Optimizing Oracle Performance Cary Millsap, Jeff Holt

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Spis treści książki

• Optimizing Oracle Performance
• Dedication
• A Note Regarding Supplemental Files
• Foreword
• Preface
  • Why I Wrote This Book
  • Audience for This Book
  • Structure of This Book
  • Which Platform and Version?
  • What This Book Is and Is Not
  • About the Tools, Examples, and Exercises
  • Citations
  • Conventions Used in This Book
  • Comments and Questions
  • Acknowledgments
• I. Method
  • 1. A Better Way to Optimize
    • 1.1. Youre Doing It Wrong
    • 1.2. Requirements of a Good Method
    • 1.3. Three Important Advances
      • 1.3.1. User Action Focus
      • 1.3.2. Response Time Focus
      • 1.3.3. Amdahls Law
      • 1.3.4. All Together Now
    • 1.4. Tools for Analyzing Response Time
      • 1.4.1. Sequence Diagram
      • 1.4.2. Resource Profile
    • 1.5. Method R
      • 1.5.1. Who Uses the Method
        • 1.5.1.1. The abominable smokestack
        • 1.5.1.2. The optimal performance analyst
        • 1.5.1.3. Your role
      • 1.5.2. Overcoming Common Objections
        • 1.5.2.1. But my whole system is slow
        • 1.5.2.2. The method only works if the problem is the database
        • 1.5.2.3. The method is unconventional
      • 1.5.3. Evaluation of Effectiveness
  • 2. Targeting the Right User Actions
    • 2.1. Specification Reliability
      • 2.1.1. The System
      • 2.1.2. Economic Constraints
    • 2.2. Making a Good Specification
      • 2.2.1. User Action
      • 2.2.2. Identifying the Right User Actions and Contexts
      • 2.2.3. Prioritizing the User Actions
      • 2.2.4. Determining Who Will Execute Each Action and When
    • 2.3. Specification Over-Constraint
  • 3. Targeting the Right Diagnostic Data
    • 3.1. Expectations About Data Collection
    • 3.2. Data Scope
      • 3.2.1. Scoping Errors
      • 3.2.2. Long-Running User Actions
      • 3.2.3. Too Much Data Is Really Not Enough Data
    • 3.3. Oracle Diagnostic Data Sources
    • 3.4. For More Information
  • 4. Targeting the Right Improvement Activity
    • 4.1. A New Standard of Customer Care
    • 4.2. How to Find the Economically Optimal Performance Improvement Activity
    • 4.3. Making Sense of Your Diagnostic Data
    • 4.4. Forecasting Project Net Payoff
      • 4.4.1. Forecasting Project Benefits
        • 4.4.1.1. Monetizing the benefits
        • 4.4.1.2. If you cant monetize the benefits
      • 4.4.2. Forecasting Project Cost
      • 4.4.3. Forecasting Project Risk
• II. Reference
  • 5. Interpreting Extended SQL Trace Data
    • 5.1. Trace File Walk-Through
    • 5.2. Extended SQL Trace Data Reference
      • 5.2.1. Trace File Element Definitions
        • 5.2.1.1. Cursor numbers
        • 5.2.1.2. Session identification and timestamps
        • 5.2.1.3. Application identification
        • 5.2.1.4. Cursor identification
        • 5.2.1.5. Database calls
        • 5.2.1.6. Wait events
        • 5.2.1.7. Bind variables
        • 5.2.1.8. Row source operations
        • 5.2.1.9. Transaction end markers
        • 5.2.1.10. Reference summary
      • 5.2.2. Oracle Time Units
    • 5.3. Response Time Accounting
      • 5.3.1. Time Within a Database Call
      • 5.3.2. Time Between Database Calls
      • 5.3.3. Recursive SQL Double-Counting
        • 5.3.3.1. Parent-child relationships
        • 5.3.3.2. Recursive statistics
    • 5.4. Evolution of the Response Time Model
    • 5.5. Walking the Clock
      • 5.5.1. Oracle Release 8 and Prior
      • 5.5.2. Oracle Release 9
      • 5.5.3. Clock Walk Formulas
    • 5.6. Forward Attribution
      • 5.6.1. Forward Attribution for Within-Call Events
      • 5.6.2. Forward Attribution for Between-Call Events
    • 5.7. Detailed Trace File Walk-Through
    • 5.8. Exercises
  • 6. Collecting Extended SQL Trace Data
    • 6.1. Understanding Your Application
    • 6.2. Activating Extended SQL Trace
      • 6.2.1. Tracing Your Own Source Code
      • 6.2.2. Tracing Someone Elses Source Code
        • 6.2.2.1. Triggering a session to activate its own trace
        • 6.2.2.2. Activating trace from a third-party session
    • 6.3. Finding Your Trace File(s)
      • 6.3.1. Trace File Names
      • 6.3.2. Simple Client-Server Applications
      • 6.3.3. Oracle Parallel Execution
      • 6.3.4. Oracle Multi-Threaded Server
      • 6.3.5. Connection-Pooling Applications
      • 6.3.6. Some Good News
    • 6.4. Eliminating Collection Error
      • 6.4.1. Time Scope Errors at Trace Activation
        • 6.4.1.1. Missing wait event data at trace activation
        • 6.4.1.2. Missing database call data at trace activation
        • 6.4.1.3. Excess database call data at trace activation
      • 6.4.2. Missing Time at Trace Deactivation
      • 6.4.3. Incomplete Recursive SQL Data
    • 6.5. Exercises
  • 7. Oracle Kernel Timings
    • 7.1. Operating System Process Management
      • 7.1.1. The sys call Transition
      • 7.1.2. The interrupt Transition
      • 7.1.3. Other States and Transitions
    • 7.2. Oracle Kernel Timings
    • 7.3. How Software Measures Itself
      • 7.3.1. Elapsed Time
      • 7.3.2. CPU Consumption
    • 7.4. Unaccounted-for Time
    • 7.5. Measurement Intrusion Effect
    • 7.6. CPU Consumption Double-Counting
    • 7.7. Quantization Error
      • 7.7.1. Measurement Resolution
      • 7.7.2. Definition of Quantization Error
      • 7.7.3. Complications in Measuring CPU Consumption
        • 7.7.3.1. How gettimeofday works
        • 7.7.3.2. How getrusage works
      • 7.7.4. Detection of Quantization Error
      • 7.7.5. Bounds of Quantization Error
    • 7.8. Time Spent Not Executing
      • 7.8.1. Instrumenting the Experiment
      • 7.8.2. Process States and Transitions Revisited
    • 7.9. Un-Instrumented Oracle Kernel Code
      • 7.9.1. Effect
      • 7.9.2. Trace Writing
    • 7.10. Exercises
  • 8. Oracle Fixed View Data
    • 8.1. Deficiencies of Fixed View Data
      • 8.1.1. Too Many Data Sources
      • 8.1.2. Lack of Detail
      • 8.1.3. Measurement Intrusion Effect of Polling
      • 8.1.4. Difficulty of Proper Action-Scoping
      • 8.1.5. Difficulty of Proper Time-Scoping
      • 8.1.6. Susceptibility to Overflow and Other Errors
      • 8.1.7. Lack of Database Call Duration Data
      • 8.1.8. Lack of Read Consistency
    • 8.2. Fixed View Reference
      • 8.2.1. V$SQL
      • 8.2.2. V$SESS_IO
      • 8.2.3. V$SYSSTAT
      • 8.2.4. V$SESSTAT
      • 8.2.5. V$SYSTEM_EVENT
      • 8.2.6. V$SESSION_EVENT
      • 8.2.7. V$SESSION_WAIT
    • 8.3. Useful Fixed View Queries
      • 8.3.1. Tom Kytes Test Harness
      • 8.3.2. Finding a Fixed View Definition
      • 8.3.3. Finding Inefficient SQL
      • 8.3.4. Finding Where a Session Is Stuck
      • 8.3.5. Finding Where a System Is Stuck
      • 8.3.6. Approximating a Sessions Resource Profile
      • 8.3.7. Viewing Waits System-Wide
        • 8.3.7.1. The idle events problem
        • 8.3.7.2. The denominator problem
        • 8.3.7.3. Infinite capacity for waiting
        • 8.3.7.4. Idle events in background sessions
        • 8.3.7.5. Targeting revisited
    • 8.4. The Oracle Wait Interface
    • 8.5. Exercises
  • 9. Queueing Theory for the Oracle Practitioner
    • 9.1. Performance Models
    • 9.2. Queueing
      • 9.2.1. Queueing Economics
      • 9.2.2. Queueing Visualized
    • 9.3. Queueing Theory
      • 9.3.1. Model Input and Output Values
        • 9.3.1.1. Arrivals and completions
        • 9.3.1.2. Service channels, utilization, and stability
        • 9.3.1.3. Service time and service rate
        • 9.3.1.4. Queueing delay and response time
        • 9.3.1.5. Maximum effective throughput
        • 9.3.1.6. Cumulative distribution function (CDF) of response time
      • 9.3.2. Random Variables
        • 9.3.2.1. Expected value
        • 9.3.2.2. Probability density function (pdf)
        • 9.3.2.3. Using the pdf
        • 9.3.2.4. Why understanding distribution is important
      • 9.3.3. Queueing Theory Versus the Wait Interface
        • 9.3.3.1. Oracle wait times
        • 9.3.3.2. Differences in queueing theory notation
    • 9.4. The M/M/m Queueing Model
      • 9.4.1. M/M/m Systems
      • 9.4.2. Non-M/M/m Systems
      • 9.4.3. Exponential Distribution
        • 9.4.3.1. Poisson-exponential relationship
        • 9.4.3.2. Testing for fit to exponential distribution
        • 9.4.3.3. A program to test for exponential distribution
      • 9.4.4. Behavior of M/M/m Systems
        • 9.4.4.1. Multi-channel scalability
        • 9.4.4.2. The knee
        • 9.4.4.3. Response time fluctuations
        • 9.4.4.4. Parameter sensitivity
      • 9.4.5. Using M/M/m: Worked Example
        • 9.4.5.1. Suitability for modeling with M/M/m
        • 9.4.5.2. Computing the required number of CPUs
        • 9.4.5.3. What we can learn from an optimistic model
        • 9.4.5.4. Negotiating the negotiable parameters
        • 9.4.5.5. Using Goal Seek in Microsoft Excel
        • 9.4.5.6. Sensitivity analysis
    • 9.5. Perspective
    • 9.6. Exercises
• III. Deployment
  • 10. Working the Resource Profile
    • 10.1. How to Work a Resource Profile
      • 10.1.1. Work in Descending Response Time Order
        • 10.1.1.1. Why targeting is vital
        • 10.1.1.2. Possible benefits of low-return improvements
      • 10.1.2. Eliminate Unnecessary Calls
        • 10.1.2.1. Why workload elimination works so well
        • 10.1.2.2. Supply and demand in the technology stack
        • 10.1.2.3. How to eliminate calls
        • 10.1.2.4. Thinking in a bigger box
      • 10.1.3. Eliminate Inter-Process Competition
        • 10.1.3.1. How to attack a latency problem
        • 10.1.3.2. How to find competing workload
      • 10.1.4. Upgrade Capacity
    • 10.2. How to Forecast Improvement
    • 10.3. How to Tell When Your Work Is Done
  • 11. Responding to the Diagnosis
    • 11.1. Beyond the Resource Profile
    • 11.2. Response Time Components
      • 11.2.1. Oracle Pseudoevents
        • 11.2.1.1. CPU service
        • 11.2.1.2. unaccounted-for
      • 11.2.2. No Event Is Inherently Unimportant
        • 11.2.2.1. Responding to large SQL*Net response time contributions
        • 11.2.2.2. Responding to large response time contributions from other events
    • 11.3. Eliminating Wasteful Work
      • 11.3.1. Logical I/O Optimization
        • 11.3.1.1. Why LIO problems are so common
        • 11.3.1.2. How to optimize SQL
      • 11.3.2. Parse Optimization
      • 11.3.3. Write Optimization
    • 11.4. Attributes of a Scalable Application
  • 12. Case Studies
    • 12.1. Case 1: Misled by System-Wide Data
      • 12.1.1. Targeting
      • 12.1.2. Diagnosis and Response
      • 12.1.3. Results
      • 12.1.4. Lessons Learned
    • 12.2. Case 2: Large CPU Service Duration
      • 12.2.1. Targeting
      • 12.2.2. Diagnosis and Response
      • 12.2.3. Results
      • 12.2.4. Lessons Learned
    • 12.3. Case 3: Large SQL*Net Event Duration
      • 12.3.1. Targeting
      • 12.3.2. Diagnosis and Response
      • 12.3.3. Results
      • 12.3.4. Lessons Learned
    • 12.4. Case 4: Large Read Event Duration
      • 12.4.1. Targeting
      • 12.4.2. Diagnosis and Repair
      • 12.4.3. Results
      • 12.4.4. Lessons Learned
    • 12.5. Conclusion
• IV. Appendixes
  • Glossary
  • A. Greek Alphabet
  • B. Optimizing Your Database Buffer Cache Hit Ratio
  • C. M/M/m Queueing Theory Formulas
  • D. References
• Index
• About the Authors
• Colophon
• Copyright

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