I ain't being smug. If I were, I might use fancy words like "discourse". But we're not playing cards here, are we?
JIT-ed code can be pretty fast, but so can calls to libraries like NumPy. PyPy can also be somewhat speedy, though it hasn't had as much dev time as v8. And if you somehow find yourself breaking new ground with an algorithm no one's written a fast implementation of yet, there's always Cython.
Besides, if there's any task that takes more than a negligible amount of time, I usually push it to a different process and respond immediately. The callback speed in that case is bound by the time it takes to write to a message queue or append to a database table, not the language itself.
JIT-ed code can be pretty fast, but so can calls to libraries like NumPy. PyPy can also be somewhat speedy, though it hasn't had as much dev time as v8. And if you somehow find yourself breaking new ground with an algorithm no one's written a fast implementation of yet, there's always Cython.
Besides, if there's any task that takes more than a negligible amount of time, I usually push it to a different process and respond immediately. The callback speed in that case is bound by the time it takes to write to a message queue or append to a database table, not the language itself.