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Static typing is a useful constraint, but it's not the only constraint. Focusing too much on dynamic vs. static typing can make one miss the more general problem: we want code that's expressive enough to do what we want, while being constrained enough to not do what we don't.
Immutability, for example, is another great constraint that's not considered in the article, but should certainly be on your mind if you're deciding between, say, Rust and Java.
The article delves into some of the drawbacks of static typing, in that while it can be more expressive, it can also contain a lot of information that's useful for the compiler but decidedly less useful for a reader. The Rust example that loads a SQL resultset into a collection of structs is a standard problem with dealing with data that's coming from outside of your static type system.
The author's solution to this is the classic one: we just need a Sufficiently Smart Compiler™. Now, don't me wrong; compilers have gotten a lot better, and Rust is the poster child of what a good compiler can accomplish. But it feels optimistic to believe that a future compiler will entirely solve the current drawbacks of static typing.
I was also slightly surprised when templates were suggested. Surely if you're aiming for rigor and correctness, you want to be dealing with properly typed data structures.
I have typically understood the "Sufficiently Smart Compiler" to be one that can arrive at the platonic performance ideal of some procedure, regardless of how the steps in that procedure are actually expressed (as long as they are technically correct). This is probably impossible.
What I'm proposing is quite a bit more reasonable—so reasonable that versions of it exist in various ecosystems. I just think they can be better and am essentially thinking out loud about how I'd like that to work.
> we want code that's expressive enough to do what we want, while being constrained enough to not do what we don't
I don't think that's an ideal mental model. Code in any (useful) language can do what you want, and can not do what you don't want. The question is how far that code is from code that breaks those properties -- using a distance measure that takes into account likelihood of a given defect being written by a coder, passing code review, being missed in testing, etc. (Which is a key point -- the distance metric changes with your quality processes! The ideal language for a person writing on their own with maybe some unit testing is not the same as for a team with rigorous quality processes.) Static typing is not about making correct code better, it's about making incorrect code more likely to be detected earlier in the process (by you, not your customers).
I was being glib, so let me expand on what I said a little.
By 'constraint' I mean something the language disallows or at least discourages. Constraints in software development are generally intended to eliminate certain classes of errors. Static typing, immutability, variable scoping, automatic memory management and encapsulation are all examples of constraints, and represent control that the language takes away from the developer (or at least hides behind 'unsafe' APIs).
By 'expressiveness' I mean a rough measurement of how concisely a language can implement functionality. I'm not talking code golf here; I mean more the size of the AST than the actual number of bytes in the source files.
Adding constraints to a language does not necessarily reduce its overall expressiveness, but static typing is one of those constraints that typically does have a negative effect on language expressiveness. Some will argue that static typing is worth it regardless, or that this isn't an inherent problem with static typing, but one that stems from inadequate compilers.
Something that the type system should do is "make impossible states impossible" as Evan Czaplicki said (maybe others too)
We have started to use typed HTML templates in Ruby using Sorbet. It definitely prevents some production bugs (our old HAML templates would have `nil` errors when first going into production).
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> we want code that's expressive enough to do what we want, while being constrained enough to not do what we don't.
Have you somehow solved the halting problem? AFAIK, all Turing complete languages are perfectly capable of expressing the exact same programs.
The price for not making a Turing Complete language is that you can't solve all possible problems. But, you probably didn't want to solve all possible problems.
That's one of the insights in WUFFS. Yes, most problems cannot be solved with WUFFS, but, we often don't want to solve those problems so that's fine. WUFFS code, even written by an incompetent noob, categorically does not have most of the notorious problems from systems languages, yet in the hands of an expert it's as fast or faster. It has a very limited purpose, but... why aren't we making more of these special purpose languages with their excellent safety and performance, rather than building so many Swiss Army Chainsaw languages which are more dangerous but slower ?
DSLs serve an important purpose but the entire thread is about general purpose Turing complete languages. DSLs fail very quickly as soon as you need to leave the domain which can easily result in needing many many DSLs for a given project which has other forms of complexity and sources of bugs that easily arise from such an approach (and that’s assuming you can just cobble together DSLs), not the least of which that proprietary DSLs and languages quickly become difficult to hire for and maintain long term. And DSLs more specifically suffer from the domains they can solve. WUFFs is a notable exception that proves the rule just because format parsing is very well studied and has very sharp edges on the things you need to support / accomplish.
There's a difference 'theoretically possible' and 'practically feasible'. Assume I'm talking about the latter.
A program does not tell the machine what to do. It tells the next programmer what we wanted the machine to do.
What I say is that the types exist in your code whether you write them down or not.
When you don’t write them down, you’re saying, “trust me, this works.” But now the types are written down in your head (a very volatile form of memory that can’t be accessed easily by others).
One time when dynamic works well is when “prove this works” is simply not worth the time. Eg. a quick script.
> you’re saying, “trust me, this works.” But now the types are written down in your head
Which is only even true during the initial phases of a project. The moment the initial team shares bus numbers with other developers, the 'you' becomes a collective you, and you have three people who all believe they have it written down in their head, and often at least one is wrong.
You can make a project that relies on memorization. It will eventually fail, in one sense of the term or another. Or you can make it so that you can verify things you think you used to know about the project cheaply. But then you need to remember to check your assumptions when doing new work.
> whether you write them down or not.
Note that "types have been written down" != "the program was written with static typing". See, for example, what's doable with clojure.spec.
While loose, "from the hip" code (w.r.t. typing) often correlates with the use of dynamically typed languages, we shouldn't make the mistake of assuming a total causality there.
(Also, I still find the number of people willing to put e.g. `object` parameters in their public C# APIs concerningly high. Not that one has to deal with such APIs all the time, but they're not exactly eradicated, either.)
Dynamic, strongly typed code is a quadrant that has confused a substantial number of conversations about how to handle typing.
A fundamental limitation is that static analysis enforces guarantees when a compiler can see and understand all the code at the same time.
It's great when you compile large amounts of code written in the same language into a single binary.
It's not so great for calls between languages or for servers communicating across a network. To get static guarantees back again, you often need to validate your inputs, for example using something like Zod for TypeScript. And then it's not a static guarantee anymore; it's a runtime error.
Database tables often live on a different server than the server-side processes that access them, so mix-and-match between different schema versions that the compiler never saw together is possible in most systems.
To prevent this, you would need some kind of monolithic release process. That runs into lifecycle issues, since data is often much longer-lived than the code that accesses it.
> you often need to validate your inputs, for example using something like Zod for TypeScript. And then it's not a static guarantee anymore; it's a runtime error.
True, but validating at the boundaries and having a safe core is much better than having the unsafe portion everywhere imo.
It depends on the system. Some servers just don't do very much. If a server validates the input and then just sends it on without doing any calculations, there's very little to go wrong that static analysis can warn you about.
And then the next server in line has to validate the data again.
Most languages have a way to represent a blob of bytes that you don’t care about the internal shape or meaning of. The point of parsing is to validate the stuff that you want to use. To use the Zod example from up-thread, you can use z.unknown() or use z.looseObject() if you care about some keys but not others (while wanting to propagate the whole object).
Yes you have to validate untrusted input at runtime. Seems a bit odd to call that a fundamental limitation of static types.
Another way to put it is that in some programs, there's relatively little for static analysis to do because the program isn't doing much internal calculation, but there are a lot of potential I/O errors that it won't be able to catch. Programming embedded devices is often like that.
Parse, don't validate. Alexis King's article by that title should be fundamental reading for all distributed application developers. Go directly from on-the-wire representation to data structure with a well-defined type, and error if you can't. Use the type system to describe all representable objects. While this results in runtime errors on parse failure on the receiver's side, an advantage is that if the sender uses those same type definitions, you can guarantee that it will always send valid data.
Schemas should be derived from the type of the representation used in the application code, or vice-versa. If there are multiple schemas in use, there should be multiple corresponding types, perhaps with ways to interconvert between them. Schema changes will be less frequent, and easier to manage, with a bit of upfront systems analysis and design, including use of a data dictionary or complete IRM repository.
The more time you invest in thinking and planning what you want to build ahead of time, the fewer bugs and changes you'll have to worry about at runtime. The payoff is almost always more than worth the investment; "we don't have time to do things the right way" implies you have plenty of time to do things the wrong way.
Very good summary of why static types are so useful, and when they're not so much...
BTW, Java has a framework called Manifold that provides type-safe SQL just like the author wants, with IDE support and everything:
https://github.com/manifold-systems/manifold/blob/master/man...
HTML with interpolation is not a good idea.
You really should want a template engine that knows if the variable it's injecting goes into a tag as text or into an attribute, etc.
The problem with the webstack is that these things are easily hard, or comes to you by using an ORM, or DSLs in a generic language.
Personal I think that things like JSX or: https://www.gomponents.com/ Aren't that bad, as they are basically modeling HTML.
JSX doesn't have to mean react, you could render server side into plain html.
Tests are how one constrains dynamically typed programs to make sure they work as expected. I have found that adding tests to untyped python code is far better at exposing bugs than adding typing is. I like my IDE to do completion too but I tend to add typing where I think it helps most rather than everywhere.
It just seems that some people like static types and good for them. Dynamic languages are for those of us who are, perhaps, lazy and want to be free to think about how to solve the problem at a slightly higher level without having to get into details.
For me the details activate my perfectionism and that blocks me from advancing with thinking about the goal and the effort of creating an elegant set of types makes me less willing to suddenly throw everything away when I realise there's a better approach. Dynamic languages give me that ability to "sketch" until I have an answer and at that point I have to ask: do I really need this to be in a higher performance language? Once or twice the answer has been yes but working out the approach in a dynamic language allowed me to do it right in the static language first time.
Type checks are proofs across all inputs. Tests are proofs across a given set of inputs. They are complimentary. If you want really robust code, use both.
As a general rule of thumb, once you have proper tests, static typing doesn't find very much, if anything.
complementary
Agree on the importance of testing. Among the production-grade codebases I've worked on, I've found that the dynamically-typed ones have more comprehensive testing. It's just so easy to think that a successful compile means your code will work. I've also found that it's harder to set up great test systems for static languages because you often have to modify your logic just to make it testable (looking at you, IoC). A delightful test system is one that engineers will use during development because it saves time, not an afterthought. For whatever reason, I haven't ever found something that provides this type of experience out of the box. In one organization, we spent months building a rig and achieved true TDD. The result was fewer production issues, faster development, and of course, better test coverage numbers. We eventually switched from javascript to typescript, but it didn't compare to the difference that test system made.
Sure but nobody ever writes tests that are as comprehensive at checking the basic types of stuff as simply adding static type hints (which is very easy if you start with them).
How many times have you seen a NoneType error or a KeyError? It's probably the most common class of bugs in Python and there's an easy way to eliminate it. And as a nice side effect your code is way easier to understand, navigate and edit.
I still sometimes have to give up and use Any in Python, partly because half the Python community haven't realised their mistake yet. But that's fine. It's still way better.
Very much enjoyed the article, it aligns with a lot of my own thoughts on coding :P.
nitpick - in the Rust example it seems the events are not passed into rendering the events.html template.
https://unplannedobsolescence.com/blog/what-dynamic-typing-i...
Just fixed it. Nice catch, thank you!
I agree, but I also thing that static analysis is a requirement for high quality. The obvious solution is (global) type inference, but I have yet to see a language that embraces it.
Do you really want type inference to be global? The idea that changing one line in a function might change the type of a whole other function — and thus variables assigned to its result, which affect the types of other functions, which ... — well it just seems like a lot to have to reason about.
Yes, with the caveat that I'm assuming generics here. I'm working on a language where you can have:
def add(a, b):
return a + b
add(1, 2) # Works
add('a', 'b') # Works
add(1, 'b') # Type error!
There are practical limits on global type inference; eg, sometimes writing Haskell youll be not sure what’s going on with some code that won’t typecheck, and the way to deal is break it down piece be piece, add type annotations, until you see where your mental model is failing.
This only gets harder with more type inference, as the actual issue may be far away from where the type error occurs.
This is why in haskell it is considered best practice to add type annotations to all top level definitions, at the very least.
Hindley - Milner dates from the 70s, but the only language to fully embrace it is ML.
What about Miranda, Haskell, OCaml and F#?
Have you seen OCaml? Though its inference stops at module boundaries (but you don't need to annotate them) and for some more special type features. In addition it solves some type inference problems by having e.g. different operators on integers and floats, there are no type-directed traits.
But in practice it's quite handy to annotate module interfaces and even individual functions.
I hear that OCaml without interface files can cause spooky-action-at-a-distance type errors[1]. Have you had experience with that?
> But in practice it's quite handy to annotate module interfaces and even individual functions.
Yes, but it also limits what information the types can represent because of syntax and human patience limitations. A fully inferred language could associate numbers with possible value ranges, lists with their sizes, enumeration of fixed string values, etc.
> spooky-action-at-a-distance type errors
Global type inference is literally this. Inferring types leaves gaps in the typing information, being global means the gaps can be arbitrarily large, and so a type conflict could theoretically span every line of a large program.
I agree with the author. DSLs are often more reasonable, readable, and authentic than weirdly ported versions for general purpose languages. On the other hand, language servers checking type mismatches and providing suggestions are very useful. I too hope they will become good enough so we can use the same capacity for the DSL in a general purpose language like CSS/SQL in JavaScript. They are some, but not enough.
I find statically typed code harder to read and maintain on average because interfaces tend to be more complex on average. With dynamically typed languages, there is more pressure to keep the function signatures as simple as possible as developers try to work around their mental limitations of remembering all the types.
IMO, the value of simple interfaces is enormous and yields substantial benefits in terms of separation of concerns and modularization. The benefits of such architecture make arguments around static vs dynamic types pointless.
Once you know how to architect code properly and test appropriately, it makes almost no difference whether the languages is statically typed or dynamically typed... A lot of the constraints of static typing become annoyances.
The most infuriating line I hear from proponents of dynamically typed languages is how statically typed languages make refactoring easier. It's infuriating because I know exactly what they mean but it's incorrect. Having coded with statically typed languages, I'm familiar with the feeling of refactoring code and "the friendly compiler" guiding me and reminding me of all the places were I forgot to update the code when I changed one of the types... But unfortunately, the very fact that refactorings often require updating code in so many places is a testament to poor architecture. I don't have this problem when refactoring dynamically typed codebases. On average, the code is more modular. With dynamically typed languages, people don't try to design types that cut across the entire codebase. Oftentimes, many 'refactorings' in static typed land are just type modifications which wouldn't even be a problem in the first place if a dynamically typed language was used.
Just read this article and it vibes with my personal journey. I've built and maintained many web applications for more than 20 years. PHP, Ruby, Python, Java, Dotnet, Go, Node, Bun etc.
The most maintainable codebase is the one that requires the least guessing from the developers perspective. Where everyone that looks at the code immediately gets a good understanding of what is going on. When it comes to this I personally had the best experience with Go so far as it is verbose when it comes to error handling (I don't need to guess what happens or what can go wrong etc.). Is it the most elegant? No, but I don't need to build a big context window in my brain to understand what is going on immediately (looking at you Rails callbacks).
Anyways I found the article interesting and as I am also using SQLC I think it is something that goes in that direction.
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Nitpick: SQL is a programming language. But for most CRUD tasks you should rely less on SQL's programming capabilities until processing information is too expensive outside the SQL server/engine. The advice is also for maintainability.
I'm thinking... couldn't a zig library infer the result columns at compile time with comptime and return a proper type to it?
> Unsurprisingly, the equivalent Rust code is much more explicit.
Okay, but you can do the same in dynamically typed Python, while still using familiar exception logic and not requiring the type annotation on `req` (although of course you can still use one):
def authenticate(req):
match req.cookies.get("token"):
case None:
raise AuthenticationFailure("Token not included in request")
case cookie_token:
pass
match req.db.get_user_by_token(cookie_token):
case None:
raise AuthenticationFailure("Could not find user for token")
case user:
return user
def authenticate(req):
cookie_token = req.cookies.get("token")
if cookie_token is None:
raise AuthenticationFailure("Token not included in request")
user = req.db.get_user_by_token(cookie_token)
if user is None:
raise AuthenticationFailure("Could not find user for token")
return user
The nice thing about the Rust syntax shown is that constructs like `let` and `match` allow for a bit of type inference, so you aren't declaring manifest-typed temporaries like you'd have to in many other languages.
> It's possible to write sloppier Rust than this, but the baseline is quite a bit higher.
The flip side: the baseline for Python might be low, but that's deliberate. Because there are common idioms and expectations: dictionaries have both a `.get` method and key-indexing syntax for a reason (and user-defined types are free to emulate that approach). So indeed we could rewrite again:
def authenticate(req):
try:
cookie_token = req.cookies.get("token")
except KeyError:
raise AuthenticationFailure("Token not included in request")
user = req.db.get_user_by_token(cookie_token)
if user is None:
raise AuthenticationFailure("Could not find user for token")
return user
You can always add more checks. The Zen says "explicit is better than implicit", but I would add that "implicit is better than redundant".
> In essence, dynamically-typed languages help you write the least amount of server code possible, leaning heavily on the DSLs that define web programming while validating small amounts of server code via means other than static type checking.
Well, no; it's not because of access to the DSLs. It's because (as seen later) you aren't expected to worry about declaring types for the interfaces between the DSLs and the main code. (Interfaces that, as correctly pointed out, could fail at runtime anyway, if e.g. an underlying SQL database's column types can't be checked against the code's declared data structures at compile time.)
The main thing that personally bothers me with static typing is that, well, sometimes the type calculus is difficult. When your types don't check, it's still on you to figure out whether that's because you supplied the wrong thing, or because you had the wrong specification of what to supply. And understanding the resulting compile-time error message (which is fundamentally written in terms of what the type is) is typically harder than understanding a run-time dynamic type error (which can usually be understood in terms of what the object does).
> Okay, but you can do the same in dynamically typed Python
But the rust code is still safer, e.g. you haven't checked for an `AttributeError` in case `req.cookies`, the point is Rust protects you from this rabbit-hole, if you're prepared to pay the price of wrestling the compiler.
Walrus operator: if (cookie_token := req.cookies.get("token")) is None: raise ...
I really, really like this blogger’s philosophy of coding.
Pulling everything into the type system does lead to madness.
Luckily i'm just the mad scientist to recreate a non-async rust web stack, here's the sqlite bit if you're interested.
Well... Yeah but then you lose all the advantages of static typing that he rightly acknowledges!
TSX in particular is a terrible example to make the point. It's very very similar to HTML so there is barely anything more to learn, and in return you get type checking, auto complete, go-to-definition, etc. Its so good I use it for completely static sites.
The SQL example is more compelling, but again it's really hard to paint "SQL makes static typing a bit awkward" as "dynamic typing is great here!". Like, you shouldn't be super happy about walking across a desert because your car can't drive on sand. "Look how simple legs are, and they work on sand".
That said, the SQL example does suck. I'm surprised nobody has made something like sqlx that generates the Rust types and does all the mapping for you from your schema. That's clearly the way to go IMO.
> Like, you shouldn't be super happy about walking across a desert because your car can't drive on sand. "Look how simple legs are, and they work on sand".
I didn't say I was happy about it—I just said I needed to get across the desert!
Kysley and others give you the types in TS. I believe you can write SQL in Linq C# but it's been ages since I've done it. If rust truly doesn't have that yet.. I guess they're missing out.
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nice article !
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The thing I think most arch enemies of dynamic typing miss is the underlying economics of writing software.
~98% of software gets thrown on the trash heap before it reaches the point where it really needs to be maintained.
That last 2% of gold dust desperately needs to be maintained well and to work, but to get to the point where you have that asset you'll probably find you need to throw away an awful lot of code first.
If you write that 98% using static typing it will slow you right down - potentially to the point where you dont even get to the gold dust.
If you write that 2% with dynamic typing it will fall apart. A lot of programmers see this process without ever appreciating the fast prototyping that got it there in the first place.
So, this is why Im a fan of gradual typing.
I find static types actually speed up writing new code, since that's when I'm most likely to make typos in fields -- just today I had to fix some goofs where I used snake_cased fields when it was expecting camelCase. LLMs love types, and any hallucinations are instantly red-lined. Agreed on gradual typing, but I'd rather approach it from a strict language with an opt-out dynamic type like TS's 'unknown' rather than a duck-typed language with type "hints" the runtime has to always check anyway.
Structural subtyping usually hits the sweet spot for me: I can just create random structures on the fly, the type system makes sure I use them consistently, and my IDE helps me extract a named type out of them after the fact with just a few keystrokes.
I like common lisp, where sbcl will catch the worst type errors while compiling, but you can also specify types and they will speed up your code.
I'm the same. When writing python I write some lines, run and test, write some more, run and test... with Rust I bash out lines of code for an hour then have the compiler and Clippy help me fix it all afterward.
Can the addition of type hints to the 2% be largely automated?
or just use primitive types - maps, arrays & you skip this noise. if a map key is empty then it's empty.
maybe people should watch Rich Hickey's Value of Values.
I agree strong typing is a necessity in the front end. To prevent the language explosion issue, I recommend writing your single page apps in Java. The Flavour framework makes this quick and easy with complete maven tooling and powerful components.
Flavour book: https://frequal.com/Flavour/book.html
Flavour home page: https://flavour.sourceforge.io/
Example app: https://frequal.com/wordii
The article isn’t about strong typing. It’s about static vs dynamic typing. For example, Python is strongly and dynamically typed.
Personally, I’d rather stop writing software than write everything in Java, and writing software is just about my favorite thing in the world. I don’t contend that Java is a bad language. However, it’s almost diametrically opposed to how I think about programming logic, and its style and conventions set my teeth on edge. I’m sure Java programmers would say the same about my preferences, too, and that’s OK! I’m not saying that my opinions are objectively right. But I am saying that no one would be willing to pay me what it would take to get me to write Java for a living.
You can use the Java ecosystem with a number of other languages that are completely different paradigms, like Scala or Clojure, or you can use Kotlin as a "better Java"
Sure, that would be my strategy if I were forced to. But honestly, I can’t imagine a likely scenario where a JVM language would be better for the kinds of things I do than a natively compiled language like Rust.
Crafted by Rajat
Source Code