I hope it did because I dont see the multiple tuple failure error. But I see another issue. I have stopped loading the batch process that sends messages to Kafka. I killed my topology and then restarted again. I still see that message are been loaded into Cassandra. Does that mean that storm still trying to process the failed messages? Is htere a way to flush the old message out from storm so I can fresh start it?
-- Kushan Maskey 817.403.7500 On Tue, Sep 9, 2014 at 10:09 AM, Naresh Kosgi <[email protected]> wrote: > Yes, that is what I was talking about. Hopefully that fixes it. > > On Tue, Sep 9, 2014 at 10:59 AM, Kushan Maskey < > [email protected]> wrote: > >> 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 < >> [email protected]> 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/localdir >>> storm.local.mode.zmqfalsestorm.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 <[email protected]> >>> 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 < >>>> [email protected]> 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 >>>>> >>>> >>>> >>> >> >
