IBM’s MQ is very widely used across today’s z/OS environments. But reporting capabilities for MQ SMF data have lagged behind those of other subsystems, due to the limitations of existing tooling, the need to learn unfamiliar tooling that is siloed by area, and limitations in the time and expertise required to develop in-house programs.
This session will present the benefits of modernizing how you understand and leverage SMF data for MQ performance analysis. You will see how having an intuitive interface to easily explore the data and dynamically drilldown to view relationships between various metrics can enable you to quickly derive insights from MQ SMF data.
Both MQ Statistics (SMF 115) and MQ Accounting (SMF 116) will be covered in this session:
- You will see how rated health assessments of dozens of MQ Statistics metrics visualized in easy-to-use views can provide insights to help you identify potential performance and availability issues before they impact production.
- And instead of being overwhelmed by massive volumes of MQ Accounting data, with very limited visibility and at best facing the prospect of combing through countless static reports, discover how interactive analysis using context-sensitive drilldowns can elevate your effectiveness to an entirely new level.
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Selected MQ Accounting Data SMF Metrics – Part 2
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Selected MQ Accounting Data SMF Metrics – Part 1
Examples of the key SMF metrics captured in MQ Accounting data including buffer pools, Thread Elapsed Time, CPU, elapsed time per get, etc.