb4c3f2) at org.apache.flink.client.deployment.ClusterClientJobClientAdapter.lambda$null$6(ClusterClientJobClientAdapter.java:112) at java.util.concurrent.CompletableFuture.uniApply(CompletableFuture.java:602) at java.util.concurrent.CompletableFuture$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.client.program.rest.RestClusterClient.lambda$pollResourceAsync$21(RestClusterClient.java:565) at java.util.concurrent.CompletableFuture.uniWhenComplete(CompletableFuture.java:760) at java.util.concurrent.CompletableFuture$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.lambda$retryOperationWithDelay$8(FutureUtils.java:291) at java.util.concurrent.CompletableFuture.uniWhenComplete(CompletableFuture.java:760) at java.util.concurrent.CompletableFuture$UniWhenComplete.tryFire(CompletableFuture.java:736) at java.util.concurrent.CompletableFuture.postComplete(CompletableFuture.java:474) at java.util.concurrent.CompletableFuture.postFire(CompletableFuture.java:561) at java.util.concurrent.CompletableFuture$UniCompose.tryFire(CompletableFuture.java:929) at java.util.concurrent.CompletableFuture$Completion.run(CompletableFuture.java:442) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) Caused by: org.apache.flink.runtime.client.JobExecutionException: Job execution failed. at org.apache.flink.runtime.jobmaster.JobResult.toJobExecutionResult(JobResult.java:147) at org.apache.flink.client.deployment.ClusterClientJobClientAdapter.lambda$null$6(ClusterClientJobClientAdapter.java:110) ... 19 more Caused by: org.apache.flink.runtime.JobException: Recovery is suppressed by NoRestartBackoffTimeStrategy at org.apache.flink.runtime.executiongraph.failover.flip1.ExecutionFailureHandler.handleFailure(ExecutionFailureHandler.java:110) at org.apache.flink.runtime.executiongraph.failover.flip1.ExecutionFailureHandler.getFailureHandlingResult(ExecutionFailureHandler.java:76) at org.apache.flink.runtime.scheduler.DefaultScheduler.handleTaskFailure(DefaultScheduler.java:192) at org.apache.flink.runtime.scheduler.DefaultScheduler.maybeHandleTaskFailure(DefaultScheduler.java:186) at org.apache.flink.runtime.scheduler.DefaultScheduler.updateTaskExecutionStateInternal(DefaultScheduler.java:180) at org.apache.flink.runtime.scheduler.SchedulerBase.updateTaskExecutionState(SchedulerBase.java:496) at org.apache.flink.runtime.scheduler.UpdateSchedulerNgOnInternalFailuresListener.notifyTaskFailure(UpdateSchedulerNgOnInternalFailuresListener.java:49) at org.apache.flink.runtime.executiongraph.ExecutionGraph.notifySchedulerNgAboutInternalTaskFailure(ExecutionGraph.java:1703) at org.apache.flink.runtime.executiongraph.Execution.processFail(Execution.java:1252) at org.apache.flink.runtime.executiongraph.Execution.processFail(Execution.java:1220) at org.apache.flink.runtime.executiongraph.Execution.markFailed(Execution.java:1051) at org.apache.flink.runtime.executiongraph.ExecutionVertex.markFailed(ExecutionVertex.java:748) at org.apache.flink.runtime.scheduler.DefaultExecutionVertexOperations.markFailed(DefaultExecutionVertexOperations.java:41) at org.apache.flink.runtime.scheduler.DefaultScheduler.handleTaskDeploymentFailure(DefaultScheduler.java:446) at org.apache.flink.runtime.scheduler.DefaultScheduler.lambda$assignResourceOrHandleError$5(DefaultScheduler.java:433) at java.util.concurrent.CompletableFuture.uniHandle(CompletableFuture.java:822) at java.util.concurrent.CompletableFuture$UniHandle.tryFire(CompletableFuture.java:797) at java.util.concurrent.CompletableFuture.postComplete(CompletableFuture.java:474) at java.util.concurrent.CompletableFuture.completeExceptionally(CompletableFuture.java:1977) at org.apache.flink.runtime.jobmaster.slotpool.SchedulerImpl.lambda$internalAllocateSlot$0(SchedulerImpl.java:168) at java.util.concurrent.CompletableFuture.uniWhenComplete(CompletableFuture.java:760) at java.util.concurrent.CompletableFuture$UniWhenComplete.tryFire(CompletableFuture.java:736) at java.util.concurrent.CompletableFuture.postComplete(CompletableFuture.java:474) at java.util.concurrent.CompletableFuture.completeExceptionally(CompletableFuture.java:1977) at org.apache.flink.runtime.jobmaster.slotpool.SlotSharingManager$SingleTaskSlot.release(SlotSharingManager.java:726) at org.apache.flink.runtime.jobmaster.slotpool.SlotSharingManager$MultiTaskSlot.release(SlotSharingManager.java:537) at org.apache.flink.runtime.jobmaster.slotpool.SlotSharingManager$MultiTaskSlot.lambda$new$0(SlotSharingManager.java:432) at java.util.concurrent.CompletableFuture.uniHandle(CompletableFuture.java:822) at java.util.concurrent.CompletableFuture$UniHandle.tryFire(CompletableFuture.java:797) at java.util.concurrent.CompletableFuture.postComplete(CompletableFuture.java:474) at java.util.concurrent.CompletableFuture.completeExceptionally(CompletableFuture.java:1977) at org.apache.flink.runtime.concurrent.FutureUtils.lambda$forward$21(FutureUtils.java:1065) at java.util.concurrent.CompletableFuture.uniWhenComplete(CompletableFuture.java:760) at java.util.concurrent.CompletableFuture$UniWhenComplete.tryFire(CompletableFuture.java:736) at java.util.concurrent.CompletableFuture.postComplete(CompletableFuture.java:474) at java.util.concurrent.CompletableFuture.completeExceptionally(CompletableFuture.java:1977) at org.apache.flink.runtime.concurrent.FutureUtils$Timeout.run(FutureUtils.java:999) at org.apache.flink.runtime.rpc.akka.AkkaRpcActor.handleRunAsync(AkkaRpcActor.java:402) at org.apache.flink.runtime.rpc.akka.AkkaRpcActor.handleRpcMessage(AkkaRpcActor.java:195) at org.apache.flink.runtime.rpc.akka.FencedAkkaRpcActor.handleRpcMessage(FencedAkkaRpcActor.java:74) at org.apache.flink.runtime.rpc.akka.AkkaRpcActor.handleMessage(AkkaRpcActor.java:152) at akka.japi.pf.UnitCaseStatement.apply(CaseStatements.scala:26) at akka.japi.pf.UnitCaseStatement.apply(CaseStatements.scala:21) at scala.PartialFunction.applyOrElse(PartialFunction.scala:123) at scala.PartialFunction.applyOrElse$(PartialFunction.scala:122) at akka.japi.pf.UnitCaseStatement.applyOrElse(CaseStatements.scala:21) at scala.PartialFunction$OrElse.applyOrElse(PartialFunction.scala:171) at scala.PartialFunction$OrElse.applyOrElse(PartialFunction.scala:172) at scala.PartialFunction$OrElse.applyOrElse(PartialFunction.scala:172) at akka.actor.Actor.aroundReceive(Actor.scala:517) at akka.actor.Actor.aroundReceive$(Actor.scala:515) 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) at akka.dispatch.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260) at akka.dispatch.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339) at akka.dispatch.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979) at akka.dispatch.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107) Caused by: org.apache.flink.runtime.jobmanager.scheduler.NoResourceAvailableException: Could not allocate the required slot within slot request timeout. Please make sure that the cluster has enough resources. at org.apache.flink.runtime.scheduler.DefaultScheduler.maybeWrapWithNoResourceAvailableException(DefaultScheduler.java:452) ... 47 more Caused by: java.util.concurrent.CompletionException: java.util.concurrent.TimeoutException at java.util.concurrent.CompletableFuture.encodeThrowable(CompletableFuture.java:292) at java.util.concurrent.CompletableFuture.completeThrowable(CompletableFuture.java:308) at java.util.concurrent.CompletableFuture.uniApply(CompletableFuture.java:593) at java.util.concurrent.CompletableFuture$UniApply.tryFire(CompletableFuture.java:577) ... 27 more Caused by: java.util.concurrent.TimeoutException ... 25 more
我的服务器运行情况
一共有四台服务器,jps命令信息如下:
第一台服务器(tuge1):
5794 ResourceManager
5459 NameNode
5689 DFSZKFailoverController
10297 Jps
1834 Application
8123 JobHistoryServer
2686 QuorumPeerMain
第二台服务器(tuge2):
4929 DFSZKFailoverController
4822 NameNode
4748 JournalNode
12429 Jps
4654 QuorumPeerMain
第三台服务器(tuge3):
9700 Jps
4965 JournalNode
5157 NodeManager
5048 DataNode
4877 QuorumPeerMain
第四台服务器(tuge4):
4771 JournalNode
4846 DataNode
4958 NodeManager
11758 Jps
PS:我的虚拟机配置的每台服务器都是2核2G.
我的flink配置情况
flink-conf.yaml配置如下:
################################################################################ # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ################################################################################ #============================================================================== # Common #============================================================================== # The external address of the host on which the JobManager runs and can be # reached by the TaskManagers and any clients which want to connect. This setting # is only used in Standalone mode and may be overwritten on the JobManager side # by specifying the --host <hostname> parameter of the bin/jobmanager.sh executable. # In high availability mode, if you use the bin/start-cluster.sh script and setup # the conf/masters file, this will be taken care of automatically. Yarn/Mesos # automatically configure the host name based on the hostname of the node where the #设置task内存 taskmanager.network.memory.fraction: 0.1 taskmanager.network.memory.min: 64mb taskmanager.network.memory.max: 1gb # JobManager runs. jobmanager.rpc.address: tuge1 # The RPC port where the JobManager is reachable. jobmanager.rpc.port: 6123 # The heap size for the JobManager JVM jobmanager.heap.size: 1024m # The total process memory size for the TaskManager. # # Note this accounts for all memory usage within the TaskManager process, including JVM metaspace and other overhead. taskmanager.memory.process.size: 1024m # To exclude JVM metaspace and overhead, please, use total Flink memory size instead of 'taskmanager.memory.process.size'. # It is not recommended to set both 'taskmanager.memory.process.size' and Flink memory. # # taskmanager.memory.flink.size: 1280m # The number of task slots that each TaskManager offers. Each slot runs one parallel pipeline. taskmanager.numberOfTaskSlots: 2 # The parallelism used for programs that did not specify and other parallelism. parallelism.default: 1 # The default file system scheme and authority. # # By default file paths without scheme are interpreted relative to the local # root file system 'file:///'. Use this to override the default and interpret # relative paths relative to a different file system, # for example 'hdfs://mynamenode:12345' # # fs.default-scheme #============================================================================== # High Availability #============================================================================== # The high-availability mode. Possible options are 'NONE' or 'zookeeper'. # high-availability: zookeeper # The path where metadata for master recovery is persisted. While ZooKeeper stores # the small ground truth for checkpoint and leader election, this location stores # the larger objects, like persisted dataflow graphs. # # Must be a durable file system that is accessible from all nodes # (like HDFS, S3, Ceph, nfs, ...) # high-availability.storageDir: hdfs://tuge1:9000/ha/ # The list of ZooKeeper quorum peers that coordinate the high-availability # setup. This must be a list of the form: # "host1:clientPort,host2:clientPort,..." (default clientPort: 2181) # high-availability.zookeeper.quorum: tuge1:2181,tuge2:2181,tuge3:2181 # ACL options are based on https://zookeeper.apache.org/doc/r3.1.2/zookeeperProgrammers.html#sc_BuiltinACLSchemes # It can be either "creator" (ZOO_CREATE_ALL_ACL) or "open" (ZOO_OPEN_ACL_UNSAFE) # The default value is "open" and it can be changed to "creator" if ZK security is enabled # # high-availability.zookeeper.client.acl: open #============================================================================== # Fault tolerance and checkpointing #============================================================================== # The backend that will be used to store operator state checkpoints if # checkpointing is enabled. # # Supported backends are 'jobmanager', 'filesystem', 'rocksdb', or the # <class-name-of-factory>. # # state.backend: filesystem # Directory for checkpoints filesystem, when using any of the default bundled # state backends. # # state.checkpoints.dir: hdfs://namenode-host:port/flink-checkpoints # Default target directory for savepoints, optional. # state.savepoints.dir: hdfs://tuge1:9000/flink-checkpoints # Flag to enable/disable incremental checkpoints for backends that # support incremental checkpoints (like the RocksDB state backend). # # state.backend.incremental: false # The failover strategy, i.e., how the job computation recovers from task failures. # Only restart tasks that may have been affected by the task failure, which typically includes # downstream tasks and potentially upstream tasks if their produced data is no longer available for consumption. jobmanager.execution.failover-strategy: region #============================================================================== # Rest & web frontend #============================================================================== # The port to which the REST client connects to. If rest.bind-port has # not been specified, then the server will bind to this port as well. # #rest.port: 8081 # The address to which the REST client will connect to # #rest.address: 0.0.0.0 # Port range for the REST and web server to bind to. # #rest.bind-port: 8080-8090 # The address that the REST & web server binds to # #rest.bind-address: 0.0.0.0 # Flag to specify whether job submission is enabled from the web-based # runtime monitor. Uncomment to disable. # web.submit.enable: true #============================================================================== # Advanced #============================================================================== # Override the directories for temporary files. If not specified, the # system-specific Java temporary directory (java.io.tmpdir property) is taken. # # For framework setups on Yarn or Mesos, Flink will automatically pick up the # containers' temp directories without any need for configuration. # # Add a delimited list for multiple directories, using the system directory # delimiter (colon ':' on unix) or a comma, e.g.: # /data1/tmp:/data2/tmp:/data3/tmp # # Note: Each directory entry is read from and written to by a different I/O # thread. You can include the same directory multiple times in order to create # multiple I/O threads against that directory. This is for example relevant for # high-throughput RAIDs. # io.tmp.dirs: /tmp # The classloading resolve order. Possible values are 'child-first' (Flink's default) # and 'parent-first' (Java's default). # # Child first classloading allows users to use different dependency/library # versions in their application than those in the classpath. Switching back # to 'parent-first' may help with debugging dependency issues. # # classloader.resolve-order: child-first # The amount of memory going to the network stack. These numbers usually need # no tuning. Adjusting them may be necessary in case of an "Insufficient number # of network buffers" error. The default min is 64MB, the default max is 1GB. # # taskmanager.memory.network.fraction: 0.1 # taskmanager.memory.network.min: 64mb # taskmanager.memory.network.max: 1gb #============================================================================== # Flink Cluster Security Configuration #============================================================================== # Kerberos authentication for various components - Hadoop, ZooKeeper, and connectors - # may be enabled in four steps: # 1. configure the local krb5.conf file # 2. provide Kerberos credentials (either a keytab or a ticket cache w/ kinit) # 3. make the credentials available to various JAAS login contexts # 4. configure the connector to use JAAS/SASL # The below configure how Kerberos credentials are provided. A keytab will be used instead of # a ticket cache if the keytab path and principal are set. # security.kerberos.login.use-ticket-cache: true # security.kerberos.login.keytab: /path/to/kerberos/keytab # security.kerberos.login.principal: flink-user # The configuration below defines which JAAS login contexts # security.kerberos.login.contexts: Client,KafkaClient #============================================================================== # ZK Security Configuration #============================================================================== # Below configurations are applicable if ZK ensemble is configured for security # Override below configuration to provide custom ZK service name if configured # zookeeper.sasl.service-name: zookeeper # The configuration below must match one of the values set in "security.kerberos.login.contexts" # zookeeper.sasl.login-context-name: Client #============================================================================== # HistoryServer #============================================================================== # The HistoryServer is started and stopped via bin/historyserver.sh (start|stop) # Directory to upload completed jobs to. Add this directory to the list of # monitored directories of the HistoryServer as well (see below). #jobmanager.archive.fs.dir: hdfs:///completed-jobs/ # The address under which the web-based HistoryServer listens. #historyserver.web.address: 0.0.0.0 # The port under which the web-based HistoryServer listens. #historyserver.web.port: 8082 # Comma separated list of directories to monitor for completed jobs. #historyserver.archive.fs.dir: hdfs:///completed-jobs/ # Interval in milliseconds for refreshing the monitored directories. #historyserver.archive.fs.refresh-interval: 10000 yarn.application-attempts: 10
期望结果
我希望可以正常运行。大佬们帮忙看下啥原因~
Caused by: org.apache.flink.runtime.jobmanager.scheduler.NoResourceAvailableException: Could not allocate the required slot within slot request timeout. Please make sure that the cluster has enough resources
你的YARN里面队列没有分配资源,在YARN里面划分一个队列,给到CPU核数和内存。然后在提交Flink任务应该就可以了。
例如在文件fair-scheduler.xml
创建一个queue_hadoop_01的队列:
<allocations>
<queue name="root">
<aclSubmitApps>hadoop</aclSubmitApps>
<aclAdministerApps>hadoop</aclAdministerApps>
<queue name="queue_hadoop_01">
<maxRunningApps>10</maxRunningApps>
<minResources>1024mb,1vcores</minResources>
<maxResources>6144mb,6vcores</maxResources>
<schedulingPolicy>fair</schedulingPolicy>
<weight>1.0</weight>
<aclSubmitApps>hadoop</aclSubmitApps>
<aclAdministerApps>hadoop</aclAdministerApps>
</queue>
</queue>
<fairSharePreemptionTimeout>600000</fairSharePreemptionTimeout>
<defaultMinSharePreemptionTimeout>600000</defaultMinSharePreemptionTimeout>
</allocations>
然后在执行Flink提交命令:
flink run -m yarn-cluster -yn 2 -yjm 1024 -ytm 1024 -yqu queue_hadoop_01 flink_task.jar