bhavishyagopesh

# Time of a long running request

from socket import *
import time

sock = socket(AF_INET, SOCK_STREAM)
sock.connect(('localhost', 25000))

while True:
    start = time.time()

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python server.py

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# server code

from fib import fib
from socket import *

def fib_server(address):
    # This sets up the socket(you can think that of as pipe over which communication happens) object 
    sock = socket(AF_INET, SOCK_STREAM)
    sock.setsockopt(SOL_SOCKET, SO_REUSEADDR, 1)
    # It binds to the address and listens for any connections

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# Utility function for demonstration

def fib(n):
    if n <= 2:
        return 1
    else:
        return fib(n-1) + fib(n-2)

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# Utility function for demonstration


def fib(n):
    if n <= 2:
        return 1
    else:
return fib(n-1) + fib(n-2)

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"""
Benchmark math module.
"""


import perf
from six.moves import xrange
import math

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"""
Benchmark smtplib module.
"""

# This benchmark requires a running smtp service on port 25
# For Eg: PostFix

import perf
from six.moves import xrange
import smtplib

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"""
Benchmark zlib module.
"""


import perf
from six.moves import xrange
import zlib
import binascii

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import perf
from six.moves import xrange
def add_cmdline_args(cmd, args):
    if args.benchmark:
        cmd.append(args.benchmark)

CRUNCH_NO=10000000
def bench_number_crunching(loops):
    range_it = xrange(loops)
    t0 = perf.perf_counter()

bhavishyagopesh

The above script tries to benchmark "Concurrency" implemented using threading and multiprocessing.Actually "threads" in cpython are restricted by "GIL"
,so it's not actually concurrent...On the other hand "multiprocessing" module creates whole different processes but there is substaintial cost involved in 
spawing a whole new process.

So the there is a trade-off involved which is evident as we increase "CRUNCH_NO" variable.

But the benchmark actually tries to compare the same phenomenon in py2 and py3.And py2 looks faster here.

I'm adding two graphs comparing the timings for py2 and py3.