The vast complexity of Db2 statistics data and Db2 accounting data make it challenging to derive value from the rich metrics available. Clear visibility into Db2 metrics through SMF records helps you to prevent availability risks and manage and optimize performance.
Access built-in health insights that rate hundreds of critical metrics to proactively identify risks to your application health and performance. AI-derived anomaly detection highlights statistically significant changes, accelerating problem-solving.
Use thousands of out-of-the-box reports combined with a powerful, intuitive GUI, real-time comparisons and editing, and context-sensitive drill-down capabilities to maximize time spent preventing and resolving issues. Minimize downtime without the need for custom coding.
Augment the effectiveness of staff with interactive, customizable and shareable dashboards, built-in explanations and extensive drill downs. Use AI as a force multiplier to expedite learning, promote collaboration and enhance analytical effectiveness.
What's included
The volume and complexity of Db2 Statistics data (SMF 100) and Db2 Accounting data (SMF 101) data is difficult to analyze. Easy visibility into key Db2 metrics through SMF records is crucial to proactively prevent availability risks and to effectively manage and optimize performance.
Automated assessments of over 80 metrics for every Db2 member and buffer pool in your environment help identify potential risks to availability and performance. This image shows an example of the Db2 Health Insights interactive report. All of the warnings and exceptions are shown in this table with the ability to drilldown to the individual exceptions and perform deep-dive root cause analysis.
Numerous drill downs can move from a high-level enterprise-wide view into focused analysis to help identify actionable insights for specific Db2 members, buffer pools, and so on. This image captures two such drill down steps, initially “Pool by Size” and then by “Buffer Pool” to isolate the exceptions to specific buffer pools (shown here).
You can generate “time charts” of all assessed metrics to examine potential high-level relationships between the metrics at any phase of the analytical process. In this example, possible time-of-day correlations between the two metrics with exceptions (with the orange and red borders) and overall get-page activity (in the first chart) can be evaluated.
Since Db2 relies on needed data residing in a buffer to avoid I/Os that are synchronous with the unit of work (“random sync read I/Os”), extensive visibility into buffer pool and I/O metrics is crucial for Db2 performance tuning.
Context-sensitive drill downs of large data quantities let you focus on the data relevant to your analysis. Experts often suggest analyses that are focused by connection type, since online work (for example, coming into Db2 from CICS) typically has a different profile from batch work (for example, coming in through IMS batch BMPs).
View disk I/O and cache performance by Db2 buffer pool and database by integrating data set I/O performance data (from SMF 42 records) with Db2 dataset I/O statistics (IFCID 199) data. See metrics including disk response time by component (IOSQ, Pend, Disc, Conn) and disk cache hits and misses by Db2 buffer pool and database.
In Db2 accounting data, combining “class 2” (CPU) and “class 3” (wait) times provides an elapsed time profile of time that is spent within Db2. This profile for work coming from CICS shows that Other Read I/O Commit (green), Not Accounted Time per Commit (light purple), and Local Lock Contention Time per Commit (orange) are the primary contributors to elapsed time.
For work coming into CICS from Db2, you can take advantage of the fact that the calling transaction ID is included in the correlation name field that is found in the Db2 accounting data to facilitate numerous types of analyses by CICS transaction. This example presents a view of Db2 elapsed time profiles by CICS transaction.
More than 250 non-timing fields in the CICS 110.1 records enable detailed analysis and are organized into subgroups. The customized dashboard in this image shows examples of several of these, including Db2 SQL calls per CICS transaction, log stream writes, program loads and file gets.
Advantages to adopting a cloud model include rapid implementation (no lead time to install and set up the product locally), minimal setup (only for transmitting SMF data), offloading staff resources and access to IntelliMagic consulting services to supplement local skills.