Elixir Technical Debt Cleanup

Bloated Contexts, Misused GenServers, and Tangled Supervision Trees

Elixir apps accumulate debt differently than OOP codebases. The most common issue: Phoenix contexts that started clean but became catch-all modules with 40+ functions. Accounts context handles authentication, authorization, user profiles, and billing. The boundary that was supposed to organize your code now obscures it.

GenServer misuse is another pattern. Not everything needs a process. We see stateless operations wrapped in GenServers, creating unnecessary serialization points. What should be concurrent becomes sequential because every request routes through a single process mailbox.

Mapping Domain Boundaries and Splitting Monolithic Contexts

We map your system’s actual domain boundaries using the codebase, not assumptions. Which functions call which? Where do data flows cross context lines? This analysis reveals the natural seams in your application.

Then we restructure. Large contexts split along domain boundaries. GenServers get evaluated - does this need a process, or is it a plain module? Ecto queries move from scattered inline calls to composable query modules. We do this incrementally, merging changes daily so there’s never a big-bang rewrite moment.

Faster Responses, Cleaner Hot Code Upgrades, and Focused Modules

Response times improve because we eliminate process bottlenecks. Your system uses the BEAM’s concurrency model correctly - processes for state and concurrency, plain functions for everything else.

Deployments become less stressful. Clean supervision trees mean hot code upgrades work reliably. Smaller, focused contexts mean a change to billing logic doesn’t require understanding the user authentication flow. Your team ships features faster because they can reason about the code in front of them.

Untangling Changeset Sprawl and Composing Ecto Query Pipelines

Data layer debt in Elixir applications often hides inside schema modules that double as query builders, validators, and business logic containers. We see schemas with ten or more changeset functions, each encoding a different set of business rules. The schema becomes the de facto service layer, and understanding what validations apply in which context requires reading hundreds of lines of changeset logic.

Our refactoring separates concerns. Schemas define fields and associations. Changeset functions are organized by use case - registration_changeset, admin_update_changeset - and live in the context module that owns that workflow. Complex queries are extracted into dedicated query modules that compose with Ecto.Query pipelines, making them reusable and testable in isolation. N+1 queries are identified through telemetry instrumentation and resolved with explicit preloads or batched data loaders.

We also address migration debt. Long-running migrations that lock tables are rewritten as concurrent index builds and batched updates. Missing indexes on frequently queried columns are added. Unused indexes that slow down writes are removed. The result is a data layer where the ORM is a thin mapping, not a hidden service layer.

Compile-Time Boundary Checks, Dialyzer Specs, and Architecture Records

We introduce boundary enforcement using tools like Boundary (the library) to define allowed dependencies between contexts at compile time. Cross-boundary calls that violate your architecture fail the build, not just code review.

Credo rules are configured for your project’s conventions. Dialyzer typespecs are added to public APIs so type errors surface during development. We document the architecture decisions - why contexts are split where they are, when to use a process versus a function, how to add new domains.