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IBM® SMF Explorer is a Python framework to access SMF data directly from dump data sets. The framework uses the z/OS® Data Gatherer: SMF Data REST Services to fetch data from a z/OS host. IBM SMF Explorer is a Python library, which means that you can use it to write scripts, embed it into other applications or just fetch data interactively. Additionally the Python ecosystem provides access to a large set of libraries for visualization, data analysis, machine learning and many more.

The framework enables easy SMF data record fetching, provides information on selected SMF fields, and serves chunks of SMF data, suitable for different analysis types, that can be selected for further processing. For various SMF records and subtypes, sets of SMF fields are provided to make getting relevant data even easier (e.g. system utilization, LPAR utilization, cache statistics, …).

An easy way to use IBM SMF Explorer is through JupyterLab. JupyterLab is a web-based interface to execute Python interactively and makes data visualization and handling easy. For that reason the IBM SMF Explorer Github repository provides you with a JupyterLab environment to get started quickly.

There are some technical requirements that are listed below. In addition to the setup itself, fundamental Python knowledge and a basic understanding of the Pandas library is helpful to get you started.

Python is relatively easy to learn, and if you are used to scripting languages or programming in general you might find the Tutorial Jupyter Notebooks provided with IBM SMF Explorer sufficient to learn Python on the go. Additionally you may find very good publicly available guides and other resources to get you started with Python (e.g. https://www.learnpython.org/ ).

System Management Facility (SMF) records represent a wealth of information that can be extracted to get insights into the activities of your z/OS systems. Novice users like system programmers, data scientists and data engineers might be struggling when trying to understand and interpret SMF data if they are still not acquainted with z/OS. 

Thanks to the convenient interface to access SMF data using Python provided by IBM SMF Explorer, you can retrieve SMF data in tabular form, which can further be processed for the task of data analysis and machine learning.

You can use IBM SMF Explorer stand-alone in Python scripts or use it with the provided JupyterLab setup.

IBM SMF Explorer has some prerequisites that need to be installed and set up:

• z/OS Host:
  • z/OS Data Gatherer: SMF Data REST Services (requires z/OS 2.5)
    • PTF: UJ07630
    • See Data Gatherer User’s Guide (here)
  • A User ID with access to z/OSMF (connected to group IZUUSER)
• Workstation (Installation):
  • Python 3.8 to 3.10
  • Git (optional)
  • JupyterLab environment