Python's queue module provides thread-safe queues, suitable for multi-thread data delivery; 2. Queue() is a FIFO queue, LifoQueue() is a last-in first-out queue, and PriorityQueue() is dequeued according to priority; 3. In multi-thread, put() is enqueued and get() is dequeued, and task_done() and join() are used to ensure the completion of the task; 4. The producer-consumer model is a typical application, and the consumer needs to be notified to end with None signal. The program exits correctly after all tasks are completed.
queue module in Python provides thread-safe queue implementations, which are often used to safely pass data in multi-threaded programming. Below is a simple and practical example of using queue to help you quickly understand its basic usage.
1. Basic Queue (FIFO queue)
import queue
# Create a first-in-first-out queue q = queue.Queue()
# Add elements (join)
q.put("Task1")
q.put("Task 2")
q.put("Task 3")
# Get elements (dequeue)
while not q.empty():
item = q.get()
print(f"processing: {item}")
q.task_done() # means that the task has been completed (it is important in the thread)Output:
Processing: Task 1 Processing: Task 2 Processing: Task 3
Queue()is FIFO (first in, first out) by default, suitable for most scenarios.
2. LIFO queue (similar to stack)
import queue
q = queue.LifoQueue() # Last in first out q.put("Task 1")
q.put("Task 2")
q.put("Task 3")
while not q.empty():
item = q.get()
print(f"processing: {item}")
q.task_done()Output:
Processing: Task 3 Processing: Task 2 Processing: Task 1
LifoQueuecan be used to simulate stack structures, such as undo operations, recursive substitution, etc.
3. Priority Queue
import queue
q = queue.PriorityQueue()
# Add task in format (priority, data)
# The smaller the number, the higher the priority q.put((3, "Low Priority Task"))
q.put((1, "High priority task"))
q.put((2, "Medium Priority Task"))
while not q.empty():
priority, task = q.get()
print(f"processing priority {priority}: {task}")
q.task_done()Output:
Processing priority 1: High priority task processing priority 2: Medium priority task processing priority 3: Low priority task
Commonly used in task scheduling systems, such as job queuing, event processing, etc.
4. Use Queue in multithreading (producer-consumer model)
import queue
import threading
import time
def producer(q):
for i in range(5):
q.put(f"task-{i}")
print(f"Production: Task-{i}")
time.sleep(0.5)
def consumer(q):
While True:
item = q.get()
if item is None:
break
print(f"Consumption: {item}")
time.sleep(1)
q.task_done()
# Create queue q = queue.Queue()
# Create thread t1 = threading.Thread(target=producer, args=(q,))
t2 = threading.Thread(target=consumer, args=(q,))
t1.start()
t2.start()
t1.join() # Wait for the producer to complete# Send the end signal q.put(None) # Notify the consumer to end t2.join() # Wait for the consumer to complete print("All tasks are completed")This is the most typical use of
queue: communication between threads , avoiding race conditions.
Tips
-
q.put(item): join the queue, optional parametersblock=True, timeout=None -
q.get(): dequeue, also supports blocking and timeout -
q.task_done(): Must be called after processing the task -
q.join(): Block until all tasks in the queue are processed bytask_done() -
q.empty()andq.full()are not thread-safe and are for reference only
Basically that's it. queue module is simple but powerful, especially suitable for multithreaded task scheduling. Not complicated, but it is easy to ignore the combination of task_done() and join() .
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