![]() ![]() The class has an insertion_count attribute. We define a PriorityQueue class with a constructor that instantiates an empty list and a _str_ method that returns a readable representation of the priority queue object. ![]() Let’s go over our priority queue implementation. The reason for using heapq instead of other data types is that you can push and pop in O(log n) time while keeping the underlying data in order of priority. We’ll use the heapq available from the Lib/heapq.py module in Python for our priority queue implementation. pq.is_empty(): returns True if the priority queue is empty.pq.size(): returns the number of task in the priority queue.pq.peek(): returns the highest priority task from the priority queue.pq.pull(): returns and removes the highest priority task from the priority queue.pq.insert(task, priority): adds a task with a given priority to the priority queue.Most priority queue implementations have the following methods: They are also used in bandwidth management to prioritize important data packets.Ĭertain foundational algorithms, such as Dijkstra’s algorithm, also rely on priority queues. Operating systems use priority queues to select the following process to run, load balancing and interrupt handling. In a max-priority queue, you assign a higher priority to a task with a higher priority number.In a min-priority queue, you assign a higher priority to a task with a lower priority number.While the next element in a queue is the first element inserted and in a stack is the last element inserted, in a priority queue, the next element to be retrieve is the one with the highest priority. Priority queues differ from queues and stack in the way your retrieve the elements. In computer science, a priority queue is an abstract data type in which each element has a priority associated with it. A Priority Queue Implementation in Python (this article).I based the series on my notes while studying for a Python technical interview.įor quick access, here is the list of posts on this series: This post is the third in a miniseries exploring the implementation of linear data structures. # python3 test_priority_queue.pyįile "test_priority_queue.In this article, I’ll explore the priority queue, a linear data structure we can easily implement in Python. Note that it's still one of the first elements placed into the priority queue that is in the wrong order. get():īad, not completely sorted output (~30% of the time): # python3 test_priority_queue.pyįile "test_priority_queue.py", line 129, in Īdditional, bad, example. Priorities of the items in the PriorityQueue as they were popped off of the queue via. Good, expected output (~70% of the time): # python3 test_priority_queue.py Print("All checks have passed successfully!") # priorities read off of the priority queue are sorted low to highĪssert len(priorities) = len(data) = NUM_EXAMPLES # Ensure that the queues have been fully read from and the Print(f"Placing into the PriorityQueue: ") # priority_number: float = round(np.random.normal(), 3)Ī = The final item placed in the priority queue will have a priority of 999.0 Write data to the priority queue with random priorities Prioritized item for priority queue as recommended at:ĭef _init_(self, priority: float, data: int = 0):ĭef writer_function(priority_queue_under_test: PriorityQueue, PQManager.register("PriorityQueue", PriorityQueue) PriorityQueue and a reader process to read data from the PriorityQueue.Įnsure the data read out of the PriorityQueue is sorted by priorityįrom _future_ import annotations # For type hinting the values in PriorityQueueįrom multiprocessing.managers import SyncManager Test the PriorityQueue class by making a writer process to write data into the I have verified the issue in both Python 3.7.13 and 3.10.7. ![]() Perhaps the answered PriorityQueue isn't actually thread/process safe? If so, how can I make it thread/process safe? If you run it a few times, eventually you should see an assertion error. I wrote a unit test of sorts for the answer mentioned above. I tried having the priority queue take in tuples of (priority, data), I also tried using a same result. I followed this answer to create the multiprocessing priority queue. I think that should be a hint to the problem but I don't know why that is. Most often when they are returned incorrectly, the first item returned from the priority queue is incorrect and it corresponds to the first (or one of the first) item placed in the priority queue. get() returns from my priority queue are ~70% of the time 100% correctly sorted, but ~30% of the time they will only be ~70% sorted correctly, with most of the elements correct but a few elements shuffled incorrectly. ![]()
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