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# Original code at http://code.activestate.com/recipes/203871/
# I have added a limit for the number of tasks waiting
# The method queueTask will block if the current taskList size 
# exceeds the specified limit

import threading
from time import sleep

# Ensure booleans exist (not needed for Python 2.2.1 or higher)

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from concurrent.futures import ThreadPoolExecutor, Future
import asyncio
import time

async def sum_numbers(x, y):
    await asyncio.sleep(1)
    return x + y

def sum_numbers_blocking(x, y):
    time.sleep(1)

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Easy parallel python with concurrent.futures

As of version 3.3, python includes the very promising concurrent.futures module, with elegant context managers for running tasks concurrently. Thanks to the simple and consistent interface you can use both threads and processes with minimal effort.

For most CPU bound tasks - anything that is heavy number crunching - you want your program to use all the CPUs in your PC. The simplest way to get a CPU bound task to run in parallel is to use the ProcessPoolExecutor, which will create enough sub-processes to keep all your CPUs busy.

We use the context manager thusly:

with concurrent.futures.ProcessPoolExecutor() as executor:

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#!/usr/bin/env python3
# ~*~ coding: utf-8 ~*~
#
# >>
#   .. created: 5/20/16
#   .. author:  blake.vandemerwe
#
# LICENSE
# <<

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#!/usr/bin/env python3
# coding: utf-8

from db import db
import time
import calendar

month = 43200*60
day = 24*60*60
week = 24*60*60*7

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// $ 6g echo.go && 6l -o echo echo.6
// $ ./echo
//
//  ~ in another terminal ~
//
// $ nc localhost 3540

package main

import (

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from hashlib import md5
from base64 import b64decode
from base64 import b64encode
from Crypto.Cipher import AES


# Padding for the input string --not
# related to encryption itself.
BLOCK_SIZE = 16  # Bytes
pad = lambda s: s + (BLOCK_SIZE - len(s) % BLOCK_SIZE) * \

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sudo rm /System/Library/LaunchAgents/com.apple.mediaanalysisd.plist

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To send the ip addres of the client/webbrowser to the server/webserver behind it there are a few options:
 1- option forwardfor
 2- send-proxy
 3- source 0.0.0.0 usesrc clientip

1- option forwardfor
This is an easy option to configure in haproxy, it does require that http layer7 processing is used 'mode http' and the webserver/ webapplication that wants to log or use the ip of the client must use the http-header 'X-Forwarded-For' to read the clientip.

2- send-proxy / send-proxy-v2 / send-proxy-*
This is can be used both with mode tcp and http, it does however require that the server also understands the proxyprotocol. Some applications have added support for this protocol which adds a few bytes with ip information before the actual request. 

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from pymongo import Connection
from bson import ObjectId
from itertools import imap


class Model(dict):
    """
    A simple model that wraps mongodb document
    """
    __getattr__ = dict.get