Thread Priority

In Python, currently thread priority is not directly supported by the threading 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 on Java’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.

Open Compiler

import threading
import time

classDummyThread(threading.Thread):def__init__(self, name, priority):
      threading.Thread.__init__(self)
      self.name = name
      self.priority = priority

   defrun(self):
      name = self.name
      time.sleep(1.0* self.priority)print(f"{name} thread with priority {self.priority} is running")# Creating threads with different priorities
t1 = DummyThread(name='Thread-1', priority=4)
t2 = DummyThread(name='Thread-2', priority=1)# Starting the threads
t1.start()
t2.start()# Waiting for both threads to complete
t1.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 running
Thread-1 thread with priority 4 is running
All Threads are executed

Adjusting Python Thread Priority on Windows

On Windows Operating system you can manipulate the thread priority using the ctypes module, This is one of the Python’s 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 the ctypes module.

import threading
import ctypes
import time

# Constants for Windows API
w32 = ctypes.windll.kernel32
SET_THREAD =0x20
PRIORITIZE_THE_THREAD =1classMyThread(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

   defset_priority(self, priority):ifnot 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)ifnot success:print('Failed to set thread priority:', w32.GetLastError())defrun(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 start
start_event = threading.Event()# Create threads
thread_normal = MyThread(start_event, name='normal', iterations=4)
thread_high = MyThread(start_event, name='high', iterations=4)# Start the threads
thread_normal.start()
thread_high.start()# Adjusting priority of 'high' thread
thread_high.set_priority(PRIORITIZE_THE_THREAD)# Trigger thread execution
start_event.set()

Output

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

high running
normal running
high running
normal running
high running
normal running
high running
normal running

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Prioritizing Python Threads Using the Queue Module

The queue 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 the heapq 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 the PriorityQueue class in the queue module to manage task priorities between the two threads.

Open Compiler

from time import sleep
from random import random, randint
from threading import Thread
from queue import PriorityQueue

queue = PriorityQueue()defproducer(queue):print('Producer: Running')for i inrange(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')defconsumer(queue):print('Consumer: Running')whileTrue:# get a unit of work
      item = queue.get()if item isNone: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: Running
Consumer: Running
(0, 0.15332707626852804)
(2, 0.4730737391435892)
(2, 0.8679231358257962)
(3, 0.051924220435665025)
(4, 0.23945882716108446)
Producer: Done
Consumer: Done