Python - Thread Priority

In Python, currently thread priority is not directly supported by thethreading module. unlike Java, Python does not support thread priorities, thread groups, or certain thread control mechanisms like destroying, stopping, suspending, resuming, or interrupting threads.

Even thought Python threads are designed simple and is loosely based onJava's threading model. This is because of Python's Global Interpreter Lock (GIL), which manages Python threads.

However, you can simulate priority-based behavior using techniques such as sleep durations, custom scheduling logic within threads or using the additional module which manages task priorities.

Setting the Thread Priority Using Sleep()

You can simulate thread priority by introducing delays or using other mechanisms to control the execution order of threads. One common approach to simulate thread priority is by adjusting the sleep duration of your threads.

Threads with a lower priority sleep longer, and threads with a high priority sleep shorter.

Example

Here's a simple example to demonstrate how to customize the thread priorities using the delays in Python threads. In this example, Thread-2 completes before Thread-1 because it has a lower priority value, resulting in a shorter sleep time.

import threadingimport timeclass DummyThread(threading.Thread):   def __init__(self, name, priority):      threading.Thread.__init__(self)      self.name = name      self.priority = priority   def run(self):      name = self.name      time.sleep(1.0 * self.priority)      print(f"{name} thread with priority {self.priority} is running")# Creating threads with different prioritiest1 = DummyThread(name='Thread-1', priority=4)t2 = DummyThread(name='Thread-2', priority=1)# Starting the threadst1.start()t2.start()# Waiting for both threads to completet1.join()t2.join()print('All Threads are executed')

Output

On executing the above program, you will get the following results −

Thread-2 thread with priority 1 is runningThread-1 thread with priority 4 is runningAll Threads are executed

Adjusting Python Thread Priority on Windows

On Windows Operating system you can manipulate the thread priority using thectypes module, This is one of the Pythons standard module used for interacting with the Windows API.

Example

This example demonstrates how to manually set the priority of threads in Python on a Windows system using thectypes module.

import threadingimport ctypesimport time# Constants for Windows APIw32 = ctypes.windll.kernel32SET_THREAD = 0x20PRIORITIZE_THE_THREAD = 1class MyThread(threading.Thread):   def __init__(self, start_event, name, iterations):      super().__init__()      self.start_event = start_event      self.thread_id = None      self.iterations = iterations      self.name = name   def set_priority(self, priority):      if not self.is_alive():         print('Cannot set priority for a non-active thread')         return      thread_handle = w32.OpenThread(SET_THREAD, False, self.thread_id)      success = w32.SetThreadPriority(thread_handle, priority)      w32.CloseHandle(thread_handle)      if not success:         print('Failed to set thread priority:', w32.GetLastError())   def run(self):      self.thread_id = w32.GetCurrentThreadId()      self.start_event.wait()      while self.iterations:         print(f"{self.name} running")         start_time = time.time()         while time.time() - start_time < 1:            pass         self.iterations -= 1# Create an event to synchronize thread startstart_event = threading.Event()# Create threadsthread_normal = MyThread(start_event, name='normal', iterations=4)thread_high = MyThread(start_event, name='high', iterations=4)# Start the threadsthread_normal.start()thread_high.start()# Adjusting priority of 'high' threadthread_high.set_priority(PRIORITIZE_THE_THREAD)# Trigger thread executionstart_event.set()

Output

While executing this code in your Python interpreter, you will get the following results −

high runningnormal runninghigh runningnormal runninghigh runningnormal runninghigh runningnormal running

Prioritizing Python Threads Using the Queue Module

Thequeue module in Python's standard library is useful in threaded programming when information must be exchanged safely between multiple threads. The Priority Queue class in this module implements all the required locking semantics.

With a priority queue, the entries are kept sorted (using theheapq module) and the lowest valued entry is retrieved first.

The Queue objects have following methods to control the Queue −

• get() − The get() removes and returns an item from the queue.

• put() − The put adds item to a queue.

• qsize() − The qsize() returns the number of items that are currently in the queue.

• empty() − The empty( ) returns True if queue is empty; otherwise, False.

• full() − the full() returns True if queue is full; otherwise, False.

queue.PriorityQueue(maxsize=0)

This is the Constructor for a priority queue. maxsize is an integer that sets the upper limit on the number of items that can be placed in the queue. If maxsize is less than or equal to zero, the queue size is infinite.

The lowest valued entries are retrieved first (the lowest valued entry is the one that would be returned by min(entries)). A typical pattern for entries is a tuple in the form −

(priority_number, data)

Example

This example demonstrates the use of thePriorityQueue class in thequeue module to manage task priorities between the two threads.

from time import sleepfrom random import random, randintfrom threading import Threadfrom queue import PriorityQueuequeue = PriorityQueue()def producer(queue):   print('Producer: Running')   for i in range(5):      # create item with priority      value = random()      priority = randint(0, 5)      item = (priority, value)      queue.put(item)   # wait for all items to be processed   queue.join()   queue.put(None)   print('Producer: Done')def consumer(queue):   print('Consumer: Running')   while True:      # get a unit of work      item = queue.get()      if item is None:         break      sleep(item[1])      print(item)      queue.task_done()   print('Consumer: Done')producer = Thread(target=producer, args=(queue,))producer.start()consumer = Thread(target=consumer, args=(queue,))consumer.start()producer.join()consumer.join()

Output

On execution, It will produce the following output −

Producer: RunningConsumer: Running(0, 0.15332707626852804)(2, 0.4730737391435892)(2, 0.8679231358257962)(3, 0.051924220435665025)(4, 0.23945882716108446)Producer: DoneConsumer: Done