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Comment by dbaupp | Hacker News Reader

I don't know the details, but the memory difference was almost certainly due to the different approaches to concurrency. Python coroutines just need to save their exact stack frame while a Go goroutine will spawn a "massive" stack that likely has much more space than needed. The first case might only need tens or hundreds of bytes for a dozen local variables, while the latter case is a fixed overhead of several KB (8 previously, but 2 with Go 1.4 according to the slides).

The JIT memory is constant at runtime (proportional(-ish) to the amount of code, which is fixed) while it is desirable to have the number of coroutines be as large as possible.

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The memory used by the JIT will at some point remain constant but before that happens, it will grow as the JIT considers more and more traces to be worthy of compilation.

It can take a very long time until the memory consumed by the JIT actually remains constant. If you do continuous integration and deploy several times per day, your application might never reach that point.

The fact it may be less than the long term result isn't so relevant: there's an upper bound so the memory use of the JIT is O(1) with respect to the number of coroutines.

Your theory regarding PyPy's JIT is disproved rather easily. Python's grammar has been shown to not be context free. This implies that in the best case, the parser is supralinear in both time and space. Just the parser. Now add the rest of the JIT.

My hand-waving is "disproved" even more easily than that, by passing a dynamically generated string to `eval`. However, that's missing my point (and my "-ish"): the JIT is a constant overhead. Assuming they're being careful to not dynamically generate code for handling each coroutine (seems like an obvious thing to avoid, and a reasonable assumption), the JIT'd code is shared across all coroutines, so as you accept more and more connections, requiring less memory for each coroutine will eventually outweight the memory cost of the JIT.

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