Leveraging Machine Learning to diagnose Real Application Clusters Issues & Incidents

Today's post is a shout out to a much needed presentation by Anil Nair Oracle Senior Principal Product Manager on Leveraging Machine Learning to diagnose Real Application Clusters Issues & Incidents.

Here is a brief summary of the presentation by Anil:

• Scalability without changing Application Code.
• Problem Resolution Paths & Approaches.
• Reactive & Proactive Diagnosis.
• Common Problems such as Hangs, Performance Issues, Deadlocks etc.
• Case Studies of Incidents & Issues in Real Application Clusters (RAC).
• Automatic Notification & Diagnosis Collection with Trace File Analyzer (TFA).
• Cluster Health Advisor TFA SMTP Notifications.
• Configuring OraChk to run in DAEMON mode.
• Node Evictions due to Memory Pressure.
• RAC Misconfigurations.
• Oracle Memory Guard.
• Aggregated Data by Process Type provided by CHM.
• Sample Problems & Resolutions.
• Reconfiguration Diagnosability.
• Dynamic Remastering (DRM) Diagnosability.
• Grid Infrastructure Management Repository (GIMR).
• Autonomous Health Framework (AHF) collects much of the data that OSWatcher collects.
• Documents & Resources
• Whats New?
• Oracle Autonomous Health Framework (New).
• Applied Machine Learning Diagnostics (New).
• Autonomous Health - Database Performance (New).
• Cluster Health Advisor Graphical (CHAG) (New).

As should be clear by this excellent presentation, Leveraging Machine Learning to diagnose Real Application Clusters Issues & Incidents makes the whole RAC diagnosis process easier & faster.

Cheers.