The profiles of work generated by the various callers of Db2 services often differ dramatically, highlighting the importance of dynamic navigation enabling great visibility into Db2 metrics by connection type.


More Db2 Accounting Videos

  1. Exploring Analysis by Connection Type
  2. Exploring Analysis by Correlation ID
  3. Exploring Elapsed Time Profiles
  4. Exploring Analysis by Authorization ID
  5. Exploring Prefetch Activity and Suspension Events
  6. Exploring Analysis by Plan or Package Name
  7. Exploring Database Sync I/O Activity
  8. Case Study: Isolating Change Drivers
  9. Exploring Other Metrics in Accounting Data by Plan
  10. Accounting Data: Customized Dashboard Recap


Video Transcript

All right. So let’s begin our exploration of accounting data by connection type. So, a common way to subset Db2 accounting data is by connection type, and that reflects the fact that the profiles of work coming from various Db2 callers often differ dramatically from one another. Let’s begin with views of some of the key accounting data metrics by connection type that we mentioned on the earlier slide. So, in this example of SQL statements, the total volumes between the CICS and the IMS BMP batch are not too different, but looking one layer beneath this over time, the time of day profiles for CICS follow a typical online profile. Whereas the IMS batch BNP connection type activity is entirely skewed to nighttime batch.

So as we proceed with today’s analysis, I’m going to collect some of the key charts in a customized dashboard. We’ll call this Db2 Accounting. So as we identify and customize views of interest, we can view them in the future at a glance or as a springboard for additional analysis.  So now looking here at log throughputs again, similarly though, there are not big differences in the logging volumes among the top three connection types here. Again, view them in the time of day profile there they’re entirelyy different, right? Db2 utilities is driving the early morning hours, CICS during the day and IMS batch at night. I’ll go ahead and add that one to the dashboard as well. Now, elapsed time profiles are one area where huge differences between Db2 callers are often immediately apparent as we can see here between the lapse time profile between the work coming in from CICS and the work coming in from IMS batch are dramatically different. And so we’re going to look at this in some more detail in the next section when we are exploring by correlation ID.

All right, one more example here. If I want to explore what types of work are driving what types of prefetch activity. So dynamic prefetch, the blue here, generates the majority of the requests and the top four callers drive most of that activity, but let’s explore what’s driving the other two types of prefetch. So let’s look at the sequential prefetch requests, get this over time, and indicate that it’s sequential in the title. So here I see a couple of different spikes, one with the TSO call attach, and the other by DDF. So we’ll come back to both of those later when we’re drilling down into additional levels of detail. So for right now, I’m going to capture that on the dashboard. And then let’s also look at the list prefetch requests again, make that the only thing on the chart and turn this into a timeline and indicate the title now that this is list Prefetch requests. I see interesting patterns here of spikes every three hours driven by IMS Batch BMP. So again, we’ll come back to this later, when we’re exploring analysis at additional levels of detail for now, we’ll just stash it away on the dashboard.

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