Walkthrough of Db2 Statistics Dashboard for Buffer Pool Tuning

A brief walkthrough of a sample customized dashboard that provides easy accessibility into key Db2 Statistics metrics used to carry out buffer pool tuning initiatives. This type of dashboard enables team members to share a common view of all key metrics and facilitates future iterations of the analysis applied to different time intervals.

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

So, this is what we created today, right? We started out and found out that the I/O volumes were overwhelmingly in a particular data sharing group and they were overwhelmingly in the 4K pools. And then from there, we saw the buffer pool 20 was the highest I/O driver, so that was on a methodology that looked at I/O rate. If we look at residency times, again buffer pool 20 came out higher there, and then that’s how they compare to the hit ratios. And again, if we looked at hit ratios, buffer pool 20 came out there as well, and then cache hits and misses and buffer pool 20, the response time profile stayed relatively the same, no matter what was going on with the I/O rate. But the response times for databases were quite different across time there. And then this was the buffer efficiency metric looking at it by buffer pool, looking at it by database cache hits. We saw how that could figure in as kind of a second tier of access in our buffer pool tuning methodology. We saw that for buffer pool 20 DDF work was the dominant contributor. And then here were how those response time components, the key focus ones shaped up over time. So again, we could take any one of these, we can change the time interval and assess it further. So that’s kind of the value of the dashboard as well.

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