Archive for July, 2022

How to scale IBM MQ clusters and client applications in OpenShift

Tuesday, July 19th, 2022

Overview

You’re running a cluster of IBM MQ queue managers in Red Hat OpenShift, together with a large number of client applications putting and getting messages to them. This workload will vary over time, so you need flexibility in how you scale all of this.

This tutorial will show how you can easily scale the number of instances of your client applications up and down, without having to reconfigure their connection details and without needing to manually distribute or load balance them.

And it will show how to quickly and easily grow the queue manager cluster – adding a new queue manager to the cluster without complex, new, custom configuration.

Background

The IBM MQ feature demonstrated in this tutorial is Uniform Clusters. Dave Ware has a great introduction and demo of Uniform Clusters, so if you’re looking for background about how the feature works, I’d highly recommend it.

This tutorial is heavily inspired by that demo (thanks, Dave!), but my focus here is mainly on how to apply the techniques that Dave showed in OpenShift.

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How to transcribe and analyse a phone call in real-time

Saturday, July 16th, 2022

In this post, I want to share an example of how to stream phone call audio through IBM Watson Speech to Text and IBM Watson Natural Language Understanding services, and show some ideas of what you could use this for.

Let’s start with a demo

That’s what I want to show you how to build.

At a high-level, this is what you will have seen in that video:

1.
Faith made a phone call to a phone number managed by Twilio.

2.
Twilio routed the phone call to me, and I answered the call.

We then started talking to each other. And while we were doing this:

3.
Twilio streamed a copy of the audio from the phone call to a demo Node.js app

4.
The Node.js app sent audio to the Watson Speech to Text service for transcribing.

5.
Watson Speech to Text asynchronously sent transcriptions to the Node.js app as soon as they were available.

6.
The app then submitted the transcription text to Watson Natural Language Understanding for analysis.

7.
All of this – the transcriptions and analyses – were displayed on the demo web page.

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How to use MQ Streaming Queues and Kafka Connect to make an auditable copy of IBM MQ messages

Sunday, July 10th, 2022

Scenario

You have an IBM MQ queue manager. An application is putting messages to a command queue. Another application gets these messages from the queue and takes actions in response.

diagram

Objective

You want multiple separate audit applications to be able to review the commands that go through the command queue.

They should be able to replay a history of these command messages as many times as they want.

This must not impact the application that is currently getting the messages from the queue.

diagram

Solution

You can use Streaming Queues to make a duplicate of every message put to the command queue to a separate copy queue.

This copy queue can be used to feed a connector that can produce every message to a Kafka topic. This Kafka topic can be used by audit applications

diagram

Details

The final solution works like this:

diagram

  1. A JMS application called Putter puts messages onto an IBM MQ queue called COMMANDS
  2. For the purposes of this demo, a development-instance of LDAP is used to authenticate access to IBM MQ
  3. A JMS application called Getter gets messages from the COMMANDS queue
  4. Copies of every message put to the COMMANDS queue will be made to the COMMANDS.COPY queue
  5. A Connector will get every message from the COMMANDS.COPY queue
  6. The Connector transforms each JMS message into a string, and produces it to the MQ.COMMANDS Kafka topic
  7. A Java application called Audit can replay the history of all messages on the Kafka topic

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