Synchronizing Threads in Python With Locks (69/100 Days of Python)

Day 69 of the “100 Days of Python” blog post series covering synchronization in threads with locks

Thread locks, also known as mutexes (short for mutual exclusion), are synchronization mechanisms used to prevent race conditions in multithreaded applications. When multiple threads try to access a shared resource concurrently, a race condition can occur, leading to unpredictable results and potentially crashing the program. To avoid this, thread locks can be used to ensure that only one thread can access the shared resource at a time.

What are Thread Locks?

Thread locks are synchronization mechanisms that allow multiple threads to access a shared resource safely. When a thread wants to access the shared resource, it must first acquire the lock. Once a thread has acquired the lock, no other thread can access the resource until the lock is released. This ensures that only one thread can access the shared resource at a time, preventing race conditions and ensuring that the program behaves predictably.

There are two types of thread locks: binary locks and counting locks. Binary locks are simple locks that can be either locked or unlocked. When a thread tries to acquire a locked binary lock, it will be blocked until the lock is released. Counting locks, on the other hand, can be locked and unlocked multiple times. When a thread acquires a counting lock, it will decrement the lock count, and when it releases the lock, the count will be incremented again.

How do Thread Locks Work?

Thread locks work by providing a mutual exclusion mechanism that allows only one thread to access a shared resource at a time. When a thread wants to access the resource, it must first acquire the lock. If the lock is available, the thread can acquire it and access the resource. If the lock is not available, the thread will be blocked until the lock becomes available.

Once a thread has acquired the lock, it can access the shared resource safely. While the thread is accessing the resource, no other thread can access it until the lock is released. When the thread is done accessing the resource, it must release the lock so that other threads can access it.

Thread locks can be used to protect any shared resource that is accessed by multiple threads, including variables, files, and network connections.

How to Use Thread Locks in Python

In Python, thread locks are implemented using the threading.Lock() class. To use a lock in your code, you first need to create an instance of the Lock() class:

import threading

lock = threading.Lock()

Once you have created a lock instance, you can use it to protect a shared resource. To acquire the lock, you use the acquire() method:

lock.acquire()

This will block the thread until the lock becomes available. Once the lock is acquired, the thread can access the shared resource safely. When the thread is done accessing the resource, it must release the lock using the release() method:

lock.release()

This will release the lock and allow other threads to access the shared resource.

It’s important to note that when using locks, you must ensure that you release the lock in all possible code paths. If you acquire a lock and then exit the function without releasing the lock, the lock will remain locked, preventing other threads from accessing the shared resource. To avoid this, it’s a good practice to use a try-finally block to ensure that the lock is released, even if an exception occurs:

lock.acquire()
try:
    # access the shared resource
finally:
    lock.release()

As a challenge, try to fix the example from the previous tutorial using locks.

What’s next?

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• Full series: 100 Days of Python
• Previous topic: Multithreading
• Next topic: Synchronizing Threads in Python With Semaphores