> The problem that I’ve just expressed ultimately occurs because languages like C force us to encode both kinds of assumption with a single assert statement: either all asserts are compiled in or none are.

It’s trivial to write a debug_assert macro in C, so no, you’re not forced to do that.

I remember someone arguing that disabling assertions in prod is like wearing a life jacket in the harbor but throwing it overboard going to sea. And Moore's Law paid for them years ago.

Swift calls these precondition() and assert(). Preconditions are enabled in release (and debug) builds, and asserts are enabled in debug builds only.

Asserts are convenient, but every time I encounter them I ask "Can the compiler prove this can't happen?"

In the example of "min(ages) > 0", making age a NonZero type renders the assert unnecessary. Rust even has some fancy perf optimizations it can do with that information. It's a win all around.

Is it common to use parenthesis for python assert statements?

Common Lisp does assertions right: you can let `assert' provide a restart which allows the user to fix a problem when it crops up. For example:

  (assert (< index (length some-vector))
    (index)
    "Can't change the element at index ~D, index must be smaller than ~D."
    index
    (length some-vector))

That will print a message when the index is too large and give you the option of providing another index.

Nim has this too:

  - `assert` is disabled in unsafe `danger` mode or can be disabled with a flag for performance
  - `doAssert` cannot be disabled

While I've never come across an argument for why there are two types of assert, over time I’ve naturally started using them in the same way as the author.

    > These days I thus view asserts as falling into two categories:
    >    1. Checking problem domain assumptions.
    >    2. Checking internal assumptions.

(1) is the category of assert that should not be an assert. That is an error to be handled, not asserted.

Ok, to be fair, (1) is really a combination of two categories: (1a) assumptions about uncontrolled external input, and (1b) assumptions about supposedly controlled or known input. Both should be handled with error checking most of the time, but it's forgivable for (1b) to be asserted if it's too inconvenient to do proper error handling. (1b) and (2) are problems that you need to fix, and the sooner and clearer the issue is announced, the more likely and easier it is to be fixed.

One thing I didn't see mentioned is that asserts, especially category (2), enable fuzz testing to be vastly more effective. A fuzz test doesn't have to stumble across something that causes a crash or a recognizably bad output; it just needs to trigger an assert. Which is another reason to not use asserts for unexpected input; fuzzers are supposed to give unexpected input, and your program is supposed to handle it reasonably gracefully. If you over-constrain the input, then first you'll be wrong because weirdness will sneak in anyway, and second the fuzzer is harder to implement correctly and is less powerful. The fuzzer is supposed to be a chaos monkey, and it works best if you allow it to be one.

"Using runtime assert() in a tight performance loop can impact performance?" Um, like ... duh?

Run time checks should simply be enabled. Normally, you're checking some context on entry or exit from a function. 99% of the time it simply won't matter to performance. And, when it does, it will pop out at you and you can remove it.

The bigger issue as alluded to is assert() in libraries. As a user, you can't add an assert() to a library. And also, as a user, you can't remove an assert() that is getting in your way.

Similarly python in optimised mode with -O or -OO flags will disable asserts.

I see asserts as a less temporary version of print() based debugging. Sometimes very useful as a quick and dirty fix, but 9 times out of 10 you’re better off with some combination of a real debugger, unit tests, tracing/logging, and better typing or validation.

The distinction between debug and release mode wrt my code (assertions, debug logging) was always confusing to me. The experience told me uncountable times that “release” (iow “prod”) is where you want debug information to be produced, cause that’s where your code meets full-blown reality. Otherwise your system silently breaks in production after few weeks, and two weeks later someone will ask you why and to repair it asap. Good luck investigating it without megabytes of the debug info. And don’t expect that you’ll get only definitive questions. In “who’s right” and disputes the question you receive will often have a great share of uncertainty.

Assertions are basically opposite to that. Useless in development because you watch/test closely anyway and useless in “release” because they vanish.

As I rarely write #CLK-level performance-required code - like most developers I believe - and mostly create systems that just do things rather than doing things in really tight loops many times a second, I always leave as much debug info and explicit failure modes as it is reasonable in my production code, so that the few-weeks failure would be immediate and explained in the logs (“reasonable” being readable without holding pgdn and not filling the ssd just after a week). It doesn’t mean that the system must crash hard as in sigsegv, ofc. It means it reports and logs all(!) steps and errors as they occur in a form that is obviously greppable by all important params (client id, task id, module tag, etc), and it stops a logical process in which an inconsistency occurred from advancing it further. If someone asks you later why it happened or what happened at the specific time with a specific process, you always have the answer almost immediately.

Tldr. Unless you write performance-first systems, i.e. you have performance requirements document in any form to follow, don’t turn off assertions and do log everything. You’ll thank yourself later.

One point I didn't see mentioned that asserts can be used by the compiler to enable certain optimisations.

For instance, if you assert that an incoming index into a function is within the bounds of a vector, then during the rest of the function the compiler can elide any bounds checking.

What about the fact that the assert causes a panic that crashes the program? Is there not a better alternative of doing your bounds checking as a first class aspect of the function and returning an error? If someone is deep into a session with your application and the cursor somehow drifts into an unknown state (which could be corrected with the “home” key or something else), for example, getting too crazy with assertions seems like a much worse result. (E.g. “assert!(is_on_screen())”)

I don't think I've ever in 30+ years of programming seen a runtime assertion that wouldn't be improved by doing something else instead. Writing unit tests and choosing designs that codify the constraints in the type system are two things that are obvious.

The cost of a failing assertion is often incurred at some point where it is most expensive. I know this because I've worked for companies that have charged exorbitant prices for my time to diagnose these sorts of failures.

The cost of a unit test is some learning and some time. LLMs are making the time portion drive towards zero.

As a general rule, I'd say avoid littering your code with assertions. It's a crappy engineering practice.

I agree with the others here that classic assert statements are in a weird spot. You enable it when testing, in other words when the data flowing through your program is likely to be constrained and lazy. And then turn it off when your program is about to meet real data (production). There's some wrinkle to this where you might want to log certain things in production instead of aborting with an assertion failure. But let's assume failing hard and fast is preferred for now.

Basically there are three cases.

1. The performance hit of the assertion checks are okay in production

2. It has a cost but can be lived with

3. Cannot be tolerated

For number two there is some space to play with.

What I've wanted is something more fine-grained. Not a global off/on switch but a way to turn on asserts for certain modules/classes/subsection, certain kinds of checks, and so on. It would also be nice to get some sort of data about the code coverage for assertions which have lived in a deployed program for months. You could then use that data to change the program (hopefully you can toggle these dynamically); maybe there is some often-hit assertion that has a noticeable impact which checks a code path that has been stable for months. Turning it off would not mean that you lose that experience wholesale if you have some way to store that data. I mean: imagining that there is some history tool that you can load the data about this code path into. You see the data travels through it and what range it uses. Then you have empirical data across however many runs (in various places) that indeed this assertion is never triggered. Which you can use to make a case about the likelihood of regression if you turn off that assertion.

That assumes that the code path is stable. You might need to enable the assertion again if the code path changes.

Just as a concrete example. You are working with some generated code in a language which doesn't support macros or some other, better way than code generation. You need to assert that the code is true to whatever it is modelling and doesn't fall out of sync. You could then enable some kind of reflection code that checks that every time the generated code interacts with the rest of the system. Then you eventually end up with twenty classes like that one and performance degrades. Well you could run these assertions until you have enough data to argue that the generated code is correct with a high level of certainty.

Then every time you need to regenerate and edit the code you would turn the assertions back on.

It's worth pointing out that Python's asserts can also be "compiled" away if you use the -O flag (or PYTHONOPTIMIZE=1), which eliminates their runtime cost.

It's also worth pointing out that this is the reason you should *never* put side-effects or security-relevant checks in assert statements. For example, you should never do something like this:

  assert f.read(4) == b"\x89PNG", "Not a PNG file"
  # proceed to read and parse the rest of the file

but rather, you should do

  magic = f.read(4)
  assert magic == b"\x89PNG", "Not a PNG file"

so that your code doesn't suddenly break when someone decides to be clever and use -O.

Also, fun unrelated fact: Python does have something like a preprocessor, although it's rarely used. If you condition on the flag __debug__:

  if __debug__:
    expensive_runtime_check()

and then run Python with -O, the if statement and its body will be entirely deleted from the bytecode - even the `if` check will be deleted. It can be used for including "debug" code in hot code, where the extra flag check itself might be expensive.

Swift has assert (removed when compiling w/ optimizations) and precondition (usually kept but can be removed w/ compiler option, and compiler might make assumptions depending on content of precondition).