Category: IBM MQ

Presentations for virtual events

This week I’ve been preparing material for two virtual events that are happening at the same time: SHARE and IBM’s Integration Technical Conference. In normal times, I’d have had to make a choice as to which conference to attend, but both are now using remote presentations. So I can give talks (different ones) at both.

The TechCon event is using “live” presentations, streamed with the presenter talking from home. I’ve been doing a lot of presentations like that already over the last few months – I talked about that in another recent post. While the platform running the TechCon stream is different, and some details will change, it’s probably not really going to affect how I work with that material.

The SHARE event is a little more interesting technically as they want the sessions to be prerecorded, submitted as a video, and then having just a short live Q&A at the end. I guess that helps with risky situations like bandwidth restrictions or network failures at the presenter’s end. Running everything centrally makes it more controllable. But I found a couple of challenges doing the recordings.

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This post was last updated on July 26th, 2020 at 05:22 pm

Updates for MQ metric exporters

The MQ metric exporters are a set of Go programs that deliver queue manager statistics and status to databases such as Prometheus and Influx. They have recently been updated, giving more consistent function and a much easier configuration. This post will explore and explain these changes.

For an introduction to these exporters, see some of my earlier posts in this blog.

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This post was last updated on November 27th, 2021 at 02:59 pm

A “major” update to the MQ Go package version

The Go language and toolchain did not have a good version control system when created. Systems built on Go could not easily define the levels of the dependencies underpinning the system. Various tools were developed to help with that such as dep and glide. But more recently, the Go compiler environment has defined modules as the way forward. The MQ Go packages are now available in a format that works with modules, with a major number version update to match. This post describes what has been done in the core MQ packages.

A separate post talks about changes in the mq-metric-samples repository that exploits these packages and enables monitoring in tools like Prometheus and Influx.

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This post was last updated on June 4th, 2020 at 09:44 am

MQ API Exits – FAQ

In the past few months I seem to have been been sent a bunch of questions about MQ API Exits from a variety of developers. Some of the questions were repeated so I thought I’d try to collect them, tidy them up, and turn them into an FAQ article.

API Exits were originally made available for one platform in MQSeries V5.2 and then extended to the rest of the Distributed family in V5.3 so they have been around for a while.

Exits are an advanced topic and I’m not planning on going into much general information about these exits. I will assume that someone interested in this article already knows the basic principles and is comfortable working in this environment. For more information on the interfaces see the product documentation and the sample amqsaxe0.c exit.

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This post was last updated on December 30th, 2025 at 10:04 am

MQ JMS and Spring Boot – improved efficiency

The efficiency of MQ JMS is now improved when used in a Spring Boot application.

The Spring Framework provides simple ways for Java programs to use a variety of interfaces. Its JMS component includes classes that help a program wait for new messages, similar to a Message Driven Bean. The default behaviour of the Spring implementation is known to be non-optimal when working with IBM MQ and I wanted to improve the efficiency.

This article shows recent improvements to Spring Boot and the corresponding MQ JMS Spring Boot component. They remove the need for application developers to know about, and to write code to deal with that inefficiency.

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This post was last updated on June 24th, 2025 at 02:44 pm

Viewing MQ configurations with Grafana

This post shows how you can use Grafana to selectively view information about your MQ configuration. Which may sound a little odd. Grafana’s strength is primarily to show statistics and metrics in pretty graphs. So why would we want to use it to look at queue definitions? The answer is that you usually would not! There are many more appropriate tools for displaying and updating the queue manager configuration – even the MQ Explorer or MQ Console are better. But there may be times when a limited set of information may be desirable, so you can link from a graph to a different view, within the same tool.

But another important aspect that I hope this shows is the power of a common data format. The techniques I’ll show here could be used to combine a variety of different tools, and perhaps this will give you some ideas.

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This post was last updated on November 25th, 2019 at 02:18 pm

Exploring the MQ archives: Education 1996-style

Recently, we have been creating and publishing a lot of material to help educate people about MQ. You can find that here.

But how might you have learned about MQ in its early days? While hunting through archives for something that I was, in the end, unable to find, I did come across one piece of education that was created over 20 years ago.

You can now see what it was like in this video.

Table of Contents for the training program

Just a few thoughts that I had:

  • While the style may be different, and details vary, a lot of the content is recognisably the same
  • It’s nice to see the MQ Dancers logo return, even as a tiny icon
  • In 1996, the course ended by saying that MQ was “long-term”. Yes, they got that right.

I hope you enjoy it.

This post was last updated on November 20th, 2019 at 09:14 pm

Using Prometheus to monitor MQ channel status

In 2016 I wrote about how MQ’s resource statistics can work with a number of time-series databases, including Prometheus. This permits monitoring using the same tools that many customers use for monitoring other products. It allows easy creation of dashboards using tools such as Grafana.

Since that original version, we’ve made a number of enhancements to the packages that underpin that monitoring capability. For example, more database options were added; a JSON formatter appeared. One notable change was when we split the monitoring agent programs into a separate GitHub repository, making it easier to work with just the pieces you needed.

And now, I’ve released some changes that allow Prometheus and generic JSON processors to see some key channel status information. In particular, a Grafana dashboard can easily highlight channels that are not running.

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This post was last updated on November 25th, 2019 at 09:53 am