- Foreword
- Preface
- I. Setting the Stage
- 1. Introduction
- 2. The Origins of Streaming
- 3. Is Kafka What You Think It Is?
- 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
- 6. Processing Events with Stateful Functions
- 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
- 9. Event Streams as a Shared Source of Truth
- 10. Lean Data
- IV. Consistency, Concurrency, and Evolution
- 11. Consistency and Concurrency in Event-Driven Systems
- 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
- V. Implementing Streaming Services with Kafka
- 14. Kafka Streams and KSQL
- 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
Part V. Implementing Streaming Services with Kafka
A reactive system does not compute or perform a function, it maintains a certain ongoing relationship with its environment.
David Harel and Amir Pnueli, “On the Development of Reactive Systems,” 1985
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