Architecture Patterns with Python. Enabling Test-Driven Development, Domain-Driven Design, and Event-Driven Microservices (e-book) Zabrze

As Python continues to grow in popularity, projects are becoming larger and more complex. Many Python developers are taking an interest in high-level software design patterns such as hexagonal/clean architecture, event-driven architecture, and the strategic patterns prescribed by domain-driven design (DDD). But translating those patterns into Python isn...t always straightforward.With this hands-on guide, Harry Percival and Bob Gregory from MADE.com introduce proven architectural design patterns to help Python developers manage application complexity-and get the most value out of their test suites. Each pattern is illustrated with concrete examples in beautiful, idiomatic Python, avoiding some of the verbosity of Java and C# syntax. Patterns include:Dependency inversion and its links to ports and adapters (hexagonal/clean architecture)Domain-driven design...s distinction between Entities, Value Objects, and Aggregates Repository and Unit of Work patterns for persistent storageEvents, commands, and the message busCommand-query responsibility segregation (CQRS)Event-driven architecture and reactive microservices Spis treści: Preface Managing Complexity, Solving Business Problems Why Python? TDD, DDD, and Event-Driven Architecture Who Should Read This Book A Brief Overview of What Youll Learn Part I, Building an Architecture to Support Domain Modeling Part II, Event-Driven Architecture Addtional Content Example Code and Coding Along License Conventions Used in This Book OReilly Online Learning How to Contact OReilly Acknowledgments Introduction Why Do Our Designs Go Wrong? Encapsulation and Abstractions Layering The Dependency Inversion Principle A Place for All Our Business Logic: The Domain Model I. Building an Architecture to Support Domain Modeling 1. Domain Modeling What Is a Domain Model? Exploring the Domain Language Unit Testing Domain Models Dataclasses Are Great for Value Objects Value Objects and Entities Not Everything Has to Be an Object: A Domain Service Function Pythons Magic Methods Let Us Use Our Models with Idiomatic Python Exceptions Can Express Domain Concepts Too 2. Repository Pattern Persisting Our Domain Model Some Pseudocode: What Are We Going to Need? Applying the DIP to Data Access Reminder: Our Model The Normal ORM Way: Model Depends on ORM Inverting the Dependency: ORM Depends on Model Introducing the Repository Pattern The Repository in the Abstract What Is the Trade-Off? Building a Fake Repository for Tests Is Now Trivial! What Is a Port and What Is an Adapter, in Python? Wrap-Up 3. A Brief Interlude: On Coupling and Abstractions Abstracting State Aids Testability Choosing the Right Abstraction(s) Implementing Our Chosen Abstractions Testing Edge to Edge with Fakes and Dependency Injection Why Not Just Patch It Out? Wrap-Up 4. Our First Use Case: Flask API and Service Layer Connecting Our Application to the Real World A First End-to-End Test The Straightforward Implementation Error Conditions That Require Database Checks Introducing a Service Layer, and Using FakeRepository to Unit Test It A Typical Service Function Why Is Everything Called a Service? Putting Things in Folders to See Where It All Belongs Wrap-Up The DIP in Action 5. TDD in High Gear and Low Gear How Is Our Test Pyramid Looking? Should Domain Layer Tests Move to the Service Layer? On Deciding What Kind of Tests to Write High and Low Gear Fully Decoupling the Service-Layer Tests from the Domain Mitigation: Keep All Domain Dependencies in Fixture Functions Adding a Missing Service Carrying the Improvement Through to the E2E Tests Wrap-Up 6. Unit of Work Pattern The Unit of Work Collaborates with the Repository Test-Driving a UoW with Integration Tests Unit of Work and Its Context Manager The Real Unit of Work Uses SQLAlchemy Sessions Fake Unit of Work for Testing Using the UoW in the Service Layer Explicit Tests for Commit/Rollback Behavior Explicit Versus Implicit Commits Examples: Using UoW to Group Multiple Operations into an Atomic Unit Example 1: Reallocate Example 2: Change Batch Quantity Tidying Up the Integration Tests Wrap-Up 7. Aggregates and Consistency Boundaries Why Not Just Run Everything in a Spreadsheet? Invariants, Constraints, and Consistency Invariants, Concurrency, and Locks What Is an Aggregate? Choosing an Aggregate One Aggregate = One Repository What About Performance? Optimistic Concurrency with Version Numbers Implementation Options for Version Numbers Testing for Our Data Integrity Rules Enforcing Concurrency Rules by Using Database Transaction Isolation Levels Pessimistic Concurrency Control Example: SELECT FOR UPDATE Wrap-Up Part I Recap II. Event-Driven Architecture 8. Events and the Message Bus Avoiding Making a Mess First, Lets Avoid Making a Mess of Our Web Controllers And Lets Not Make a Mess of Our Model Either Or the Service Layer! Single Responsibility Principle All Aboard the Message Bus! The Model Records Events Events Are Simple Dataclasses The Model Raises Events The Message Bus Maps Events to Handlers Option 1: The Service Layer Takes Events from the Model and Puts Them on the Message Bus Option 2: The Service Layer Raises Its Own Events Option 3: The UoW Publishes Events to the Message Bus Wrap-Up 9. Going to Town on the Message Bus A New Requirement Leads Us to a New Architecture Imagining an Architecture Change: Everything Will Be an Event Handler Refactoring Service Functions to Message Handlers The Message Bus Now Collects Events from the UoW Our Tests Are All Written in Terms of Events Too A Temporary Ugly Hack: The Message Bus Has to Return Results Modifying Our API to Work with Events Implementing Our New Requirement Our New Event Test-Driving a New Handler Implementation A New Method on the Domain Model Optionally: Unit Testing Event Handlers in Isolation with a Fake Message Bus Wrap-Up What Have We Achieved? Why Have We Achieved? 10. Commands and Command Handler Commands and Events Differences in Exception Handling Discussion: Events, Commands, and Error Handling Recovering from Errors Synchronously Wrap-Up 11. Event-Driven Architecture: Using Events to Integrate Microservices Distributed Ball of Mud, and Thinking in Nouns Error Handling in Distributed Systems The Alternative: Temporal Decoupling Using Asynchronous Messaging Using a Redis Pub/Sub Channel for Integration Test-Driving It All Using an End-to-End Test Redis Is Another Thin Adapter Around Our Message Bus Our New Outgoing Event Internal Versus External Events Wrap-Up 12. Command-Query Responsibility Segregation (CQRS) Domain Models Are for Writing Most Users Arent Going to Buy Your Furniture Post/Redirect/Get and CQS Hold On to Your Lunch, Folks Testing CQRS Views Obvious Alternative 1: Using the Existing Repository Your Domain Model Is Not Optimized for Read Operations Obvious Alternative 2: Using the ORM SELECT N+1 and Other Performance Considerations Time to Completely Jump the Shark Updating a Read Model Table Using an Event Handler Changing Our Read Model Implementation Is Easy Wrap-Up 13. Dependency Injection (and Bootstrapping) Implicit Versus Explicit Dependencies Arent Explicit Dependencies Totally Weird and Java-y? Preparing Handlers: Manual DI with Closures and Partials An Alternative Using Classes A Bootstrap Script Message Bus Is Given Handlers at Runtime Using Bootstrap in Our Entrypoints Initializing DI in Our Tests Building an Adapter Properly: A Worked Example Define the Abstract and Concrete Implementations Make a Fake Version for Your Tests Figure Out How to Integration Test the Real Thing Wrap-Up Epilogue What Now? How Do I Get There from Here? Separating Entangled Responsibilities Identifying Aggregates and Bounded Contexts An Event-Driven Approach to Go to Microservices via Strangler Pattern Convincing Your Stakeholders to Try Something New Questions Our Tech Reviewers Asked That We Couldnt Work into Prose Footguns More Required Reading Wrap-Up A. Summary Diagram and Table B. A Template Project Structure Env Vars, 12-Factor, and Config, Inside and Outside Containers Config.py Docker-Compose and Containers Config Installing Your Source as a Package Dockerfile Tests Wrap-Up C. Swapping Out the Infrastructure: Do Everything with CSVs Implementing a Repository and Unit of Work for CSVs D. Repository and Unit of Work Patterns with Django Repository Pattern with Django Custom Methods on Django ORM Classes to Translate to/from Our Domain Model Unit of Work Pattern with Django API: Django Views Are Adapters Why Was This All So Hard? What to Do If You Already Have Django Steps Along the Way E. Validation What Is Validation, Anyway? Validating Syntax Postels Law and the Tolerant Reader Pattern Validating at the Edge Validating Semantics Validating Pragmatics Index