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python cache api requests

Narkw Brahma

Narkw Brahma

• 2 min read

Python Cache API Requests

Python is a popular language used for web scraping and performing API requests. When making API requests, there is a possibility of sending too many requests in a short period of time, which can lead to a denial of service or rate-limiting issues. To avoid these problems, caching API requests is a useful technique.

Using the Python Requests Cache library

The Python Requests library provides built-in support for caching using the cache-control header. However, it is limited and not very configurable. To overcome this limitation, there is another library called Requests Cache. This library provides more control over the caching mechanism and allows us to cache API responses to disk or memory.

To use the Requests Cache library, you need first to install it via pip:


    pip install requests-cache

After installation, you can start using it in your code by importing it:


    import requests_cache
    
    # enable caching
    requests_cache.install_cache('api_cache', expire_after=600)  # cache for 10 minutes

The above code installs a cache for API requests with an expiration time of 10 minutes. After enabling the cache, all requests made using the requests module will be cached automatically. If you make the same request again, it will return the cached response instead of making a new request.

Using the caching feature of the Python Redis library

Redis is an in-memory data structure store that can be used as a database, cache, and message broker. It is often used for caching API responses because of its fast read and write times. The Python Redis library provides built-in support for caching API responses using Redis.

First, you need to install the Redis library via pip:


    pip install redis

After installation, you can start using Redis in your code by importing it:


    import redis
    
    # create a Redis client
    r = redis.Redis(host='localhost', port=6379, db=0)

The above code creates a Redis client that connects to a Redis server running on localhost at port 6379. You can then use this client to store API responses in Redis:


    import requests
    import json
    
    url = 'https://jsonplaceholder.typicode.com/todos/1'
    
    # check if response is in cache
    response = r.get(url)
    
    if response:
        data = json.loads(response)
    else:
        # make API request and store response in cache
        response = requests.get(url)
        data = response.json()
        r.set(url, json.dumps(data))
    
    print(data)

The above code first checks if the API response is already cached in Redis. If it is, it loads the cached data from Redis. Otherwise, it makes an API request and stores the response in Redis for future use.

Conclusion

Caching API responses can improve your application's performance and avoid rate-limiting issues. The Python Requests Cache library and Redis are two popular choices for caching API responses. The Requests Cache library provides a more flexible caching mechanism, whereas Redis provides fast read and write times for caching.

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