Quick Multiprocessing Queue for Python (Wrap of multiprocessing.queue to increase data transfer velocity between processes)

# Quick Multiprocessing Queue

This is an implementation of Quick Multiprocessing Queue for Python and work similar to multiprocessing.queue (more information about multiprocessing.queue in https://docs.python.org/3/library/multiprocessing.html?highlight=process#pipes-and-queues).

## Install

Last release version of the project to install in: https://pypi.org/project/quick_queue_project/

pip install quick-queue

## Introduction

The motivation to create this class is due to multiprocessing.queue is too slow putting and getting elements to transfer data transfer between python processes.

But if you put or get one list with elements work similar as put or get one single element; this list is getting as fast as usually but this has too many elements for process in the subprocess and this action is very quickly.

In other words, Multiprocess queue is pretty slow putting and getting individual data, then QuickQueue wrap several data in one list, this list is one single data that is enqueue in the queue than is more quickly than put one individual data.

While consumer produce and put lists of elements in queue, subprocesses consume those lists and iterate every element, then subprocesses have elements very quickly.

## Quick use

Import:

from quick_queue import QQueue

Pseudocode without process:

qq = QQueue()

# << Add here qq to new process(es) and start process(es) >>

qq.put("value")
# Put all the values you need

qq.end()
# When end put values call to end() to mark you will not put more values and close QQueue

Complete example (it needs import multiprocessing):

def _process(qq):
print(qq.get())
print(qq.get())
print(qq.get())

if __name__ == "__main__":

qq = QQueue()

p = multiprocessing.Process(target=_process, args=(qq,))
p.start()

qq.put("A")
qq.put("B")
qq.put("C")

qq.end()

p.join()

Note: you need to call end method to perform remain operation and close queue. If you only want put remain data in queue, you can call put_remain, then you need to call manually to close (or end, this performs close operation too).

You can put al values in one iterable or several iterables whit put_iterable method (put_iterable perform remain 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.get())
print(qq.get())
print(qq.get())

if __name__ == "__main__":

qq = QQueue()

p = multiprocessing.Process(target=_process, args=(qq,))
p.start()

qq.put_iterable(["A", "B", "C"])
qq.put_iterable(["D", "E", "F"])

qq.end()

p.join()

An important fact is the size of list (named here "bucket list") in relation productor and consumers process to have the best performance:

• If queue is full, mean consumers are slower than productor.
• If queue is empty, mean productor is slower than consumers.

Then, best size of bucket list (size_bucket_list) is where queue is not full and not empty; for this, I implemented one sensor to determinate in realtime the size_bucket_list, you can enable this sensor if size_bucket_list is None (if you define a number in size_bucket_list, then you want a constant value to size_bucket_list and sensor disable). by default sensor is enabled (size_bucket_list=None), because depend on Hardware in your computer this size_bucket_list value should change, I recommend you test the best performance for your computer modifying size_bucket_list (with None and with number value).

You can delimite sensor scope with min_size_bucket_list and max_size_bucket_list (if max_size_bucket_list is None then is infinite):

qq = QQueue(min_size_bucket_list=10, max_size_bucket_list=1000)

To disable the sensor define a size in size_bucket_list:

qq = QQueue(size_bucket_list=120)

## Performance test

Hardware where the tests have been done:

• Processor: Intel i5 3.2GHz
• Operating System: Windows 10 x64

Use python3 tests\performance_qqueue_vs_queue.py

Put in a producer process and get in a consumer process N elements with QuickQueue and multiprocessing.queue:

10,000,000 elements (time: Queue = QuickQueue x 13.28 faster):

QuickQueue: 0:00:24.436001 | Queue: 0:05:24.488149

1,000,000 elements (time: Queue = QuickQueue x 17.55 faster):

QuickQueue: 0:00:01.877998 | Queue: 0:00:32.951001

100,000 elements (ftime: Queue = QuickQueue x 6.32 faster):

QuickQueue: 0:00:00.591002 | Queue: 0:00:03.736011

## Documentation

### Functions:

• QQueue: Main method to create a QuickQueue object configured. Args:
• maxsize: maxsize of bucket lists in queue. If maxsize<=0 then queue is infinite (and sensor is disabled, I recommend always define one positive number to save RAM memory). By default: 1000
• size_bucket_list: None to enable sensor size bucket list (require maxsize>0). If a number is defined here then use this number to size_bucket_list and disable sensor. If maxsize<=0 and size_bucket_list==None then size_bucket_list is default to 1000; other wise, if maxsize<=0 and size_bucket_list is defined, then use this number. By default: None
• min_size_bucket_list: (only if sensor is enabled) min size bucket list. Min == 1 and max == max_size_bucket_list - 1. By default: 10
• max_size_bucket_list: (only if sensor is enabled) max size bucket list. If None is infinite. By defatult: None

### Class:

This is a class whit heritage multiprocessing.queues.Queue. Methods overwritten:

• put_bucket: This put in queue a list of data.
• put: This put in queue a data wrapped in a list. Accumulate data until size_bucket_list, then put in queue.
• put_remain: Call to enqueue rest values that remains.
• put_iterable: This put in this QQueue all data from an iterable.
• end: Helper to call to put_remain and close queue in one method.
• get_bucket: This get from queue a list of data.
• get: This get from queue a data unwrapped from the list.
• qsize: This return the number of bucket lists (not the number of elements)