Cache invalidation strategies in Rest API - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this cache invalidation simulation?

Consider a REST API cache that stores user data. The cache invalidation is triggered when a user updates their profile. What will be the output of the following Python simulation code?

Rest API

cache = {'user_1': 'John Doe', 'user_2': 'Jane Smith'}

# User 1 updates profile
cache['user_1'] = 'John A. Doe'

# Invalidate cache for user_2
cache.pop('user_2', None)

print(cache)

A {'user_1': 'John A. Doe', 'user_2': 'Jane Smith'}

B {'user_1': 'John Doe', 'user_2': 'Jane Smith'}

C {'user_1': 'John A. Doe'}

D {'user_2': 'Jane Smith'}

💡 Hint

Think about what happens when you update a cache entry and then remove another.

The cache updates the value for 'user_1' to 'John A. Doe'. Then it removes 'user_2' from the cache. So only 'user_1' remains with the updated value.

🧠 Conceptual

intermediate

Which cache invalidation strategy is demonstrated here?

A REST API cache is set to expire entries automatically after 10 minutes. Which cache invalidation strategy does this represent?

A Time-to-live (TTL) expiration

B Write-through caching

C Manual invalidation

D Cache aside

💡 Hint

Think about automatic expiration based on time.

TTL expiration means cache entries are invalidated automatically after a set time period.

🔧 Debug

advanced

Why does this cache invalidation code fail to remove the stale entry?

Look at the code below that tries to invalidate a cache entry after updating the database. Why does the stale cache entry remain?

Rest API

cache = {'item_1': 'old_value'}

def update_database():
    # Simulate database update
    return 'new_value'

new_val = update_database()

# Later code tries to remove stale cache but fails
if 'item_1' in cache:
    pass  # supposed to remove stale cache but does nothing

print(cache)

A The cache invalidation code does not remove the entry; it only checks existence.

B The cache dictionary is immutable, so assignment fails.

C The cache entry is overwritten, so no stale entry remains.

D The update_database function returns null, so cache is not updated.

💡 Hint

Look carefully at the 'if' block that is supposed to remove the cache entry.

The 'if' block only checks if 'item_1' is in cache but does not remove it. So the stale entry remains.

📝 Syntax

advanced

Which option causes a runtime error in cache invalidation code?

Identify the option that will cause a runtime error when trying to invalidate a cache entry in Python.

Rest API

cache = {'key1': 'value1', 'key2': 'value2'}

A cache.pop('key1')

B del cache['key2']

C cache['key1'] = None

D cache.remove('key1')

💡 Hint

Check which method is not valid for Python dictionaries.

Dictionaries do not have a 'remove' method. Using cache.remove('key1') causes an AttributeError.

🚀 Application

expert

How many cache entries remain after this sequence of invalidations?

Given the following cache invalidation sequence, how many entries remain in the cache?

Rest API

cache = {'a': 1, 'b': 2, 'c': 3, 'd': 4}

# Invalidate entries where value is even
cache = {k: v for k, v in cache.items() if v % 2 != 0}

# Add new entry
cache['e'] = 5

# Remove entry 'a'
cache.pop('a', None)

print(len(cache))

A 1

B 2

C 4

D 3

💡 Hint

Count entries after filtering, adding, and removing.

After removing even values (b:2, d:4), cache has 'a':1, 'c':3 (2 entries). Then 'e':5 added (3 entries). Removing 'a' leaves 'c':3, 'e':5 (2 entries).

Practice

1. Which cache invalidation strategy automatically removes cached data after a set period of time?

easy

A. Manual invalidation

B. Event-based invalidation

C. Time-based invalidation

D. Versioning invalidation

Solution

1. Step 1: Understand time-based invalidation

   Time-based invalidation removes cached data after a fixed time, ensuring data freshness automatically.

2. Step 2: Compare with other strategies

   Manual invalidation requires user action, event-based depends on data changes, and versioning uses data versions.

3. Final Answer:

   Time-based invalidation -> Option C

4. Quick Check:

   Automatic removal after time = Time-based invalidation [OK]

Hint: Time-based means cache expires after set time [OK]

Common Mistakes:

• Confusing manual with automatic removal
• Thinking event-based triggers on time
• Mixing versioning with time expiry

2. Which of the following is the correct way to manually invalidate a cache entry in a REST API?

easy

A. Send a PUT request to update cached data

B. Send a GET request to the cache endpoint

C. Send a POST request to retrieve cached data

D. Send a DELETE request to the cache endpoint

Solution

1. Step 1: Identify HTTP method for deletion

   DELETE requests are used to remove resources, including cache entries.

2. Step 2: Understand other methods

   GET retrieves data, POST creates, and PUT updates; none remove cache.

3. Final Answer:

   Send a DELETE request to the cache endpoint -> Option D

4. Quick Check:

   DELETE removes cache entry manually [OK]

Hint: DELETE method removes resources like cache entries [OK]

Common Mistakes:

• Using GET to remove cache
• Confusing POST with deletion
• Thinking PUT deletes cache

3. Consider this pseudo-code for event-based cache invalidation:

if (dataChangedEvent) {
  invalidateCache(key);
}
return getCachedData(key);

What will happen if dataChangedEvent is true?

medium

A. Cache is invalidated and fresh data is fetched next time

B. Cache remains unchanged and stale data is returned

C. Cache is updated without invalidation

D. An error occurs because cache cannot be invalidated

Solution

1. Step 1: Analyze the condition

   If dataChangedEvent is true, invalidateCache(key) runs, removing cached data.

2. Step 2: Understand cache behavior after invalidation

   After invalidation, next data fetch will get fresh data, not stale cache.

3. Final Answer:

   Cache is invalidated and fresh data is fetched next time -> Option A

4. Quick Check:

   Event triggers invalidation = fresh data next fetch [OK]

Hint: Event true means cache cleared for fresh data [OK]

Common Mistakes:

• Assuming cache stays after invalidation
• Thinking cache updates automatically
• Believing invalidation causes errors

4. You have this code snippet for versioning cache invalidation:

cache = { 'v1': data1 }
function getData(version) {
  return cache[version];
}

// Later
cache['v2'] = newData;

What is the main bug if the client still requests v1 after update?

medium

A. Cache throws an error because version key is missing

B. Client gets stale data because old version is still cached

C. Cache automatically updates old version to new data

D. Client cannot access any cached data

Solution

1. Step 1: Understand versioning cache behavior

   Old version 'v1' remains in cache, so requests for it get stale data.

2. Step 2: Identify bug impact

   Client requesting 'v1' does not see 'v2' data, causing stale responses.

3. Final Answer:

   Client gets stale data because old version is still cached -> Option B

4. Quick Check:

   Old version key returns stale data [OK]

Hint: Old version keys keep stale data unless removed [OK]

Common Mistakes:

• Assuming cache auto-updates old versions
• Expecting errors on missing keys
• Thinking client loses all cache access

5. You want to design a cache invalidation strategy for a REST API where data changes frequently but must stay fresh within 5 minutes. Which combined strategy is best?

hard

A. Use time-based invalidation with 5-minute expiry plus event-based invalidation on data changes

B. Use manual invalidation only, requiring user to clear cache

C. Use versioning only without time limits

D. Use time-based invalidation with 1-hour expiry only

Solution

1. Step 1: Analyze freshness requirement

   Data must be fresh within 5 minutes, so time-based expiry should be 5 minutes or less.

2. Step 2: Combine with event-based invalidation

   Event-based invalidation clears cache immediately on data changes, improving freshness.

3. Step 3: Evaluate other options

   Manual invalidation is slow, versioning alone may keep stale data, and 1-hour expiry is too long.

4. Final Answer:

   Use time-based invalidation with 5-minute expiry plus event-based invalidation on data changes -> Option A

5. Quick Check:

   Combine time and event for fresh cache [OK]

Hint: Combine time and event for best freshness [OK]

Common Mistakes:

• Relying only on manual invalidation
• Using too long expiry times
• Ignoring event triggers for changes