Metadata-Version: 2.1
Name: quick-queue
Version: 1.0.0
Summary: Quick Multiprocessing Queue for Python (Wrap of multiprocessing.queue to increase data transfer velocity between processes)
Home-page: https://github.com/Invarato/sort_in_disk_project
Author: Ramon Invarato Menendez
Author-email: r.invarato@gmail.com
License: UNKNOWN
Description: # 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_project
        ```
        
        ## 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:
        ```python
        from quick_queue import QQueue
        ```
        
        Pseudocode without process:
        ```python
        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`):
        ```python
        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):
        ```python
        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()
        ```
        
        
        ## About performance
        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).
        
        
        ## Performance test
        Hardware where the tests have been done:
         * Processor: Intel i5 3 Generation 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)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.0
Description-Content-Type: text/markdown
