u can set it using the config object. The method I believe is called num_ackers(int)
On Wed, Sep 10, 2014 at 11:53 AM, Kushan Maskey < kushan.mas...@mmillerassociates.com> wrote: > Thanks for the responses. > > Based on the document > https://storm.incubator.apache.org/documentation/Running-topologies-on-a-production-cluster.html, > I am able to set the TOPOLOGY_MAX_SPOUT_PENDING, but how can you set the > TOPOLOGY_AKERS? > I see TOPOLOGY_AKERS_EXECUTORS. Does that mean the same? How do you > determine the TOPOLOGY_AKERS for a batch load of more than 100K data at 20 > messages per seconds? Can any of you give me an idea on this? > > > -- > Kushan Maskey > 817.403.7500 > > On Wed, Sep 10, 2014 at 4:18 AM, Spico Florin <spicoflo...@gmail.com> > wrote: > >> Hello! >> I'll consider to slow down the spout. Set up a value >> for Config.TOPOLOGY_MAX_SPOUT_PENDING. It can happen that Cassanda, Solr >> and CouchDatabase do not cope with the requency that you emit your messages >> and thus you have backpressure. >> Also, the spout should emit the messages anchored (( not sure here see >> my post about this))in order that the set up of >> Config.TOPOLOGY_MAX_SPOUT_PENDING to take effect. >> Check this blog: >> http://brianoneill.blogspot.ro/2012/08/a-big-data-trifecta-storm-kafka-and.html >> >> Hope that these help. >> Best regards, >> Florin >> >> >> On Wed, Sep 10, 2014 at 6:25 AM, Vikas Agarwal <vi...@infoobjects.com> >> wrote: >> >>> Kafka still contains the logs and they would be there upto the >>> configured time of log retention period. Check server.properties of kafka >>> and update the log retention period to 5 min and restart kafka and when >>> kafka stablizes, shut down it and restart the it with original value of log >>> retentions period property. >>> >>> >>> On Tue, Sep 9, 2014 at 10:40 PM, Kushan Maskey < >>> kushan.mas...@mmillerassociates.com> wrote: >>> >>>> 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 <nareshko...@gmail.com> >>>> wrote: >>>> >>>>> Yes, that is what I was talking about. Hopefully that fixes it. >>>>> >>>>> On Tue, Sep 9, 2014 at 10:59 AM, Kushan Maskey < >>>>> kushan.mas...@mmillerassociates.com> 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 < >>>>>> 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 >>>>>>> -Xmx1024mnimbus.cleanup.inbox.freq.secs600 >>>>>>> nimbus.file.copy.expiration.secs600nimbus.hostnmcxstrmd001 >>>>>>> nimbus.inbox.jar.expiration.secs3600nimbus.monitor.freq.secs10 >>>>>>> nimbus.reassigntruenimbus.supervisor.timeout.secs60 >>>>>>> nimbus.task.launch.secs120nimbus.task.timeout.secs30 >>>>>>> nimbus.thrift.max_buffer_size1048576nimbus.thrift.port6627 >>>>>>> nimbus.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_ms >>>>>>> 1000storm.messaging.netty.min_wait_ms100 >>>>>>> storm.messaging.netty.server_worker_threads1 >>>>>>> storm.messaging.netty.transfer.batch.size262144 >>>>>>> storm.messaging.transportbacktype.storm.messaging.netty.Context >>>>>>> storm.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.timeout >>>>>>> 20000supervisor.childopts-Xmx256msupervisor.enabletrue >>>>>>> supervisor.heartbeat.frequency.secs5 >>>>>>> supervisor.monitor.frequency.secs3supervisor.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.secs120 >>>>>>> supervisor.worker.timeout.secs30task.heartbeat.frequency.secs3 >>>>>>> task.refresh.poll.secs10topology.acker.executors >>>>>>> topology.builtin.metrics.bucket.size.secs60topology.debugfalse >>>>>>> topology.disruptor.wait.strategy >>>>>>> com.lmax.disruptor.BlockingWaitStrategy >>>>>>> topology.enable.message.timeoutstrue >>>>>>> topology.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.size8 >>>>>>> topology.skip.missing.kryo.registrationsfalse >>>>>>> topology.sleep.spout.wait.strategy.time.ms1 >>>>>>> topology.spout.wait.strategy >>>>>>> backtype.storm.spout.SleepSpoutWaitStrategy >>>>>>> topology.state.synchronization.timeout.secs60 >>>>>>> topology.stats.sample.rate0.05topology.tasks >>>>>>> topology.tick.tuple.freq.secstopology.transfer.buffer.size1024 >>>>>>> topology.trident.batch.emit.interval.millis500 >>>>>>> topology.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 >>>>>>> /transactionaltransactional.zookeeper.serversui.childopts-Xmx768m >>>>>>> ui.port8080worker.childopts-Xmx768mworker.heartbeat.frequency.secs1 >>>>>>> zmq.hwm0zmq.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 >>>>>>>>> >>>>>>>> >>>>>>>> >>>>>>> >>>>>> >>>>> >>>> >>> >>> >>> -- >>> Regards, >>> Vikas Agarwal >>> 91 – 9928301411 >>> >>> InfoObjects, Inc. >>> Execution Matters >>> http://www.infoobjects.com >>> 2041 Mission College Boulevard, #280 >>> Santa Clara, CA 95054 >>> +1 (408) 988-2000 Work >>> +1 (408) 716-2726 Fax >>> >>> >> >
