Designing Distributed Systems. Patterns and Paradigms for Scalable, Reliable Services

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• Preface
  • Who Should Read This Book
  • Why I Wrote This Book
  • The World of Distributed Systems Today
  • Navigating This Book
  • Conventions Used in This Book
  • Online Resources
  • Using Code Examples
  • OReilly Safari
  • How to Contact Us
  • Acknowledgments
• 1. Introduction
  • A Brief History of Systems Development
  • A Brief History of Patterns in Software Development
    • Formalization of Algorithmic Programming
    • Patterns for Object-Oriented Programming
    • The Rise of Open Source Software
  • The Value of Patterns, Practices, and Components
    • Standing on the Shoulders of Giants
    • A Shared Language for Discussing Our Practice
    • Shared Components for Easy Reuse
  • Summary
• I. Single-Node Patterns
  • Motivations
  • Summary
• 2. The Sidecar Pattern
  • An Example Sidecar: Adding HTTPS to a Legacy Service
  • Dynamic Configuration with Sidecars
  • Modular Application Containers
    • Hands On: Deploying the topz Container
  • Building a Simple PaaS with Sidecars
  • Designing Sidecars for Modularity and Reusability
    • Parameterized Containers
    • Define Each Containers API
    • Documenting Your Containers
  • Summary
• 3. Ambassadors
  • Using an Ambassador to Shard a Service
    • Hands On: Implementing a Sharded Redis
  • Using an Ambassador for Service Brokering
  • Using an Ambassador to Do Experimentation or Request Splitting
    • Hands On: Implementing 10% Experiments
• 4. Adapters
  • Monitoring
    • Hands On: Using Prometheus for Monitoring
  • Logging
    • Hands On: Normalizing Different Logging Formats with Fluentd
  • Adding a Health Monitor
    • Hands On: Adding Rich Health Monitoring for MySQL
• II. Serving Patterns
  • Introduction to Microservices
• 5. Replicated Load-Balanced Services
  • Stateless Services
    • Readiness Probes for Load Balancing
    • Hands On: Creating a Replicated Service in Kubernetes
  • Session Tracked Services
  • Application-Layer Replicated Services
  • Introducing a Caching Layer
    • Deploying Your Cache
    • Hands On: Deploying the Caching Layer
  • Expanding the Caching Layer
    • Rate Limiting and Denial-of-Service Defense
    • SSL Termination
    • Hands On: Deploying nginx and SSL Termination
  • Summary
• 6. Sharded Services
  • Sharded Caching
    • Why You Might Need a Sharded Cache
    • The Role of the Cache in System Performance
    • Replicated, Sharded Caches
    • Hands On: Deploying an Ambassador and Memcache for a Sharded Cache
  • An Examination of Sharding Functions
    • Selecting a Key
    • Consistent Hashing Functions
    • Hands On: Building a Consistent HTTP Sharding Proxy
  • Sharded, Replicated Serving
  • Hot Sharding Systems
• 7. Scatter/Gather
  • Scatter/Gather with Root Distribution
    • Hands On: Distributed Document Search
  • Scatter/Gather with Leaf Sharding
    • Hands On: Sharded Document Search
    • Choosing the Right Number of Leaves
  • Scaling Scatter/Gather for Reliability and Scale
• 8. Functions and Event-Driven Processing
  • Determining When FaaS Makes Sense
    • The Benefits of FaaS
    • The Challenges of FaaS
    • The Need for Background Processing
    • The Need to Hold Data in Memory
    • The Costs of Sustained Request-Based Processing
  • Patterns for FaaS
    • The Decorator Pattern: Request or Response Transformation
    • Hands On: Adding Request Defaulting Prior to Request Processing
    • Handling Events
    • Hands On: Implementing Two-Factor Authentication
    • Event-Based Pipelines
    • Hands On: Implementing a Pipeline for New-User Signup
• 9. Ownership Election
  • Determining If You Even Need Master Election
  • The Basics of Master Election
    • Hands On: Deploying etcd
    • Implementing Locks
    • Hands On: Implementing Locks in etcd
    • Implementing Ownership
    • Hands On: Implementing Leases in etcd
  • Handling Concurrent Data Manipulation
• III. Batch Computational Patterns
• 10. Work Queue Systems
  • A Generic Work Queue System
    • The Source Container Interface
      • Work queue API
    • The Worker Container Interface
    • The Shared Work Queue Infrastructure
  • Hands On: Implementing a Video Thumbnailer
  • Dynamic Scaling of the Workers
  • The Multi-Worker Pattern
• 11. Event-Driven Batch Processing
  • Patterns of Event-Driven Processing
    • Copier
    • Filter
    • Splitter
    • Sharder
    • Merger
  • Hands On: Building an Event-Driven Flow for New User Sign-Up
  • Publisher/Subscriber Infrastructure
  • Hands On: Deploying Kafka
• 12. Coordinated Batch Processing
  • Join (or Barrier Synchronization)
  • Reduce
    • Hands On: Count
    • Sum
    • Histogram
  • Hands On: An Image Tagging and Processing Pipeline
• 13. Conclusion: A New Beginning?
• Index

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