President & Founder, OraPub Inc. Oracle Ace Director Applied AI Scientist
OraPub, Inc. USA OraPub/Viscosity
In this presentation I will show you how to find the DML SQL associated with a commit. Using an AWR report won't help because there is no "Top DML SQL" section in the report. Analyzing AWR SQL data is also problematic because it is based on multi-minute snapshots and summarized data. One of our clues is the wait event associated with a commit is, log file sync. But it's not as simple as it seems because a commit has no SQL_ID. So, how are we supposed to find the application DML SQL associated with a commit? With ASH sample session based data, we have a chance to solve this mystery. By isolating an active log file sync session and closely looking at the session activity, it is possible to infer the DML SQL associated with a commit. This process pushes ASH data analysis to the limit by using a variety of sample based data analysis techniques. Join me as we diagnose one of the most complicated and frustrating of all Oracle performance issues.
Even in the Oracle Cloud, rare and random short-lived performance incidents can occur. These incidents present Oracle DBAs with a challenge. In this presentation, I begin with why ASH (active session history) is so powerful, how it works and how we use ASH to solve complex performance incidents. Using only four free scripts, I will lead you step-by-step through an incident analysis process. Plus, I will show you how to visualize the performance situation using a Python script.
It is true. You can laugh your way to understanding queuing theory, performance analysis and why systems behave the way they do. From driving in traffic to being served at a restaurant, every person feels the impact of queuing theory. Queuing theory beautifully relates time and work into terms we can feel, like utilization, workload intensity, response time, elapsed time and systems architecture design. With only the basics we can use foundational queuing theory to derive targeted performance solutions and goals, filter and evaluate any performance solution thrown at us and help non-technical people understand why our solutions make perfect sense. Join us for a shockingly practical and fun session that will impact your Oracle career and beyond.
Key business SLA objectives were not being met. IT Operations was stuck in the middle of declining service levels, a tight labor force, and IT needed to be better aligned with business goals. To be better aligned, the IT Operations capacity had to be increased by reducing costs of both IT and business operations. More specifically, IT Operations needed to better detect problem trends and unusual activity across all their Oracle systems. False alerts were a real problem wasting employee time and energy. A solid operations picture required Oracle's Active Workload Repository data for near real-time analysis, in conjunction with machine learning and AI Join us for a key business goals-focused perspective into how a business problem was solved using a variety of Oracle products, machine learning and AI.