Cloud Native. Using Containers, Functions, and Data to Build Next-Generation Applications Boris Scholl, Trent Swanson, Peter Jausovec

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• Preface
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Introduction to Cloud Native
  • Distributed Systems
    • Fallacies of Distributed Systems
    • CAP Theorem
  • The Twelve-Factor App
  • Availability and Service-Level Agreements
  • Summary
• 2. Fundamentals
  • Containers
    • Container Isolation Levels
    • Container Orchestration
    • Kubernetes Overview
    • Kubernetes and Containers
  • Serverless Computing
  • Functions
  • From VMs to Cloud Native
    • Lift-and-Shift
    • Application Modernization
    • Application Optimization
  • Microservices
    • Benefits of a Microservices Architecture
      • Agility
      • Continuous innovation
      • Evolutionary design
      • Small, focused teams
      • Fault isolation
      • Improved scale and resource usage
      • Improved observability
    • Challenges with a Microservices Architecture
      • Complexity
      • Data integrity and consistency
      • Performance
      • Development and testing
      • Versioning and integration
      • Monitoring and logging
      • Service dependency management
      • Availability
  • Summary
• 3. Designing Cloud Native Applications
  • Fundamentals of Cloud Native Applications
    • Operational Excellence
    • Security
    • Reliability and Availability
    • Scalability and Cost
  • Cloud Native versus Traditional Architectures
  • Functions versus Services
    • Function Scenarios
    • Considerations for Using Functions
    • Composite of Functions and Services
  • API Design and Versioning
    • API Backward and Forward Compatibility
    • Semantic Versioning
  • Service Communication
    • Protocols
      • WebSockets
      • HTTP/2
      • gRPC
    • Messaging Protocols
      • Message Queue Telemetry Transport
      • Advanced Message Queuing Protocol
    • Serialization Considerations
    • Idempotency
    • Request/Response
    • Publisher/Subscriber
    • Choosing Between Pub/Sub and Request Response
    • Synchronous versus Asynchronous
  • Gateways
    • Routing
    • Aggregation
    • Offloading
    • Implementing Gateways
  • Egress
  • Service Mesh
    • Traffic management
    • Failure handling
    • Security
      • Authentication, mutual TLS, and JWT tokens
      • Authorization
      • Tracing and monitoring
  • Example Architecture
  • Summary
• 4. Working with Data
  • Data Storage Systems
    • Objects, Files, and Disks
    • Databases
      • Key/value
      • Document
      • Relational
      • Graph
      • Column family
      • Time-series
      • Search
    • Streams and Queues
    • Blockchain
    • Selecting a Datastore
      • Functional requirements
      • Nonfunctional requirements
      • Management and cost
  • Data in Multiple Datastores
    • Change Data Capture
    • Write Changes as an Event to a Change Log
    • Transaction Supervisor
    • Compensating Transactions
    • Extract, Transform, and Load
    • Microservices and Data Lakes
  • Client Access to Data
    • Restricted Client Tokens (Valet-Key)
    • Database Services with Fine-Grained Access Control
    • GraphQL Data Service
  • Fast Scalable Data
    • Sharding Data
    • Caching Data
    • Content Delivery Networks
  • Analyzing Data
    • Streams
    • Batch
    • Data Lakes on Object Storage
    • Data Lakes and Data Warehouses
    • Distributed Query Engines
  • Databases on Kubernetes
    • Storage Volumes
    • StatefulSets
    • DaemonSets
  • Summary
• 5. DevOps
  • What Is DevOps?
    • Collaboration
    • Automation
    • Lean Principles and Processes
    • Measurement
    • Sharing
  • Testing
    • Test Doubles
    • Test Automation Pyramid
      • Unit tests
      • Service tests
      • UI tests
      • Jepsen tests
      • Performance tests
      • Load tests
      • Security/penetration tests
      • A/B tests
      • Acceptance tests
      • Usability tests
      • Configuration tests
      • Smoke tests
      • Integration tests
      • Chaos tests
      • Fuzz tests
    • When to Run Which Types of Tests
    • Testing Cadence
    • Testing in Production
      • Predeployment
      • Deployment
      • Release
      • Post-release
  • Development Environments and Tools
    • Development Tools
    • Development Environments
    • Local Development Environments
    • Local Development with a Remote Cluster
    • Skaffold Development Workflow
    • Remote Cluster Routed to Local Development
    • Cloud Development Environments
  • CI/CD
    • Source Code Control
    • Build Stage (CI)
    • Test Stage (CI)
    • Deploy Stage (CD)
    • Release Stage (CD)
    • Post-Release Stage
  • Monitoring
    • Collecting Metrics
      • Alerting
    • Observable Services
      • Logging
      • Distributed tracing
      • Service health, liveness, and readiness
  • Configuration Management
    • Single-Environment Variable
    • Multiple-Environment Variables
    • Adding ConfigMap Data to a Volume
    • Storing Secrets
    • Deployment Configuration
  • Sample CI/CD Flows
  • Summary
• 6. Best Practices
  • Moving to Cloud Native
    • Breaking Up the Monolith for the Right Reasons
    • Decouple Simple Services First
    • Learn to Operate on a Small Scale
    • Use an Anticorruption Layer Pattern
    • Use a Strangler Pattern
    • Come Up with a Data Migration Strategy
    • Rewrite Any Boilerplate Code
    • Reconsider Frameworks, Languages, Data Structures, and Datastores
    • Retire Code
  • Ensuring Resiliency
    • Handle Transient Failures with Retries
    • Use a Finite Number of Retries
    • Use Circuit Breakers for Nontransient Failures
    • Graceful Degradation
    • Use a Bulkhead Pattern
    • Implement Health Checks and Readiness Checks
    • Define CPU and Memory Limits for Your Containers
    • Implement Rate Limiting and Throttling
  • Ensuring Security
    • Treat Security Requirements the Same as Any Other Requirements
    • Incorporate Security in Your Designs
    • Grant Least-Privileged Access
    • Use Separate Accounts/Subscriptions/Tenants
    • Securely Store All Secrets
    • Obfuscate Data
    • Encrypt Data in Transit
    • Use Federated Identity Management
    • Use Role-Based Access Control
    • Isolate Kubernetes Pods
  • Working with Data
    • Use Managed Databases and Analytics Services
    • Use a Datastore That Best Fits Data Requirements
    • Keep Data in Multiple Regions or Zones
    • Use Data Partitioning and Replication for Scale
    • Avoid Overfetching and Chatty I/O
    • Dont Put Business Logic in the Database
    • Test with Production-like Data
    • Handle Transient Failures
  • Performance and Scalability
    • Design Stateless Services That Scale Out
    • Use Platform Autoscaling Features
    • Use Caching
    • Use Partitioning to Scale Beyond Service Limits
  • Functions
    • Write Single-Purpose Functions
    • Dont Chain Functions
    • Keep Functions Light and Simple
    • Make Functions Stateless
    • Separate Function Entry Point from the Function Logic
    • Avoid Long-Running Functions
    • Use Queues for Cross-Function Communication
  • Operations
    • Deployments and Releases Are Separate Activities
    • Keep Deployments Small
    • CI/CD Definition Lives with the Component
    • Consistent Application Deployment
    • Use Zero-Downtime Releases
    • Dont Modify Deployed Infrastructure
    • Use Containerized Build
    • Describe Infrastructure Using Code
    • Use Namespaces to Organize Services in Kubernetes
    • Isolate the Environments
    • Separate Function Source Code
    • Correlate Deployments with Commits
  • Logging, Monitoring, and Alerting
    • Use a Unified Logging System
    • Use Correlation IDs
    • Include Context with Log Entries
    • Common and Structured Logging Format
    • Tag Your Metrics Appropriately
    • Avoid Alert Fatigue
    • Define and Alert on Key Performance Indicators
    • Continuous Testing in Production
    • Start with Basic Metrics
  • Service Communication
    • Design for Backward and Forward Compatibility
    • Define Service Contracts That Do Not Leak Internal Details
    • Prefer Asynchronous Communication
    • Use Efficient Serialization Techniques
    • Use Queues or Streams to Handle Heavy Loads and Traffic Spikes
    • Batch Requests for Efficiency
    • Split Up Large Messages
  • Containers
    • Store Images in a Trusted Registry
    • Utilize the Docker Build Cache
    • Dont Run Containers in Privileged Mode
    • Use Explicit Container Image Tags
    • Keep Container Images Small
    • Run One Application per Container
    • Use Verified Images from Trusted Repositories
    • Use Vulnerability Scanning Tools on Images
    • Dont Store Data in Containers
    • Never Store Secrets or Configuration Inside an Image
  • Summary
• 7. Portability
  • Why Make Applications Portable?
  • The Costs of Portability
    • Data Gravity and Portability
  • When and How to Implement Portability
    • Standardized Interfaces
      • Containers
    • Common Services and Features
    • Abstractions and Layers
      • Component substitution
      • Service facade
      • Transforms
    • Managed Services from Other Vendors
    • Portability Tooling
      • Serverless framework
      • Infrastructure
      • Storage abstractions
    • Kubernetes as a Portability Layer
      • Cloud Controller Manager
      • Service catalog
      • Virtual Kubelet
  • Summary
• Index

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