Inferring SQL Run Times And Analysis Using Raw ASH Data

Posted on Posted in Webinars

Imagine knowing SQL run times without instrumenting your application. That is exactly what I will teach you how to do by analyzing raw ASH data.

Suppose the support ticket shows, “This query takes 45 seconds!” How can you confirm that? Is 45 seconds unusual? Has it happened before? Perhaps there is a bad plan being used? What is a good plan for the SQL? Where in the application does the SQL reside? This is valuable information that creatively analyzing ASH data will reveal.

In this presentation, using ASH data, I will show you how to manually infer SQL run times. Then I’ll show you how to use a simple yet flexible SQL script to analyze ASH data, infer SQL run times and report the results… even at the execution plan level. Next I’ll show you how to analyze the run time samples using simple Python.

Join me as we explore one of the untapped analysis opportunities ASH data provides.

Presented by Craig Shallahamer, Applied AI Scientist, Viscosity North AmericaFounder, OraPub and Oracle ACE Director

Craig is a long time Oracle DBA specializing in predictive analytics, machine learning and Oracle performance tuning. Craig is a researcher, blogger, consultant, author, an enthusiastic conference speaker, passionate teacher and an Oracle ACE Dir.

This event is co-hosted by the New York Oracle Users Group (www.nyoug.org) and Oracle Professional Services firm,
Viscosity North America (www.viscosityna.com)

YouTube player

Related posts:

  1. NYOUG 2018 Summer General Meeting: Introduction of Jupyter Notebook And Packages for Data Analysis and Plotting
  2. “Tune that SQL” now what do I do?
  3. NYOUG 2019 Winter Meeting: Conquer Big Data with Oracle 19c, In-Memory External Tables and Analytic Function
  4. NYOUG Webinar: The future of AI & Machine Learning (ML) in Oracle Products, Presented by Rich Niemiec co-hosted by Viscosity