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Tag Archives: JMS

ActiveMQ – Network of Brokers Explained – Part 2

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In this blog we will see how duplex network connectors work. In the previous part 1 we created a network connector from broker-1 and broker-2. We were able to see how messages for queue “foo.bar” on broker-1 were forwarded queue “foo.bar” on broker-2 when there was a consumer on broker-2 for queue “foo.bar” Let’s try doing the reverse by producing messages into broker-2’s queue ...

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ActiveMQ – Network of Brokers Explained

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Objective This 7 part blog series is to share about how to create network of ActiveMQ brokers in order to achieve high availability and scalability. Why network of brokers? ActiveMQ message broker is a core component of messaging infrastructure in an enterprise. It needs to be highly available and dynamically scalable to facilitate communication between dynamic heterogeneous distributed applications which ...

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Using ActiveMQ – “Master/Slave” configuration with failover protocol

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Introduction ActiveMQ broker(s) tends to be a core piece of messaging infrastructure in an enterprise. It is vital for this messaging infrastructure to be highly available and scalable. Please read this link in order to learn more about creating network of brokers to support various use cases. One of the popular use case for ActiveMQ is the Master/Slave configuration with ...

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Temp, Store and Memory Percent Usage in ActiveMQ

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In order to effectively use ActiveMQ, it is very important to understand how ActiveMQ manages memory and disk resources to handle non-persistent and persistent messages. ActiveMQ has three key parameters which need to be kept under check. Temp Percent Usage This is the % of assigned disk storage that has been used up to spool non-persistent messages Non persistent messages ...

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Lessons learned: ActiveMQ, Apache Camel and connection pooling

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Every once in a while, I run into an interesting problem related to connections and pooling with ActiveMQ, and today I’d like to discuss something that is not always very clear and could potentially cause you to drink heavily when using ActiveMQ and Camel JMS. Not to say that you won’t want to drink heavily when using ActiveMQ and Camel ...

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Spring JMS: Processing messages within transactions

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1.Introduction This post will show you how an error in the execution of the consumer during the asynchronous reception of messages with JMS, can lead to the loss of messages. I then will explain how you can solve this problem using local transactions. You will also see that this solution can cause in some cases, message duplication (for example, when ...

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Running multiple ActiveMQ instances on one machine

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A few weeks ago I started making use of Apache ActiveMQ again as the JMS provider with my Mule ESB solution. Since it had been a few years that I used ActiveMQ I thought it would be nice to check out some of the (new) features like the failover transport and other clustering features. To be able to test these ...

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Apache ActiveMQ 5.9 released

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The Apache ActiveMQ team has just released the new ActiveMQ 5.9 release. This release is 8 months of hard work since the previous 5.8 release. In this release we have as usual more hardening of the broker, and the recent new protocols such as AMQP and MQTT. Also very interesting is the hardening of the leveldb persistent store, which now ...

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All Other Metrics Are Useless

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When it comes to queues, whether they’re implemented as JMS, database tables (i.e. what Ruby’s Delayed::Job uses for a queue), or even Amazon’s SQS, the most common metric used to evaluate the state of a queue is its length. In essence, one derives an efficiency metric based upon how many messages are residing in a queue at any given time. ...

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Java 8: Definitive guide to CompletableFuture

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Java 8 is coming so it’s time to study new features. While Java 7 and Java 6 were rather minor releases, version 8 will be a big step forward. Maybe even too big? Today I will give you a thorough explanation of new abstraction in JDK 8 – CompletableFuture<T>. As you all know Java 8 will hopefully be released in ...

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