Microservices

System Design Workbook – Edição 2026 é uma obra que traduz, organiza e conecta os principais fundamentos da engenharia de sistemas distribuídos sob uma perspectiva prática, moderna e orientada à realidade de produção.

The “perfect architecture” is a myth. The adaptable enterprise wins. Learn how to design evolutionary systems, quantify technical debt, and align engineering with business ROI.

Go services often fail in production for boring reasons: weak probe semantics, missing timeout budgets, unsafe retries, noisy telemetry, and rollout settings that look fine until traffic spikes. Go Apps on Kubernetes is a practical pocketbook for engineers who ship and run Go microservices on K8s. It gives you concise, production-focused guidance you can apply immediately: health/readiness contracts, graceful shutdown and draining, retry/backoff patterns, observability defaults (metrics, logs, traces), security hardening baselines, and deployment/autoscaling templates. No theory dump, no platform-admin detours. Just reusable patterns and incident-oriented checklists to help your services start cleanly, degrade predictably, and recover fast.

Rapid-development frameworks across Ruby on Rails, Node.js, PHP, Python, and .NET revolutionized how quickly we can ship software. But what happens when your business domain grows complex? Relying on Active Record and heavy ORM patterns tightly couples your database to your business logic. As your product matures, this coupling creates brittle, difficult-to-maintain codebases that resist change and slow down your team. The solution is the Domain Model Pattern (the foundation of Domain-Driven Design). It isolates business rules from infrastructure, creating a pure, scalable architecture. Historically, however, this pattern required a massive amount of effort and boilerplate, making it too slow for fast-paced development. That changes today. We are at an AI inflection point. Modern AI coding assistants excel at generating boilerplate and wiring infrastructure, effectively eliminating the traditional drawbacks of DDD. AI finally bridges the gap between the rapid speed of framework scaffolding and the architectural purity of a Domain Model. But to harness AI reliably, you cannot simply let it generate code unguided. You need a structured methodology to guide the machine. In this book, you will learn:How to build reliable, maintainable software using a strict, AI-assisted pipeline: Use Cases → Gherkin → Event Storming → Strategic & Tactical DDD.How to create implementation-agnostic class diagrams that serve as universal blueprints, allowing you to generate code in OOP languages (Java, C#, PHP, Python) or Functional languages (Elixir, Clojure, F#).How to future-proof your systems with a timeless methodology that prioritizes the longevity of business logic over the ephemeral nature of web frameworks.How to scale your architecture gracefully from a Majestic Monolith to Microservices without a complete rewrite. Whether you want to accelerate your development velocity, improve your system's resilience, or simply reclaim your craft by building beautiful, majestic systems, this book provides the blueprint. Discover how to combine the timeless principles of DDD with the power of AI. Read the free sample today.

From containers and Kubernetes to autoscaling, observability, Prometheus, Grafana, SLOs, security, and production failure. Volume III shows how distributed architecture becomes an operable, measurable, evidence-driven production system.

Cross the process boundary with a systems-level guide to concurrency, async I/O, IPC, TCP, TLS, HTTP/2, gRPC, retries, queues, consistency, observability, and the real cost of distribution.

Go beneath C# and .NET—from CPU, kernel, processes and threads to IL, JIT, native code, stacks, heaps and GC. Build the runtime mental model you need before deciding where, why, and whether a system should be distributed.

Migrating a stateless application to Google Cloud , think of it like moving to a new apartment: you need a plan, the right tools, and a clear understanding of where everything goes. "Migrating Stateless Workloads to Google Cloud: A Practical Guide for the Modern Developer" breaks down this process into 12 manageable steps, from assessing your current setup to post-cutover cleanup

Migrating stateful workloads to the cloud is like moving a fish tank—every detail matters. Unlike stateless applications, which can be redeployed with ease, stateful workloads demand careful handling to preserve data integrity, minimize downtime, and ensure a smooth transition.this guideis your practical guide to navigating this complex process.

Microservices aren't built with boxes on a diagram—they're built with owned truth, stable contracts, idempotent messages, and controlled failure.

Build financial systems that stay correct when requests repeat, events arrive late, and services fail. Learn practical patterns for money modeling, double-entry ledgers, idempotency, reconciliation, auditability, security, and invariant-driven testing—so every balance and transaction can be explained and trusted.

In this book, we are building a complete working server, and again we rebuild the whole thing on a different framework. It's about testing out a new approach. If the architecture is honest, the second version costs almost nothing and the business logic never moves. We ran the test, and I let you watch the result rather than describing it.

Stop overload from becoming unbounded queues, goroutines, and downstream calls. Build bounded backpressure that fails predictably.

It's a practical book. We're going to build something real together, a University API, and we're going to grow it chapter by chapter until it looks like a system you'd be proud to deploy. We'll start with a few endpoints, give it databases, make it concurrent, lock it down with authentication, wrap it in containers, and finally split it into cooperating services that talk over the network.

Another Production Field Manual: protect APIs before overload becomes an outage with rate limiting, throttling, and tenant fairness.