How can I use **python timing code** effectively to achieve this?

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1

How can I time a code segment for testing performance with Python’s timeit?

I have a Python script that works as expected, but I need to log the execution time. I’ve read that I should use timeit, but I can’t seem to get it to work.

Here’s my Python script:

import sys
import getopt
import timeit
import random
import os
import re
import ibm_db
import time
from string import maketrans

myfile = open("results_update.txt", "a")

for r in range(100):
    rannumber = random.randint(0, 100)

    update = "update TABLE set val = %i where MyCount >= '2010' and MyCount < '2012' and number = '250'" % rannumber
    #print rannumber

    conn = ibm_db.pconnect("dsn=myDB","usrname","secretPWD")

for r in range(5):
    print "Run %s\n" % r        
    ibm_db.execute(query_stmt)
query_stmt = ibm_db.prepare(conn, update)

myfile.close()
ibm_db.close(conn)

What I need is to capture the time it takes to execute the query and write it to the file results_update.txt. The purpose is to test an update statement for my database with different indexes and tuning mechanisms.

How can I use python timing code effectively to achieve this?

2

Hey All!

You can start by using time.time() or time.clock() to measure how long a code block takes to run. Basically, you record the time right before and after running your block of code. Here’s an example with time.time():

import time

t0 = time.time()
# Your code block
t1 = time.time()

total = t1 - t0

This method is simple and works well, but it might not be the most precise, especially for very fast functions. The time difference might be too small to get accurate results. To improve this, you could run your function multiple times and average the result:

import time

def myfast():
    # Your code

n = 10000
t0 = time.time()
for i in range(n):
    myfast()
t1 = time.time()

total_n = t1 - t0
3

Hey @Shielagaa

I totally agree with @yanisleidi-rodriguez s approach, but if you’re looking for a more reliable measurement, especially for smaller code snippets, I’d recommend using the timeit module. It’s designed to handle multiple executions of the same block of code and averages the results for more accuracy. Here’s how you can use it:

import timeit

code_block = '''
# Your code here
'''

total_time = timeit.timeit(code_block, number=10000)
print(f"Time taken: {total_time} seconds")

With timeit, you’re basically running your code multiple times behind the scenes, so it’s perfect when you need more precision, especially for quick functions. It’ll give you a more stable and reliable result.

4

Great point, @dimplesaini.230! But if you really need the highest possible precision, then I’d suggest using time.perf_counter(). It’s better than time.time() for timing small intervals because it offers much higher resolution. Here’s how you can use it:

import time

t0 = time.perf_counter()
# Your code block
t1 = time.perf_counter()

total = t1 - t0
print(f"Time taken: {total} seconds")

perf_counter() is your go-to when you’re measuring performance on a really small scale, as it gives you the most accurate timing available. So, for critical performance testing, especially with tiny time intervals, it’s the best choice.