Design Patterns for Cloud Native Applications - Helion

ebook

Autor: Kasun Indrasiri, Sriskandarajah Suhothayan
ISBN: 9781492090663
stron: 314, Format: ebook
Data wydania: 2021-05-17
Księgarnia: Helion

Cena książki: 186,15 zł (poprzednio: 216,45 zł)
Oszczędzasz: 14% (-30,30 zł)

Osoby, które kupiły tę książkę, wybierały także »

With the immense cost savings and scalability the cloud provides, the rationale for building cloud native applications is no longer in question. The real issue is how. With this practical guide, developers will learn about the most commonly used design patterns for building cloud native applications using APIs, data, events, and streams in both greenfield and brownfield development.

You'll learn how to incrementally design, develop, and deploy large and effective cloud native applications that you can manage and maintain at scale with minimal cost, time, and effort. Authors Kasun Indrasiri and Sriskandarajah Suhothayan highlight use cases that effectively demonstrate the challenges you might encounter at each step.

Learn the fundamentals of cloud native applications
Explore key cloud native communication, connectivity, and composition patterns
Learn decentralized data management techniques
Use event-driven architecture to build distributed and scalable cloud native applications
Explore the most commonly used patterns for API management and consumption
Examine some of the tools and technologies you'll need for building cloud native systems

Osoby które kupowały "Design Patterns for Cloud Native Applications", wybierały także:

Windows Media Center. Domowe centrum rozrywki 66,67 zł, (8,00 zł -88%)
Ruby on Rails. Ćwiczenia 18,75 zł, (3,00 zł -84%)
DevOps w praktyce. Kurs video. Jenkins, Ansible, Terraform i Docker 190,00 zł, (39,90 zł -79%)
Przywództwo w świecie VUCA. Jak być skutecznym liderem w niepewnym środowisku 58,64 zł, (12,90 zł -78%)
Scrum. O zwinnym zarządzaniu projektami. Wydanie II rozszerzone 58,64 zł, (12,90 zł -78%)

Spis treści

Design Patterns for Cloud Native Applications eBook -- spis treści

Preface
- Conventions Used in This Book
- OReilly Online Learning
- How to Contact Us
- Acknowledgments
1. Introduction to Cloud Native
- What Is Cloud Native?
  - Designed as a Collection of Microservices
  - Use Containerization and Container Orchestration
    - What are containers?
    - Why container orchestration?
    - Kubernetes
    - Serverless functions
    - Virtual machines
  - Automate the Development Life Cycle
  - Dynamic Management
- Methodology for Building Cloud Native Apps
  - Designing the Application
  - Developing the Application
    - Independent codebase
    - Explicit dependencies
    - Decoupled configurations
    - Independent testing
    - Containerization
  - Connectivity, Compositions, and APIs
    - Service-led interactions
    - Interservice communication and compositions
    - Exposing capabilities as managed APIs
  - Automating the Development, Release, and Deployment
  - Running in a Dynamic Environment
  - Control Plane for Dynamic Management
  - Observability and Monitoring
- Design Patterns for Building Cloud Native Apps
  - Communication Patterns
  - Connectivity and Composition Patterns
    - Connectivity
    - Compositions
  - Data Management Patterns
  - Event-Driven Architecture Patterns
  - Stream-Processing Patterns
  - API Management and Consumption Patterns
- Reference Architecture for Cloud Native Apps
- Summary
2. Communication Patterns
- Synchronous Messaging Patterns
  - Request-Response Pattern
    - How it works
    - How its used in practice
    - Considerations
    - Related patterns
  - Remote Procedure Calls Pattern
    - How it works
    - How its used in practice
    - Considerations
    - Related patterns
  - Summary of Synchronous Messaging Patterns
- Asynchronous Messaging Patterns
  - Single-Receiver Pattern
    - How it works
    - How its used in practice
    - Considerations
    - Related patterns
  - Multiple-Receiver Pattern
    - How it works
    - How its used in practice
    - Considerations
    - Related patterns
  - Asynchronous Request-Reply Pattern
    - How it works
    - How its used in practice
    - Considerations
    - Related patterns
  - Summary of Asynchronous Messaging Patterns
- Service Definition Patterns
  - Service Definitions in Synchronous Communication
    - How it works
    - How its used in practice
      - Service definitions for RESTful services with OpenAPI and Consul
    - Considerations
    - Related patterns
  - Service Definition in Asynchronous Communication
    - How it works
    - How its used in practice
      - Schema definitions with Kafka Schema Registry
      - Service definitions with AsyncAPI
    - Considerations
    - Related patterns
- Technologies to Implement Synchronous Messaging Patterns
  - RESTful Services
  - GraphQL
  - WebSocket
  - gRPC
  - Summary of Synchronous Messaging Technologies
- Technologies to Implement Asynchronous Messaging Patterns
  - AMQP
  - Kafka
  - NATS
- Testing
- Security
- Observability and Monitoring
- DevOps
- Summary
3. Connectivity and Composition Patterns
- Connectivity Patterns
  - Service Connectivity Pattern
    - How it works
    - How its used in practice
    - Considerations
    - Related patterns
  - Service Abstraction Pattern
    - How it works
    - How its used in practice
      - Kubernetes services
    - Considerations
    - Related patterns
  - Service Registry and Discovery Pattern
    - How it works
    - How its used in practice
      - Service discovery in Kubernetes
    - Considerations
    - Related patterns
  - Resilient Connectivity Pattern
    - How it works
    - How its used in practice
      - Time-out
      - Retry
      - Deadlines
      - Circuit breaker
      - Fail-fast
    - Considerations
    - Related patterns
  - Sidecar Pattern
    - How it works
    - How its used in practice
      - Sidecar proxy
      - Sidecar bridge
    - Considerations
    - Related patterns
  - Service Mesh Pattern
    - How it works
    - How its used in practice
    - Considerations
    - Related patterns
  - Sidecarless Service Mesh Pattern
    - How it works
    - How its used in practice
      - Sidecarless gRPC services in Google Traffic Director
    - Considerations
    - Related patterns
  - Technologies for Implementing Service Connectivity Patterns
  - Summary of Connectivity Patterns
- Service Composition Patterns
  - Service Orchestration Pattern
    - How it works
    - How its used in practice
    - Considerations
    - Related patterns
  - Service Choreography Pattern
    - How it works
    - How its used in practice
    - Considerations
    - Related patterns
  - Saga Pattern
    - How it works
    - How its used in practice
    - Considerations
    - Related patterns
  - Technologies for Implementing Service Composition Patterns
  - Summary of Service Composition Patterns
- Summary
4. Data Management Patterns
- Data Architecture
- Types and Forms of Data
- Data Stores
  - Relational Databases
  - NoSQL Databases
  - Filesystem Storage
  - Data Store Summary
- Data Management
  - Centralized Data Management
  - Decentralized Data Management
  - Hybrid Data Management
  - Data Management Summary
- Data Composition Patterns
  - Data Service Pattern
    - How it works
    - How its used in practice
      - Allow multiple microservices to access the same data
      - Expose abstract legacy/proprietary data stores
    - Considerations
    - Related patterns
  - Composite Data Services Pattern
    - How it works
    - How its used in practice
    - Considerations
    - Related patterns
  - Client-Side Mashup Pattern
    - How it works
    - How its used in practice
      - Present critical data with low latency
      - Give a perception that the web page is loading faster
    - Considerations
    - Related patterns
  - Summary of Data Composition Patterns
- Data Scaling Patterns
  - Data Sharding Pattern
    - How it works
    - How its used in practice
      - Scale beyond a single node
      - Segregate data to improve data-retrieval time
      - Geographically distribute data
    - Considerations
    - Related patterns
  - Command and Query Responsibility Segregation Pattern
    - How it works
    - How its used in practice
      - Use different domain models for command and query
      - Distribute operations and reduce data contention
    - Considerations
    - Related patterns
  - Summary of Data Scaling Patterns
- Performance Optimization Patterns
  - Materialized View Pattern
    - How it works
    - How its used in practice
      - Improve data-retrieval efficiency
      - Provide access to nonsensitive data hosted in secure systems
    - Considerations
    - Related patterns
  - Data Locality Pattern
    - How it works
    - How its used in practice
      - Reduce latency when retrieving data
      - Reduce bandwidth usage when retrieving data
    - Considerations
    - Related patterns
  - Caching Pattern
    - How it works
    - How its used in practice
      - Improve time to retrieve data
      - Improve static content loading
      - Reduce data store contention
      - Prefetch data to improve data-retrieval time
      - Achieve high availability by relaxing the data store dependency
      - Cache more data than a single node can hold
    - Considerations
    - Related patterns
  - Static Content Hosting Pattern
    - How it works
    - How its used in practice
      - Provide faster static content delivery
      - Reduce resource utilization on rendering services
    - Considerations
    - Related patterns
  - Summary of Performance Optimization Patterns
- Reliability Patterns
  - Transaction Pattern
    - How it works
    - How its used in practice
      - Combine multiple operations as a single unit of work
      - Combine operations across multiple systems
    - Considerations
    - Related pattern
  - Summary of Transaction Reliability Pattern
- Security: Vault Key Pattern
  - How it works
    - How its used in practice
    - Considerations
    - Related pattern
  - Summary of the Vault Key Pattern
- Technologies for Implementing Data Management Patterns
  - Relational Database Management Systems
  - Apache Cassandra
  - Apache HBase
  - MongoDB
  - Redis
  - Amazon DynamoDB
  - Apache HDFS
  - Amazon S3
  - Azure Cosmos DB
  - Google Cloud Spanner
  - Summary of Technologies
- Testing
- Security
- Observability and Monitoring
- DevOps
- Summary
5. Event-Driven Architecture Patterns
- Event-Driven Architecture
  - Exactly Once Processing
  - Message Broker Categories
  - CloudEvents
  - Event Schema
- Event-Delivery Patterns
  - Producer-Consumer Pattern
    - How it works
    - How its used in practice
      - Provide asynchronous event delivery
      - Process each event by a single application
      - Ensure event delivery
      - Handle sudden bursts of events
      - Share workload with fairness
    - Considerations
    - Related patterns
  - Publisher-Subscriber Pattern
    - How it works
    - How its used in practice
      - Broadcast events
      - Deliver events with best effort
      - Make sure all events are delivered to all subscribers
      - Selectively deliver events to subscribers
      - Share workloads
    - Considerations
    - Related pattern
  - Fire and Forget Pattern
    - How it works
    - How its used in practice
      - Deliver events with best effort
      - Deliver events to systems that do not support subscription
    - Considerations
    - Related patterns
  - Store and Forward Pattern
    - How it works
    - How its used in practice
      - Deliver events to services that do not support subscription
      - Ensure event delivery during service unavailability
    - Considerations
    - Related patterns
  - Polling Pattern
    - How it works
    - How its used in practice
      - Retrieve results from a long-running process that cannot notify of job completion
      - Deliver events to a client that cannot initiate subscription or callbacks
    - Considerations
    - Related patterns
  - Request Callback Pattern
    - How it works
      - WebSockets
      - Webhooks
    - How its used in practice
      - Deliver response asynchronously
      - Deliver updates continuously
    - Considerations
    - Related patterns
  - Summary of Event-Delivery Patterns
- State Management Patterns
  - Event Sourcing Pattern
    - How it works
    - How its used in practice
      - Re-create application state
      - Build different domain models
      - Run temporal queries
      - Replay events
    - Considerations
    - Related patterns
  - Summary of State Management Pattern
- Orchestration Patterns
  - Mediator Pattern
    - How it works
    - How its used in practice
      - Sort and distribute events
      - Split events into multiple subevents
      - Ensure task execution order
    - Considerations
    - Related patterns
  - Pipe and Filter Pattern
    - How it works
    - How its used in practice
      - Build large-scale decoupled systems
      - Seamlessly add and remove functionality
      - Provide segregation of duties
    - Considerations
    - Related patterns
  - Priority Queue Pattern
    - How it works
    - How its used in practice
      - Process some events quicker than others
      - Optimize resource utilization
    - Considerations
    - Related patterns
  - Summary of Orchestration Patterns
- Technologies for Event-Driven Architecture
  - Apache ActiveMQ
  - RabbitMQ
  - Amazon SQS
  - Amazon SNS
  - Azure Event Grid
  - Azure Service Bus Queues
  - Google Cloud Pub/Sub
  - Summary of Message Broker Technologies
- Testing
- Security
- Observability and Monitoring
- DevOps
- Summary
6. Stream-Processing Patterns
- What Is a Stream?
- What Is Stream Processing?
- Streaming Data Processing Patterns
  - Transformation Pattern
    - How it works
    - How its used in practice
      - Message transformation
      - Protocol switching
    - Considerations
    - Related patterns
  - Filters and Thresholds Pattern
    - How it works
    - How its used in practice
      - Filter events by category
      - Scenario: Apply a threshold for alerting
    - Considerations
    - Related patterns
  - Windowed Aggregation Pattern
    - How it works
    - How its used in practice
      - Aggregate events over time
      - Aggregate events over length
    - Considerations
    - Related patterns
  - Stream Join Pattern
    - How it works
    - How its used in practice
      - Scatter and gather
      - Join various types of events
    - Considerations
    - Related patterns
  - Temporal Event Ordering Pattern
    - How it works
    - How its used in practice
      - Detect sequence of event occurrence
      - Detect nonoccurrence of event
    - Considerations
    - Related patterns
  - Machine Learner Pattern
    - How it works
      - Prebuilt machine learning models
      - Online machine learning models
    - How its used in practice
      - Predict based on prebuilt machine learning models
      - Continuous learning with data
    - Considerations
    - Related patterns
  - Summary of Streaming Data Processing Patterns
- Scaling and Performance Optimization Patterns
  - Sequential Convoy Pattern
    - How it works
    - How its used in practice
      - Scale stream-processing applications
      - Partition the stream processing
    - Considerations
    - Related patterns
  - Buffered Event Ordering Pattern
    - How it works
    - How its used in practice
      - Order events generated on distributed event sources
      - Reorder events generated from the same event sources
    - Considerations
    - Related patterns
  - Course Correction Pattern
    - How it works
    - How its used in practice
      - Update results with new information
      - Correct previous decisions
    - Considerations
    - Related patterns
  - Watermark Pattern
    - How it works
    - How its used in practice
      - Synchronize events generated from event sources that are time synchronized
      - Synchronize events generated from nonsynchronized sources
    - Considerations
    - Related patterns
  - Summary of Scaling and Performance Optimization Patterns
- Reliability Patterns
  - Replay Pattern
    - How it works
    - How its used in practice
      - Replay events when system state is not persisted
      - Replay events when the system persists its state
    - Considerations
    - Related patterns
  - Periodic Snapshot State Persistence Pattern
    - How it works
    - How its used in practice
    - Considerations
    - Related patterns
  - Two-Node Failover Pattern
    - How it works
    - How its used in practice
    - Considerations
    - Related patterns
  - Summary of Reliability Patterns
- Technologies
  - Esper
  - Siddhi
  - ksqlDB
  - Apache Spark
  - Apache Flink
  - Amazon Kinesis
  - Azure Stream Analytics
  - Google Dataflow
  - Summary of Stream-Processing Technologies
- Testing
- Security
- Observability and Monitoring
- DevOps
- Summary
7. API Management and Consumption Patterns
- API Management Patterns
  - API Gateway Pattern
    - How it works
      - API gateway
      - API management plane
      - API developer portal
    - How its used in practice
    - Considerations
    - Related patterns
  - API Microgateway Pattern
    - How it works
    - How its used in practice
    - Considerations
    - Related patterns
  - Service Mesh Sidecar as an API Gateway Pattern
    - How it works
    - How its used in practice
    - Considerations
    - Related patterns
  - Technologies for Implementing API Management Patterns
  - Summary of API Management Patterns
- API Consumption Patterns
  - Direct Frontend-to-Microservices Communication Pattern
    - How it works
    - How its used in practice
    - Considerations
    - Related patterns
  - Frontends Consuming Services Through API Gateway Pattern
    - How it works
    - How its used in practice
    - Considerations
    - Related patterns
  - Backend for Frontends Pattern
    - How it works
    - How its used in practice
    - Considerations
    - Related patterns
  - Summary of API Consumption Patterns
- Summary
8. Cloud Native Patterns in Practice
- Building an Online Retail System
  - Product Catalog
  - Order Management
  - Order Tracking and Prediction
  - Product Recommendations
  - Customer and Partner Management
- Building the High-Level Architecture
- Building External APIs
- Connecting Services
- Performing Data Management
- Using Event-Driven Architecture
- Using Stream Processing
- Implementing Dynamic Management in a Cloud Environment
- Summary
Index