This article will show you how to use caching in Python with your web

scraping tasks. You can read the [<u>full

article</u>](https://oxylabs.io/blog/python-cache-how-to-use-effectively)

on our blog, where we delve deeper into the different caching

strategies.

There are different ways to implement caching in Python for different

caching strategies. Here we’ll see two methods of Python caching for a

simple web scraping example. If you’re new to web scraping, take a look

at our [<u>step-by-step Python web scraping

guide</u>](https://oxylabs.io/blog/python-web-scraping).

We’ll use the [<u>requests

library</u>](https://pypi.org/project/requests/) to make HTTP requests

to a website. Install it with

[<u>pip</u>](https://pypi.org/project/pip/) by entering the following

command in your terminal:

python -m pip install requests

Other libraries we’ll use in this project, specifically time and

functools, come natively with Python 3.11.2, so you don’t have to

install them.

A [<u>decorator</u>](https://peps.python.org/pep-0318/) in Python is a

function that accepts another function as an argument and outputs a new

function. We can alter the behavior of the original function using a

decorator without changing its source code.

One common use case for decorators is to implement caching. This

involves creating a dictionary to store the function's results and then

saving them in the cache for future use.

Let’s start by creating a simple function that takes a URL as a function

argument, requests that URL, and returns the response text:

def get_html_data(url):

response = requests.get(url)

return response.text

Now, let's move toward creating a memoized version of this function:

def memoize(func):

cache = {}

def wrapper(\*args):

if args in cache:

return cache\[args\]

else:

result = func(\*args)

cache\[args\] = result

return result

return wrapper

def get_html_data_cached(url):

response = requests.get(url)

return response.text

The wrapper function determines whether the current input arguments have

been previously cached and, if so, returns the previously cached result.

If not, the code calls the original function and caches the result

before being returned. In this case, we define a memoize decorator that

generates a cache dictionary to hold the results of previous function

calls.

By adding @memoize above the function definition, we can use the memoize

decorator to enhance the get_html_data function. This generates a new

memoized function that we’ve called get_html_data_cached. It only makes

a single network request for a URL and then stores the response in the

cache for further requests.

Let’s use the time module to compare the execution speeds of the

get_html_data function and the memoized get_html_data_cached function:

import time

start_time = time.time()

get_html_data('https://books.toscrape.com/')

print('Time taken (normal function):', time.time() - start_time)

start_time = time.time()

get_html_data_cached('https://books.toscrape.com/')

print('Time taken (memoized function using manual decorator):',

time.time() - start_time)

Here’s what the complete code looks like:

\# Import the required modules

from functools import lru_cache

import time

import requests

\# Function to get the HTML Content

def get_html_data(url):

response = requests.get(url)

return response.text

\# Memoize function to cache the data

def memoize(func):

cache = {}

\# Inner wrapper function to store the data in the cache

def wrapper(\*args):

if args in cache:

return cache\[args\]

else:

result = func(\*args)

cache\[args\] = result

return result

return wrapper

\# Memoized function to get the HTML Content

def get_html_data_cached(url):

response = requests.get(url)

return response.text

\# Get the time it took for a normal function

start_time = time.time()

get_html_data('https://books.toscrape.com/')

print('Time taken (normal function):', time.time() - start_time)

\# Get the time it took for a memoized function (manual decorator)

start_time = time.time()

get_html_data_cached('https://books.toscrape.com/')

print('Time taken (memoized function using manual decorator):',

time.time() - start_time)

Here’s the output:

Notice the time difference between the two functions. Both take almost

the same time, but the supremacy of caching lies behind the re-access.

Since we’re making only one request, the memoized function also has to

access data from the main memory. Therefore, with our example, a

significant time difference in execution isn’t expected. However, if you

increase the number of calls to these functions, the time difference

will significantly increase (see [<u>Performance

Comparison</u>](#performance-comparison)). 

Another method to implement caching in Python is to use the built-in

@lru_cache decorator from functools. This decorator implements cache

using the least recently used (LRU) caching strategy. This LRU cache is

a fixed-size cache, which means it’ll discard the data from the cache

that hasn’t been used recently.

To use the @lru_cache decorator, we can create a new function for

extracting HTML content and place the decorator name at the top. Make

sure to import the functools module before using the decorator: 

from functools import lru_cache

@lru_cache(maxsize=None)

def get_html_data_lru(url):

response = requests.get(url)

return response.text

In the above example, the get_html_data_lru method is memoized using the

@lru_cache decorator. The cache can grow indefinitely when the maxsize

option is set to None.

To use the @lru_cache decorator, just add it above the get_html_data_lru

function. Here’s the complete code sample:

\# Import the required modules

from functools import lru_cache

import time

import requests

\# Function for getting HTML Content

def get_html_data(url):

response = requests.get(url)

return response.text

\# Memoized using LRU Cache

@lru_cache(maxsize=None)

def get_html_data_lru(url):

response = requests.get(url)

return response.text

\# Getting time for Normal function to extract HTML content

start_time = time.time()

get_html_data('https://books.toscrape.com/')

print('Time taken (normal function):', time.time() - start_time)

\# Getting time for Memoized function (LRU cache) to extract HTML

content

start_time = time.time()

get_html_data_lru('https://books.toscrape.com/')

print('Time taken (memoized function with LRU cache):', time.time() -

start_time)

This produced the following output:

In the following table, we’ve determined the execution times of all

three functions for different numbers of requests to these functions:

| **No. of requests** | **Time taken by normal function** | **Time taken by memoized function (manual decorator)** | **Time taken by memoized function (lru_cache decorator)** |

|---------------------|-----------------------------------|--------------------------------------------------------|-----------------------------------------------------------|

| 1 | 2.1 Seconds | 2.0 Seconds | 1.7 Seconds |

| 10 | 17.3 Seconds | 2.1 Seconds | 1.8 Seconds |

| 20 | 32.2 Seconds | 2.2 Seconds | 2.1 Seconds |

| 30 | 57.3 Seconds | 2.22 Seconds | 2.12 Seconds |

As the number of requests to the functions increases, you can see a

significant reduction in execution times using the caching strategy. The

following comparison chart depicts these results:

The comparison results clearly show that using a caching strategy in

your code can significantly improve overall performance and speed.

Feel free to visit our [<u>blog</u>](https://oxylabs.io/blog) for an

array of intriguing web scraping topics that will keep you hooked!

from functools import lru_cache

import time

import requests

def get_html_data(url):

response = requests.get(url)

return response.text

def get_html_data_lru(url):

response = requests.get(url)

return response.text

start_time = time.time()

get_html_data('https://books.toscrape.com/')

print('Time taken (normal function):', time.time() - start_time)

start_time = time.time()

get_html_data_lru('https://books.toscrape.com/')

print('Time taken (memoized function with LRU cache):', time.time() - start_time)

from functools import lru_cache

import time

import requests

def get_html_data(url):

response = requests.get(url)

return response.text

def memoize(func):

cache = {}

# Inner wrapper function to store the data in the cache

def wrapper(*args):

if args in cache:

return cache[args]

else:

result = func(*args)

cache[args] = result

return result

return wrapper

def get_html_data_cached(url):

response = requests.get(url)

return response.text

start_time = time.time()

get_html_data('https://books.toscrape.com/')

print('Time taken (normal function):', time.time() - start_time)

start_time = time.time()

get_html_data_cached('https://books.toscrape.com/')

print('Time taken (memoized function using manual decorator):', time.time() - start_time)