Why caching reduces server load in Rest API - Challenge Your Understanding

Challenge - 5 Problems

🧠 Conceptual

intermediate

How does caching reduce server load?

Imagine a busy coffee shop where many customers order the same drink. Instead of making each drink from scratch every time, the barista prepares a batch in advance. How does this idea relate to caching in REST APIs?

Choose the best explanation for why caching reduces server load.

A Caching stores responses so the server can skip processing repeated requests, reducing work and speeding up responses.

B Caching increases server load by storing extra data and making the server check the cache for every request.

C Caching forces the server to process every request twice, once for the cache and once for the client.

D Caching deletes old data from the server, which reduces the amount of data the server holds.

💡 Hint

Think about how reusing prepared data saves time and effort.

Caching saves server resources by reusing stored responses for repeated requests. This avoids repeating expensive operations like database queries or computations, thus reducing server load and improving response time.

❓ Predict Output

intermediate

Output of caching simulation code

Consider this Python code simulating a simple cache for API responses. What is the output?

Rest API

cache = {}
def get_data(key):
    if key in cache:
        return f"Cache hit: {cache[key]}"
    else:
        data = f"Data for {key}"
        cache[key] = data
        return f"Cache miss: {data}"

print(get_data('user1'))
print(get_data('user1'))
print(get_data('user2'))

A

Cache hit: Data for user1
Cache miss: Data for user1
Cache hit: Data for user2

B

Cache hit: Data for user1
Cache hit: Data for user1
Cache hit: Data for user2

C

Cache miss: Data for user1
Cache miss: Data for user1
Cache miss: Data for user2

D

Cache miss: Data for user1
Cache hit: Data for user1
Cache miss: Data for user2

💡 Hint

Check when the cache is empty and when it has stored data.

The first call for 'user1' is a miss because the cache is empty. It stores the data. The second call for 'user1' hits the cache and returns stored data. The call for 'user2' is a miss because it's new.

🔧 Debug

advanced

Identify the error causing cache to not reduce server load

This code tries to cache API responses but does not reduce server load as expected. What is the main problem?

Rest API

cache = {}
def fetch_data(key):
    if key not in cache:
        data = f"Data for {key}"
    cache[key] = data
    return cache[key]

print(fetch_data('item1'))
print(fetch_data('item1'))

A Variable 'data' is used before assignment when key is in cache, causing a runtime error.

B Cache is never updated because the condition is wrong, so it always fetches new data.

C The function always returns None because it lacks a return statement.

D The cache dictionary is cleared after each call, so caching does not work.

💡 Hint

Look at what happens when the key is already in the cache.

If the key is in cache, the 'if' block is skipped, so 'data' is never assigned. Then 'cache[key] = data' tries to use 'data' which is undefined, causing an error.

📝 Syntax

advanced

Which code snippet correctly implements caching?

Which of the following Python code snippets correctly implements a cache to reduce server load?

A

cache = {}
def get_response(key):
    if key not in cache:
        response = f"Response for {key}"
    cache[key] = response
    return cache[key]

B

cache = []
def get_response(key):
    if key in cache:
        return cache[key]
    response = f"Response for {key}"
    cache.append(response)
    return response

C

cache = {}
def get_response(key):
    if key in cache:
        return cache[key]
    response = f"Response for {key}"
    cache[key] = response
    return response

D

cache = {}
def get_response(key):
    response = f"Response for {key}"
    cache[key] = response
    if key in cache:
        return cache[key]

💡 Hint

Check which code stores and returns cached data properly.

Option C correctly checks if the key is in cache and returns it. If not, it creates the response, stores it, and returns it. Others have errors like wrong data structure, missing return, or overwriting cache incorrectly.

🚀 Application

expert

Calculate server load reduction with caching

A server receives 1000 identical requests per minute. Without caching, each request takes 50ms of server processing time. With caching, 80% of requests are served from cache instantly (0ms processing), and 20% require full processing.

What is the total server processing time per minute with caching?

A 1000 ms

B 10000 ms

C 50000 ms

D 40000 ms

💡 Hint

Calculate time for cached and non-cached requests separately, then add.

20% of 1000 requests = 200 requests need processing. Each takes 50ms, so 200 * 50ms = 10000ms. Cached requests take 0ms. Total = 10000ms per minute.