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Comment by nomel | Hacker News Reader

By I/O limits, do you mean memory size limits? If so, this wording will lead to much confusion, since addressing limits (which is what a memory limit is) is a somewhat unusual use of "I/O limits" which, in a streaming context, most would perceive as a bandwidth limit (either memory or network).

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By I/O limits, I meant network bandwidth and disk throughput limits, not memory capacity.

Thanks for pointing out the ambiguity.

Now I'm more confused. An infinitely efficient system would saturate the network. An infinitely inefficient system would saturate the CPU. " The implementation saturates CPU before reaching I/O limits." is true infinitely inefficient system, but false for an infinitely efficient system. That means it's an undesirable.

The metric that actually matters is efficiency of the task, given a hardware constraint. In this context, that's entirely network throughput (streaming ability/hardware, with hardware being constant, you can just compare streaming ability directly).

For a litmus test of the concept, if you rewrote this in C or Rust, would the CPU bottleneck earlier or later? Would the network throughput be closer or further from its bottleneck?

You're right - this represents computational duress, not optimal efficiency. The 1 CPU struggles to handle the 50 concurrent user scenario and was chosen to demonstrate worst-case behavior rather than peak performance. I intended to stress test the framework. I did not mean to indicate that CPU saturation is ideal but rather highlight that performance remained predictable even at the limits.

Lower-level languages would certainly offer higher performance. I was hoping to showcase how Python can perform when architecture is restrained. The goal was to show that careful design choices (bounded memory, generator-based streaming) can maintain predictable behavior even when computational resources are exhausted.

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