I'm curious what you mean by "data-level programming."
For example, JSON describes a logical structure of nested lists and dictionaries. If you were doing data-level programming, you would just map the JSON into actual nested lists of dictionaries and get on about your business.
The alternative, which is more common in static languages like Java, is to transform it all into some set of domain model objects, and probably validate it up-front, too. Even the bits you don't actually need to look at in order to accomplish the job at hand. IMO, that approach tends to mean creating a lot of unnecessary work for oneself. It also makes it harder to obey Postel's law.
(The corollary to that last bit is that it is also possible for static typing to create bugs.)
One way is to treat the JSON as a generic JSON structure, and traverse it manually. Of course, you will have to be explicit about what should happen when children are of different types from what you expect, though this explicitness could just be throwing an exception or ignoring it. Haskell's Aeson and Rust's serde_json both support this, as does .NET's JsonElement type.
Unfortunately, this means you're passing around a lot of objects called something like "JSON" without any information about what they contain at the type level, and as an alternative between that approach and creating domain objects, there are row polymorphic records, which allow you to write functions that accept any record that has certain fields, and also specify that they may also contain other fields which you do not handle. This allows you to program to what you know about the types you're ingesting without having to write a lot of new types.
A personal example of this was Httpd used to accept standard headers with spaces instead of dashes, this leads to strange behavior if you accidentally include both. So they decided to stop doing that in a major version. This major version was opaquely included by ops accidentally into our base images. This lead to a very long day of debugging on our end.
Point is, by being liberal with what you accept you create ambiguity, which you may not totally understand at the time. By putting that out into the wild you basically are forced to keep this ambiguous, undocumented spec alive or you no doubt will end up breaking some client.
I'm talking about situations where the JSON is formatted fine, it's just that some field wasn't specified, so then the entire input gets rejected. Even though there was zero need to read the contents of that field in the first place. It just happened to be included in some domain object that gets re-used everywhere, including some other places where the field's contents do matter.
Keep in mind that, when we're dealing with anything that might be transmitted in JSON, thinking that there might be a published spec, and that it manages to accurately cover all these details, is really optimistic. I've honestly never seen it happen in the wild. Oftentimes, any validation rules you might try to impose are guesswork as much as they are anything else. So complaining that a piece of data didn't conform to the spec might not even be a valid thing to do. All you can say for sure is that the data didn't meet the needs of some piece of business logic.
It's not perfect, but it's life. This tension, for example, is at the heart of why proto2 got replaced with proto3, and why using proto3 is strongly encouraged if you're looking to build a robust infrastructure.
Beyond that I think you’re operating from a misconception about JSON parsing in static languages. There’s no requirement to convert to domain objects and reject data that doesn’t fit on a triviality, you’re just required to specify explicitly what happens when you encounter unexpected structure or data.
Whereas the ergonomics of popular dynamic languages tend to favor an approach that I find, for this specific purpose, to be both less verbose and more robust.
> For example, suppose we have JSON that represents a set of metric data (this isn't our real JSON, this is just a thought experiment) that should look like this, with "tags" being optional attribute: { "id": "1", "timestamp":"12:30pm", "value":"999", "tags": [ "myapp" ] }
> Suppose a python client sends tags but calls the attribute "tag" rather than "tags" (its missing the "s"). Its an optional attribute, so the server won't consider it an error if the "tags" attribute is missing. But it also won't fail due to this unknown attribute called "tag" - it will just silently ignore it now. The Python developer is wondering why his tags aren't being stored - he is getting no errors but they are just silently being ignored. He would need to figure out he is sending in the wrong attribute name, with no error messages to help him out.
> That's the use-case I'm asking about - the "silent error" that will occur due to malformed JSON messages.
They're orthogonal concerns. C is statically and weakly typed. Clojure is dynamically and strongly typed. PHP is dynamically and weakly typed. Haskell is statically and strongly typed. Java, as the most design-by-committe language ever, manages to be a mix of all four.
I'd consider Python to be more strongly typed than JavaScript. It doesn't do quite so many automatic conversions. For example, in Python, `1 + "foo"` is a TypeError. In JavaScript, it's "1foo". Sadly, `1 == True` in Python, so it certainly doesn't get full marks.
I think a more interesting question is typecasts, like happens in languages like Java and C#. These languages are nominally statically typed, but they retains some type information at run-time, so that you can perform run-time type conversions, which requires run-time type checking. Which is the defining feature of dynamic typing.
C# is a little bit more straightforward about being a hybrid static/dynamic language, with its reified generics and dynamic references. But teasing out the details of where, how, and the extent to which Java is statically or dynamically typed would make a decent topic for a master's thesis.
It also hints at a deeper thing that one must be mindful of: static/dynamic and strong/weak are not binary categories. They're not even the extremes of two binary scales. They are somewhat vague descriptions that are meant to serve as useful shorthands for certain sets of choices that one must make when designing a language's type discipline.
But the fact that they're not cut-and-dry terms does not mean that they're meaningless. It just means that one must disabuse oneself of the notion that they're cut-and-dry before one can have a conversation about type discipline that goes beyond a certain level of detail.
Java is a statically typed language with late binding implemented through subtype polymorphism and its type system has been explored pretty extensively in the literature.
{-# LANGUAGE MultiParamTypeClasses, TypeSynonymInstances, FlexibleInstances #-}
import Prelude (String, (++), show, Int, (==))
import qualified Prelude
class Add x y where
(+) :: x -> y -> y
instance Add Int String where
(+) x y = show x ++ y
instance Add Int Int where
(+) x y = x Prelude.+ y
instance Add String String where
(+) x y = x ++ y
a = ((1 :: Int) + (1 :: Int)) == 2
b = ((1 :: Int) + "aa") == "1aa"
c = ("a" + "aa") == "aaa"Static typing — types checked at compile time. Dynamic typing — types checked at runtime.