Powerful Why Reliability Demands Functional Programming

In banking, telecom, and payments, reliability is not a nice to have. It is table stakes. The most reliable systems I have worked on reduce entire classes of bugs before the code even runs. Functional programming and Algebraic Data Types (ADTs) let you push correctness into the type system, so illegal states cannot be constructed in the first place.

Most production incidents are not due to complex algorithms. They are due to the code entering a state that should never have been possible. If you have been on call, you have seen variants of these:

Functional programming helps by modeling the domain with types that make invalid states unrepresentable. Pure functions and immutability keep behavior predictable and testable.

Product types combine fields, think "and". Sum types choose one of several cases, think "or". Together they model your domain rules.

With this shape, "paypal" cannot exist as a Payment. The compiler refuses the value.

When you pattern match on a sum type, the compiler can force you to handle every variant. If you later add a new case, every non exhaustive match becomes a compilation error or warning. This is how refactors become safe by default.

Add a new Crypto method and both code bases will point out every place you must update.

Incident story A payout worker retries on network timeouts and calls settle() twice. The table allows pending = false and settled = true twice with the same ledger id. Reconciliation finds duplicates and accounting needs a manual fix.

Why it happened State is spread across booleans and strings. The database does not express the lifecycle. The application code does, but only by convention and tests.

Transitions become total functions. You can return a Result when a transition is not allowed.

Source: HackerNews