One optimization that we take is letting yarn to reuse the flink-dist jar which was localized when running previous jobs.
Thanks, Zhu Zhu Jörn Franke <jornfra...@gmail.com> 于2019年8月30日周五 下午4:02写道： > Increase replication factor and/or use HDFS cache > https://hadoop.apache.org/docs/r2.4.1/hadoop-project-dist/hadoop-hdfs/CentralizedCacheManagement.html > Try to reduce the size of the Jar, eg the Flink libraries do not need to > be included. > > Am 30.08.2019 um 01:09 schrieb Elkhan Dadashov <elkhan.dadas...@gmail.com > >: > > Dear Flink developers, > > Having difficulty of getting a Flink job started. > > The job's uberjar/fat jar is around 400MB, and I need to kick 800+ > containers. > > The default HDFS replication is 3. > > *The Yarn queue is empty, and 800 containers are allocated > almost immediately by Yarn RM.* > > It takes very long time until all 800 nodes (node managers) will download > Uberjar from HDFS to local machines. > > *Q1:* > > a) Do all those 800 nodes download of batch of 3 at a time ? ( batch > size = HDFS replication size) > > b) Or Do Flink TM's can replicate from each other ? or already started > TM's replicate to yet-started nodes? > > Most probably answer is (a), but want to confirm. > > *Q2:* > > What is the recommended way of handling 400MB+ Uberjar with 800+ > containers ? > > Any specific params to tune? > > Thanks. > > Because downloading the UberJar takes really long time, after around 15 > minutes since the job kicked, facing this exception: > > org.apache.hadoop.yarn.exceptions.YarnException: Unauthorized request to > start container. > This token is expired. current time is 1567116179193 found 1567116001610 > Note: System times on machines may be out of sync. Check system time and time > zones. > at sun.reflect.GeneratedConstructorAccessor35.newInstance(Unknown > Source) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:423) > at > org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.instantiateException(SerializedExceptionPBImpl.java:168) > at > org.apache.hadoop.yarn.api.records.impl.pb.SerializedExceptionPBImpl.deSerialize(SerializedExceptionPBImpl.java:106) > at > org.apache.hadoop.yarn.client.api.impl.NMClientImpl.startContainer(NMClientImpl.java:205) > at > org.apache.flink.yarn.YarnResourceManager.lambda$onContainersAllocated$1(YarnResourceManager.java:400) > at > org.apache.flink.runtime.rpc.akka.AkkaRpcActor.handleRunAsync(AkkaRpcActor.java:332) > at > org.apache.flink.runtime.rpc.akka.AkkaRpcActor.handleRpcMessage(AkkaRpcActor.java:158) > at > org.apache.flink.runtime.rpc.akka.FencedAkkaRpcActor.handleRpcMessage(FencedAkkaRpcActor.java:70) > at > org.apache.flink.runtime.rpc.akka.AkkaRpcActor.onReceive(AkkaRpcActor.java:142) > at > org.apache.flink.runtime.rpc.akka.FencedAkkaRpcActor.onReceive(FencedAkkaRpcActor.java:40) > at > akka.actor.UntypedActor$$anonfun$receive$1.applyOrElse(UntypedActor.scala:165) > at akka.actor.Actor$class.aroundReceive(Actor.scala:502) > at akka.actor.UntypedActor.aroundReceive(UntypedActor.scala:95) > at akka.actor.ActorCell.receiveMessage(ActorCell.scala:526) > at akka.actor.ActorCell.invoke(ActorCell.scala:495) > at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:257) > at akka.dispatch.Mailbox.run(Mailbox.scala:224) > at akka.dispatch.Mailbox.exec(Mailbox.scala:234) > 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) > > > >
