Dynamic Oracle Performance Analytics : Using Normalized Metrics to Improve Database Speed

Roger Cornejo

ISBN 10: 1484241363 / ISBN 13: 9781484241363

Published by APRESS L.P., 2018

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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 sourcesApply statistical techniques to select relevant metricsCreate a taxonomy to provide additional insight into problem areasProvide a metrics-based root cause analysis regarding the performance issueGenerate an actionable tuning plan prioritized according to problem areasMonitor performance using database-specific normal ranges Who This Book Is ForProfessional 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. Seller Inventory # 9781484241363

Bibliographic Details

Title: Dynamic Oracle Performance Analytics : Using...

Publisher: APRESS L.P.

Publication Date: 2018

Binding: Taschenbuch

Condition: Neu

About this title

Synopsis:

IOUG Press

From the Back Cover:

Use an innovative approach that relies on big data and advanced analytical techniques to analyze and improve Oracle Database performance. The approach in this book is a step-change 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 draw impactful, focused performance improvement conclusions. 
This book reviews past and present practices, along with available tools, to help you pinpoint areas for improvement. 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 Learn:

• Collect 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