2/10/2024 0 Comments Python queue to list fastThen, best size of bucket list ( size_bucket_list) is where queue is not full and not empty for this, I implemented If queue is empty, mean productor is slower than consumers.If queue is full, mean consumers are slower than productor.join () About performanceĪn important fact is the size of list (named here "bucket list") in relation productor and consumers process to have Process ( target = _process, args = ( qq ,)) p. get ()) if _name_ = "_main_" : qq = QQueue () p = multiprocessing. Operation when iterable is consumed but this not close queue, you need call to close() or to end() in this case): def _process ( qq ): print ( qq. You can put al values in one iterable or several iterables whit put_iterable method ( put_iterable perform remain Queue, you can call put_remain, then you need to call manually to close (or end, this performs close operation Note: you need to call end method to perform remain operation and close queue. ![]() end () # When end put values call to end() to mark you will not put more values and close QQueueĬomplete example (it needs import multiprocessing): def _process ( qq ): print ( qq. put ( "value" ) # Put all the values you need qq. Pseudocode without process: qq = QQueue () # > qq. Then subprocesses have elements very quickly. While consumer produce and put lists of elements in queue, subprocesses consume those lists and iterate every element, In other words, Multiprocess queue is pretty slow putting and getting individual data, then QuickQueue wrap severalĭata in one list, this list is one single data that is enqueue in the queue than is more quickly than put one To transfer data transfer between python processes.īut if you put or get one list with elements work similar as put or get one single element this list is getting asįast as usually but this has too many elements for process in the subprocess and this action is very quickly. The motivation to create this class is due to multiprocessing.queue is too slow putting and getting elements Last release version of the project to install in: pip install quick_queue_project Information about multiprocessing.queue in Measure execution time with timeit in PythonĪs mentioned at the beginning, the deque class from the collections module is imported as follows.This is an implementation of Quick Multiprocessing Queue for Python and work similar to multiprocessing.queue (more.To decide between list and deque based on your specific needs, consider benchmarking the processing time with the timeit module. ![]() In most cases, using list is sufficient unless you need to reduce processing time at the millisecond level. However, the difference in processing speed between list and deque is usually not noticeable when dealing with a few hundred or a few thousand elements, depending on the environment and conditions. It's recommended to use deque when you specifically want to handle data as a queue, stack, or deque.
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