Just realized that the tuple timeout you are talking about is the "topology.message.timeout.secs" which was set to 30 sec and now I made to to 120.
-- Kushan Maskey 817.403.7500 On Tue, Sep 9, 2014 at 9:43 AM, Kushan Maskey < kushan.mas...@mmillerassociates.com> wrote: > > Thanks and apologies, I should I mentioned that in my question earlier. I > am using storm 0.9.2 and using the inbuilt KafkaSpout. I do not implement > any failure my self. Do I need to create my own custom KafkaSpout? > > I have not set timeout for tuples. In fact I dont know where to set that. > Here is my storm config if that is where I need to set the time out. But > non of them say anything about tuple timeout. > > dev.zookeeper.path/tmp/dev-storm-zookeeperdrpc.childopts-Xmx768m > drpc.invocations.port3773drpc.port3772drpc.queue.size128 > drpc.request.timeout.secs600drpc.worker.threads64java.library.path > /usr/local/lib:/opt/local/lib:/usr/liblogviewer.appender.nameA1 > logviewer.childopts-Xmx128mlogviewer.port8000nimbus.childopts-Xmx1024m > nimbus.cleanup.inbox.freq.secs600nimbus.file.copy.expiration.secs600 > nimbus.hostnmcxstrmd001nimbus.inbox.jar.expiration.secs3600 > nimbus.monitor.freq.secs10nimbus.reassigntrue > nimbus.supervisor.timeout.secs60nimbus.task.launch.secs120 > nimbus.task.timeout.secs30nimbus.thrift.max_buffer_size1048576 > nimbus.thrift.port6627nimbus.topology.validator > backtype.storm.nimbus.DefaultTopologyValidatorstorm.cluster.mode > distributedstorm.local.dir/data/disk00/storm/localdirstorm.local.mode.zmq > falsestorm.messaging.netty.buffer_size5242880 > storm.messaging.netty.client_worker_threads1 > storm.messaging.netty.flush.check.interval.ms10 > storm.messaging.netty.max_retries30storm.messaging.netty.max_wait_ms1000 > storm.messaging.netty.min_wait_ms100 > storm.messaging.netty.server_worker_threads1 > storm.messaging.netty.transfer.batch.size262144storm.messaging.transport > backtype.storm.messaging.netty.Contextstorm.thrift.transport > backtype.storm.security.auth.SimpleTransportPlugin > storm.zookeeper.connection.timeout15000storm.zookeeper.port2181 > storm.zookeeper.retry.interval1000 > storm.zookeeper.retry.intervalceiling.millis30000 > storm.zookeeper.retry.times5storm.zookeeper.root/storm > storm.zookeeper.serversnmcxstrmd001storm.zookeeper.session.timeout20000 > supervisor.childopts-Xmx256msupervisor.enabletrue > supervisor.heartbeat.frequency.secs5supervisor.monitor.frequency.secs3 > supervisor.slots.ports > 6700,6701,6702,6703,6704,6705,6706,6707,6708,6709,6710,6711,6712,6713,6714,6715,6716,6717,6718,6719,6720,6721,6722,6723,6724,6725,6726,6727,6728 > supervisor.worker.start.timeout.secs120supervisor.worker.timeout.secs30 > task.heartbeat.frequency.secs3task.refresh.poll.secs10 > topology.acker.executorstopology.builtin.metrics.bucket.size.secs60 > topology.debugfalsetopology.disruptor.wait.strategy > com.lmax.disruptor.BlockingWaitStrategytopology.enable.message.timeouts > truetopology.error.throttle.interval.secs10 > topology.executor.receive.buffer.size1024 > topology.executor.send.buffer.size1024 > topology.fall.back.on.java.serializationtruetopology.kryo.factory > backtype.storm.serialization.DefaultKryoFactory > topology.max.error.report.per.interval5topology.max.spout.pending > topology.max.task.parallelismtopology.message.timeout.secs30 > topology.multilang.serializerbacktype.storm.multilang.JsonSerializer > topology.receiver.buffer.size8topology.skip.missing.kryo.registrations > falsetopology.sleep.spout.wait.strategy.time.ms1 > topology.spout.wait.strategybacktype.storm.spout.SleepSpoutWaitStrategy > topology.state.synchronization.timeout.secs60topology.stats.sample.rate > 0.05topology.taskstopology.tick.tuple.freq.secs > topology.transfer.buffer.size1024 > topology.trident.batch.emit.interval.millis500topology.tuple.serializer > backtype.storm.serialization.types.ListDelegateSerializer > topology.worker.childoptstopology.worker.receiver.thread.count1 > topology.worker.shared.thread.pool.size4topology.workers1 > transactional.zookeeper.porttransactional.zookeeper.root/transactional > transactional.zookeeper.serversui.childopts-Xmx768mui.port8080 > worker.childopts-Xmx768mworker.heartbeat.frequency.secs1zmq.hwm0 > zmq.linger.millis5000zmq.threads1 > > -- > Kushan Maskey > 817.403.7500 > > On Tue, Sep 9, 2014 at 9:23 AM, Naresh Kosgi <nareshko...@gmail.com> > wrote: > >> What is your timeout setting for failing a tuple? Its hard to say what is >> causing this issue without more information but the default timeout on >> tuples is 30 seconds and for some tuples it maybe taking longer then 30 >> seconds to process. Try increasing the timeout to 1 or 2 min? >> >> >> "Why the ack/failure ack counts are so much higher than the number of >> records I am trying to process?" >> >> how are you implementing the fail() method in your spout? on failure, >> this method is called by the framework. It could be you are reemitting the >> tuple to be processed and its failing again. This could be a reason why u >> have more failed tuples then records >> >> On Tue, Sep 9, 2014 at 10:06 AM, Kushan Maskey < >> kushan.mas...@mmillerassociates.com> wrote: >> >>> I have a batch job where I process more than 100k records from file. I >>> post all these message to Kafka topic. I have a topology that goes and >>> fetches these records and dumps them into Cassandra database and also >>> updates solr and couch databases. >>> >>> I have been trying to run the process multiple times to make sure that >>> the process completes successfully. It does run successfully sometimes and >>> sometimes it errors out saying the following error that says "Too many >>> tuple failures" in the storm UI. >>> >>> java.lang.RuntimeException: java.lang.RuntimeException: Too many tuple >>> failures at >>> backtype.storm.utils.DisruptorQueue.consumeBatchToCursor(DisruptorQueue.java:128) >>> at backtype.storm.utils.DisruptorQueue.consumeBatch(DisruptorQueue.java:87) >>> at backtype.storm.disruptor$consume_batch.invoke(disruptor.clj:76) at >>> backtype.storm.daemon.executor$fn__5573$fn__5588$fn__5617.invoke(executor.clj:540) >>> at backtype.storm.util$async_loop$fn__457.invoke(util.clj:431) at >>> clojure.lang.AFn.run(AFn.java:24) at java.lang.Thread.run(Thread.java:744) >>> Caused by: java.lang.RuntimeException: Too many tuple failures at >>> storm.kafka.PartitionManager.fail(PartitionManager.java:210) at >>> storm.kafka.KafkaSpout.fail(KafkaSpout.java:174) at >>> backtype.storm.daemon.executor$fail_spout_msg.invoke(executor.clj:370) at >>> backtype.storm.daemon.executor$fn$reify__5576.expire(executor.clj:430) at >>> backtype.storm.utils.RotatingMap.rotate(RotatingMap.java:73) at >>> backtype.storm.daemon.executor$fn__5573$tuple_action_fn__5579.invoke(executor.clj:435) >>> at >>> backtype.storm.daemon.executor$mk_task_receiver$fn__5564.invoke(executor.clj:402) >>> at >>> backtype.storm.disruptor$clojure_handler$reify__745.onEvent(disruptor.clj:58) >>> at >>> backtype.storm.utils.DisruptorQueue.consumeBatchToCursor(DisruptorQueue.java:125) >>> ... 6 more >>> >>> once this failure happens, i also see that the number of records stored >>> in Cassandra database if way much higher than the actual batch records >>> count. How do I handle this error? Also when there is any kind of >>> error/exception occurs then the ack failed values goes up form 0 to >>> thousands. Why the ack/failure ack counts are so much higher thank the >>> number of records I am trying to process? >>> >>> >>> -- >>> Kushan Maskey >>> >> >> >
