MQ Statistics data provided by the Message Manager component can be leveraged to establish a workload profile of MQ activity. When supplemented by MQ Accounting data, drivers of message activity can be isolated by connection type and even down to the address space issuing the messages.
More MQ Statistics Videos
- Overview of MQ Statistics and Health Assessments
- Assessing MQ Buffer Manager Health
- Assessing MQ Log Manager Health
- MQ Message Manager Metrics Supplemented by Accounting Data
- Log Manager and Buffer Manager Metrics
- Sample MQ Statistics and Accounting Dashboards
Now let’s go on and look at some other things that we can learn from MQ Statistics data. The message manager puts out information about the number of requests that are made for each type of MQ command. So here, we’re looking at the volume of requests to put messages to the queue, and also we see which queue managers have the most activity, a time of day profile, and so on. So this can be a good starting point for identifying a workload baseline, as well as an indicator of any workload changes. As here, we see an example of that, right, with a spike in this high-volume queue manager. So let’s go ahead and just broaden the interval out a bit and see if that’s a usual or unusual situation.
So looking in the context of the entire week, we can see indeed, that spike did represent an anomaly for the week. So let’s go ahead and capture that in the dashboard. So that’s as far as we can go with this with the statistics data, but we’re going to come back later and see how accounting data can help shed light on what drove that spike.
We might also be interested in comparing the number of puts to the number of gets, see what the relationship is between those. When we do that here, we see that most of the time there’s more gets than puts, which may indicate multiple queue managers competing to get the messages. That’s the case all the time, except when there was that spike. We made the point at the time that you couldn’t go any further with the statistics data to get below that, to see what was responsible, but you can with the accounting data. So, we’ll go ahead and look at that now. And this site is only generating the class one accounting data that comes by default, not the class three data, but that will still be sufficient to provide helpful insights here.
All right. So let’s go down to the accounting data. And again, you might notice here there’s, you know, a very limited amount. So we’ll go to look at the get and put rates. And again, we’re looking at a spike in puts, so let’s go ahead and focus our attention on that. And then also it was for a particular queue manager. So I’m going to go ahead and limit what we’re viewing here to only data from that queue manager. So we’ll have apples to apples from what we were looking at before. All right. So when we do that, and now if we look at this data by connection type, so the total number of messages we had that before from statistics, but now that we’re in accounting data, we can look at the data by connection type.
When we do that, we see that the spike was coming from batch work, right? So now, and we’ve mentioned for batch, the connection name also had the address space ID. So let’s go ahead and look at that. Again, let’s eliminate the other types and only look at the batch and TSO work. And now we want to look at this by connection name, and again, there’s going to be a lot of callers. So let’s go ahead and limit this to the top address spaces that are doing the most volume of put messages. All right. So now that we do that, we see that we’ve got this particular job that’s generating close to 14,000 messages a second for that 15-minute interval. Let’s go ahead and capture that in that dashboard that we had started there. So that’s an example of how accounting data enables investigation at levels of detail that are not available from the statistics data.
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