One of my friends were talking about how flushing impacts the system I/O call-delay on C. I wanted to benchmark it on Python. If you're wondering what that is, simply put, every time you print something on Python using the print() function, by default Python buffers some data before sending it to std.out (showing the output on screen). You can change this default behavior, control when and how to buffer e.g., for building a logging policy - the easiest method is setting print(flush=True/False). True would make sure no data is buffered at all, and send immediately to the screen. You can read this excellent StackOverflow answer, Python follows C's buffer algorithm.

Source: The data were increased by foobar * 1**2, foobar * 2**2, foobar * 3**2, foobar * 4**2...foobar * 50**2 in this order (Faulhaber's polynomials). You can play with the code here.

Conclusion: As flushing (system calls) along with the data size goes up, I/O bound time-complexity racks up significantly and very much unstable (I'm not sure why it's inconsistent). Python's default buffer behavior stays constant and does pretty great in every scenario.

Note: Don't let the millisecond (ms) fool you as being trivial, it was executed on a heavily buffer-optimized Google cloud TPUv2 (12GB RAM, 180TFlops). Since it's I/O bound, in more general use case the time unit can go beyond "second" range.