Metrics for Exadata deliver to you one way to deeply see, and understand, what it is happening for Exadata Storage Server and Exadata Software. Understand it is fundamental to identify and solve problems that can be hidden (or even unsee) from the database side. In this post, I will explain details about these metrics and what you can do using them.
My last article about Exadata Storage Server metrics was about one example of how to use them to identify problems that do not appear in the database side. In that post, I showed how I used the metric DB_FC_IO_BY_SEC to identify bad queries.
The point for Exadata (that I made in that article), is that most of the time, Exadata is so powerful that bad statements are handled without a problem because of the features that exist (flashcache, smartio, and others). But another point is that usually, Exadata is a high consolidated environment, where you “consolidate” a lot of databases and it is normal that some of them have different workloads and needs. Using metrics can help you to do a fine tune of your environment, but besides that, it delivers to you one way to check and control everything that’s happening.
In this post, I will not explain each metric one by one, but guide you to understand metrics and some interesting and important details about them.