Python - Synchronizing Threads

In Python, when multiple threads are working concurrently with shared resources, it's important to synchronize their access to maintain data integrity and program correctness. Synchronizing threads in python can be achieved using various synchronization primitives provided by the threading module, such as locks, conditions, semaphores, and barriers to control access to shared resources and coordinate the execution of multiple threads.

In this tutorial, we'll learn about various synchronization primitives provided by Python's threading module.

Thread Synchronization using Locks

The lock object in the Python's threading module provide the simplest synchronization primitive. They allow threads to acquire and release locks around critical sections of code, ensuring that only one thread can execute the protected code at a time.

A new lock is created by calling the Lock() method, which returns a lock object. The lock can be acquired using the acquire(blocking) method, which force the threads to run synchronously. The optional blocking parameter enables you to control whether the thread waits to acquire the lock and released using the release() method.

Example

The following example demonstrates how to use locks (the threading.Lock() method) to synchronize threads in Python, ensuring that multiple threads access shared resources safely and correctly.

import threading

counter = 10

def increment(theLock, N):
   global counter
   for i in range(N):
      theLock.acquire()
      counter += 1
      theLock.release()

lock = threading.Lock()
t1 = threading.Thread(target=increment, args=[lock, 2])
t2 = threading.Thread(target=increment, args=[lock, 10])
t3 = threading.Thread(target=increment, args=[lock, 4])

t1.start()
t2.start()
t3.start()

# Wait for all threads to complete
for thread in (t1, t2, t3):
   thread.join()

print("All threads have completed")
print("The Final Counter Value:", counter)

Output

When the above code is executed, it produces the following output −

All threads have completed
The Final Counter Value: 26

Condition Objects for Synchronizing Python Threads

Condition variables enable threads to wait until notified by another thread. They are useful for providing communication between the threads. The wait() method is used to block a thread until it is notified by another thread through notify() or notify_all().

Example

This example demonstrates how Condition objects can synchronize threads using the notify() and wait() methods.

import threading

counter = 0  

# Consumer function
def consumer(cv):
   global counter
   with cv:
      print("Consumer is waiting")
      cv.wait()  # Wait until notified by increment
      print("Consumer has been notified. Current Counter value:", counter)

# increment function
def increment(cv, N):
   global counter
   with cv:
      print("increment is producing items")
      for i in range(1, N + 1):
         counter += i  # Increment counter by i
        
      # Notify the consumer 
      cv.notify()  
      print("Increment has finished")

# Create a Condition object
cv = threading.Condition()

# Create and start threads
consumer_thread = threading.Thread(target=consumer, args=[cv])
increment_thread = threading.Thread(target=increment, args=[cv, 5])

consumer_thread.start()
increment_thread.start()

consumer_thread.join()
increment_thread.join()

print("The Final Counter Value:", counter)

Output

On executing the above program, it will produce the following output −

Consumer is waiting
increment is producing items
Increment has finished
Consumer has been notified. Current Counter value: 15
The Final Counter Value: 15

Synchronizing threads using the join() Method

The join() method in Python's threading module is used to wait until all threads have completed their execution. This is a straightforward way to synchronize the main thread with the completion of other threads.

Example

This demonstrates synchronization of threads using the join() method to ensure that the main thread waits for all started threads to complete their work before proceeding.

import threading
import time

class MyThread(threading.Thread):
   def __init__(self, threadID, name, counter):
      threading.Thread.__init__(self)
      self.threadID = threadID
      self.name = name
      self.counter = counter
      
   def run(self):
      print("Starting " + self.name)    
      print_time(self.name, self.counter, 3)
      
def print_time(threadName, delay, counter):
   while counter:
      time.sleep(delay)
      print("%s: %s" % (threadName, time.ctime(time.time())))
      counter -= 1
      
threads = []

# Create new threads
thread1 = MyThread(1, "Thread-1", 1)
thread2 = MyThread(2, "Thread-2", 2)

# Start the new Threads
thread1.start()
thread2.start()

# Join the threads
thread1.join()
thread2.join()

print("Exiting Main Thread")

Output

On executing the above program, it will produce the following output −

Starting Thread-1
Starting Thread-2
Thread-1: Mon Jul  1 16:05:14 2024
Thread-2: Mon Jul  1 16:05:15 2024
Thread-1: Mon Jul  1 16:05:15 2024
Thread-1: Mon Jul  1 16:05:16 2024
Thread-2: Mon Jul  1 16:05:17 2024
Thread-2: Mon Jul  1 16:05:19 2024
Exiting Main Thread

Additional Synchronization Primitives

In addition to the above synchronization primitives, Python's threading module offers: −

• RLocks (Reentrant Locks): A variant of locks that allow a thread to acquire the same lock multiple times before releasing it, useful in recursive functions or nested function calls.
• Semaphores:Similar to locks but with a counter. Threads can acquire the semaphore up to a certain limit defined during initialization. Semaphores are useful for limiting access to resources with a fixed capacity.
• Barriers: Allows a fixed number of threads to synchronize at a barrier point and continue executing only when all threads have reached that point. Barriers are useful for coordinating a group of threads that must all complete a certain phase of execution before any of them can proceed further.