Site Reliability Engineering. How Google Runs Production Systems
- Autorzy:
- Niall Richard Murphy, Betsy Beyer, Chris Jones
- Ocena:
- Bądź pierwszym, który oceni tę książkę
- Stron:
- 552
- Dostępne formaty:
-
ePubMobi
Opis ebooka: Site Reliability Engineering. How Google Runs Production Systems
The overwhelming majority of a software systemâ??s lifespan is spent in use, not in design or implementation. So, why does conventional wisdom insist that software engineers focus primarily on the design and development of large-scale computing systems?
In this collection of essays and articles, key members of Googleâ??s Site Reliability Team explain how and why their commitment to the entire lifecycle has enabled the company to successfully build, deploy, monitor, and maintain some of the largest software systems in the world. Youâ??ll learn the principles and practices that enable Google engineers to make systems more scalable, reliable, and efficientâ??lessons directly applicable to your organization.
This book is divided into four sections:
- Introductionâ??Learn what site reliability engineering is and why it differs from conventional IT industry practices
- Principlesâ??Examine the patterns, behaviors, and areas of concern that influence the work of a site reliability engineer (SRE)
- Practicesâ??Understand the theory and practice of an SREâ??s day-to-day work: building and operating large distributed computing systems
- Managementâ??Explore Google's best practices for training, communication, and meetings that your organization can use
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O autorze ebooka
Chris Jones jest inżynierem SRE odpowiedzialnym za Google App Engine. Wcześniej odpowiadał za statystyki reklam, hurtownie danych i system pomocy technicznej w Google.
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Site Reliability Engineering. Jak Google zarządza systemami producyjnymi Site Reliability Engineering. Jak Google zarządza systemami producyjnymi
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In 2016, Google’s Site Reliability Engineering book ignited an industry discussion on what it means to run production services today—and why reliability considerations are fundamental to service design. Now, Google engineers who worked on that bestseller introduce The Site Reliability...
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Kup polskie wydanie:
Site Reliability Engineering. Jak Google zarządza systemami producyjnymi
39,50 zł
79,00 zł
(39.50 zł najniższa cena z 30 dni)
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Szczegóły ebooka
- ISBN Ebooka:
- 978-14-919-5117-0, 9781491951170
- Data wydania ebooka:
- 2016-03-23 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@ebookpoint.pl.
- Język publikacji:
- angielski
- Rozmiar pliku ePub:
- 6.4MB
- Rozmiar pliku Mobi:
- 15.6MB
Spis treści ebooka
- Foreword
- Preface
- Conventions Used in This Book
- Using Code Examples
- Safari Books Online
- How to Contact Us
- Acknowledgments
- I. Introduction
- 1. Introduction
- The Sysadmin Approach to Service Management
- Googles Approach to Service Management: Site Reliability Engineering
- Tenets of SRE
- Ensuring a Durable Focus on Engineering
- Pursuing Maximum Change Velocity Without Violating a Services SLO
- Monitoring
- Emergency Response
- Change Management
- Demand Forecasting and Capacity Planning
- Provisioning
- Efficiency and Performance
- The End of the Beginning
- 2. The Production Environment at Google, from the Viewpoint of an SRE
- Hardware
- System Software That Organizes the Hardware
- Managing Machines
- Storage
- Networking
- Other System Software
- Lock Service
- Monitoring and Alerting
- Our Software Infrastructure
- Our Development Environment
- Shakespeare: A Sample Service
- Life of a Request
- Job and Data Organization
- II. Principles
- 3. Embracing Risk
- Managing Risk
- Measuring Service Risk
- Risk Tolerance of Services
- Identifying the Risk Tolerance of Consumer Services
- Target level of availability
- Types of failures
- Cost
- Other service metrics
- Identifying the Risk Tolerance of Consumer Services
- Identifying the Risk Tolerance of Infrastructure Services
- Target level of availability
- Types of failures
- Cost
- Example: Frontend infrastructure
- Motivation for Error Budgets
- Forming Your Error Budget
- Benefits
- 4. Service Level Objectives
- Service Level Terminology
- Indicators
- Objectives
- Agreements
- Service Level Terminology
- Indicators in Practice
- What Do You and Your Users Care About?
- Collecting Indicators
- Aggregation
- Standardize Indicators
- Objectives in Practice
- Defining Objectives
- Choosing Targets
- Control Measures
- SLOs Set Expectations
- Agreements in Practice
- 5. Eliminating Toil
- Toil Defined
- Why Less Toil Is Better
- What Qualifies as Engineering?
- Is Toil Always Bad?
- Conclusion
- 6. Monitoring Distributed Systems
- Definitions
- Why Monitor?
- Setting Reasonable Expectations for Monitoring
- Symptoms Versus Causes
- Black-Box Versus White-Box
- The Four Golden Signals
- Worrying About Your Tail (or, Instrumentation and Performance)
- Choosing an Appropriate Resolution for Measurements
- As Simple as Possible, No Simpler
- Tying These Principles Together
- Monitoring for the Long Term
- Bigtable SRE: A Tale of Over-Alerting
- Gmail: Predictable, Scriptable Responses from Humans
- The Long Run
- Conclusion
- 7. The Evolution of Automation at Google
- The Value of Automation
- Consistency
- A Platform
- Faster Repairs
- Faster Action
- Time Saving
- The Value of Automation
- The Value for Google SRE
- The Use Cases for Automation
- Google SREs Use Cases for Automation
- A Hierarchy of Automation Classes
- Automate Yourself Out of a Job: Automate ALL the Things!
- Soothing the Pain: Applying Automation to Cluster Turnups
- Detecting Inconsistencies with Prodtest
- Resolving Inconsistencies Idempotently
- The Inclination to Specialize
- Service-Oriented Cluster-Turnup
- Borg: Birth of the Warehouse-Scale Computer
- Reliability Is the Fundamental Feature
- Recommendations
- 8. Release Engineering
- The Role of a Release Engineer
- Philosophy
- Self-Service Model
- High Velocity
- Hermetic Builds
- Enforcement of Policies and Procedures
- Continuous Build and Deployment
- Building
- Branching
- Testing
- Packaging
- Rapid
- Deployment
- Configuration Management
- Conclusions
- Its Not Just for Googlers
- Start Release Engineering at the Beginning
- 9. Simplicity
- System Stability Versus Agility
- The Virtue of Boring
- I Wont Give Up My Code!
- The Negative Lines of Code Metric
- Minimal APIs
- Modularity
- Release Simplicity
- A Simple Conclusion
- III. Practices
- 10. Practical Alerting from Time-Series Data
- The Rise of Borgmon
- Instrumentation of Applications
- Collection of Exported Data
- Storage in the Time-Series Arena
- Labels and Vectors
- Rule Evaluation
- Alerting
- Sharding the Monitoring Topology
- Black-Box Monitoring
- Maintaining the Configuration
- Ten Years On
- 11. Being On-Call
- Introduction
- Life of an On-Call Engineer
- Balanced On-Call
- Balance in Quantity
- Balance in Quality
- Compensation
- Feeling Safe
- Avoiding Inappropriate Operational Load
- Operational Overload
- A Treacherous Enemy: Operational Underload
- Conclusions
- 12. Effective Troubleshooting
- Theory
- In Practice
- Problem Report
- Triage
- Examine
- Diagnose
- Simplify and reduce
- Ask what, where, and why
- What touched it last
- Specific diagnoses
- Test and Treat
- Negative Results Are Magic
- Cure
- Case Study
- Making Troubleshooting Easier
- Conclusion
- 13. Emergency Response
- What to Do When Systems Break
- Test-Induced Emergency
- Details
- Response
- Findings
- What went well
- What we learned
- Change-Induced Emergency
- Details
- Response
- Findings
- What went well
- What we learned
- Process-Induced Emergency
- Details
- Response
- Findings
- What went well
- What we learned
- All Problems Have Solutions
- Learn from the Past. Dont Repeat It.
- Keep a History of Outages
- Ask the Big, Even Improbable, Questions: What If?
- Encourage Proactive Testing
- Conclusion
- 14. Managing Incidents
- Unmanaged Incidents
- The Anatomy of an Unmanaged Incident
- Sharp Focus on the Technical Problem
- Poor Communication
- Freelancing
- Elements of Incident Management Process
- Recursive Separation of Responsibilities
- A Recognized Command Post
- Live Incident State Document
- Clear, Live Handoff
- A Managed Incident
- When to Declare an Incident
- In Summary
- 15. Postmortem Culture: Learning from Failure
- Googles Postmortem Philosophy
- Collaborate and Share Knowledge
- Introducing a Postmortem Culture
- Conclusion and Ongoing Improvements
- 16. Tracking Outages
- Escalator
- Outalator
- Aggregation
- Tagging
- Analysis
- Reporting and communication
- Unexpected Benefits
- 17. Testing for Reliability
- Types of Software Testing
- Traditional Tests
- Unit tests
- Integration tests
- System tests
- Traditional Tests
- Production Tests
- Configuration test
- Stress test
- Canary test
- Types of Software Testing
- Creating a Test and Build Environment
- Testing at Scale
- Testing Scalable Tools
- Testing Disaster
- The Need for Speed
- Pushing to Production
- Expect Testing Fail
- Integration
- Production Probes
- Conclusion
- 18. Software Engineering in SRE
- Why Is Software Engineering Within SRE Important?
- Auxon Case Study: Project Background and Problem Space
- Traditional Capacity Planning
- Brittle by nature
- Laborious and imprecise
- Traditional Capacity Planning
- Our Solution: Intent-Based Capacity Planning
- Intent-Based Capacity Planning
- Precursors to Intent
- Dependencies
- Performance metrics
- Prioritization
- Precursors to Intent
- Introduction to Auxon
- Requirements and Implementation: Successes and Lessons Learned
- Approximation
- Raising Awareness and Driving Adoption
- Set expectations
- Identify appropriate customers
- Customer service
- Designing at the right level
- Team Dynamics
- Fostering Software Engineering in SRE
- Successfully Building a Software Engineering Culture in SRE: Staffing and Development Time
- Getting There
- Conclusions
- 19. Load Balancing at the Frontend
- Power Isnt the Answer
- Load Balancing Using DNS
- Load Balancing at the Virtual IP Address
- 20. Load Balancing in the Datacenter
- The Ideal Case
- Identifying Bad Tasks: Flow Control and Lame Ducks
- A Simple Approach to Unhealthy Tasks: Flow Control
- A Robust Approach to Unhealthy Tasks: Lame Duck State
- Limiting the Connections Pool with Subsetting
- Picking the Right Subset
- A Subset Selection Algorithm: Random Subsetting
- A Subset Selection Algorithm: Deterministic Subsetting
- Load Balancing Policies
- Simple Round Robin
- Small subsetting
- Varying query costs
- Machine diversity
- Unpredictable performance factors
- Simple Round Robin
- Least-Loaded Round Robin
- Weighted Round Robin
- 21. Handling Overload
- The Pitfalls of Queries per Second
- Per-Customer Limits
- Client-Side Throttling
- Criticality
- Utilization Signals
- Handling Overload Errors
- Deciding to Retry
- Load from Connections
- Conclusions
- 22. Addressing Cascading Failures
- Causes of Cascading Failures and Designing to Avoid Them
- Server Overload
- Resource Exhaustion
- CPU
- Memory
- Threads
- File descriptors
- Dependencies among resources
- Service Unavailability
- Causes of Cascading Failures and Designing to Avoid Them
- Preventing Server Overload
- Queue Management
- Load Shedding and Graceful Degradation
- Retries
- Latency and Deadlines
- Picking a deadline
- Missing deadlines
- Deadline propagation
- Bimodal latency
- Slow Startup and Cold Caching
- Always Go Downward in the Stack
- Triggering Conditions for Cascading Failures
- Process Death
- Process Updates
- New Rollouts
- Organic Growth
- Planned Changes, Drains, or Turndowns
- Request profile changes
- Resource limits
- Testing for Cascading Failures
- Test Until Failure and Beyond
- Test Popular Clients
- Test Noncritical Backends
- Immediate Steps to Address Cascading Failures
- Increase Resources
- Stop Health Check Failures/Deaths
- Restart Servers
- Drop Traffic
- Enter Degraded Modes
- Eliminate Batch Load
- Eliminate Bad Traffic
- Closing Remarks
- 23. Managing Critical State: Distributed Consensus for Reliability
- Motivating the Use of Consensus: Distributed Systems Coordination Failure
- Case Study 1: The Split-Brain Problem
- Case Study 2: Failover Requires Human Intervention
- Case Study 3: Faulty Group-Membership Algorithms
- Motivating the Use of Consensus: Distributed Systems Coordination Failure
- How Distributed Consensus Works
- Paxos Overview: An Example Protocol
- System Architecture Patterns for Distributed Consensus
- Reliable Replicated State Machines
- Reliable Replicated Datastores and Configuration Stores
- Highly Available Processing Using Leader Election
- Distributed Coordination and Locking Services
- Reliable Distributed Queuing and Messaging
- Distributed Consensus Performance
- Multi-Paxos: Detailed Message Flow
- Scaling Read-Heavy Workloads
- Quorum Leases
- Distributed Consensus Performance and Network Latency
- Reasoning About Performance: Fast Paxos
- Stable Leaders
- Batching
- Disk Access
- Deploying Distributed Consensus-Based Systems
- Number of Replicas
- Location of Replicas
- Capacity and Load Balancing
- Quorum composition
- Monitoring Distributed Consensus Systems
- Conclusion
- 24. Distributed Periodic Scheduling with Cron
- Cron
- Introduction
- Reliability Perspective
- Cron
- Cron Jobs and Idempotency
- Cron at Large Scale
- Extended Infrastructure
- Extended Requirements
- Building Cron at Google
- Tracking the State of Cron Jobs
- The Use of Paxos
- The Roles of the Leader and the Follower
- The leader
- The follower
- Resolving partial failures
- Storing the State
- Running Large Cron
- Summary
- 25. Data Processing Pipelines
- Origin of the Pipeline Design Pattern
- Initial Effect of Big Data on the Simple Pipeline Pattern
- Challenges with the Periodic Pipeline Pattern
- Trouble Caused By Uneven Work Distribution
- Drawbacks of Periodic Pipelines in Distributed Environments
- Monitoring Problems in Periodic Pipelines
- Thundering Herd Problems
- Moiré Load Pattern
- Introduction to Google Workflow
- Workflow as Model-View-Controller Pattern
- Stages of Execution in Workflow
- Workflow Correctness Guarantees
- Ensuring Business Continuity
- Summary and Concluding Remarks
- 26. Data Integrity: What You Read Is What You Wrote
- Data Integritys Strict Requirements
- Choosing a Strategy for Superior Data Integrity
- Backups Versus Archives
- Requirements of the Cloud Environment in Perspective
- Data Integritys Strict Requirements
- Google SRE Objectives in Maintaining Data Integrity and Availability
- Data Integrity Is the Means; Data Availability Is the Goal
- Delivering a Recovery System, Rather Than a Backup System
- Types of Failures That Lead to Data Loss
- Challenges of Maintaining Data Integrity Deep and Wide
- Scaling issues: Fulls, incrementals, and the competing forces of backups and restores
- Retention
- How Google SRE Faces the Challenges of Data Integrity
- The 24 Combinations of Data Integrity Failure Modes
- First Layer: Soft Deletion
- Second Layer: Backups and Their Related Recovery Methods
- Overarching Layer: Replication
- 1T Versus 1E: Not Just a Bigger Backup
- Third Layer: Early Detection
- Challenges faced by cloud developers
- Out-of-band data validation
- Knowing That Data Recovery Will Work
- Case Studies
- GmailFebruary, 2011: Restore from GTape
- Sunday, February 27, 2011, late in the evening
- GmailFebruary, 2011: Restore from GTape
- Google MusicMarch 2012: Runaway Deletion Detection
- Tuesday, March 6th, 2012, mid-afternoon
- Discovering the problem
- Assessing the damage
- Resolving the issue
- Parallel bug identification and recovery efforts
- First wave of recovery
- Second wave of recovery
- Addressing the root cause
- General Principles of SRE as Applied to Data Integrity
- Beginners Mind
- Trust but Verify
- Hope Is Not a Strategy
- Defense in Depth
- Conclusion
- 27. Reliable Product Launches at Scale
- Launch Coordination Engineering
- The Role of the Launch Coordination Engineer
- Launch Coordination Engineering
- Setting Up a Launch Process
- The Launch Checklist
- Driving Convergence and Simplification
- Launching the Unexpected
- Developing a Launch Checklist
- Architecture and Dependencies
- Example checklist questions
- Example action items
- Architecture and Dependencies
- Integration
- Example action items
- Capacity Planning
- Example checklist questions
- Failure Modes
- Example checklist questions
- Example action items
- Client Behavior
- Example checklist question
- Example action items
- Processes and Automation
- Example checklist question
- Example action items
- Development Process
- Example action items
- External Dependencies
- Example checklist questions
- Rollout Planning
- Example action items
- Selected Techniques for Reliable Launches
- Gradual and Staged Rollouts
- Feature Flag Frameworks
- Dealing with Abusive Client Behavior
- Overload Behavior and Load Tests
- Development of LCE
- Evolution of the LCE Checklist
- Problems LCE Didnt Solve
- Scalability changes
- Growing operational load
- Infrastructure churn
- Conclusion
- IV. Management
- 28. Accelerating SREs to On-Call and Beyond
- Youve Hired Your Next SRE(s), Now What?
- Initial Learning Experiences: The Case for Structure Over Chaos
- Learning Paths That Are Cumulative and Orderly
- Targeted Project Work, Not Menial Work
- Creating Stellar Reverse Engineers and Improvisational Thinkers
- Reverse Engineers: Figuring Out How Things Work
- Statistical and Comparative Thinkers: Stewards of the Scientific Method Under Pressure
- Improv Artists: When the Unexpected Happens
- Tying This Together: Reverse Engineering a Production Service
- Five Practices for Aspiring On-Callers
- A Hunger for Failure: Reading and Sharing Postmortems
- Disaster Role Playing
- Break Real Things, Fix Real Things
- Documentation as Apprenticeship
- Shadow On-Call Early and Often
- On-Call and Beyond: Rites of Passage, and Practicing Continuing Education
- Closing Thoughts
- 29. Dealing with Interrupts
- Managing Operational Load
- Factors in Determining How Interrupts Are Handled
- Imperfect Machines
- Cognitive Flow State
- Cognitive flow state: Creative and engaged
- Cognitive flow state: Angry Birds
- Cognitive Flow State
- Do One Thing Well
- Distractibility
- Polarizing time
- Seriously, Tell Me What to Do
- General suggestions
- On-call
- Tickets
- Ongoing responsibilities
- Be on interrupts, or dont be
- Reducing Interrupts
- Actually analyze tickets
- Respect yourself, as well as your customers
- 30. Embedding an SRE to Recover from Operational Overload
- Phase 1: Learn the Service and Get Context
- Identify the Largest Sources of Stress
- Identify Kindling
- Phase 1: Learn the Service and Get Context
- Phase 2: Sharing Context
- Write a Good Postmortem for the Team
- Sort Fires According to Type
- Phase 3: Driving Change
- Start with the Basics
- Get Help Clearing Kindling
- Explain Your Reasoning
- Ask Leading Questions
- Conclusion
- 31. Communication and Collaboration in SRE
- Communications: Production Meetings
- Agenda
- Attendance
- Communications: Production Meetings
- Collaboration within SRE
- Team Composition
- Techniques for Working Effectively
- Case Study of Collaboration in SRE: Viceroy
- The Coming of the Viceroy
- Challenges
- Recommendations
- Collaboration Outside SRE
- Case Study: Migrating DFP to F1
- Conclusion
- 32. The Evolving SRE Engagement Model
- SRE Engagement: What, How, and Why
- The PRR Model
- The SRE Engagement Model
- Alternative Support
- Documentation
- Consultation
- Alternative Support
- Production Readiness Reviews: Simple PRR Model
- Engagement
- Analysis
- Improvements and Refactoring
- Training
- Onboarding
- Continuous Improvement
- Evolving the Simple PRR Model: Early Engagement
- Candidates for Early Engagement
- Benefits of the Early Engagement Model
- Design phase
- Build and implementation
- Launch
- Post-launch
- Disengaging from a service
- Evolving Services Development: Frameworks and SRE Platform
- Lessons Learned
- External Factors Affecting SRE
- Toward a Structural Solution: Frameworks
- New Service and Management Benefits
- Significantly lower operational overhead
- Universal support by design
- Faster, lower overhead engagements
- A new engagement model based on shared responsibility
- Conclusion
- V. Conclusions
- 33. Lessons Learned from Other Industries
- Meet Our Industry Veterans
- Preparedness and Disaster Testing
- Relentless Organizational Focus on Safety
- Attention to Detail
- Swing Capacity
- Simulations and Live Drills
- Training and Certification
- Focus on Detailed Requirements Gathering and Design
- Defense in Depth and Breadth
- Postmortem Culture
- Automating Away Repetitive Work and Operational Overhead
- Structured and Rational Decision Making
- Conclusions
- 34. Conclusion
- A. Availability Table
- B. A Collection of Best Practices for Production Services
- Fail Sanely
- Progressive Rollouts
- Define SLOs Like a User
- Error Budgets
- Monitoring
- Postmortems
- Capacity Planning
- Overloads and Failure
- SRE Teams
- C. Example Incident State Document
- D. Example Postmortem
- Lessons Learned
- What went well
- What went wrong
- Where we got lucky
- Lessons Learned
- Timeline
- Supporting information:
- E. Launch Coordination Checklist
- F. Example Production Meeting Minutes
- Bibliography
- Index
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