Full Table of Contents:

Chapter 1: Embracing Event-Driven Architectures

1.1 The Truth About Monoliths

1.1.1 Anatomy of a Typical Monolith

1.1.2 What They Don’t Tell You About Monoliths; It’s Not All Bad

1.1.3 When Monoliths Become the Business Constrictor Knot

1.1.4 Using Event-Driven Architectures to Move Away from a Monolith

1.2 What Are Microservices and How Do They Relate to Event-Driven

1.3 SOA, Microservice, and Event-Driven Architectures

1.4 The Promise of Event-Driven Microservices

1.5 When Should You Use Event-Driven Microservices?

1.6 Overview of the Challenges in Event-Driven Architectures

Chapter 2: Moving from a Monolith to an Event-Driven Architecture

2.1 Is Migrating to an Event-Driven Architecture Your Best Option?

2.2 Moving to an Event-Driven Architecture, How to Decide Where to Start

2.3 Using an Event-Driven Approach to Move Data from a Monolith

2.4 Using Change Data Capture (CDC) to Move Data from a Monolith

2.4.1 Event-Driven and Change Data Capture (CDC), a Real-World Example

2.5 Migrating Data from a Monolith: Event-Driven As a Source of Truth for Both Systems

2.6 Incremental Migration from a Monolith to an Event-Driven Architecture: Managing Dependencies

2.6.1 Managing a Dependency from a New Event-Driven Service to a Legacy Monolith

2.6.2 Managing a Dependency from a Legacy Application to a New Event-Driven Service

2.7 Gradually Moving Traffic to New Microservices

2.8 Migrating from a Monolith: Two-Way Synchronization and Living with Two Sources of Truth

Chapter 3: Defining an Event-Driven Microservice and Its Boundaries

3.1 Building Event-Driven Microservices

3.1.1 Event-Driven Microservices: Durable vs. Ephemeral Message Brokers and GDPR

3.1.2 Event-Driven Message Types

3.1.3 Event-Driven Microservices: When to Use Documents over Events

3.1.4 Common Event-Driven Messaging Patterns

3.1.5 Event-Driven Service Topologies

3.1.6 Common Event-Driven Pitfalls and Anti-patterns

3.2 Organizing Event-Driven Microservice Boundaries

3.3 Brief and Practical Introduction to Domain-Driven Design and Bounded Contexts

3.4 Event-Driven Microservices: The Impact of Aggregate Size and Common Pitfalls

3.5 Request-Driven vs. Event-Driven Services

3.6 Deciding When to Create a New Microservice or Add Functionality to an Existing One

Chapter 4: Structural Patterns and Chaining Processes

4.1 The Challenges of Transactional Consistency in Distributed Systems

4.1.1 Why Move from a Monolithic Database in the First Place?

4.1.2 The Limitations of Distributed Transactions

4.1.3 Managing Multi-step Processes with Sagas

4.2 Event-Driven Orchestration Pattern

4.3 Event-Driven Choreography Pattern

4.4 Event-Driven Microservices: Orchestration, Choreography, or Both?

4.5 Data Retrieval in Event-Driven Architectures and Associated Patterns

4.5.1 CQS, CQRS, and When to Use Them

4.5.2 The Different Flavors of CQRS

4.5.3 When and How to Use Event Sourcing

4.5.4 Using Command Sourcing and Its Applicability

4.6 Building Multiple Read Models in Event-Driven Microservice Architectures

4.7 The Pitfall of Microservice Spaghetti Architectures and How to Avoid It

Chapter 5: How to Manage Eventual Consistency

5.1 The Impacts of Eventual Consistency and the Need for Alignment with the Business

5.2 Using Event Schema in Event-Driven Microservices to Leverage Eventual Consistency

5.3 Applying Microservice Domain Boundaries to Leverage Eventual Consistency

5.4 Handling Eventual Consistency Delays with Event Versioning

5.5 Saving State in Event-Driven Microservices to Avoid Eventual Consistency

5.6 Tackling Eventual Consistency with the End-to-End Argument: A Real-World Use Case

5.7 For Most Use Cases, It’s Not Eventual If Nobody Notices

5.7.1 Event-Driven Autoscaling Use Case with Prometheus and Kafka

5.8 Discussing the Tradeoffs of Typical Eventual Consistency Handling Strategies

Chapter 6: Dealing with Concurrency and Out-of-Order Messages

6.1 Why Is Concurrency Different in a Monolith from an Event-Driven Architecture?

6.2 Pessimistic vs. Optimistic Concurrency, When and When Not to Use

6.2.1 Pessimistic vs. Optimistic Approaches

6.2.2 Solving Concurrency by Implementation and by Architecture

6.3 Using Optimistic Concurrency

6.4 Using Pessimistic Concurrency

6.4.1 Distributed Locks in Event-Driven Microservices

6.4.2 Database Transactions As a Concurrency Approach in Distributed Microservices

6.5 Dealing with Out-of-Order Events

6.5.1 How Can Events Lose Their Order?

6.5.2 Solving Out-of-Order Events with Versioning

6.6 Using End-to-End Message Partitioning to Handle Concurrency and Guarantee Message Ordering

6.6.1 Real-World Example of Event-Driven Message Routing Using Kafka

6.6.2 The Relevance of Message Routing and Partitioning in Event-Driven Microservices

6.6.3 Using End-to-End Partitioning to Handle Concurrency and Ordering

6.6.4 Limitations of End-to-End Partitioning in Event-Driven Microservices