Time Comparison of heapq vs PriorityQueue
Here, we have defined 2 functions for two different purpose. p_queue() function for using PriorityQueue on 10^5 data items. And heap_queue() on same size of data input list for performing operations using heapq Python module. And the main() function for time profiling on each of the mentioned 2 functions.
Python3
import timefrom queue import PriorityQueue, Emptyimport heapqdef p_queue(): prior_queue = PriorityQueue() jobs = [(x, f"This is item: {x}") for x in range(1, 10 ** 5 + 1)] for job in jobs: prior_queue.put(job) while 1: try: popped_item = prior_queue.get_nowait() except Empty: breakdef heap_queue(): jobs = [(x, f"This is item: {x}") \ for x in range(1, 10 ** 5 + 1)] heapq.heapify(jobs) for _ in range(len(jobs)): popped_item = heapq.heappop(jobs)def main(): start_time = time.perf_counter_ns() heap_queue() end_time = time.perf_counter_ns() print(f"Adding and popping item using heapq \ took: {(end_time - start_time) // 10 ** 6:.02f}ms") start_time = time.perf_counter_ns() p_queue() end_time = time.perf_counter_ns() print(f"Adding and popping item using PriorityQueue\ took: {(end_time - start_time) // 10 ** 6:.02f}ms")if __name__ == '__main__': main() |
Output:
Adding and popping item using heapq took: 154.00ms
Adding and popping item using PriorityQueue took: 375.00ms
Conclusion: It is clear from the time profiling that, heapq runs faster than PriorityQueue function. And this is obvious because PriorityQueue uses the threading module to implement a mutex structure for thread safety while manipulating items in the queue.
Difference Between heapq and PriorityQueue in Python
In this article, we are going to see the difference between heapq and PriorityQueue in Python.
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