Python - Concurrency - Examples

	#ConcurrentExamples.iy
	#https://learning.oreilly.com/library/view/python-parallel-programming/9781785289583/ch04s02.html
	import concurrent.futures
	import time
	number_list = [1,2,3,4,5,6,7,8,9,10]
	def evaluate_item(x):
	#count...just to make an operation
	result_item = count(x)
	#print the input item and the result
	print ("item " + str(x) + " result " + str(result_item))
	def count(number) :
	for i in range(0,10000000):
	i=i+1
	return i*number
	if __name__ == "__main__":
	##Sequential Execution
	start_time = time.process_time()
	for item in number_list:
	evaluate_item(item)
	print ("Sequential execution in " + \
	str(time.process_time() - start_time), "seconds")
	#The ThreadPoolExecutor executes the given task using one of its internally pooled threads.
	#It manages five threads working on its pool. Each thread takes a job out from the pool and executes it.
	#When the job is executed, it takes the next job to be processed from the thread pool.
	##Thread pool Execution
	start_time_1 = time.process_time()
	with concurrent.futures.ThreadPoolExecutor(max_workers=5)\
	as executor:
	for item in number_list:
	executor.submit(evaluate_item, item)
	print ("Thread pool execution in " + \
	str(time.process_time() - start_time_1), "seconds")
	##Process pool Execution
	#the ProcessPoolExecutor class is an executor subclass that uses a pool of processes to execute calls asynchronously.
	#However, unlike ThreadPoolExecutor, the ProcessPoolExecutor uses the multiprocessing module,
	#which allows us to outflank the global interpreter lock and obtain a shorter execution time.
	start_time_2 = time.process_time()
	with concurrent.futures.ProcessPoolExecutor(max_workers=5)\
	as executor:
	for item in number_list:
	executor.submit(evaluate_item, item)
	print ("Process pool execution in " + \
	str(time.process_time() - start_time_2), "seconds")

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