Why Cache invalidation strategies in Rest API? - Purpose & Use Cases

The Big Idea

What if your app could always show fresh data without you lifting a finger?

The Scenario

Imagine you have a website that shows product prices. You store these prices in a cache to make the site faster. But when prices change, the cache still shows old prices. You have to manually find and update every cached price everywhere.

The Problem

Manually updating cached data is slow and easy to forget. It causes users to see wrong information, leading to confusion and lost sales. It's like trying to update every copy of a flyer by hand whenever the price changes.

The Solution

Cache invalidation strategies automatically keep cached data fresh. They decide when to remove or update old cache entries so users always get the latest info without manual work.

Before vs After

✗ Before

cache['product_123'] = old_price  # forget to update after price change

✓ After

cache.invalidate('product_123')  # automatically remove old price from cache

What It Enables

It enables fast, reliable apps that always show up-to-date data without slowing down users.

Real Life Example

An online store updates product prices daily. With cache invalidation, the site instantly shows new prices without waiting or errors.

Key Takeaways

Manual cache updates are slow and error-prone.

Cache invalidation strategies automate keeping data fresh.

This improves user experience and app reliability.

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