DDevelopment & Design

JMS redelivery with ActiveMQ and Servicemix

The other day I felt a compelling need to implement a JMS redelivery scenario. The exact scenario I’d been trying to handle was:

  1. my message is in an ActiveMQ queue or topic
  2. its processing fails, because of some exception – ie. database access exception due to server nonavailability
  3. since we get an exception, the message is not handled properly, we may want to retry processing attempt some time later
  4. of course, for the redelivery to happen we need the message to stay in the ActiveMQ queue – fetching messages from the queue will be stopped until the redelivery succeeds or expires

See how this can be done after the jump :)

For this to happen, I’ve tried implementing Apache Camel route, but as it turns out, Camel fails to deliver facilities for exact JMS redelivery. It is possible to set JMS connection in transacted mode, but the redeliveries happen one after another and fixed times.

What I’ve ended up doing was implement a servicemix-jms endpoint. I’ve used this configuration for it:


            activemq/connectionFactory

            activemq/resourceAdapter

As you can see, we lookup a couple of things in JNDI registry, so you need to have them configured on the Servicemix side – a sample config presented farther in this entry.

The bean responsible for configuring redelivery settings is activationSpec. You can set various things with it, like:

  • initial redelivery delay
  • maximum number of redeliveries
  • backoff multiplier
  • …

What is really important in jms:endpoint config for this to work are:

  • processorName=”jca”
  • rollbackOnError=”true”

Servicemix should have the following entries in its jndi registry:

          

(...) 

       xmlns:jencks="http://jencks.org/2.0"
       xmlns:amqra="http://activemq.apache.org/schema/ra" -->

When the redeliveries are exhausted, message is routed to global Dead Letter Queue called ActiveMQ.DLQ. Since this is a single bag for all the failed messages from all queues, you may want to configure this aspect differently. For example you can tell ActiveMQ to create a single DLQ for each queue. Use this config to achieve it – the changes should be made to Broker configuration.


        

            

  ...

More on the subject of redelivieries in ActiveMQ can be found at http://activemq.apache.org/message-redelivery-and-dlq-handling.html.

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DDevelopment & Design

Recently at storm-users

I've been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa's post "Football zero, Apache Pig hero". Since I've encountered a lot of insightful and very interesting information I've decided to describe some of those in this post.

  • nimbus will work in HA mode - There's a pull request open for it already... but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we'll look at reworking the HA pull request. (storm’s pull request)

  • pig on storm - Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal.

  • implementing topologies in pure python with petrel looks like this:

class Bolt(storm.BasicBolt):
    def initialize(self, conf, context):
       ''' This method executed only once '''
        storm.log('initializing bolt')

    def process(self, tup):
       ''' This method executed every time a new tuple arrived '''       
       msg = tup.values[0]
       storm.log('Got tuple %s' %msg)

if __name__ == "__main__":
    Bolt().run()

  • Fliptop is happy with storm - see their presentation here

  • topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics.

  • storm vs flume - some users' point of view: I use Storm and Flume and find that they are better at different things - it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don't necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors)

That's all for this day - however, I'll keep on reading through storm-users, so watch this space for more info on storm development.

I've been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa's post "Football zero, Apache Pig hero". Since I've encountered a lot of insightful and very interesting information I've decided to describe some of those in this post.

  • nimbus will work in HA mode - There's a pull request open for it already... but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we'll look at reworking the HA pull request. (storm’s pull request)

  • pig on storm - Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal.

  • implementing topologies in pure python with petrel looks like this:

class Bolt(storm.BasicBolt):
    def initialize(self, conf, context):
       ''' This method executed only once '''
        storm.log('initializing bolt')

    def process(self, tup):
       ''' This method executed every time a new tuple arrived '''       
       msg = tup.values[0]
       storm.log('Got tuple %s' %msg)

if __name__ == "__main__":
    Bolt().run()

  • Fliptop is happy with storm - see their presentation here

  • topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics.

  • storm vs flume - some users' point of view: I use Storm and Flume and find that they are better at different things - it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don't necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors)

That's all for this day - however, I'll keep on reading through storm-users, so watch this space for more info on storm development.