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RMM has been working on bringing complex features from Mainframe TSO and ISPF interfaces to z/OSMF with the goal of providing modern interfaces and reducing the learning curve for users new to the Mainframe or RMM. In this iteration, RMM targeted querying and reporting on data that can be found within the RMM database.
A New Landing Page
The first thing you will notice upon opening the RMM Plugin in z/OS 3.2 is a new landing page! The RMM Dashboard gives users the ability to get quick insights to their RMM Address Space in an at-a-glance format; Customizable widgets give information in the following categories:
· Parmlib settings and general Address Space status
· Details on the Address Space last housekeeping run
· Statistics on Volumes in your Control Data Set (CDS) based on:
o Retention Method
o Volume status
Each of the described widgets are interactable so the user can gain deeper insights. For example, clicking on the pie chart sections of the Retention Method Statistics will bring you to the Volumes page with a pre-populated query to list all the volumes of the selected Retention Method.
Customizations:
The page may also be customized and persistent for a user across sessions within z/OSMF. Currently, the customizations include toggling the available widgets as enabled or disabled and the statistics shown on the Last Housekeeping widget. These persistent settings can be found by clicking on the “Screen Options” icon on the page or on the widget itself.
RMM Reports
In addition to the Dashboard page, you may notice another new page within our navigation menu: Reports. We are bringing the RMM Report Generator from the green screen to the browser! In this iteration, the reporting capability is all based on default templates provided by RMM. These can be customized to submit queries and saved as Report Templates. Templates or default reports can be submitted and their output saved into a PDS based on the report name. The report output can be retrieved by the browser for viewing or to download, eliminating the need for a user to be familiar with ISPF dialogues or complicated Sort JCL!
Search and Scalability
The RMM team investigated the overall scalability and discovered that RMM instances with large CDS were having a less-than-ideal interaction with the available interfaces. The RMM Plugin is growing in features and usage, so we made significant scalability improvements to accompany the growth in z/OS 3.2! Within the plugin, we implemented server-side pagination on volume and dataset requests that enables users to view all contents of their CDS as single pages at a time. This improvement has shown large performance gains in controlled test environments! Previously, a broad search query for RMM could take minutes to retrieve any information and is now retrieving individual pages within seconds on large tables! While this was taken into consideration for the tabular views within the RMM Plugin, it has a growing effect on reporting upon the data within the CDS across the product as a whole.
The change in data query also changed how our export to CSV worked; existing users may notice a new modal interface upon an export request. This gives the user requesting the export options on what the data export will contain, while providing a better user experience to show work is being done.
Looking into search more broadly, we took this opportunity to expose more fields from the CDS into the RMM Search capabilities within the RMM Plugin and the TSO interfaces. You may notice the fields for searching on dates now include time (where applicable) as well as more dates to search on. While many of these fields have existed within the RMM interfaces and CDS, they had not been available within commands for RMM users to search on these fields.
Why the RMM Plugin is valuable to YOU!
The latest RMM plugin enhancements deliver significant performance and usability improvements for z/OS users. Data browsing is now faster, request wait times are shorter, and full CDS access is fully supported—enabling a more complete and responsive user experience.
The new dashboard landing page provides immediate visibility into the status of the RMM Address Space, recent housekeeping activity, and volume metrics—making system monitoring more intuitive and actionable.
The integration of the Report Generator into the modern interface streamlines report creation. Users can now generate both default and custom reports with fewer keystrokes, reducing complexity and saving time. The “Saved Templates” feature further accelerates this process, allowing one-click report submission without the need to locate JCL manually.
Together, these enhancements simplify workflows, improve performance, and empower both new and experienced users to manage and report on RMM data more efficiently than ever before.
We would love to hear your thoughts on RMM and these new enhancements! Leave a comment on this blog post, start a discussion on the DFSMS Community Page, or join Mainframe Data Management LinkedIn group to discuss there!
Ready to get started with RMM?! Continue reading below!
Get Started
Configuration can be broken down into two distinct sections for the new features:
- Dashboard options
- Report options
To start with the simpler example, configuring the dashboard, specify the Message and Report data sets from your house keeping job. Note: expected to be the most recent version of these data sets and to be a fully qualified data set name - Masking and relative GDG specifications are not valid in these fields.
The inputs are within the Settings page of the RMM Plugin, labeled as “Input Datasets: Message Dataset, and Report Dataset.” A link to take you to this input directly can be found within the Last Housekeeping widget in which these data sets are required.
To configure the Report options, some additional context may be useful. Without customization of the reports (hitting the “Edit” button), the report will submit against the default Message and Reptext data sets. Without anything specified, they run against datasets with these names “RMMUSER.APAR.MESSAGE” and “RMMUSER.APAR.REPTEXT.” To change this when submitting a report you may specify your own housekeeping datasets.
The next step on configuring the Report options is to determine where the report output will be saved. The default is upon submission, the current z/OSMF username will be used as a High Level Qualifier (HLQ) in a Generation Data Group (GDG) dataset for that unique report name, following general GDG convention of incrementing the version value for each new submission.
For example, assuming the current user logged in to z/OSMF is “USER1” the default data set name would result in the following:
“USER1.REPORT1.G0001V00”
Generated GDG data sets and stores 5 generations of output before rolling off. The HLQ can be customized from the default “SYSUID” on the File Attributes card of the Settings page. Additionally, found within this card are attributes for determining where this dataset should be stored. If you would like to leverage your SMS environment, the SMS constructs and Unit parameters are available for use on the data set creation JCL, as well as a Volume Serial parameter if you would like to specify the volume directly.
Author:
Parker Mathewson
Editor:
Alexis Kapica
