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3727 | 12-31-2017 10:12 PM |
02-22-2023
08:30 AM
for now we have 15 Kafka machines in the cluster , all machines are are installed with RHEL 7.9 and the HW machine is DELL physical machine Kafka version is 2.7 , and we have 3 zookeeper servers that serve the Kafka cluster we decided to extend the Kafka cluster to ~100 machines , because Total Throughput In Megabytes increased dramatic - note according to Kafka confluent calculator we need around 100 Kafka machines in that case I am wonder if our 3 zookeeper servers are enough to serve this huge cluster machines? addition I want to say that our 3 zookeeper servers are already serve other application as HDFS , YARN , HIVE , spark etc.
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Apache Zookeeper
01-24-2023
09:23 AM
we have HDP cluster version - 2.6.5 with Ambari platform here is example from our Ambari lab cluster with 5 mode managers machines regarding to YARN service - is it possible to add in Ambari the widget that can show CPU core consuming ? if not what are the other ways to find the CORE consuming by YARN from cli ? other way that we found is from the `resource_manager:8088/cluster` as the following so is it possible to find some API / CLI that can capture the VCores Used ?
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Ambari Blueprints
01-24-2023
08:41 AM
we have spark production cluster with YARN service ( based on HDP 2.6.5 version ) total node-managers services are - 745 ( actually 745 Linux machines ) and yarn active resource-manager and standby resourcemanager are installed on different masters machines we found that the following parameters not defined in our YARN configuration ( yarn-site.xml ) ! yarn.scheduler.increment-allocation-vcores
yarn.scheduler.increment-allocation-mb and above parameters not defined not in Ambari and not in YARN XML configuration files! I want to know what the meaning of the parameter - yarn.scheduler.increment-allocation-vcores ? and what is the affect if this parameters are not defined in our configuration? from YARN best practice configuration we are understanding that both parameters are part of YARN configuration , but we not sure if we must to add them to YARN custom configuration from documentation we found: Minimum and maximum allocation unit in YARN Two resources—memory and CPU, as of in Hadoop 2.5.1, have minimum and maximum allocation unit in YARN, as set by the configurations in yarn-site.xml. Basically, it means RM can only allocate memory to containers in increments of “yarn.scheduler.minimum-allocation-mb” and not exceed “yarn.scheduler.maximum-allocation-mb” It can only allocate CPU vcores to containers in increments of “yarn.scheduler.minimum-allocation-vcores” and not exceed “yarn.scheduler.maximum-allocation-vcores” If changes required, set above configurations in yarn-site.xml on RM nodes, and restart RM. reference: https://docs.trifacta.com/display/r076/Tune+Cluster+Performance https://stackoverflow.com/questions/58522138/how-to-control-yarn-container-allocation-increment-properly https://pratikbarjatya.github.io/learning/best-practices-for-yarn-resource-management/ https://stackoverflow.com/questions/58522138/how-to-control-yarn-container-allocation-increment-properly
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Ambari Blueprints
01-12-2023
12:09 AM
I want to say also that node-manager restart or fully restart of yarn service fixed the problem , but as you know this isn't the right solution that should be every time that one of the node manager became die
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01-11-2023
11:36 PM
Dear @Shelton , long time that we not meet , glad to see you again back to my Question , since we are talking on node manager , my goal is to avoid cases like node-manager service is die or not sync with the resource manager , please forgive me but I not understand why you talking about data node and exclude data node from the cluster , because the question is on different subject , and as I mention we want to understand the root cause of lost node manager and how to do proactive steps in order to avoid such of this problems additionally as I understand most of this problems are as results of bad network that break the connectivity between node manager to resources manager , so in spite some times this behavior is happening , we are trying to set the configuration that give the cluster to be stable in spite all networking problems or INFA problems let me know if my question is clear so we can continue with our discussion , and sorry again if my first post was not clearly
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01-11-2023
08:33 AM
we have huge production Hadoop cluster, with HDP version 2.6.5 and ambari version 2.6.2.2 , and all machines are with OS RHEL 7.6 version the cluster size is as the following : Total workers machines - 425 ( each worker include data node and node manager service ) from time to time we get indication of lost one or two **node-manager** and this identified from Ambari as ( 424/425 when total node-manager are 425 ) in order to fix it we just restart the **node-manager** and this action fix the problem and as results we get 425/425 after some googling , we found the following parameters that maybe should be tune better yarn.client.nodemanager-connect.max-wait-ms ( its configured to 60000 ms and we think to increase it ) yarn.client.nodemanager-connect.retry-interval-ms ( its configured to 10 sec ms and we think to increase it ) yarn.nm.liveness-monitor.expiry-interval-ms ( this parameter not configured yet and we think to add it with value of 1500000 ms ) based on above details , I will appreciate to get comments or others ideas background: NodeManager is LOST means that ResourceManager haven't received heartbeats from it for a duration of nm.liveness-monitor.expiry-interval-ms milliseconds (default is 10 minutes).
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Ambari Blueprints
12-04-2022
05:58 AM
we want to find the **approach / test /cli / API** that gives us the results about HeartBeat Lost between Ambari agent to Ambari server HeartBeat Lost could be as results of poor connection between Ambari agent to Ambari server or because Ambari server was down for along time , etc Note - from Ambari GUI the machine with **HeartBeat Lost** state usually colored by yellow state clarification: the case as described here appears when `ambari-agent status` is in running state as the following ambari-agent status Found ambari-agent PID: 119315 ambari-agent running. Agent PID at: /run/ambari-agent/ambari-agent.pid Agent out at: /var/log/ambari-agent/ambari-agent.out Agent log at: /var/log/ambari-agent/ambari-agent.log
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Ambari Blueprints
11-29-2022
08:07 AM
we have 3 Kafka brokers machines on on RHEL 7.9 Linux version , ( when each machine is physical strong `DELL HW` - memory = 512G and 96 CORE CPU ) Kafka cluster is in production mode Kafka version is 2.7.x , and Kafka disks are in Jbod configuration each Kafka broker has 8 Jbod disks , as we can see from the following ( df -h details ) df -h /dev/sdc 1.7T 929G 748G 56% /kafka/kafka_logs2 /dev/sdd 1.7T 950G 727G 57% /kafka/kafka_logs3 /dev/sde 1.7T 999G 678G 60% /kafka/kafka_logs4 /dev/sdf 1.7T 971G 706G 58% /kafka/kafka_logs5 /dev/sdg 1.7T 1.7T 20K 100% /kafka/kafka-logs6 <----------------- /dev/sdh 1.7T 962G 714G 58% /kafka/kafka_logs7 /dev/sdi 1.7T 1.1T 621G 63% /kafka/kafka_logs8 as we can see from above disk - `/kafka/kafka_logs6` get `100%` used after short investigation we found that Kafka broker isn't tolerant when one disk is failed or disk reached to 100% , as results of this Kafka broker now is down here the Kafka `server.log` [2022-11-29 15:43:59,723] ERROR Error while writing to checkpoint file /kafka/kafka-logs6 ............. java.io.IOException: No space left on device at java.io.FileOutputStream.writeBytes(Native Method) at java.io.FileOutputStream.write(FileOutputStream.java:326) at sun.nio.cs.StreamEncoder.writeBytes(StreamEncoder.java:221) at sun.nio.cs.StreamEncoder.implFlushBuffer(StreamEncoder.java:291) at sun.nio.cs.StreamEncoder.implFlush(StreamEncoder.java:295) at sun.nio.cs.StreamEncoder.flush(StreamEncoder.java:141) at java.io.OutputStreamWriter.flush(OutputStreamWriter.java:229) at java.io.BufferedWriter.flush(BufferedWriter.java:254) at kafka.server.checkpoints.CheckpointFile.liftedTree1$1(CheckpointFile.scala:108) at kafka.server.checkpoints.CheckpointFile.write(CheckpointFile.scala:92) at kafka.server.checkpoints.LeaderEpochCheckpointFile.write(LeaderEpochCheckpointFile.scala:70) at kafka.server.epoch.LeaderEpochFileCache.flush(LeaderEpochFileCache.scala:292) at kafka.server.epoch.LeaderEpochFileCache.$anonfun$truncateFromEnd$1(LeaderEpochFileCache.scala:238) at kafka.server.epoch.LeaderEpochFileCache.truncateFromEnd(LeaderEpochFileCache.scala:235) at kafka.log.Log.$anonfun$new$1(Log.scala:305) at kafka.log.Log.<init>(Log.scala:305) at kafka.log.Log$.apply(Log.scala:2549) at kafka.log.LogManager.loadLog(LogManager.scala:273) at kafka.log.LogManager.$anonfun$loadLogs$12(LogManager.scala:352) at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511) at java.util.concurrent.FutureTask.run(FutureTask.java:266) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:750) **from my perspective** when we have **8 disks** on each broker and one disks is failed ( like reached 100% ) , then we expect Kafka broker still be alive even one disk is failed so as results of above scenario we searched in Kafka `server.properties` the parameter that can help us to configure the Kafka broker to be tolerant when one disk is failed but we not found it , or maybe we not know what to set in order to define the kafka broker to be tolerant with one disk failure the full parameters are: more server.properties auto.create.topics.enable=false auto.leader.rebalance.enable=true background.threads=10 log.retention.bytes=-1 log.retention.hours=48 delete.topic.enable=true leader.imbalance.check.interval.seconds=300 leader.imbalance.per.broker.percentage=10 log.dir=/kafka/kafka-logs2,/kafka/kafka-logs3 ............... log.flush.interval.messages=9223372036854775807 log.flush.interval.ms=1000 log.flush.offset.checkpoint.interval.ms=60000 log.flush.scheduler.interval.ms=9223372036854775807 log.flush.start.offset.checkpoint.interval.ms=60000 compression.type=producer log.roll.jitter.hours=0 log.segment.bytes=1073741824 log.segment.delete.delay.ms=60000 message.max.bytes=1000012 min.insync.replicas=1 num.io.threads=10 num.network.threads=48 num.recovery.threads.per.data.dir=1 num.replica.fetchers=1 offset.metadata.max.bytes=4096 offsets.commit.required.acks=-1 offsets.commit.timeout.ms=5000 offsets.load.buffer.size=5242880 offsets.retention.check.interval.ms=600000 offsets.retention.minutes=10080 offsets.topic.compression.codec=0 offsets.topic.num.partitions=50 offsets.topic.replication.factor=3 offsets.topic.segment.bytes=104857600 queued.max.requests=1000 quota.consumer.default=9223372036854775807 quota.producer.default=9223372036854775807 replica.fetch.min.bytes=1 replica.fetch.wait.max.ms=500 replica.high.watermark.checkpoint.interval.ms=5000 replica.lag.time.max.ms=10000 replica.socket.receive.buffer.bytes=65536 replica.socket.timeout.ms=30000 request.timeout.ms=30000 socket.receive.buffer.bytes=102400 socket.request.max.bytes=104857600 socket.send.buffer.bytes=102400 transaction.max.timeout.ms=900000 transaction.state.log.load.buffer.size=5242880 transaction.state.log.min.isr=2 transaction.state.log.num.partitions=50 transaction.state.log.replication.factor=3 transaction.state.log.segment.bytes=104857600 transactional.id.expiration.ms=604800000 unclean.leader.election.enable=false zookeeper.connection.timeout.ms=600000 zookeeper.max.in.flight.requests=10 zookeeper.session.timeout.ms=600000 zookeeper.set.acl=false broker.id.generation.enable=true connections.max.idle.ms=600000 connections.max.reauth.ms=0 controlled.shutdown.enable=true controlled.shutdown.max.retries=3 controlled.shutdown.retry.backoff.ms=5000 controller.socket.timeout.ms=30000 default.replication.factor=3 delegation.token.expiry.time.ms=86400000 delegation.token.max.lifetime.ms=604800000 delete.records.purgatory.purge.interval.requests=1 fetch.purgatory.purge.interval.requests=1000 group.initial.rebalance.delay.ms=3000 group.max.session.timeout.ms=1800000 group.max.size=2147483647 group.min.session.timeout.ms=6000 log.cleaner.backoff.ms=15000 log.cleaner.dedupe.buffer.size=134217728 log.cleaner.delete.retention.ms=86400000 log.cleaner.enable=true log.cleaner.io.buffer.load.factor=0.9 log.cleaner.io.buffer.size=524288 log.cleaner.io.max.bytes.per.second=1.7976931348623157e308 log.cleaner.max.compaction.lag.ms=9223372036854775807 log.cleaner.min.cleanable.ratio=0.5 log.cleaner.min.compaction.lag.ms=0 log.cleaner.threads=1 log.cleanup.policy=delete log.index.interval.bytes=4096 log.index.size.max.bytes=10485760 log.message.timestamp.difference.max.ms=9223372036854775807 log.message.timestamp.type=CreateTime log.preallocate=false log.retention.check.interval.ms=300000 max.connections=2147483647 max.connections.per.ip=2147483647 max.incremental.fetch.session.cache.slots=1000 num.partitions=1 producer.purgatory.purge.interval.requests=1000 queued.max.request.bytes=-1 replica.fetch.backoff.ms=1000 replica.fetch.max.bytes=1048576 replica.fetch.response.max.bytes=10485760 reserved.broker.max.id=1500 transaction.abort.timed.out.transaction.cleanup.interval.ms=60000 transaction.remove.expired.transaction.cleanup.interval.ms=3600000 zookeeper.sync.time.ms=2000 broker.rack=/default-rack I want to add my personal feeling: *just to gives here the absurder of the above scenario lets say we have on each Kafka broker 100 disks ( in Jbod ) is it make sense that Kafka broker will be shutdown just because one disk is failed ?*
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Apache Kafka
11-16-2022
09:28 AM
we have in our Hadoop cluster 3 Kafka machines, Kafka machines include the following services on kafka01: confluent kafka service confluent schema registry service zookeeper service on kafka02: confluent kafka service confluent schema registry service zookeeper service on kafka03: confluent kafka service confluent schema registry service zookeeper service on our Kafka cluster , we have different 34 topics one of the topic is topic name - `car.to.go` this topic include 3 replica in Kafka cluster what we found regarding to schema registry service , is that topic - `car.to.go` have different versions on kafka03 from other machines - `kafka01/02` so actually we get that versions are not in sync here is example: [root@kafka01 ~]# curl -X GET http://kafka01:8081/subjects/car.to.go-value/versions [1,2,3,4,5,6,7] [root@kafka01 ~]# curl -X GET http://kafka02:8081/subjects/car.to.go-value/versions [1,2,3,4,5,6,7] [root@kafka01 ~]# curl -X GET http://kafka03:8081/subjects/car.to.go-value/versions [1,2,3,4] from above info what could be the resewn that versions on kafka03 is diff from kafka01/02 regarding to topic `car.to.go` - what is the right way to fix this issue in way that we get on kafka03 the number versions as - `1,2,3,4,5,6,7` notes: all Kafka services and schema registry services and zookeeper services are up and running links: https://github.com/confluentinc/schema-registry/blob/master/README.md https://docs.confluent.io/platform/current/schema-registry/develop/using.html#check-if-a-schema-is-registered-under-subject-kafka-key https://kafkawize.com/2019/03/17/commands-for-schemaregistry/
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Apache Hadoop
10-30-2022
05:49 AM
so based on doc seems that we need to increase the CMSInitiatingOccupancyFraction from default 70% to higher value as for example 85% do you agree with that ?
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