como puedo almacenar datos y leer al mismo tiempo

[razpeitia]

Claro, puedes hacerlo con hacerlo con threads, puedes hacer tu thread pool.

PD1: Threads son una mentira en python.
PD2: Puedes usar multiprocessing y usar 100% todos los CPU que tengas

Aquí te dejo un ejemplo con threads:

Código Python:

import time
import threading
import logging
import Queue
import random
 
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.DEBUG)
 
sentinel = None
queue = Queue.Queue()
num_threads = 5
 
def foobar_task(queue):
    while True:
        n = queue.get()
        logger.info('task called: {n}'.format(n=n))
        if n is sentinel: break
        queue.task_done()
 
threads=[threading.Thread(target=foobar_task,args=(queue,))
         for n in range(num_threads)]
for t in threads:
    t.start()
 
while True:
    n = random.random()
    if n > .999:
        break
    else:
        queue.put(n)
 
queue.join()
for i in range(num_threads):
    queue.put(sentinel)
 
for t in threads:
    t.join()
logger.info("threads are closed")
 

Y aquí te dejo otro usando multiprocessing:

Código Python:

import multiprocessing
import time
 
class Consumer(multiprocessing.Process):
    
    def __init__(self, lock, task_queue, result_queue):
        multiprocessing.Process.__init__(self)
        self.lock = lock
        self.task_queue = task_queue
        self.result_queue = result_queue
 
    def process_safe_print(self, message):
        self.lock.acquire()
        print(message)
        self.lock.release()
 
    def run(self):
        proc_name = self.name
        while True:
            next_task = self.task_queue.get()
            if next_task is None:
                # Poison pill means shutdown
                self.process_safe_print('%s: Exiting' % proc_name)
                self.task_queue.task_done()
                break
            self.process_safe_print('%s: %s' % (proc_name, next_task))
            answer = next_task()
            self.task_queue.task_done()
            self.result_queue.put(answer)
        return
 
 
class Task(object):
    def __init__(self, a, b):
        self.a = a
        self.b = b
    def __call__(self):
        time.sleep(1) # pretend to take some time to do the work
        return '%s * %s = %s' % (self.a, self.b, self.a * self.b)
    def __str__(self):
        return '%s * %s' % (self.a, self.b)
 
 
if __name__ == '__main__':
    lock = multiprocessing.Lock()
 
    # Establish communication queues
    tasks = multiprocessing.JoinableQueue()
    results = multiprocessing.Queue()
    
    # Start consumers
    num_consumers = multiprocessing.cpu_count() * 2
    print('Creating %d consumers' % num_consumers)
    consumers = [ Consumer(lock, tasks, results)
                  for i in xrange(num_consumers) ]
    for w in consumers:
        w.start()
    
    # Enqueue jobs
    num_jobs = 10
    for i in xrange(num_jobs):
        tasks.put(Task(i, i))
    
    # Add a poison pill for each consumer
    for i in xrange(num_consumers):
        tasks.put(None)
 
    # Wait for all of the tasks to finish
    tasks.join()
    for w in consumers:
        w.join()
    
    # Start printing results
    while num_jobs:
        result = results.get()
        print('Result: %s' % result)
        num_jobs -= 1