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Author:   Hugo Filipe Oliveira Rocha
Publisher:   APress
Imprint:   APress
Edition:   1st ed.
Weight:   0.891kg
ISBN:  

9781484274675

ISBN 10:   1484274679
Pages:   449
Publication Date:   11 November 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

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. 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 1.7. Summary 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. How to decide where to start 2.3. Using an event-driven approach to migrate data 2.4. Using change data capture (CDC) 2.4.1. Event-driven and change data capture (CDC), a real-world example 2.5. Event-driven as a source of truth for both systems 2.6. Managing dependencies between the two systems 2.6.1. Dependency from new event-driven services to the monolith 2.6.2. Dependency from the monolith to new event-driven services 2.7. Gradually moving traffic 2.8. Two-way synchronization and living with two sources of truth 2.9. Summary Chapter 3. Defining an event-driven microservice and its boundaries 3.1. Building event-driven microservices 3.1.1. Durable vs. ephemeral message brokers and GDPR 3.1.2. Message types 3.1.3. 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. The impact of aggregate size and common pitfalls 3.5. Request-driven vs. event-driven services 3.6. Adding functionality to an existing microservice vs. creating a new one 3.7. Summary Chapter 4. Event-driven structural patterns and high-level processes 4.1. The challenges of transactional consistency in distributed systems 4.1.1. Why abandon 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. 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 4.7. The pitfall of microservice spaghetti architectures and how to avoid it 4.8. Summary 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 to leverage eventual consistency 5.3. Applying domain boundaries to leverage eventual consistency 5.4. Event versioning to manage delays 5.5. Saving state to avoid eventual consistency 5.6. 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. Autoscaling use case with Prometheus and Kafka 5.8. Tradeoffs of each solution 5.9. Summary Chapter 6. Dealing with event-driven 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 6.4.2. Database transactions 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. The relevance of message routing and partitioning 6.6.2. Real-world example of message routing using Kafka 6.6.3. Using end-to-end partitioning 6.6.4. Limitations of end-to-end partitioning 6.7. Summary Chapter 7. Achieving resilience and event processing reliability in event-driven microservices 7.1. Common failures in microservice architectures and how they relate to event-driven architectures 7.1.1. Cascading failures and event-driven services 7.1.2. Load balancing and rate limiters in event-driven services 7.2. Understanding message delivery semantics 7.3. Avoiding inconsistencies when saving state and publishing events 7.3.1. Event stream as the only source of truth 7.3.2. Outbox pattern 7.3.3. Transactions and compensating actions 7.4. Applying ACID 2.0 as a resilience strategy 7.5. Avoiding message leak 7.6. Applying common resilience patterns 7.6.1. Retries 7.6.2. Circuit breakers 7.7. Recovering data and repairing state 7.8. Bulkhead pattern 7.9. Summary Chapter 8. Choosing the correct event schema design 8.1. Event storming 8.2. Event headers and envelopes 8.2.1. Headers vs envelopes 8.2.2. Relevant event contextual information 8.3. Town crier events 8.4. Bee events 8.5. The event schema goldilocks principle 8.6. Denormalized event schema 8.7. Schema evolution 8.7.1. Event stream versioning 8.7.2. Using a downscaler/upscaler 8.8. Summary Chapter 9. How to leverage the user interface 9.1. Using an aggregating layer 9.2. Backends for frontends 9.3. UI Decomposition 9.3.1. Application decomposition 9.3.2. Page decomposition 9.3.3. Section decomposition 9.4. The limitations of API composition 9.5. Task-based UIs 9.6. Event-driven APIs 9.7. Summary Chapter 10. Overcoming the challenges in quality assurance 10.1. The only happens in production syndrome 10.2. Component tests vs integration tests 10.3. The correct mix of component validation and production validations 10.4. Monitoring and alarmistic from the ground up 10.5. Summary Chapter 11. Organizational cost of event-driven microservices 11.1. The epic journey to be onboarded 11.2. When implementation overhead impacts time to market 11.3. Dependencies management 11.4. Summary

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Hugo Rocha has nearly a decade of experience working with highly distributed event-driven microservices architectures. He currently is an engineering lead for the leading global ecommerce platform for luxury products (Farfetch), providing services to millions of active users, backed by an event-driven architecture with hundreds of microservices processing hundreds of changes per second. Before that, he worked for several reference telecommunications companies that transitioned from monolithic applications to microservice-oriented architectures. Hugo has managed several teams that directly face the caveats of event-driven architectures every day. He designed solutions for critical pieces of the platform’s highly distributed backoffice platform, handling hundreds of changes per second, concurrently, scalably, and with high performance.

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