activemq | Docker image for ActiveMQ | Continuous Deployment library

Generally speaking, more consumers (especially concurrent consumers) will process messages more quickly than fewer consumers so this looks good from that perspective.

In order to remove specific messages you need to know the ID which you can get via a browse of the scheduled messages. The only other option available is to use the start and stop time options in the remove operations to remove all messages inside a range.

The job has huge data to process , many partition messages are publishing to queue and the aggregator of response from replyChannnel is failing with timeout of messages as all messages cant be processed in a given time.

You need to increase your timeout or add more workers. The Javadoc of MessageChannelPartitionHandler is clear about that:

The application context was loading 3 times(due to code issue from our end) causing this behaviour. hope this will helpful for others. Thank you.

A socket typically behaves asynchronously (i.e., signals are emitted at some indeterminate time after calling methods), but you are setting up the mock object such that it behaves synchronously (signals are emitted immediately as a result of calling the method). You should be attempting to simulate asynchronous behavior.

Typically, you would achieve this behavior by calling the signal manually (and not as part of an invoke clause):

ActiveMQ Artemis has no default connection limit. I just wrote a quick test based on this which uses the Paho 1.2.5 MQTT client. It spun up 500 concurrent connections using both normal TCP and WebSockets. The test finished in less than 20 seconds with no errors. I'm just running this test on my laptop.

I noticed that your journal-buffer-timeout is 700000 which seems quite high which means you have a very low write speed of 1.43 writes per millisecond (i.e. a slow disk). The journal-buffer-timeout that is calculated, for example, on my laptop is 4000 which translates into a write-speed of 250 which is significantly faster than yours. My laptop is nothing special, but it does have an SSD. That said, SSDs are pretty common. If this low write-speed is indicative of the overall performance of your VM it may simply be too weak to handle the load you want. To be clear, this value isn't related directly to MQTT connections. It's just something I noticed while reviewing your configuration that may be indirect evidence of your issue.

The journal-buffer-timeout value is calculated and configured automatically when the instance is created. You can re-calculate this value later and configure it manually using the bin/artemis perf-journal command.

Ultimately, your issue looks environmental to me. I recommend you inspect your VM and network. TCP dumps may be useful to see perhaps how/why the connection is being reset. Thread dumps from the server during the time of the trouble would also be worth inspecting.

There are a couple of important points to understand before getting started:

• When using the Artemis Prometheus Metrics Plugin neither the broker nor the applications "send" metrics to Prometheus. Prometheus itself must retrieve or "scrape" metrics from the broker. This is why the plugin comes with a servlet. The servlet exposes an HTTP endpoint that Prometheus can use to scrape metrics.
• The Artemis Prometheus Metrics Plugin is part of the broker infrastructure. It is not to be deployed as part of an application. The plugin's jar and war files are deployed on the broker and configured in broker.xml and bootstrap.xml respectively.

The Artemis Prometheus Metrics Plugin provides integration with Prometheus using two modules:

• artemis-prometheus-metrics-plugin: This provides the actual implementation of org.apache.activemq.artemis.core.server.metrics.ActiveMQMetricsPlugin and packages it with the Micrometer and Prometheus dependencies in an "uber" jar.

• artemis-prometheus-metrics-plugin-servlet: This provides a war file containing a simple servlet which can be deployed to the broker's embedded web server which then Prometheus can use to scrape metrics.

Once you clone the Artemis Prometheus Metrics Plugin repository simply run mvn install to build these two modules. The output will be in their respective target directories.

After building the modules follow these steps to deploy and configure the Artemis Prometheus Metrics Plugin. If you have some kind of dev-ops group which manages and configures your broker then they would follow these steps.

1. Copy artemis-prometheus-metrics-plugin/target/artemis-prometheus-metrics-plugin-.jar to /lib.

2. Add this to your /etc/broker.xml:

The doc says the JMS MessageID is available as header. So you should be able to return it like this:

The 2.6.3.redhat-00015 version corresponds to AMQ 7.2.3 which is quite old at this point. The current AMQ release is 7.8.1. I strongly recommend you upgrade as it's likely you're hitting a bug that's already been fixed.

You may be able to work around the issue by increasing the minimum large message size (e.g using minLargeMessageSize on core client URLs or amqpMinLargeMessageSize on your AMQP acceptor). For what it's worth, the stack-trace indicates that the core JMS client (i.e. not AMQP) is in use when the exception is thrown.

Lastly, it's worth noting that the default minimum large message size is 100 KB not 2 GB as explained in the documentation.