Cache-aside (lazy loading) deep dive in Redis - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

Cache miss handling in cache-aside pattern

In a cache-aside pattern using Redis, when a cache miss occurs, what is the correct sequence of actions to retrieve and cache the data?

A Return null immediately without fetching from database

B Fetch data from database, store it in cache, then return data

C Fetch data from cache again, then return null if not found

D Store null in cache first, then fetch data from database

💡 Hint

Think about how lazy loading works to reduce database hits.

In cache-aside, on a cache miss, the application fetches data from the database, stores it in the cache, then returns it. This avoids repeated database hits for the same data.

🧠 Conceptual

intermediate

Why avoid caching null values in cache-aside?

In cache-aside pattern, why is it generally discouraged to cache null or empty results directly?

A Because caching null can cause stale data if the database later has valid data

B Because Redis does not support storing null values

C Because caching null values increases memory usage unnecessarily

D Because null values cause Redis to crash

💡 Hint

Consider what happens if the database updates after caching null.

Caching null means the cache will serve empty results even if the database later has valid data, causing stale cache issues.

📝 Syntax

advanced

Correct Redis command sequence for cache-aside get

Which Redis command sequence correctly implements a cache-aside get operation for key 'user:123' assuming a database fetch function fetchUserFromDB()?

Redis

Pseudocode:
1. Try to get value from Redis
2. If not found, fetch from DB
3. Store in Redis
4. Return value

A

GET user:123
IF null THEN fetchUserFromDB()
SET user:123 value
RETURN value

B

SET user:123 value
GET user:123
RETURN value

C

GET user:123
RETURN value

D

DEL user:123
GET user:123
fetchUserFromDB()
RETURN value

💡 Hint

Remember to fetch from DB only if cache miss occurs.

The correct flow is to GET from Redis, if null then fetch from DB, SET in Redis, then return.

❓ optimization

advanced

Reducing cache stampede in cache-aside pattern

What is an effective technique to reduce cache stampede (many requests causing DB overload) in cache-aside pattern with Redis?

A Disable caching and always fetch from DB

B Increase Redis cache TTL to infinite

C Use distributed locks or mutexes to allow only one DB fetch on cache miss

D Use Redis DEL command frequently to clear cache

💡 Hint

Think about controlling concurrent access to DB on cache miss.

Using distributed locks ensures only one request fetches from DB and others wait, preventing overload.

🔧 Debug

expert

Diagnosing stale data in cache-aside pattern

You notice your Redis cache returns outdated user profile data even after the database was updated. Which of the following is the most likely cause?

A Redis automatically updates cache when database changes

B The cache TTL is set to zero causing immediate expiration

C The database update failed silently

D The application updates the database but does not invalidate or update the cache

💡 Hint

Think about cache consistency with database updates.

Cache-aside requires explicit cache invalidation or update after DB changes; otherwise stale data remains.

Practice

1. What is the main idea behind the Cache-aside (lazy loading) pattern in Redis?

easy

A. Preload all data into cache before any request

B. Load data into cache only when it is requested and missing

C. Update cache only after database changes without reading cache

D. Keep cache and database completely separate without syncing

Solution

1. Step 1: Understand cache-aside purpose

   Cache-aside means data is loaded into cache only when requested and not found in cache.

2. Step 2: Compare options with definition

   Load data into cache only when it is requested and missing matches this lazy loading idea; others describe different caching strategies.

3. Final Answer:

   Load data into cache only when it is requested and missing -> Option B

4. Quick Check:

   Cache-aside = lazy loading on miss [OK]

Hint: Cache-aside loads data only on cache miss [OK]

Common Mistakes:

• Thinking cache preloads all data
• Confusing cache-aside with write-through caching
• Assuming cache and DB never sync

2. Which Redis command is typically used to check if a key exists in cache before loading from the database in cache-aside pattern?

easy

A. EXISTS

B. GET

C. SET

D. DEL

Solution

1. Step 1: Identify command to check key presence

   Redis command EXISTS returns if a key is present in cache.

2. Step 2: Eliminate other commands

   GET retrieves value, SET writes value, DEL deletes key; only EXISTS checks presence.

3. Final Answer:

   EXISTS -> Option A

4. Quick Check:

   Check key presence = EXISTS [OK]

Hint: Use EXISTS to check cache key before GET [OK]

Common Mistakes:

• Using GET to check existence (returns nil if missing)
• Confusing SET as check command
• Using DEL which deletes keys

3. Consider this pseudo-code for cache-aside in Redis:

if not redis.exists(key):
  data = db.query(key)
  redis.set(key, data)
return redis.get(key)

What will be returned if the key is missing in cache but present in database?

medium

A. Old stale data from cache

B. Null or empty because cache was empty initially

C. An error because key does not exist in cache

D. The data fetched from database and cached

Solution

1. Step 1: Follow code logic on cache miss

   If key missing in Redis, code queries database and sets cache with fresh data.

2. Step 2: Return cached data after setting

   After setting, code returns redis.get(key) which now holds fresh data.

3. Final Answer:

   The data fetched from database and cached -> Option D

4. Quick Check:

   Cache miss triggers DB fetch and cache set [OK]

Hint: Cache miss loads DB data then returns it [OK]

Common Mistakes:

• Assuming cache returns empty if initially missing
• Expecting error on missing cache key
• Thinking cache returns stale data without update

4. You have this cache-aside code snippet:

if redis.get(key) is None:
  data = db.query(key)
  redis.set(key, data)
return redis.get(key)

But sometimes stale data is returned after DB update. What is the likely bug?

medium

A. Setting cache before querying database

B. Using redis.get instead of redis.exists to check cache

C. Not deleting or updating cache after DB changes

D. Returning data before setting cache

Solution

1. Step 1: Identify stale data cause

   Stale data means cache was not refreshed after DB update.

2. Step 2: Check cache update strategy

   Code only sets cache on miss, but does not invalidate or update cache after DB changes.

3. Final Answer:

   Not deleting or updating cache after DB changes -> Option C

4. Quick Check:

   Cache stale if not refreshed after DB update [OK]

Hint: Always invalidate cache after DB update [OK]

Common Mistakes:

• Thinking redis.get check causes stale data
• Believing cache sets before DB query fixes stale data
• Ignoring cache invalidation on DB writes

5. You want to implement cache-aside with Redis for a user profile service. The database updates user info frequently. Which approach best ensures cache consistency and performance?

hard

A. On DB update, delete cache key; on read, load from DB if cache miss

B. Always write to cache first, then update DB asynchronously

C. Preload all user profiles into cache at service start

D. Never update cache; always read directly from DB

Solution

1. Step 1: Analyze cache consistency needs

   Frequent DB updates require cache invalidation to avoid stale data.

2. Step 2: Evaluate options for cache-aside pattern

   On DB update, delete cache key; on read, load from DB if cache miss deletes cache on DB update, so next read reloads fresh data, matching cache-aside best practice.

3. Final Answer:

   On DB update, delete cache key; on read, load from DB if cache miss -> Option A

4. Quick Check:

   Invalidate cache on update, lazy load on read [OK]

Hint: Delete cache on update, lazy load on read [OK]

Common Mistakes:

• Writing cache before DB causes inconsistency
• Preloading all data wastes memory and may be stale
• Ignoring cache leads to poor performance