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BL
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| Record Number
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851211
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| Main Entry
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Cornejo, Roger.
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| Title & Author
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Dynamic Oracle performance analytics : : using normalized metrics to improve database speed /\ Roger Cornejo.
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| Publication Statement
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New York :: Springer Nature :: Apress,, 2018.
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| Page. NO
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1 online resource
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| ISBN
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1484241371
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: 9781484241370
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9781484241363
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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Intro; Table of Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Part I: Performance Tuning Basics; Chapter 1: Performance Tuning Basics; OEM; ASH Reports; AWR; ADDM; Summary; Part II: The Dynamic Oracle Performance Analytics (DOPA) Process; Chapter 2: Gathering Problem Information; Has This Run Before?; When Did the Problem Start?; What Was the Start and End Time of the Application Run?; What Database Were You Connected To?; What User Accounts Are They Using to Connect to the Database?; What SQL Do You Believe You Were Running?
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Case #2: Basic Example of How the DOPA Process Identified a Configuration ProblemCase #3: CPU Problem Caused by SQL Execution; Case #4: Example Where the DOPA Process Was Used to Detail Problem Areas; Case #5: Performance Investigation for Ill-Described Problem (a Narrative); Case #6: Example of Where Someone Might Think It Was a Top SQL, but It's Not; Case #7: Alert Log Example (Error Alert Failure Example) Narrative; Case #8: DOPA Analysis of Oracle Memory Error-Narrative; Summary; Chapter 9: Monitoring; Development Requirements for DOPA to Be Used for Monitoring; Persisting Baselines
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Chapter 7: Building the Model and ReportingBringing Together the DOPA Process Components; Implementing the DOPA Process: The SQL Code; Precondition to Running the DOPA Process; Running the DOPA Process; Documenting Model Parameters; Variables for Building the Model; Reporting; General Instructions for Building and Tweaking the Model; How to Choose Inputs/Set Variables; Example: Building the Model and Reporting; Summary; Part III: Case Studies and Further Applications; Chapter 8: Case Studies; Case Studies; Case #1: Basic Example Showing How DOPA Process Identifies a SQL Problem
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Further Thoughts on the Study of Normal RangesAlerting via Absolute Values; Alerting via Percentiles; Summary; Chapter 10: Further Enhancements; Introductory Statement; Metrics Analysis; Implementing Other Sources/Customized Metrics; Active Session History; Regressed SQL: dba-hist-sqlstat; Advisor Analysis: DBA-ADVISOR% Queries for Recommended Action; Application of This Approach to V Tables and StatsPack; Application of This Approach to Other Metrics Collection Sources in the Infrastructure; Application of This Approach to Other Database Management Systems Such As Postgres or MongoDB
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What Is the Expected Duration?Did You Receive Any Oracle Error Messages?; Summary; Chapter 3: Data Preparation; Normalize the Data; Convert to Delta Values; Union Data from All Sources; Custom Queries; Summary; Chapter 4: Statistical Analysis; Statistics Review; Calculating the Sample Variance Using the Oracle Function; Statistics in the DOPA Process; Summary; Chapter 5: Feature Selection; Summary; Chapter 6: Taxonomy; Why Use Taxonomies?; Infrastructure Taxonomy; Oracle Taxonomy; The Taxonomy Table; Code Snippet for Building the Taxonomies; Summary
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| Abstract
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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.
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| Subject
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Database management.
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Relational databases.
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COMPUTERS-- Data Processing.
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Database management.
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Relational databases.
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| Subject
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Oracle (Computer file)
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Oracle (Computer file)
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| Dewey Classification
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005.7565
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| LC Classification
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QA76.9.D3
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