- Use Process when the task
- amount to execute is small
- has long IO wait
- Use Pool when the task
- amount to execute is big
- has short IO wait
- consumed a lot of memory, (you required to do resources saving)
In Python, the multithreading capability was disable by GIL Lock. Thus, multiprocessing is the only way library for us to achieve parallelism.
I noticed that Python has two classes that provide multiprocessing capability: Process and Pool.
Let’s have a look on the differences between these two classes.
Both Process and Pool used FIFO (First In First Out) scheduler which the first task assigned to core will get executed first.
Process will keep all the memory whereas Pool will only keep those that are under execution. When you have tons of tasks required a lot of memory, Process might not be a wise choice as it might waste a lot of memory.
However, using Pool is not a silver bullet for saving memory as well. In the case when you only have little number of tasks, using pool will create even more overhead.
Pool will not schedule another process till the task’s IO operation is complete. While Process on the other hand, it will halt the current process if it is running IO Operation.
Let’s try a small experiment for this behavior.
In the code snippet below, we will execute 2 IO tasks (
IOTask) with Process and Pool.
Then, we will observe the time required for both process and pool.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 from multiprocessing import Process, Pool import time def IO_Task(file_name): f = open(file_name, "w") f.write("hello world") f.write("hello world") f.write("hello world") f.write("hello world") f.write("hello world") f.close() process_time = time.time() p1 = Process(target=IO_Task, args=("myIOFile1.txt",)) p2 = Process(target=IO_Task, args=("myIOFile2.txt",)) p1.start() p2.start() p1.join() p2.join() print("Time taken for Process: ", (time.time() - process_time)*1000, " ms") pool_time = time.time() pool = Pool() po1 = pool.apply_async(IO_Task, args=("myIOFile3.txt",)) po2 = pool.apply_async(IO_Task, args=("myIOFile4.txt",)) po1.wait() po2.wait() print("Time taken for Pool : ", (time.time() - pool_time)*1000, " ms")
1 2 Time taken for Process: 5.030632019042969 ms Time taken for Pool : 25.95376968383789 ms
From the output we can observe that, Process only used 5ms whereas Pool used 25ms.