This video shows how viewing CICS Call Attach work by Correlation ID enables both CICS and Db2 teams to view elapsed time profiles within Db2 corresponding to each CICS transaction ID.

 

More Integrated Visibility Resources

  1. Leveraging XCF Message Activity for CPU Efficiency
  2. Troubleshooting WLM Missed Goals with CICS Transaction Data
  3. Address Space and Db2 Accounting Data
  4. Dataset Performance (42.6) and Db2 Buffer Pools & Databases (SMF 102/IFCID 199)
  5. Db2 GETPAGE Efficiency – Dataset Performance (42.6) and Db2 IFCID 199
  6. Elapsed Time Profiles by Correlation ID: CICS Transaction (110.1) and Db2 Accounting (101) Part 1
  7. Analysis of CPU By Plan: CICS Transaction (110.1) and Db2 Accounting (101) Part 2
  8. Insights You Can Gain from Integrated Visibility Across Types of SMF Data

 

Video Transcript

Okay, let’s go to another scenario. And this is gonna be CICS transaction and Db2 accounting data. And the fact that we can do good things here is made possible by the fact that for the CICS work that calls Db2, the Db2 accounting data captures the CICS transaction ID in the correlation ID field. So that enables us to be able to do a lot of good things here, right? Get a lot of insights by pulling these two types of data together.

Okay, so I’m gonna start out here. This is a lapse time profiles, again, by different connection type. Understandably, they’re gonna have very different profiles and we’ll look at the CICS call attached data. And when I drill into it by correlation id, now I can see the CICS transaction id. So here’s the profile of the work that’s going on within Db2 that’s coming from each of these top 20 activity transactions or top Db2 CPU consuming transactions.

So we can see, you know, a lot of commonality, the class 2 CPU time, which is the CPU time within Db2 Sync IO wait time when you have a buffer pool mission, you gotta go get the IO and then other components.

And so then we can take any one of these transactions and look at the profile. So here it is in an area chart about total of five milliseconds per commit – two of which come from CPU, and three of which come from the sync IO weight, kind of in general. Or if we wanted to, we can compare the components against one another and then also evaluate each one and see how it, if it, you know, kind of varies over time.

All right, so again, let’s start another CICS and Db2 dashboard here. So the point here is that by having both types of data, you know, instead of being limited to a CICS-only view, where the time spent within Db2 is pretty much a black box. By having access with a common interface, the CICS team can easily understand what’s driving that Db2 time for any given transaction.

All right, So let’s look at this, again go back to this response view. Another way to view this data is to kind of bring together perspectives that are provided by the two data types and bring ’em together in one view. So here along the top, this is CICS 110 subtype, 1 transaction data, and across the bottom, this is Db2 accounting, SMF 101 data for that particular transaction ID that we selected here, right? So we’ve got tran rate versus commit rate. We’ve got total CPU versus CPU that went on within Db2. Again, the total picture of response time versus the elapsed time of the work, specifically within Db2. In this case, we got tran rates by region and commit rates by Db2 member. So again, we can kind of see it from both perspectives and depending on what we’re trying to analyze, having both pictures can be real helpful.

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