Concurrency in Python: A Complete Guide

Concurrency is one of the most important concepts in modern programming. Python offers several ways to handle concurrent tasks—through threads, coroutines, and multiprocessing—but it’s easy to confuse concurrency with parallelism.

This guide breaks down concurrency in Python, explains the differences, and shows when to use each approach.

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Program, Process, and Thread

Before diving into concurrency, it’s essential to understand these core concepts:

• Program: Static code instructions that sit on disk until executed.
• Process: A running instance of a program with its own isolated memory space.
• Thread: A lightweight execution unit inside a process that shares memory with other threads.

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Thread States in Python

In Python (3.9+), threads can exist in multiple states:

• NEW → Thread created but not started
• STARTING → Beginning execution
• RUNNING → Actively executing
• WAITING → Paused, waiting for a resource or event (includes “Runnable”)
• LOCKED → Blocked, waiting for lock release
• FINISHED → Execution completed
• CANCELLED or TERMINATED → Stopped before completion

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Concurrency vs Parallelism

• Concurrency → Multiple tasks make progress by interleaving execution through context switching. They don’t necessarily run at the same time.
• Parallelism → Multiple tasks actually run simultaneously on different CPU cores.

👉 Key point: Concurrency ≠ Parallelism.

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Multithreading in Python

• Multiple threads run within a single process.
• Threads share the same memory space, making communication between them easier.
• Best suited for I/O-bound tasks such as file handling or network requests.
• Limitation: The Global Interpreter Lock (GIL) prevents true parallel execution of Python bytecode.

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Coroutines and Async/Await

• Coroutines are functions that can pause and resume, allowing cooperative multitasking.
• In Python, they’re implemented using async / await.
• Perfect for lightweight concurrent I/O operations without creating multiple threads.
• Example use cases: web scraping, asynchronous APIs, real-time chat servers.

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Challenges in Concurrency

Working with concurrent systems introduces complexity. Common issues include:

• Race Conditions → Multiple threads access the same resource unsafely.
• Deadlocks → Two threads wait for each other indefinitely.
• Starvation → Some threads never get CPU time.
• Synchronization Overhead → Managing thread safety can reduce performance.
• Debugging Difficulty → Concurrency bugs are often hard to reproduce.

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Synchronization Primitives

Python provides tools to manage concurrent access to resources:

• Mutex (Lock) → Ensures only one thread can access a resource at a time.
• Semaphore → Allows a fixed number of threads to access a resource simultaneously.
• Barrier → Makes threads wait until all have reached a certain point before proceeding.

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Multiprocessing in Python

• Launches multiple processes, each with its own memory space.
• Offers true parallelism (bypassing the GIL).
• Ideal for CPU-bound tasks like heavy computation or data processing.
• Trade-offs: higher memory usage and more complex data sharing between processes.

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When to Use What?

• Coroutines (async/await) → Best for I/O-bound, high-level structured concurrency.
• Threads → Good for I/O-bound concurrency with simpler setups, but limited by GIL.
• Processes → Best for CPU-intensive workloads requiring real parallelism.

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Final Thoughts

Concurrency in Python can be confusing at first, but by understanding processes, threads, and coroutines, you’ll know which tool to apply to which problem.

• Use async/await for I/O-heavy, scalable applications.
• Use threads when tasks need concurrency but not full parallelism.
• Use multiprocessing for computation-heavy tasks that demand parallel performance.

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