Cache invalidation strategies in Rest API - Mini Project: Build & Apply

Cache Invalidation Strategies in REST API

📖 Scenario: You are building a simple REST API for a bookstore. To make the API faster, you want to add caching for book details. But when book information changes, the cache must be updated or cleared so users get fresh data.

🎯 Goal: Build a small REST API simulation that stores book data, caches book details, and uses a cache invalidation strategy to update the cache when book data changes.

📋 What You'll Learn

Create a dictionary called books with 3 books and their prices

Create a cache dictionary called cache to store cached book details

Write a function get_book_details(book_id) that returns cached data if available, otherwise fetches from books and caches it

Write a function update_book_price(book_id, new_price) that updates the book price and invalidates the cache for that book

Print the cached data before and after updating a book price to show cache invalidation

💡 Why This Matters

🌍 Real World

Caching is used in real REST APIs to speed up responses by storing data temporarily. Cache invalidation ensures users get fresh data when updates happen.

💼 Career

Understanding cache invalidation is important for backend developers working with APIs, improving performance and data accuracy.

1

Create the initial book data

Create a dictionary called books with these exact entries: 1: {'title': 'Python Basics', 'price': 29.99}, 2: {'title': 'REST APIs', 'price': 39.99}, 3: {'title': 'Data Science', 'price': 49.99}

Rest API

# Create the books dictionary with 3 books
# Your code here

Need a hint?

Use a dictionary with keys 1, 2, 3 and values as dictionaries with keys 'title' and 'price'.

2

Create the cache dictionary

Create an empty dictionary called cache to store cached book details

Rest API

books = {
    1: {'title': 'Python Basics', 'price': 29.99},
    2: {'title': 'REST APIs', 'price': 39.99},
    3: {'title': 'Data Science', 'price': 49.99}
}
# Create an empty cache dictionary
# Your code here

Need a hint?

Just create an empty dictionary named cache.

3

Write functions for caching and invalidation

Write a function called get_book_details(book_id) that returns cached data if book_id is in cache. Otherwise, fetch the book from books, store it in cache, and return it. Also write a function called update_book_price(book_id, new_price) that updates the price in books and removes the book_id from cache to invalidate it.

Rest API

books = {
    1: {'title': 'Python Basics', 'price': 29.99},
    2: {'title': 'REST APIs', 'price': 39.99},
    3: {'title': 'Data Science', 'price': 49.99}
}

cache = {}

# Write get_book_details(book_id) function
# Write update_book_price(book_id, new_price) function
# Your code here

Need a hint?

Use if book_id in cache to check cache. Use del cache[book_id] to invalidate cache.

4

Print cache before and after update

Print the result of get_book_details(2) to show cached data. Then call update_book_price(2, 44.99) to update the price and invalidate the cache. Finally, print get_book_details(2) again to show updated data is fetched and cached.

Rest API

books = {
    1: {'title': 'Python Basics', 'price': 29.99},
    2: {'title': 'REST APIs', 'price': 39.99},
    3: {'title': 'Data Science', 'price': 49.99}
}

cache = {}

def get_book_details(book_id):
    if book_id in cache:
        return cache[book_id]
    else:
        book = books.get(book_id)
        if book:
            cache[book_id] = book
        return book

def update_book_price(book_id, new_price):
    if book_id in books:
        books[book_id]['price'] = new_price
        if book_id in cache:
            del cache[book_id]

# Print cached data before update
# Update price and invalidate cache
# Print cached data after update
# Your code here

Need a hint?

Print the result of get_book_details(2), then call update_book_price(2, 44.99), then print get_book_details(2) again.