Why Cache stampede prevention in HLD? - Purpose & Use Cases

The Big Idea

What if millions of users crash your system just because your cache expired at the same time?

The Scenario

Imagine a popular website where thousands of users request the same data at the same time. Without any special handling, all these requests hit the database simultaneously when the cache expires.

The Problem

This causes the database to overload, slowing down responses or even crashing the system. Manually trying to handle this by limiting requests or adding delays is complex and often fails under heavy load.

The Solution

Cache stampede prevention techniques ensure that when cached data expires, only one request fetches fresh data while others wait or get stale data. This keeps the system stable and fast without overwhelming the database.

Before vs After

✗ Before

if cache expired:
  fetch data from DB
  update cache
return cache data

✓ After

if cache expired:
  if lock acquired:
    fetch data from DB
    update cache
    release lock
  else:
    wait for cache update
return cache data

What It Enables

It enables systems to handle huge traffic spikes smoothly without crashing or slowing down.

Real Life Example

A news website updating its homepage data every few minutes can serve millions of users without database overload by preventing cache stampedes.

Key Takeaways

Manual cache refresh causes database overload under high traffic.

Cache stampede prevention lets only one request refresh cache at a time.

This keeps systems fast, stable, and scalable during traffic spikes.

Practice

1. What is the main goal of cache stampede prevention in system design?

easy

A. To prevent many requests from hitting the database simultaneously

B. To increase the size of the cache storage

C. To reduce the number of cache servers

D. To encrypt cached data for security

Solution

1. Step 1: Understand cache stampede problem

   Cache stampede happens when many requests try to fetch data from the database because the cache expired or missed.

2. Step 2: Identify the goal of prevention

   The goal is to stop all these requests from hitting the database at once to avoid overload.

3. Final Answer:

   To prevent many requests from hitting the database simultaneously -> Option A

4. Quick Check:

   Cache stampede prevention = prevent database overload [OK]

Hint: Focus on reducing database hits during cache misses [OK]

Common Mistakes:

• Confusing cache size with stampede prevention
• Thinking encryption is related to stampede
• Assuming reducing cache servers helps stampede

2. Which of the following is a common technique used to prevent cache stampede?

easy

A. Storing data only in the database

B. Increasing cache TTL to infinite

C. Disabling cache for all requests

D. Using distributed locks around cache refresh

Solution

1. Step 1: Review common stampede prevention methods

   Techniques include locking, early expiration, and request coalescing to control cache refresh.

2. Step 2: Match technique to options

   Distributed locks prevent multiple processes from refreshing cache simultaneously, stopping stampede.

3. Final Answer:

   Using distributed locks around cache refresh -> Option D

4. Quick Check:

   Locking = stampede prevention [OK]

Hint: Look for locking or coordination methods in options [OK]

Common Mistakes:

• Choosing infinite TTL which can cause stale data
• Disabling cache which increases load
• Ignoring locking as a prevention method

3. Consider this pseudo-code for cache stampede prevention:

if cache miss:
  acquire lock
  if cache still miss:
    data = fetch from DB
    update cache
  release lock
return cached data

What happens if the lock is not used here?

medium

A. Multiple requests fetch from DB simultaneously causing overload

B. Cache never updates and returns stale data

C. Requests wait indefinitely causing deadlock

D. Cache size increases exponentially

Solution

1. Step 1: Analyze the role of the lock

   The lock ensures only one request fetches data from DB and updates cache on miss.

2. Step 2: Consequence of missing lock

   Without lock, many requests detect cache miss and all fetch from DB, causing overload.

3. Final Answer:

   Multiple requests fetch from DB simultaneously causing overload -> Option A

4. Quick Check:

   No lock = DB overload [OK]

Hint: Lock prevents simultaneous DB fetch on cache miss [OK]

Common Mistakes:

• Thinking cache never updates without lock
• Confusing deadlock with missing lock
• Assuming cache size grows without lock

4. A system uses early expiration to prevent cache stampede by setting cache TTL shorter than data TTL. However, users report stale data sometimes. What is the likely cause?

medium

A. Cache TTL is longer than data TTL causing stale reads

B. Cache is not refreshed after early expiration due to missing lock

C. Database is down causing stale cache

D. Cache size is too small causing evictions

Solution

1. Step 1: Understand early expiration technique

   Early expiration refreshes cache before data TTL ends to avoid stampede.

2. Step 2: Identify cause of stale data

   If cache refresh is not coordinated (missing lock), cache may not update properly, causing stale data.

3. Final Answer:

   Cache is not refreshed after early expiration due to missing lock -> Option B

4. Quick Check:

   Missing lock = stale cache after early expiration [OK]

Hint: Check if locking coordinates cache refresh [OK]

Common Mistakes:

• Assuming TTL mismatch causes stale data
• Blaming database downtime without evidence
• Confusing cache size with freshness

5. You design a high-traffic API with cache stampede prevention. Which combination best prevents stampede while ensuring fresh data and minimal DB load?

hard

A. Disable cache and use database connection pooling

B. Use long cache TTL with no locking and rely on DB scaling

C. Use short cache TTL with distributed locking and request coalescing

D. Use infinite cache TTL and manual cache invalidation

Solution

1. Step 1: Evaluate each option for stampede prevention

   Use long cache TTL with no locking and rely on DB scaling risks DB overload without locking; C disables cache increasing DB load; D risks stale data.

2. Step 2: Identify best practice combination

   Short TTL triggers refresh often; distributed locking prevents multiple refreshes; request coalescing merges requests to reduce DB hits.

3. Final Answer:

   Use short cache TTL with distributed locking and request coalescing -> Option C

4. Quick Check:

   Locking + coalescing + short TTL = best stampede prevention [OK]

Hint: Combine locking and coalescing with short TTL for best results [OK]

Common Mistakes:

• Ignoring locking and causing DB overload
• Disabling cache increases latency and load
• Using infinite TTL causes stale data