assert-combinators VS zod

We use in prod variant of no 1. [0]. Why? Because:

* it's extremely lightweight (built on pure, functional combinators)

* it allows us to use more complex patterns ie. convention where every json field ends with Json which is automatically parsed; which, unlike datatype alone, allows us to create composable query to fetch arbitrarily nested graphs and promoting single [$] key ie. to return list of emails as `string[]` not `{ email: string }[]` with `select email as [$] from Users` etc.

* has convenience combinators for things like constructing where clauses from monodb like queries

* all usual queries like CRUD, exists etc. and some more complex ie. insertIgnore, merge1n etc has convenient api

We resort to runtime type assertions [1] which works well for this and all other i/o; runtime type assertions are necessary for cases when your running service is incorrectly attached to old or future remote schema (there are other protections against it but still happens).

[0] https://github.com/appliedblockchain/tsql

[1] https://github.com/appliedblockchain/assert-combinators

We use not so much frameworks but combination of lightweight libraries:

- runtime assertions [0] - to map unknown values at i/o boundary into statically typed code (rpc input parameters, sql results etc)

- template based sql combinators to sanitize sql/generate sql [1]

- jsonrpc over websockets - for bidirectional comms between f/e and b/e

[0] https://github.com/appliedblockchain/assert-combinators

[1] https://github.com/appliedblockchain/tsql

Types can be asserted at runtime (parsed) at IO boundaries (reading http request or response, websocket message, parsing json file etc). Once they enter statically type system they don't need to be asserted again.

The difference it makes is illusion of type-safety vs type-safety this article touches on.

You can try to bind service with client somehow but in many cases this will fail in production as you can't guarantee paired versioning, due to normal situations by design of your architecture or temporary mid-deployment state or other team doing something they were not suppose to do etc. It's hard to avoid runtime parsing in general.

Functional combinators [0] or faster [1] with predicate/assert semantics work very well with typescript, which is very pleasant language to work with.

[0] https://github.com/appliedblockchain/assert-combinators

[1] https://github.com/preludejs/refute

Once i/o boundaries are parsing unknown types into static types, your type safety is guaranteed.

[0] https://github.com/appliedblockchain/assert-combinators

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This is a very fair comment, and you seem open to understanding why types are useful.

"problems that are due to typing" is a very difficult thing to unpack because types can mean _so_ many things.

Static types are absolutely useless (and, really, a net negative) if you're not using them well.

Types don't help if you don't spend the time modeling with the type system. You can use the type system to your advantage to prevent invalid states from being represented _at all_.

As an example, consider a music player that keeps track of the current song and the current position in the song.

If you model this naively you might do something like: https://gist.github.com/shepherdjerred/d0f57c99bfd69cf9eada4...

In the example above you _are_ using types. It might not be obvious that some of these issues can be solved with stronger types, that is, you might say that "You rarely see problems that are due to typing".

Here's an example where the type system can give you a lot more safety: https://gist.github.com/shepherdjerred/0976bc9d86f0a19a75757...

You'll notice that this kind of safety is pretty limited. If you're going to write a music app, you'll probably need API calls, local storage, URL routes, etc.

TypeScript's typechecking ends at the "boundaries" of the type system, e.g. it cannot automatically typecheck your fetch or localStorage calls return the correct types. If you're casting, you're bypassing the type systems and making it worthless. Runtime type checking libraries like Zod [0] can take care of this for you and are able to typecheck at the boundaries of your app so that the type system can work _extremely_ well.

[0]: https://zod.dev/ note: I mentioned Zod because I like it. There are _many_ similar libraries.

Zod is an open-source schema declaration and validation library that emphasizes TypeScript. It can refer to any data type, from simple to complex. Zod eliminates duplicative type declarations by inferring static TypeScript types and allows easy composition of complex data structures from simpler ones. It has no dependencies, is compatible with Node.js and modern browsers, and has a concise, chainable interface. Zod is lightweight (8kb when zipped), immutable, with methods returning new instances. It encourages parsing over validation and is not limited to TypeScript but works well with JavaScript as well.

Zod is a TypeScript-first schema declaration and validation library. It helps create schemas for any data type and is very developer-friendly. Zod has the functional approach of "parse, don't validate." It supports coercion in all primitive types.

Link: https://zod.dev/

You can check out their documentation here.

You can learn more about Zod on their website here.

In JS, it's a good idea anyway to use some JSON parsing library instead of JSON.parse.

With Zod, you can use z.bigint() parser. If you take the "parse any JSON" snippet https://zod.dev/?id=json-type and change z.number() to z.bigint(), it should do what you are looking for.

We will validate our input using client-side zod. Zod handles TypeScript-first schema validation with static type inference. This means that it will not only validate your fields, it will also set types on validated fields.