Manage MQ Queue Managers and Activity with IntelliMagic Vision
IntelliMagic Vision enables performance analysts to manage and optimize their z/OS MQ configurations and activity more effectively and efficiently, as well as proactively assess the health of their queue managers.
View the video for an example of those capabilities.
Optimize and Analyze MQ Activity and Performance
MQ is widely used across z/OS environments, but sites often find it challenging to derive the valuable performance insights potentially available from MQ SMF Statistics and Accounting data due to limitations in existing reporting and available tooling.
When managing your MQ environment, you need an easy and effective way to focus your analysis so you can rapidly find what you are looking for.
IntelliMagic Vision provides GUI-based, interactive reports with dynamic navigation and context-sensitive drilldowns to facilitate rapid and focused access to MQ data to manage, tune, and optimize your environment.
With IntelliMagic Vision, you will be able to:
- Profile MQ queue manager and buffer pool activity
- Easily view key logging metrics as well as those produced by every MQ component
- Proactively assess the health of all MQ queue managers
- Analyze elapsed and CPU times for MQ activity at detailed levels (e.g., by queue name or connection type)
IntelliMagic Vision offers you the out-of-the-box visibility and seamless navigation to manage every component of your z Systems infrastructure under a single solution.
Proactively Assess Key Queue Manager Metrics to Enhance Availability
As with Db2, responsive performance from MQ relies on data residing in memory, so buffer pool management is an important aspect of managing MQ performance.
IntelliMagic Vision automatically assesses every buffer pool in every queue manager to identify areas that may warrant additional investigation and presents these findings in a red/yellow/green manner that can be quickly consumed.
Drilldown capabilities into each metric facilitate quick follow-on analysis.
Profile Queue Manager Workloads
A view of the number of requests by type of MQ command can be a good starting point for identifying a workload baseline, as well as an indicator of any significant workload changes.
Easy visibility into this data shows (in this example) the volume of requests to PUT messages to the queues, which queue managers have the most activity, the time of day profile, etc.
Analyze Buffer Pool Utilizations
Views of buffer pool utilizations over time can indicate when these values are approaching thresholds that prompt automated de-staging to disk.
This example shows an extended interval when buffer pool 4 is nearing the 85% threshold that triggers asynchronous de-staging.
Monitor MQ Logging Infrastructure
As with Db2, a well-performing MQ logging infrastructure is essential to support recovery and backout (driven largely by persistent messages) without impacting ongoing performance.
Log Manager metrics can help identify any bottlenecks that may be occurring in log processing.
One metric that may be useful from a profiling perspective is the volume of data being logged.
View Activity by Queue Name
MQ Accounting data provides detailed activity metrics at many levels, which are invaluable to performance specialists as they investigate application problems and carry out performance tuning.
This example of command rates by queue name shows unique distributions across various types of queues, including a cluster transmission queue (first, no GETs), queues that process primarily PUTs and GETs in comparable numbers (second and third), and queues with high levels of OPEN and CLOSE activity (fourth and fifth).
Compare and Correlate Multiple Metrics
The capability to customize reports to combine multiple variables to analyze potential correlations can greatly aid analysis.
In many of today’s solutions that rely on catalogs of static reports, adhoc analysis like this typically requires the coding effort to develop a new report.
This example combining the rate of MQ GET calls and the average elapsed time for work generated by a specific CICS transaction shows a strong correlation.
Drilldown to Isolate Message Length Profiles by CICS Transactions
Though most sites tend to have massive volumes of MQ Accounting data, dynamic navigation and context-sensitive drilldown capabilities enable the analyst to quickly focus on a specific subset of the data.
In this example of message length distribution data, drilldowns into work originating from CICS and then further by CICS transaction ID profile the length of messages being “PUT” by transaction.