After creating a jhipster project, tried with the following command.
**mvnw
I am getting the following error. For existing project also, i am facing the same issue.
Error :
com.netflix.discovery.shared.transport.TransportException: Cannot execute request on any known server
at com.netflix.discovery.shared.transport.decorator.RetryableEurekaHttpClient.execute(RetryableEurekaHttpClient.java:111)
at com.netflix.discovery.shared.transport.decorator.EurekaHttpClientDecorator.register(EurekaHttpClientDecorator.java:56)
at com.netflix.discovery.shared.transport.decorator.EurekaHttpClientDecorator$1.execute(EurekaHttpClientDecorator.java:59)
at com.netflix.discovery.shared.transport.decorator.SessionedEurekaHttpClient.execute(SessionedEurekaHttpClient.java:77)
at com.netflix.discovery.shared.transport.decorator.EurekaHttpClientDecorator.register(EurekaHttpClientDecorator.java:56)
at com.netflix.discovery.DiscoveryClient.register(DiscoveryClient.java:815)
at com.netflix.discovery.InstanceInfoReplicator.run(InstanceInfoReplicator.java:104)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:180)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Your question is not detailed enough but it is obvious that you are using a microservice achitecture and did not start the registry, check documentation.
Related
I am getting error while publishing message to gcp pubsub lite using spark structured streaming.
I cannot use writestream as I want to use it in forEachBatch sink in spark so I am using foreachpartition and foreach and publishing message inside foreach for each dataframe row.
Below is error I get , some messages get published but in some I can see below exception:
2022-06-07 10:08:17 WARN PartitionCountWatcherImpl:101 - Failed to refresh partition count
com.google.api.gax.rpc.ApiException:
at com.google.cloud.pubsublite.internal.CheckedApiException.<init>(CheckedApiException.java:51)
at com.google.cloud.pubsublite.internal.CheckedApiException.<init>(CheckedApiException.java:55)
at com.google.cloud.pubsublite.internal.ExtractStatus.toCanonical(ExtractStatus.java:49)
at com.google.cloud.pubsublite.internal.wire.PartitionCountWatcherImpl.pollTopicConfig(PartitionCountWatcherImpl.java:92)
at com.google.cloud.pubsublite.internal.wire.PartitionCountWatcherImpl.onAlarm(PartitionCountWatcherImpl.java:71)
at com.google.cloud.pubsublite.internal.AlarmFactory.lambda$null$0(AlarmFactory.java:41)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:308)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:180)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:294)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.InterruptedException
at com.google.common.util.concurrent.AbstractFuture.get(AbstractFuture.java:456)
at com.google.common.util.concurrent.FluentFuture$TrustedFuture.get(FluentFuture.java:100)
at com.google.common.util.concurrent.ForwardingFuture.get(ForwardingFuture.java:73)
at com.google.cloud.pubsublite.internal.wire.PartitionCountWatcherImpl.pollTopicConfig(PartitionCountWatcherImpl.java:81)
... 9 more
Im trying to download file from Azure blob storage with flowing code:
blobServiceClient = new BlobServiceClientBuilder().connectionString(connectionString)
.buildClient();
BlobClient b = blobContainerClient.getBlobClient(remotePath);
b.downloadToFile(localPath, true);
But sometimes i got this exception:
Caused by: java.util.concurrent.TimeoutException: Did not observe any item or terminal signal within 60000ms in 'map' (and no fallback has been configured)
at reactor.core.publisher.FluxTimeout$TimeoutMainSubscriber.handleTimeout(FluxTimeout.java:288)
at reactor.core.publisher.FluxTimeout$TimeoutMainSubscriber.doTimeout(FluxTimeout.java:273)
at reactor.core.publisher.FluxTimeout$TimeoutTimeoutSubscriber.onNext(FluxTimeout.java:390)
at reactor.core.publisher.StrictSubscriber.onNext(StrictSubscriber.java:89)
at reactor.core.publisher.FluxOnErrorResume$ResumeSubscriber.onNext(FluxOnErrorResume.java:73)
at reactor.core.publisher.MonoDelay$MonoDelayRunnable.run(MonoDelay.java:117)
at reactor.core.scheduler.SchedulerTask.call(SchedulerTask.java:50)
at reactor.core.scheduler.SchedulerTask.call(SchedulerTask.java:27)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:180)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Do we have any solution to make it's stable?
Version:
<azure-storage-blob.version>12.6.0</azure-storage-blob.version>
<azure-core.version>1.3.0</azure-core.version>
Trying to follow the link to add azure event into logstash, I have the below issue:
[2020-02-13T14:06:28,886][ERROR][com.microsoft.azure.eventprocessorhost.PartitionManager] host logstash-5fdbcee8-e368-44de-bc13-c640a36f646f: Exception while initializing stores, not starting partition manager com.microsoft.azure.eventhubs.IllegalEntityException: Failure getting partition ids for event hub
at com.microsoft.azure.eventprocessorhost.PartitionManager.lambda$cachePartitionIds$4(PartitionManager.java:80) ~[azure-eventhubs-eph-2.1.0.jar:?]
at java.util.concurrent.CompletableFuture.uniHandle(CompletableFuture.java:836) ~[?:1.8.0_242]
at java.util.concurrent.CompletableFuture$UniHandle.tryFire(CompletableFuture.java:811) ~[?:1.8.0_242]
at java.util.concurrent.CompletableFuture$Completion.run(CompletableFuture.java:456) [?:1.8.0_242]
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511) [?:1.8.0_242]
at java.util.concurrent.FutureTask.run(FutureTask.java:266) [?:1.8.0_242]
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:180) [?:1.8.0_242]
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293) [?:1.8.0_242]
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) [?:1.8.0_242]
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) [?:1.8.0_242]
Can someone help ?
I got the hint from this question. It appears the consumer SAS policy still needs manage privileges.
I have a cluster with CDH 5.8.4. I'm runnin a spark streaming application which reads and writes data from/to HBase by using the cloudera spark-hbase connector namely the HBaseContext.
When I start the application I give the principal and the kinit to the spark-submit script.
I'm seeing that after 7 days the application crashed with an error about the expiration of the ticket kerberos related to the HBase context. This is the error from the executors log:
ERROR executor.Executor: Exception in task 0.0 in stage 544265.0 (TID 1149098)
org.apache.hadoop.hbase.client.RetriesExhaustedException: Can't get the location
at org.apache.hadoop.hbase.client.RpcRetryingCallerWithReadReplicas.getRegionLocations(RpcRetryingCallerWithReadReplicas.java
:326)
at org.apache.hadoop.hbase.client.ScannerCallableWithReplicas.call(ScannerCallableWithReplicas.java:157)
at org.apache.hadoop.hbase.client.ScannerCallableWithReplicas.call(ScannerCallableWithReplicas.java:61)
at org.apache.hadoop.hbase.client.RpcRetryingCaller.callWithoutRetries(RpcRetryingCaller.java:200)
at org.apache.hadoop.hbase.client.ClientScanner.call(ClientScanner.java:320)
at org.apache.hadoop.hbase.client.ClientScanner.nextScanner(ClientScanner.java:295)
at org.apache.hadoop.hbase.client.ClientScanner.initializeScannerInConstruction(ClientScanner.java:160)
at org.apache.hadoop.hbase.client.ClientScanner.<init>(ClientScanner.java:155)
at org.apache.hadoop.hbase.client.HTable.getScanner(HTable.java:867)
at org.apache.hadoop.hbase.mapreduce.TableRecordReaderImpl.restart(TableRecordReaderImpl.java:91)
at org.apache.hadoop.hbase.mapreduce.TableRecordReaderImpl.initialize(TableRecordReaderImpl.java:169)
at org.apache.hadoop.hbase.mapreduce.TableRecordReader.initialize(TableRecordReader.java:134)
at org.apache.hadoop.hbase.mapreduce.TableInputFormatBase$1.initialize(TableInputFormatBase.java:211)
at org.apache.spark.rdd.NewHadoopRDD$$anon$1.<init>(NewHadoopRDD.scala:164)
at org.apache.spark.rdd.NewHadoopRDD.compute(NewHadoopRDD.scala:129)
at org.apache.hadoop.hbase.spark.NewHBaseRDD.compute(NewHBaseRDD.scala:34)
at org.apache.hadoop.hbase.spark.NewHBaseRDD.compute(NewHBaseRDD.scala:25)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: org.apache.hadoop.security.token.SecretManager$InvalidToken: Token has expired
at sun.reflect.GeneratedConstructorAccessor58.newInstance(Unknown Source)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at org.apache.hadoop.ipc.RemoteException.instantiateException(RemoteException.java:106)
at org.apache.hadoop.ipc.RemoteException.unwrapRemoteException(RemoteException.java:95)
at org.apache.hadoop.hbase.protobuf.ProtobufUtil.getRemoteException(ProtobufUtil.java:327)
at org.apache.hadoop.hbase.protobuf.ProtobufUtil.getRowOrBefore(ProtobufUtil.java:1593)
at org.apache.hadoop.hbase.client.ConnectionManager$HConnectionImplementation.locateRegionInMeta(ConnectionManager.java:1398)
at org.apache.hadoop.hbase.client.ConnectionManager$HConnectionImplementation.locateRegion(ConnectionManager.java:1199)
at org.apache.hadoop.hbase.client.RpcRetryingCallerWithReadReplicas.getRegionLocations(RpcRetryingCallerWithReadReplicas.java:315)
... 30 more
Caused by: org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.security.token.SecretManager$InvalidToken): Token has expired
at org.apache.hadoop.hbase.security.HBaseSaslRpcClient.readStatus(HBaseSaslRpcClient.java:155)
at org.apache.hadoop.hbase.security.HBaseSaslRpcClient.saslConnect(HBaseSaslRpcClient.java:222)
at org.apache.hadoop.hbase.ipc.RpcClientImpl$Connection.setupSaslConnection(RpcClientImpl.java:617)
at org.apache.hadoop.hbase.ipc.RpcClientImpl$Connection.access$700(RpcClientImpl.java:162)
at org.apache.hadoop.hbase.ipc.RpcClientImpl$Connection$2.run(RpcClientImpl.java:743)
at org.apache.hadoop.hbase.ipc.RpcClientImpl$Connection$2.run(RpcClientImpl.java:740)
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:1783)
at org.apache.hadoop.hbase.ipc.RpcClientImpl$Connection.setupIOstreams(RpcClientImpl.java:740)
at org.apache.hadoop.hbase.ipc.RpcClientImpl$Connection.writeRequest(RpcClientImpl.java:906)
at org.apache.hadoop.hbase.ipc.RpcClientImpl$Connection.tracedWriteRequest(RpcClientImpl.java:873)
at org.apache.hadoop.hbase.ipc.RpcClientImpl.call(RpcClientImpl.java:1242)
at org.apache.hadoop.hbase.ipc.AbstractRpcClient.callBlockingMethod(AbstractRpcClient.java:227)
at org.apache.hadoop.hbase.ipc.AbstractRpcClient$BlockingRpcChannelImplementation.callBlockingMethod(AbstractRpcClient.java:336)
at org.apache.hadoop.hbase.protobuf.generated.ClientProtos$ClientService$BlockingStub.get(ClientProtos.java:34070)
at org.apache.hadoop.hbase.protobuf.ProtobufUtil.getRowOrBefore(ProtobufUtil.java:1589)
Does anyone knows how to solve this issue?
Thanks in advance,
Beniamino
We (Splice Machine) had the same issue with a customer. Our issue was caused by https://issues.apache.org/jira/browse/SPARK-12646. We wrote some code to fix the _HOST issue and we also upgraded to Spark 2.2 to get around this issue.
You should not rely on an external ticket cache for distributed jobs. The best solution is to ship a keytab with your application or rely on a keytab being deployed on all nodes where your Spark task may be executed.
UserGroupInformation.loginUserFromKeytab("name#xyz.com", keyTab);
connection=ConnectionFactory.createConnection(conf);
With your approach above, you would need to do something like the following after obtaining the UserGroupInformation instance:
ugi.doAs(new PrivilegedAction<Void>() {
public Void run() {
connection = ConnectionFactory.createConnection(conf);
...
return null;
}
});
In my spark application which is run in a cluster mode, I get below exception. I know somehow this coud be due to emery issue. But as the error says, it can not connect to a node. But I ma sure the node is available and it can be connected. Can anyone know what is the main cause of this error and how to resolve it?
17/10/31 17:10:54 ERROR ShuffleBlockFetcherIterator: Failed to get block(s) from AUPER01-02-10-12-0.prod.vroc.com.au:36787
java.io.IOException: Failed to connect to AUPER01-02-10-12-0.prod.vroc.com.au/192.168.11.22:36787
at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:232)
at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:182)
at org.apache.spark.network.netty.NettyBlockTransferService$$anon$1.createAndStart(NettyBlockTransferService.scala:97)
at org.apache.spark.network.shuffle.RetryingBlockFetcher.fetchAllOutstanding(RetryingBlockFetcher.java:141)
at org.apache.spark.network.shuffle.RetryingBlockFetcher.access$200(RetryingBlockFetcher.java:43)
at org.apache.spark.network.shuffle.RetryingBlockFetcher$1.run(RetryingBlockFetcher.java:171)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:144)
at java.lang.Thread.run(Thread.java:745)
Caused by: io.netty.channel.AbstractChannel$AnnotatedConnectException: Connection refused: AUPER01-02-10-12-0.prod.vroc.com.au/192.168.11.22:36787
at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
at sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:717)
at io.netty.channel.socket.nio.NioSocketChannel.doFinishConnect(NioSocketChannel.java:257)
at io.netty.channel.nio.AbstractNioChannel$AbstractNioUnsafe.finishConnect(AbstractNioChannel.java:291)
at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:631)
at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:566)
at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:480)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:442)
at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:131)
... 2 more
It appears that one of the executors died while the other executors tried to pull blocks from earlier shuffle stages to complete a Spark job.
Right after you've spark-submited a Spark application to a cluster, the application gets a set of machines for executors. They are responsible for executing tasks and caching their results (in memory and/or disk).
Every executor has its own BlockManager that is responsible for managing datasets (as blocks).
The BlockManagers in a Spark application have all to be available or the Spark application will re-trigger task execution.
ShuffleBlockFetcherIterator is a Scala Iterator that fetches multiple shuffle blocks (aka shuffle map outputs) from local and remote BlockManagers.