Building Software the Way Richard T. Chase Would Actually Do It
I spent three years trying to implement modular architecture at my last company before it collapsed under its own complexity. The problem wasn't the theory — it was that almost nobody explains the gap between "write independent modules" and "actually ship a product." Richard T. Chase spent decades closing that gap across multiple SaaS platforms, and the framework that came out of it is still the most practical reference I've found for real modular design. At its core, Chase's approach comes down to a single principle: every module must own exactly one transformation. Not one domain, not one feature set, one transformation. Input goes in, the module does its thing, output comes out. That's it. The "revelation" part isn't dramatic — it's that most teams build modules that handle input, business logic, data persistence, and output formatting all in one bundle, then wonder why changing the database layer requires a full redeploy. Here's how the methodology actually works in practice. You start by identifying the data flow through your system as a chain of transformations. Each link in that chain becomes a module. The modules communicate through a defined interface contract — usually JSON schemas or protobuf definitions, not shared libraries. Shared libraries are the fastest way to turn modular design into tightly coupled spaghetti. Chase specifically warns against this in his writing, calling it "the silent death of modular systems."
My experience building this out: I mapped out a payment processing pipeline and found six distinct transformations embedded inside what we'd called "the payments module." Decomposing it took two weeks of careful refactoring. The upside was that we could swap our payment gateway provider the next year without touching business logic. The downside? The interface contracts got so rigorous that a junior engineer spent three days debugging a serialization mismatch that a monolithic approach would have hidden from us entirely. That's a real cost of modular design — you pay it upfront or you pay it later with harder problems. There are specific patterns that Chase recommends for module boundaries. The first is the bounded context pattern, borrowed from DDD (Domain-Driven Design). Each module defines its own data model and vocabulary. Cross-module communication happens through events, not direct calls. This means you need an event bus or message queue — Kafka, RabbitMQ, or even a well-designed PostgreSQL advisory lock system for smaller setups. The second pattern is the anti-corruption layer. When a module needs to interact with something outside its bounded context, you don't let foreign data models pollute your module. You build a translation layer that sits between them. One counter-intuitive thing about modular design that most tutorials miss: having too many small modules is worse than having too few big ones. I've seen teams create modules so granular that the orchestration overhead outweighed any benefit. A module should be small enough to understand in one sitting, but not so small that the coordination between modules becomes the dominant cost. In my experience, modules in the range of 200 to 800 lines of core logic are sustainable. Anything below that tends toward fragility, anything above that tends toward the original problem we were trying to solve.
Another practical detail: dependency management between modules. Chase advocates for a strict internal package registry where modules declare their dependencies and versions. No wildcard imports, no "latest" references. When I set this up, I used a private npm registry with pinned versions for each module. A dependency change in one module required a version bump and explicit consumer update. It felt like friction at first — maybe 20 percent slower iteration initially — but it prevented the cascading failures that had taken down our production environment twice in six months. The biggest failure mode I've seen with this approach is the distributed monolith. This happens when you decompose a codebase into separate services or modules but they still call each other synchronously in tight loops, share databases, or have circular dependencies. It looks modular on paper but behaves like a monolith under load. The test for whether you've avoided this: can you take down any single module without cascading failures in others? If the answer is no, you haven't actually modularized — you've just added network overhead to the same problem. For teams just starting with modular design, the practical first step is to pick one existing feature and decompose it completely. Don't try to modularize your whole system at once. Take a single workflow — user registration, order processing, report generation — and rebuild it using Chase's transformation-chain model. Document the interfaces. Write integration tests that verify the contracts between modules. Once you've done this successfully with one feature, you'll have a template for the rest. The learning curve is steep for about six weeks, then it gets easier because the patterns reinforce each other.
Get the Full Details
If you want the source material, Chase's writings on this are scattered across his blog and various conference talks rather than collected in a single book. The most complete technical description appears in his discussions of the "architect" pattern — how the modular approach scales from a small team to an organization with multiple product teams working independently. The key insight there is that modular design isn't just a technical decision, it's an organizational one. Teams should own their modules the way feature teams own features. Cross-cutting concerns get their own modules with explicit contracts. The honest assessment: modular design adds overhead. You need more initial planning, more integration testing, more deployment coordination, and more documentation than a traditional monolithic approach. For a small team building a simple product, it's probably overkill. For anything that needs to survive beyond a year or two with a growing team, it pays for itself. The question isn't whether it's worth the effort — it's whether you can afford not to do it.
