A survey of a sample customized dashboard where key Accounting data charts have been collected. These dashboards can promote collaboration across teams, as well as serving as a springboard for additional analysis as the views are applied to other time intervals.
More Db2 Accounting Videos
- Exploring Analysis by Connection Type
- Exploring Analysis by Correlation ID
- Exploring Elapsed Time Profiles
- Exploring Analysis by Authorization ID
- Exploring Prefetch Activity and Suspension Events
- Exploring Analysis by Plan or Package Name
- Exploring Database Sync I/O Activity
- Case Study: Isolating Change Drivers
- Exploring Other Metrics in Accounting Data by Plan
- Accounting Data: Customized Dashboard Recap
So as we’ve explored the Db2 accounting data in this session, and we’ve looked at it, we saw we could do that by connection type. And then from there, kind of go down one of three paths, maybe correlation ID, maybe auth ID or plan or package name. And so doing that, we saw all different kinds of metrics, SQL statements by type, log throughput; here was by connection type.
We spent a good time talking about the different types of prefetch requests, here’s CPU consumption and also elapsed times by I think this was the CICS transaction view that we did there. Top CPU by auth ID for the DDF work.
Here we saw those spikes for that auth ID, and we saw that show up several times, right here. We showed it up in terms of suspension events. We looked at top CPU by plan for CICS, saw the profile for the top plans and kind of saw the bump in the evening, and when we looked across all connection types, except DDF, we saw there were some batch work that was doing a lot of CPU, but then again, we saw that CICS plan.
And then since in CICS, when you drill down the correlation ID contains transaction IDs. And so we could then get in and see which transactions were generating the Db2 CPU. And we saw a couple of unusual profiles, including the transaction that’s generating the bumps at night. And then finally we looked at database sync I/Os by connection type.
So when we come back, we want to revisit the analysis at a future date. We can view any of these views, update the time interval to a different time interval, and so throughout today, as we explore the accounting data, we saw that when working with massive data volumes as is certainly the case with Db2 accounting data and the context-sensitive drill downs are a great aid to learning and exploration because they provide the capability to easily navigate and focus quickly on the precise subset of data you want to analyze. Sometimes today we looked at it by connection type, sometimes by correlation ID, sometimes by auth ID, and sometimes by plan and package name.
So again, here are the types of metrics that we saw in the accounting data, and we viewed them again with different subsets of the data, depending on the particular type of metric and the particular driver of the Db2 work, certain drill downs were more applicable. Auth ID was typically more helpful with DDF work plan, perhaps more for work coming from CICS or IMS, and so on.
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