Table of Contents
Overview
In this tutorial, we will explore the concepts of concurrency and parallelism in Python. We will understand the difference between concurrency and parallelism, and how they can be used to improve performance and efficiency in our code. By the end of this tutorial, you will have a clear understanding of how to implement concurrency and parallelism using threading and multiprocessing in Python.
Prerequisites
To follow along with this tutorial, you should have a basic understanding of Python programming language and its syntax. Familiarity with functions, classes, and basic module usage in Python will be beneficial.
Concurrency vs Parallelism
Before diving into the implementation details, let’s clarify the difference between concurrency and parallelism. Concurrency refers to the ability of a program to perform multiple tasks simultaneously, without completing any single task entirely before starting another. It allows overlapping the execution of multiple tasks, which can be useful when dealing with I/O-bound operations.
On the other hand, parallelism involves the simultaneous execution of multiple tasks on different processors or cores. It is primarily used for computationally intensive tasks, where tasks can be divided and processed independently.
In Python, we can achieve concurrency through threading and parallelism through multiprocessing. Let’s explore each of these concepts in detail.
Threading
Threading is a technique to achieve concurrency in Python. It allows multiple threads within a single process to execute concurrently and share the same memory space. However, due to Python’s Global Interpreter Lock (GIL), only one thread can execute Python bytecode at a time. This means that threading may not provide true parallelism, but it can still be useful in certain scenarios.
Creating Threads
To create a thread in Python, we need to import the threading module. We can then define a function that will be executed in the thread and create an instance of the Thread class, passing the function as an argument.
```python
import threading
def my_function():
# Code to be executed in the thread
# Create a thread
my_thread = threading.Thread(target=my_function)
``` Once the thread is created, we can start it using the `start()` method.
```python
my_thread.start()
``` ### Thread Synchronization
When working with multiple threads, it is crucial to ensure proper synchronization to prevent race conditions and data corruption. Python provides various synchronization primitives, such as locks, semaphores, and condition variables, to achieve thread synchronization.
A lock (threading.Lock()) is the most basic synchronization primitive. It allows only one thread at a time to acquire the lock and proceed with the execution. Other threads that try to acquire the lock will be blocked until the lock is released.
Example: ```python import threading
lock = threading.Lock()
def my_function():
# Acquire the lock
lock.acquire()
try:
# Code that requires exclusive access
pass
finally:
# Release the lock
lock.release()
``` ## Multiprocessing
Multiprocessing is a technique to achieve parallelism in Python. It allows the execution of multiple processes, each having its own memory space, on different processors or cores. Unlike threading, each process can execute Python bytecode simultaneously, providing true parallelism.
Creating Processes
To create a process in Python, we need to import the multiprocessing module. We can then define a function that will be executed in the process and create an instance of the Process class, passing the function as an argument.
```python
import multiprocessing
def my_function():
# Code to be executed in the process
# Create a process
my_process = multiprocessing.Process(target=my_function)
``` Once the process is created, we can start it using the `start()` method.
```python
my_process.start()
``` ### Process Communication
In multiprocessing, each process has its own memory space, and direct communication between processes is not straightforward. However, Python provides various mechanisms to facilitate inter-process communication, such as pipes, queues, and shared memory.
A queue (multiprocessing.Queue()) is a simple and safe way to exchange data between processes. It allows multiple processes to enqueue and dequeue items in a synchronized manner.
Example: ```python import multiprocessing
# Create a shared queue
shared_queue = multiprocessing.Queue()
def producer():
# Enqueue items
shared_queue.put('Item 1')
shared_queue.put('Item 2')
def consumer():
# Dequeue items
item1 = shared_queue.get()
item2 = shared_queue.get()
``` ## Conclusion