Mateen Kiani
Published on Tue Aug 05 2025·4 min read
Python’s built-in queue
module is a key tool when you need safe data passing between threads or processes. Yet many developers overlook how choosing the right queue type—FIFO, LIFO or priority—can make or break your concurrency logic. When should you grab a Queue
vs. a PriorityQueue
, and how do you prevent deadlocks or lost data in busy worker threads?
The secret is understanding each queue’s behavior under load. By matching your data flow and locking strategy to the queue class, you avoid subtle bugs, improve throughput, and write clearer code. Let’s dive into concrete examples so you can pick the best queue for your scenario.
The simplest queue is the FIFO Queue
. It guarantees first-in, first-out ordering and handles locking internally. You create one and share it across threads without extra locks.
from queue import Queueq = Queue(maxsize=10) # limit capacityq.put('task1')task = q.get()
Key points:
maxsize=0
means infinite capacity.put()
blocks when full; get()
blocks when empty.task_done()
and join()
for producer-consumer sync.Tip: Always call
task_done()
after processingget()
to signal completion.
This pattern keeps producers from flooding consumers and helps you wait until all tasks finish with q.join()
.
When you spawn worker threads, a Queue
is your safest shareable structure. Threads call get()
to pull work and put()
to add results.
Queue
.join()
to block until all tasks are processed.import threadingfrom queue import Queuedef worker(q):while True:item = q.get()if item is None:breakprocess(item)q.task_done()q = Queue()threads = []for _ in range(4):t = threading.Thread(target=worker, args=(q,))t.start()threads.append(t)for item in range(10):q.put(item)q.join()for _ in threads:q.put(None)for t in threads:t.join()
For more on threading patterns, see this Python threading example.
The multiprocessing
module offers its own Queue
for process-safe communication. Unlike queue.Queue
, this one uses pipes and locks under the hood.
from multiprocessing import Process, Queuedef worker(q):q.put('result from process')if __name__ == '__main__':q = Queue()p = Process(target=worker, args=(q,))p.start()print(q.get()) # receives data from childp.join()
Practical tips:
join()
on processes and close()
on queues when done.Manager().Queue()
.This lets you parallelize CPU-bound tasks safely across cores.
When order matters by priority, switch to PriorityQueue
. Each entry is a tuple (priority, data)
.
from queue import PriorityQueuepq = PriorityQueue()pq.put((2, 'low-priority task'))pq.put((1, 'high-priority task'))print(pq.get()[1]) # prints 'high-priority task'
Use cases:
Note: Lower numbers come out first. Tie-breaking follows FIFO for same priority.
Mixing priorities with worker threads is as simple as swapping Queue
for PriorityQueue
.
In async code, use asyncio.Queue
. It integrates with the event loop and uses await
instead of blocking calls.
import asyncioasync def producer(q):for i in range(5):await q.put(i)print(f'put {i}')async def consumer(q):while True:item = await q.get()print(f'got {item}')q.task_done()async def main():q = asyncio.Queue()prod = asyncio.create_task(producer(q))cons = asyncio.create_task(consumer(q))await prodawait q.join()cons.cancel()asyncio.run(main())
This pattern keeps your async producers and consumers in sync without threads.
Even with built-in safety, queues can be misused:
task_done()
leads to join()
hanging.Best practices:
None
sentinel).maxsize
to prevent memory bloat.get()
in try/except if you use timeouts.By spotting these issues early, you keep systems reliable when traffic spikes.
Python’s queue module is a powerful ally for managing concurrency. From simple FIFO queues to priority scheduling and async workflows, each class solves a clear need. Understanding when to use Queue
, PriorityQueue
or asyncio.Queue
helps you avoid deadlocks, lost data, and hanging threads or processes. Practicing with real examples and following the tips above will make your code robust and maintainable. Now it’s your turn: pick a queue type, build a small producer-consumer task, and see concurrency in action.
Ready to streamline your data pipelines? Experiment with queue types today and share your findings with your team!