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That was what I originally thought, as someone who grew up on static typing and then migrated to Python. But what I've discovered in practice is that, during t

That was what I originally thought, as someone who grew up on static typing and then migrated to Python. But what I've discovered in practice is that, during those early iterations, I don't really need the compiler to help me predict what will break, because it's already in my head. The more common problem is that static typing results in more breaks than there would be in the code that just uses heterogeneous maps and lists, because I've got to set up special types, constructors, etc. for different states of the data such as "an ID has/has not been assigned yet". So it kind of ends up being the best solution to a problem largely of its own making. I'm also working from the assumption here that one will go through and clean up code before putting it into production. That could be as simple as replacing dicts with dataclasses and adding type hints, but might also mean migrating modules to cython or Rust when it makes sense to do so. So you should still have good static type checking of code by the time it goes into production.

MrJohz 3y ago
As someone who has primarily gone in the other direction, my anecdata supports the opposite conclusion: static typing tends to help me to prototype over more dynamic languages, even if it takes (slightly) longer to physically write things down. I think this is because of two things: 1. If I'm prototyping things, I find I spend a lot of time trying to figure out what sorts of shapes the data in my program will have - what sort of states are allowed, what sort of cases are present, what data will always exist vs what data will be optional, etc. If I'm doing that in my head, I may as well write it down at the same time, and voila, types. So I'm not usually doing much extra works by adding types. 2. If I change my code, which I often do when prototyping (some name turns out to be wrong, some switch needs extra cases, some function needs more data), then that is much easier in typed languages than untyped ones. Many times my IDE can do the refactoring for me, and if that isn't possible, I can start making the change somewhere (e.g. in a type declaration) and just follow the red lines until I've made the change everywhere. One of the big results of this is that statically typed prototypes are often immediately ready to be developed into a product, whereas in dynamic languages, the prototype already bears so many scars from refactoring and the natural back-and-forth that comes from prototyping, that it needs to be rewritten over more from scratch. (The corollary to that being that I have never once had a chance to rewrite a prototype before releasing it to production.) I can imagine that some of this comes down to programming/architectural style. I tend to want to define my types up front, even in dynamic languages, because types and data are how I best understand the programs I work on. But if that's not how you work, the tradeoffs might not be the same. The other side is that the type systems I regularly use are almost exclusively modern ones, with decent support for things like product and sum types, so that I can use relatively simple types to model a lot of invariants.
Yoric 3y ago
> But what I've discovered in practice is that, during those early iterations, I don't really need the compiler to help me predict what will break, because it's already in my head. Reading this, I have the feeling that you're talking mostly of single-person (or at least small team) projects. Am I wrong? > The more common problem is that static typing results in more breaks than there would be in the code that just uses heterogeneous maps and lists, because I've got to set up special types, constructors, etc. for different states of the data such as "an ID has/has not been assigned yet". So it kind of ends up being the best solution to a problem largely of its own making. There is definitely truth to this. I feel that this is a tax I'm absolutely willing to pay for most of my projects, but for single-person highly experimental projects, I agree that it sometimes feels unnecessary. > I'm also working from the assumption here that one will go through and clean up code before putting it into production. That could be as simple as replacing dicts with dataclasses and adding type hints, but might also mean migrating modules to cython or Rust when it makes sense to do so. So you should still have good static type checking of code by the time it goes into production. Just to be sure that we're talking of the same thing: do we agree that dataclasses and type hints are just the first step towards actually using types correctly? Just as putting things in `struct` or `enum` in Rust are just the first step.
- mumblemumble 3y ago
  > I have the feeling that you're talking mostly of single-person (or at least small team) projects. Am I wrong? Small teams. And ones that work collaboratively, not ones that than carve the code up into bailiwicks so that they can mostly work in mini-silos. I frankly don't like to work any other way. Conway's Law all but mandates that large teams produce excess complexity, because they're basically unable to develop efficient internal communication patterns. (Geometric scaling is a heck of a thing.) And then additive bias means that we tend to deal with that problem by pulling even more complexity into our tooling and development methodologies. I used to believe that was just how it was, but now I'm getting to old for that crap. Better to push the whole "expensive communication" mess up to a macro scale where it belongs so that the day-to-day work can be easier.
  - 3y ago

MrJohz 3y ago

As someone who has primarily gone in the other direction, my anecdata supports the opposite conclusion: static typing tends to help me to prototype over more dynamic languages, even if it takes (slightly) longer to physically write things down. I think this is because of two things: 1. If I'm prototyping things, I find I spend a lot of time trying to figure out what sorts of shapes the data in my program will have - what sort of states are allowed, what sort of cases are present, what data will always exist vs what data will be optional, etc. If I'm doing that in my head, I may as well write it down at the same time, and voila, types. So I'm not usually doing much extra works by adding types. 2. If I change my code, which I often do when prototyping (some name turns out to be wrong, some switch needs extra cases, some function needs more data), then that is much easier in typed languages than untyped ones. Many times my IDE can do the refactoring for me, and if that isn't possible, I can start making the change somewhere (e.g. in a type declaration) and just follow the red lines until I've made the change everywhere. One of the big results of this is that statically typed prototypes are often immediately ready to be developed into a product, whereas in dynamic languages, the prototype already bears so many scars from refactoring and the natural back-and-forth that comes from prototyping, that it needs to be rewritten over more from scratch. (The corollary to that being that I have never once had a chance to rewrite a prototype before releasing it to production.) I can imagine that some of this comes down to programming/architectural style. I tend to want to define my types up front, even in dynamic languages, because types and data are how I best understand the programs I work on. But if that's not how you work, the tradeoffs might not be the same. The other side is that the type systems I regularly use are almost exclusively modern ones, with decent support for things like product and sum types, so that I can use relatively simple types to model a lot of invariants.

Yoric 3y ago

> But what I've discovered in practice is that, during those early iterations, I don't really need the compiler to help me predict what will break, because it's already in my head. Reading this, I have the feeling that you're talking mostly of single-person (or at least small team) projects. Am I wrong? > The more common problem is that static typing results in more breaks than there would be in the code that just uses heterogeneous maps and lists, because I've got to set up special types, constructors, etc. for different states of the data such as "an ID has/has not been assigned yet". So it kind of ends up being the best solution to a problem largely of its own making. There is definitely truth to this. I feel that this is a tax I'm absolutely willing to pay for most of my projects, but for single-person highly experimental projects, I agree that it sometimes feels unnecessary. > I'm also working from the assumption here that one will go through and clean up code before putting it into production. That could be as simple as replacing dicts with dataclasses and adding type hints, but might also mean migrating modules to cython or Rust when it makes sense to do so. So you should still have good static type checking of code by the time it goes into production. Just to be sure that we're talking of the same thing: do we agree that dataclasses and type hints are just the first step towards actually using types correctly? Just as putting things in `struct` or `enum` in Rust are just the first step.

mumblemumble 3y ago
> I have the feeling that you're talking mostly of single-person (or at least small team) projects. Am I wrong? Small teams. And ones that work collaboratively, not ones that than carve the code up into bailiwicks so that they can mostly work in mini-silos. I frankly don't like to work any other way. Conway's Law all but mandates that large teams produce excess complexity, because they're basically unable to develop efficient internal communication patterns. (Geometric scaling is a heck of a thing.) And then additive bias means that we tend to deal with that problem by pulling even more complexity into our tooling and development methodologies. I used to believe that was just how it was, but now I'm getting to old for that crap. Better to push the whole "expensive communication" mess up to a macro scale where it belongs so that the day-to-day work can be easier.
- 3y ago