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How to implement a thread-safe queue for concurrent programming in Python?
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Use queue.Queue is the most reliable method to implement Python thread-safe queues. 1. It has a built-in lock mechanism to avoid race conditions; 2. Put() and get() block by default, and support timeout to avoid infinite waiting; 3. Use task_done() and join() to coordinate task completion; 4. You can set maxsize to implement bounded queues to control memory; 5. Custom encapsulation can provide a clearer interface; 6. Note that queue.Queue is only used for inter-thread communication and is not suitable for multi-process scenarios. If you use join(), task_done() must be called in the consumer, otherwise the program may hang.
Implementing a thread-safe queue in Python is straightforward thanks to the built-in queue.Queue class, which is designed specifically for safe use across multiple threads. Here's how to do it properly and what to keep in mind.
Use queue.Queue for Thread Safety
The most reliable and simplest way to have a thread-safe queue in Python is to use the queue.Queue module. It handles all locking mechanisms internally, so you don't have to worry about race conditions when putting or getting items.
import queue
import threading
import time
# Create a thread-safe queue
q = queue.Queue()
def producer():
for i in range(5):
item = f"item-{i}"
q.put(item)
print(f"Produced: {item}")
time.sleep(0.1) # Simulate work
def consumer():
While True:
try:
# Use timeout to avoid blocking forever
item = q.get(timeout=1)
print(f"Consumed: {item}")
q.task_done() # Indicate that the task is done
except queue.Empty:
print("No more items, exiting.")
break
# Create threads
t1 = threading.Thread(target=producer)
t2 = threading.Thread(target=consumer)
# Start threads
t1.start()
t2.start()
# Wait for both threads to finish
t1.join()
t2.join() Key Points for Using queue.Queue
- Blocking Operations :
put()andget()block by default, which is useful for coordinating threads. -
task_done()andjoin(): If you usetask_done()after processing each item, you can callq.join()in the producer to wait until all items are processed. - Avoid
queue.Emptyandqueue.Fullexceptions : Always handle them when using non-blocking or timeout-based calls.
Example with q.join() :
def producer_with_join():
for i in range(3):
q.put(f"task-{i}")
q.join() # Wait until all tasks are marked as done
print("All tasks completed.")
def consumer_with_task_done():
While True:
item = q.get()
if item is None:
break
print(f"Processing {item}")
time.sleep(0.5)
q.task_done() # Mark task as doneAlternative: Bounded Queue (With Size Limit)
You can limit the queue size to control memory usage and enable backpressure:
q = queue.Queue(maxsize=3)
Now, put() will block if the queue is full until an item is consumed.
Custom Thread-Safe Queue (Advanced Use Case)
While queue.Queue covers most needs, you might want to wrap it for specific behavior:
import queue
from typing import Any
class SafeQueue:
def __init__(self, maxsize: int = 0):
self._q = queue.Queue(maxsize=maxsize)
def put(self, item: Any):
self._q.put(item)
def get(self) -> Any:
return self._q.get()
def empty(self) -> bool:
return self._q.empty()
def size(self) -> int:
return self._q.qsize()
def task_done(self):
self._q.task_done()
def join(self):
self._q.join()This encapsulates the queue and provides a clean interface.
Important Notes
- Don't use
listwith locks manually unless you have a very specific reason — it's error-prone. -
queue.Queueis for threads only. For multiprocessing, usemultiprocessing.Queue. - Always call
task_done()in consumers if you usejoin(), or your program may hang.
Basically, just use queue.Queue — it's well-tested, efficient, and built for this exact purpose.
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