Monitoring Exadata Storage Cells - Oracle Exadata Recipes: A Problem-Solution Approach - page 390

Figure 13-1. R plots for CL_CPUT metric

How It Works
Exadata DMAs generally employ a number of techniques to summarize historical Exadata Storage Server metrics,
including the following:

cellcli output to a text file, importing to Excel, and building graphs • Saving

cellcli output to a text file and using external tables to load the data into Oracle tables
and then using SQL to report on the metric values

• Saving

cellcli output to a text file and using SQL*Loader to load to Oracle tables • Saving

• Using Enterprise Manager with Exadata plug-ins to display Exadata Storage Server

performance

cellcli output • Using R to generate statistical information and plots from

Although the script in Listing 13-3 is very simple by R standards, we prefer using R to summarize Exadata metric
information for a number of reasons. First, it is relatively easy to incorporate your analysis into a small subset of
server-side scripts, thus reducing the time required to transfer files and prepare independent analysis. Second, R
provides a rich set of statistical analysis and plotting features that are often difficult and time-consuming to replicate
using other scripting languages, SQL, or Excel.

■ programming in the r language is beyond the scope of this text. to learn more
about r and oracle's direction with r, please see www.r-project.org and
www.oracle.com/technetwork/database/options/advanced-analytics/r-enterprise/index.html .

Note