Designing Event-Driven Systems pdf epub mobi txt 电子书 下载 2026

想要找书就要到 小美书屋

立刻按 ctrl+D收藏本页

你会得到大惊喜!!

Foreword
Preface
How to Read This Book
Acknowledgments
I. Setting the Stage
1. Introduction
2. The Origins of Streaming
3. Is Kafka What You Think It Is?
Kafka Is Like REST but Asynchronous?
Kafka Is Like a Service Bus?
Kafka Is Like a Database?
What Is Kafka Really? A Streaming Platform
4. Beyond Messaging: An Overview of the Kafka Broker
The Log: An Efficient Structure for Retaining and Distributing Messages
Linear Scalability
Segregating Load in Multiservice Ecosystems
Maintaining Strong Ordering Guarantees
Ensuring Messages Are Durable
Load-Balance Services and Make Them Highly Available
Compacted Topics
Long-Term Data Storage
Security
Summary
II. Designing Event-Driven Systems
5. Events: A Basis for Collaboration
Commands, Events, and Queries
Coupling and Message Brokers
Is Loose Coupling Always Good?
Essential Data Coupling Is Unavoidable
Using Events for Notification
Using Events to Provide State Transfer
Which Approach to Use
The Event Collaboration Pattern
Relationship with Stream Processing
Mixing Request- and Event-Driven Protocols
Summary
6. Processing Events with Stateful Functions
Making Services Stateful
The Event-Driven Approach
The Pure (Stateless) Streaming Approach
The Stateful Streaming Approach
The Practicalities of Being Stateful
Summary
7. Event Sourcing, CQRS, and Other Stateful Patterns
Event Sourcing, Command Sourcing, and CQRS in a Nutshell
Version Control for Your Data
Making Events the Source of Truth
Command Query Responsibility Segregation
Materialized Views
Polyglot Views
Whole Fact or Delta?
Implementing Event Sourcing and CQRS with Kafka
Build In-Process Views with Tables and State Stores in Kafka Streams
Writing Through a Database into a Kafka Topic with Kafka Connect
Writing Through a State Store to a Kafka Topic in Kafka Streams
Unlocking Legacy Systems with CDC
Query a Read-Optimized View Created in a Database
Memory Images/Prepopulated Caches
The Event-Sourced View
Summary
III. Rethinking Architecture at Company Scales
8. Sharing Data and Services Across an Organization
Encapsulation Isn’t Always Your Friend
The Data Dichotomy
What Happens to Systems as They Evolve?
The God Service Problem
The REST-to-ETL Problem
Make Data on the Outside a First-Class Citizen
Don’t Be Afraid to Evolve
Summary
9. Event Streams as a Shared Source of Truth
A Database Inside Out
Summary
10. Lean Data
If Messaging Remembers, Databases Don’t Have To
Take Only the Data You Need, Nothing More
Rebuilding Event-Sourced Views
Kafka Streams
Databases and Caches
Handling the Impracticalities of Data Movement
Automation and Schema Migration
The Data Divergence Problem
Summary
IV. Consistency, Concurrency, and Evolution
11. Consistency and Concurrency in Event-Driven Systems
Eventual Consistency
Timeliness
Collisions and Merging
The Single Writer Principle
Command Topic
Single Writer Per Transition
Atomicity with Transactions
Identity and Concurrency Control
Limitations
Summary
12. Transactions, but Not as We Know Them
The Duplicates Problem
Using the Transactions API to Remove Duplicates
Exactly Once Is Both Idempotence and Atomic Commit
How Kafka’s Transactions Work Under the Covers
Store State and Send Events Atomically
Do We Need Transactions? Can We Do All This with Idempotence?
What Can’t Transactions Do?
Making Use of Transactions in Your Services
Summary
13. Evolving Schemas and Data over Time
Using Schemas to Manage the Evolution of Data in Time
Handling Schema Change and Breaking Backward Compatibility
Collaborating over Schema Change
Handling Unreadable Messages
Deleting Data
Triggering Downstream Deletes
Segregating Public and Private Topics
Summary
V. Implementing Streaming Services with Kafka
14. Kafka Streams and KSQL
A Simple Email Service Built with Kafka Streams and KSQL
Windows, Joins, Tables, and State Stores
Summary
15. Building Streaming Services
An Order Validation Ecosystem
Join-Filter-Process
Event-Sourced Views in Kafka Streams
Collapsing CQRS with a Blocking Read
Scaling Concurrent Operations in Streaming Systems
Rekey to Join
Repartitioning and Staged Execution
Waiting for N Events
Reflecting on the Design
A More Holistic Streaming Ecosystem
Summary
· · · · · · (收起)