Parse, Don't Validate (2019)

From the Twitter link:

> IME, people in dynamic languages almost never program this way, though—they prefer to use validation and some form of shotgun parsing. My guess as to why? Writing that kind of code in dynamically-typed languages is often a lot more boilerplate than it is in statically-typed ones!

I feel that once you've got experience working in (usually functional) programming languages with strong static type checking, flakey dynamic code that relies on runtime checks and just being careful to avoid runtime errors makes your skin crawl, and you'll intuitively gravitate towards designs that takes advantage of strong static type checks.

When all you know is dynamic languages, the design guidance you get from strong static type checking is lost so there's more bad design paths you can go down. Patching up flakey code with ad-hoc runtime checks and debugging runtime errors becomes the norm because you just don't know any better and the type system isn't going to teach you.

More general advice would be "prefer strong static type checking over runtime checks" as it makes a lot of design and robustness problems go away.

Even if you can't use e.g. Haskell or OCaml in your daily work, a few weeks or just of few days of trying to learn them will open your eyes and make you a better coder elsewhere. Map/filter/reduce, immutable data structures, non-nullable types etc. have been in other languages for over 30 years before these ideas became more mainstream best practices for example (I'm still waiting for pattern matching + algebraic data types).

It's weird how long it's taking for people to rediscover why strong static types were a good idea.

This matches my personal experience, for sure. I started out writing Python (pre-type-annotations) and JS, with lots of “raw” dicts/objects. For the past ~7 years though, I’ve written mostly Scala, but also a decent amount of TypeScript, Go and Java, and it’s completely transformed how I code, dramatically for the better.

Now, even in the rare case where I write some Python, JS or PHP, I write it in a very statically typed style, immediately parsing input into well-thought-out domain classes. And for backend services, I almost always go with 3 layers of models:

1) Data Transfer Objects. Map directly to the wire format, e.g. JSON or Protobuf. Generally auto-generated from API specs, e.g. using Open API Generator or protoc. A good API spec + code gen handles most input validation well

2) Domain Objects. Hand written, purely internal to the backend service, faithfully represent the domain. The domain layer of my code works exclusively with these. Sometimes there’s a little more validation when transforming a DTO into a domain model

3) Data Access Objects. Basically a representation of DB tables. Generally auto-generated from DB schemas, e.g. using libs like Prisma for TS or SQLBoiler for Go

Can’t imagine going back to the “everything is a dictionary” style for any decent sized project, it becomes such a mess so quickly. This style is a little more work up front, when you first write the code, but WAYYYYYY easier to maintain over time, fewer bugs and easier to modify quickly and confidently, with no nasty coupling of your domain models to either DB or wire format concerns. And code gen for the DTO and DAO layers makes it barely more up-front work.