Welcome to the World of Asynchronous Programming in Python!
As developers, we often encounter scenarios where waiting for certain tasks, such as input/output operations, can lead to inefficiencies in our programs.
Quick one: Think of such situations, if any, comment, let's have a discussion
Asynchronous programming is needed in multiple situations such as: I/O-Bound Operations, Web Development, Concurrency in Networking, GUI Applications, Real-Time Applications, Long-Running Tasks.
In this article, we'll explore the shift from traditional synchronous programming to the asynchronous paradigm in Python, uncovering powerful concepts and tools that enhance the efficiency and responsiveness of our applications.
Here are brief explanations of a list of important keywords related to asynchronous programming in Python:
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Synchronous (Classic Sequential) Programming
In synchronous programming, tasks run one after the other in a linear fashion. Each task must finish before the next one begins, leading to inefficiencies, especially in scenarios involving input/output operations.
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Asynchronous Programming
Asynchronous code allows a program to signal that it will need to wait for certain tasks, such as input/output (I/O) operations, to complete. Instead of idly waiting, the program can continue executing other tasks. Common I/O-bound operations include network communication, file read/write, remote API calls, and database operations.
Concurrency, not Multiprocessing or Multithreading:
Asynchronous programming involves running one task at a time, not multithreading or multiprocessing. During the wait time of one function, another function can be executed. -
Global Interpreter Lock (GIL)
The GIL in Python ensures that only one thread holds control of the interpreter at a time. While not impactful in single-threaded programs, it can be a bottleneck in CPU-bound and multi-threaded code.
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CPython
CPython is the canonical implementation of the Python programming language, distributed on python.org. It's essential to distinguish CPython from other implementations like Jython or IronPython.
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Process
A process is a separate instance of the Python interpreter, managed by the operating system and represented by a
multiprocessing.Process
object. -
Thread
A thread is an entity within a process that can be scheduled for execution. Threads are smaller units of processing and are managed by the operating system.
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Thread Pool
A thread pool is a collection of threads created in advance and reused to execute multiple tasks. Python's
concurrent.futures.ThreadPoolExecutor
simplifies thread pool management. -
Threading
The
concurrent.futures.ThreadPoolExecutor
offers a higher-level interface for background thread execution.queue
provides a thread-safe way to exchange data between threads, andasyncio
offers task-level concurrency without the need for multiple OS threads. -
Multithreading
Multithreading allows a processor to execute multiple threads concurrently, achieved through context switching.
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Multiprocessing
Multiprocessing refers to a system's ability to support more than one processor simultaneously. Python's
multiprocessing
module leverages subprocesses, allowing full utilization of multiple processors. -
Parallelisation
While modern computers can handle parallelism with multiple cores, Python's Global Interpreter Lock (GIL) limits true parallelism. The GIL restricts Python code to single-threaded execution.
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Concurrency
Concurrency means running more than one task simultaneously, switching between tasks rather than running them in parallel.
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Coroutine
Coroutines are a more generalized form of subroutines, executed using the
async/await
syntax. They allow the suspension and resumption of tasks, enhancing concurrency. -
AsyncIO
AsyncIO is a library for writing concurrent code using
async/await
syntax. It serves as a foundation for various asynchronous frameworks in Python, suitable for IO-bound and high-level structured network code. -
Awaitable
An awaitable is a Future-like object or a coroutine object, usable in an
await
expression. -
Event Loop
An event loop runs in a thread and executes callbacks and tasks. Tasks are wrapped coroutines that can be executed independently.
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Task
A task is a wrapped coroutine that can be executed independently.
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Futures
Futures represent the result of an asynchronous computation.
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AnyIO
AnyIO is an asynchronous networking and concurrency library compatible with both asyncio and trio. It implements structured concurrency on top of asyncio.
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Aiohttp
Aiohttp is an asynchronous HTTP client/server for asyncio in Python.
Other helpful links:
With the knowledge you've been equipped with, you're ready to dive into the world of asynchronous programming. I have a follow up article here on Python Asyncio: A Guide to Asynchronous Programming and Concurrency.
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