Automated assessments of key Db2 metrics facilitate proactive analysis and prevention of outages by identifying areas that may warrant investigation. These views also expedite learning by highlighting metrics that are important to understand and analyze.


More Db2 Statistics Videos

  1. Exploring Assessments of Key Metrics
  2. Db2 Buffer Pool Tuning: Exploring Key Metrics (Part 1)
  3. Db2 Buffer Pool Tuning: Exploring Key Metrics (Part 2)
  4. Db2 I/O Cache Insights from IFCID 199 and SMF 42 Data
  5. Buffer Efficiency from IFCID 199 and SMF 42
  6. Measuring Benefits of Db2 Buffer Pool Tuning
  7. Exploring Other Db2 Statistics Metrics
  8. Walkthrough of Db2 Statistics Dashboard for Buffer Pool Tuning
  9. Integrating with RMF Metrics (70, 71, 72, 74)
  10. Integrating with RMF 70 and 72 CPU and WLM Metrics
  11. Integrating with RMF 71 Memory and Large Frame Metrics
  12. Integrating with RMF 74.4 Coupling Facility Metrics


Video Transcript

We’ll begin our exploration of Db2 Statistics data with views that identify key metrics and assess those metrics against industry best practice thresholds. In addition to expediting learning by calling to your attention metrics worth exploring, these assessments also facilitate proactive analysis and prevention of outages. These views across the top of the screen automatically assess more than 80 metrics across every member of every data sharing group to help proactively manage the entire Db2 environment. These assessments are presented in easily consumable views. Red and yellow borders call attention to areas with exception metrics that may warrant additional investigation. So in this selected view, a dozen buffer pool metrics are being assessed in this case for every buffer pool of every member of every data sharing group. And we see exceptions here for this particular data sharing group for metrics, including page residency, time and buffer pool hit ratios and Db2 buffer pools are typically managed by size.

So one logical approach from here is to drill down to see which size of buffer pools these exceptions are occurring and it’s in the 4K buffer pools. So then the next logical exploratory step would be to look into the buffer pools themselves. And when we do that, that shows several buffer pools with exceptions that we might choose to examine. Connecting these key metrics to IBM SMF field names and field descriptions from the documentation can also aid the learning process.

All right, so let’s go ahead and select this buffer pool with the largest icon. And we can view all the metrics that are being assessed in this particular view. This enables us to compare these metrics at a glance, for example, the level of get page activity as it relates to when the exceptions are occurring for these two particular metrics. So now from here, we might choose to examine the page residency time in more detail, and we’re going to see more about that metric in an upcoming section on buffer pool tuning.

And we see how often the residency time fell below the warning area, which is the light shading, and then the exception area, which is the dark shading for the thresholds that have been defined. Now, as we were looking at the thumbnails, we briefly glanced at the level of get page activity. Customizing reports to examine potential correlations between metrics can also provide helpful insights. So in this case, let’s go ahead and bring in get pages and, of course, put it on a secondary access because it’s a different unit of measure.

And so, when we do that, we do see a correlation between the get page rate and the residency time when the get page rate is higher. The residency time is lower for most of the day. There’s that correlation, perhaps not in the late evening. Another great aid to learning can be to compare various time intervals to see whether the current interval is a typical behavior or represents an anomaly. And in this case, when we compare these two-time intervals, the patterns are somewhat similar. So that example illustrated how assessing key metrics can help expedite learning by highlighting a subset of metrics that are significant, as well as calling your attention to areas where the values of those metrics may warrant additional investigation.

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