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Über dieses Buch

Use an innovative approach that relies on big data and advanced analytical techniques to analyze and improve Oracle Database performance. The approach used in this book represents a step-change paradigm shift away from traditional methods. Instead of relying on a few hand-picked, favorite metrics, or wading through multiple specialized tables of information such as those found in an automatic workload repository (AWR) report, you will draw on all available data, applying big data methods and analytical techniques to help the performance tuner draw impactful, focused performance improvement conclusions.

This book briefly reviews past and present practices, along with available tools, to help you recognize areas where improvements can be made. The book then guides you through a step-by-step method that can be used to take advantage of all available metrics to identify problem areas and work toward improving them. The method presented simplifies the tuning process and solves the problem of metric overload.
You will learn how to: collect and normalize data, generate deltas that are useful in performing statistical analysis, create and use a taxonomy to enhance your understanding of problem performance areas in your database and its applications, and create a root cause analysis report that enables understanding of a specific performance problem and its likely solutions.

What You'll LearnCollect and prepare metrics for analysis from a wide array of sources
Apply statistical techniques to select relevant metrics
Create a taxonomy to provide additional insight into problem areas
Provide a metrics-based root cause analysis regarding the performance issue
Generate an actionable tuning plan prioritized according to problem areas
Monitor performance using database-specific normal ranges

​Who This Book Is For
Professional tuners: responsible for maintaining the efficient operation of large-scale databases who wish to focus on analysis, who want to expand their repertoire to include a big data methodology and use metrics without being overwhelmed, who desire to provide accurate root cause analysis and avoid the cyclical fix-test cycles that are inevitable when speculation is used

Inhaltsverzeichnis

Frontmatter

Performance Tuning Basics

Frontmatter

Chapter 1. Performance Tuning Basics

Before we begin Chapter 1, let me introduce you to Part I of this book.
Roger Cornejo

The Dynamic Oracle Performance Analytics (DOPA) Process

Frontmatter

Chapter 2. Gathering Problem Information

Before I begin Chapter 2, let me introduce Part II of this book, which is made up of Chapters 27.
Roger Cornejo

Chapter 3. Data Preparation

Information from the client related to the performance issue being experienced is an important element in determining root cause. This information can be viewed as a source of data for the analytic process. The other source of data that is invaluable to the analytic process is data obtained from the database itself. This chapter provides step-by-step instructions for how to collect and prepare the data from the database in preparation for the next step in the analysis process which is statistical analysis. The process of preparing data occurs in the following sequence:
Roger Cornejo

Chapter 4. Statistical Analysis

This chapter addresses the statistical manipulation of the data that has been collected and formatted. I’ll start by reviewing some basic statistical concepts, including normal ranges, outliers, and variance, and then discuss how these concepts are applied as part of the DOPA process to establish normal ranges for the host of metrics that don’t have well-known norms. Don’t worry, you don’t have to break out the statistics books; I will show you how to use the embedded Oracle functions to easily accomplish this statistical analysis.
Roger Cornejo

Chapter 5. Feature Selection

In the last chapter, we discussed the statistical calculations necessary for establishing values that could be considered normal for a given metric. Once a normal is established for each metric, it is possible to identify metrics outside of normal and highlight them. These “abnormal” metrics are the ones that will be most useful for predicting the cause of performance issues in the database. In this chapter, I’ll discuss the process of drawing out the Oracle performance metrics that are abnormal and therefore important for our analysis and discerning which ones can be safely ignored using a process that eliminates personal bias.
Roger Cornejo

Chapter 6. Taxonomy

Feature selection, which was discussed in the last chapter, is a powerful component of the DOPA process. It enables the tuning analyst to quickly identify areas of the database that are performing outside of normal. The metrics with a high incidence of flagged values are assumed to have a high predictive value of pointing to the problem area. And this is definitely true in my experience. While the feature selection/flagging process is sufficient by itself to solve many problems, I learned another analytics “trick” from my son that enabled me to take my analysis one step further. The concept I brought into the analysis is that of taxonomy.
Roger Cornejo

Chapter 7. Building the Model and Reporting

In the preceding chapters, I introduced the individual steps of the DOPA process. It is finally time to put it all together, build the model, and report the results. As I have repeated throughout the book, the DOPA process is dynamic. You essentially create a new, unique predictive model with each execution of the code by altering the model inputs as you refine your tuning efforts. It is also versatile because the data can be subset in such a way that it makes the analysis easy and clearly shows the metrics that enable you to discover the cause of the performance problem.
Roger Cornejo

Case Studies and Further Applications

Frontmatter

Chapter 8. Case Studies

In the previous chapter, I provided a framework for making the many decisions necessary when implementing the DOPA process. The discussion focused upon how you would choose to run the analysis, the various parameters, and views you would choose. In this chapter, I’ll lead you through several real examples.
Roger Cornejo

Chapter 9. Monitoring

Sadly, and all too often, we learn of database performance problems as a complaint from database users. Most DBAs and managers would like to stay ahead of performance problems by monitoring their databases and taking peremptory action to avoid the types of performance complaints to which most of us have been accustomed. Some level of automated monitoring is or should be an integral and important task for DBAs, especially in environments with hundreds if not thousands of databases, where the sheer volume makes it impossible to monitor manually.
Roger Cornejo

Chapter 10. Further Enhancements

As I have stated repeatedly throughout this work, the DOPA process is something I have been developing over a period of time. I consider it an effective tool as is, but there are many enhancements and further applications which I would like to explore as time allows. In this chapter I discuss further enhancements/possible future work in a general way. I hope these brief comments will stimulate the reader to experiment with these ideas as well. If so, I would love to hear about the results of your exploration.
Roger Cornejo

Backmatter

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