Asynchronous Python Development

Python GIL - Global Interpreter Lock

• Two threads cannot be run in one process at the same time
  • Each process in Python creates a key resource(GIL)
  • When a thread is running, it must acquire the resource
  • Since there is only one key resource per process, only one thread can run in a process at once

When to use concurrency

• IO bound
  • general rule of thumb is to use asyncio when you can, threading when you must.
  • threading is better for IO-bound tasks.
• CPU bound
  • CPU-bound problems only really gain from using multiprocessing.
  • threading and asyncio do not help this type of problem at all.
  • Multiprocessing is well-suited for CPU-bound tasks such as, tightly bound for loops and mathematical computations.

async IO

• async IO is a single-threaded, single-process design: it uses cooperative multitasking.
• async IO gives a feeling of concurrency despite using a single thread in a single process.
• Coroutines (a central feature of async IO) can be scheduled concurrently, but they are not inherently concurrent.
• asynchronous
  • Asynchronous routines are able to “pause” while waiting on their ultimate result and let other routines run in the meantime.
  • Asynchronous code facilitates concurrent execution.
• At the heart of async IO are coroutines.
  • A coroutine is a specialized version of a Python generator function.
  • a coroutine is a function that can suspend its execution before reaching return, and it can indirectly pass control to another coroutine for some time.