Antipattern necessity
Can we always act virtuously?
I was thinking about what to write today, and as I reflected on the week, a particular project came to mind. One of the initiatives I'm currently following involves migrating a suite of service integration tests to the cloud as part of a broader microservices migration. The infrastructure is a complex, hybrid mix of cloud and on-premises systems, which is common in non-big-bang migrations.
Everything seemed to be on track until a discussion arose in a meeting about configuring Kafka topics across the on-prem and cloud environments. The decision was made to use a replication mechanism where services always publish to the "real" topic, while consumers retrieve messages from a "replica" topic.
This approach introduces a challenge: many services must now configure both the real and replica topics solely to enable testing. And here is where the debate began.
What Are Architectural Antipatterns?
At the core of this issue is a fundamental question: should production configurations (or even code) be altered for the sake of tests? This is a classic antipattern.
In software architecture, an antipattern is a seemingly reasonable solution that, over time, introduces significant long-term problems. Unlike bad practices, which are poor choices from the outset, antipatterns often stem from well-intentioned decisions that gradually degrade system quality.
Example: Changing Production Code to Allow Tests
Returning to the Kafka configuration case, modifying production settings to facilitate testing introduces several risks:
Increased complexity in production configurations, raising the likelihood of misconfigurations.
Unintended dependencies between test environments and production settings, reducing flexibility and increasing operational risks.
Higher maintenance burden, as teams must validate and manage multiple configuration paths.
A better approach would involve designing the system for testability from the outset, using techniques like dependency injection, feature toggles, or environment-specific overrides. This way, teams can ensure comprehensive test coverage without introducing risky production changes.
How Far Should We Go to Avoid Architectural Antipatterns?
Avoiding antipatterns is important, but rigidly rejecting anything that resembles one can lead to paralysis by analysis another form of inefficiency. Instead of focusing solely on avoidance, architects should prioritize pragmatic trade-offs.
1. Recognize Context and Constraints
Some antipatterns arise from real-world constraints such as tight deadlines, team expertise, or evolving business needs. For instance, a startup might allow minor code structure shortcuts to meet time-to-market goals, with the understanding that refactoring will follow. In contrast, a large enterprise with strict maintainability requirements may find the same decision introduces long-term risks.
Example Edge Case: A team working on a legacy system with poor test coverage might temporarily expose private methods to improve testability. While this could be justifiable as an interim step, making it a widespread practice would weaken system encapsulation over time.
2. Prioritize Long-Term Sustainability
Short-term workarounds can be tempting, but architects should advocate for decisions that minimize future refactoring costs. Strategies such as modular design, clear API boundaries, and automated testing help prevent antipatterns from spiraling out of control.
Example: Instead of modifying production code to accommodate tests, better alternatives include:
Applying dependency inversion to support test mocks.
Refactoring monolithic functions into smaller, testable components.
Using test doubles (mocks, stubs, fakes) rather than exposing implementation details.
A real-world failure occurred when multiple teams needed to test private logic in a shared library. To accommodate testing, they made private methods public, creating unintended dependencies that later obstructed refactoring. A better approach would have been using a well-defined interface with dependency injection to facilitate testing without compromising design principles.
3. Balance Theory and Practice
Architectural purity isn’t always practical. The key is early detection and incremental refactoring identifying when an antipattern is forming and addressing it before it becomes an unmanageable problem.
Example: A team working with a legacy codebase might need to introduce an antipattern as a temporary workaround. Instead of rejecting the change outright, they could document it with a clear plan for future refactoring.
Edge Case: In highly regulated industries like finance or healthcare, exposing production logic for tests could introduce security and compliance risks. In such cases, alternative approaches such as testing through APIs or improving testability through refactoring should be prioritized.
Finding the Right Balance
Architectural antipatterns emerge when seemingly good solutions introduce long-term complexity (The good old temporary fix). While avoiding them is important, blindly chasing architectural perfection can be just as harmful. A skilled architect understands when to accept imperfection in service of progress and when to invest in long-term maintainability. Making pragmatic trade-offs, considering business constraints, and planning for refactoring are key to sustainable software architecture.
Articles I enjoyed this week
4 API Design Mistakes You Must Avoid (Or Risk Total Chaos!), 6 Essential Kafka Concepts You Need to Master, Forward vs Reverse Proxies Explained in 7 Easy Steps! - Sketech #17 by Nina
Nice read, Riccardo!
Nice article Riccardo.
I'd say anti-patterns should be acceptable in unavoidable circumstances (deadlines, cost, etc.) as long as there is a long-term fix that's planned and accounted for (the right solution, so to speak). Otherwise, using anti-patterns can become a habit that keeps making things worse.