Resources

Integrating IFCID 199 and SMF 42 data provides extensive insights into I/O cache characteristics and response time data at the Db2 buffer pool and Db2 database levels.

The profiles of work generated by the various callers of Db2 services often differ dramatically, highlighting the importance of dynamic navigation enabling great visibility into Db2 metrics by connection type.

Easy accessibility to key Db2 buffer metrics enhances buffer pool tuning analyses so that they can be quickly completed. This video explores key metrics for one buffer pool tuning methodology.

Easy accessibility to key Db2 buffer metrics simplifies buffer pool tuning analyses, increasing the likelihood they will be periodically revisited using data from new time intervals. This video explores key metrics for two other buffer pool tuning methodologies.

To help enhance the value you can derive from that SMF data, IntelliMagic Vision provides two complementary types of automated anomaly detection: health insights and change detection.

Change Detection and Health Assessments (based on pre-defined best practice conditions) play complementary roles, but there can be scenarios where they both flag issues. This use case with CICS Max Tasks is one such example.

Elapsed and CPU times can be of interest for batch jobs, especially those on the critical path for key business job cycles. This use case reflects an abnormally long run time for a daily batch job, and illustrates how potential causes might be investigated.

Key coupling facility performance metrics at the structure level include synchronous and asynchronous request rates and service times, making them attractive candidates for Change Detection.