Distributed caching (Redis, Memcached) in HLD - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Architecture

intermediate

Designing a distributed cache with Redis

You need to design a distributed caching layer using Redis for a web application that handles millions of users. Which architecture component is essential to ensure data consistency and high availability?

A Deploy Redis as a single instance on a powerful server

B Use Redis Sentinel for automatic failover and monitoring

C Use Memcached with no replication for simplicity

D Store cache data only on the application server memory

💡 Hint

Think about how Redis handles failover and keeps the cache available during node failures.

Redis Sentinel provides monitoring, notification, and automatic failover to keep the cache highly available and consistent. Single instances or local memory do not provide failover, and Memcached lacks built-in replication.

❓ scaling

intermediate

Scaling Memcached for large datasets

Your application uses Memcached to cache user session data. The dataset is growing beyond the capacity of a single Memcached server. What is the best approach to scale Memcached horizontally?

A Use Redis instead because Memcached cannot scale horizontally

B Increase the RAM of the single Memcached server to hold all data

C Use consistent hashing to distribute keys across multiple Memcached nodes

D Store all data in a single Memcached node and replicate it to others

💡 Hint

Consider how Memcached clients decide which server to use for a given key.

Consistent hashing allows clients to distribute keys evenly across multiple Memcached servers, enabling horizontal scaling. Increasing RAM on one server has limits, and Memcached supports horizontal scaling via client-side hashing.

❓ tradeoff

advanced

Choosing between Redis and Memcached for session caching

Your team must choose between Redis and Memcached for caching user sessions. Which tradeoff is most important when deciding?

A Redis supports data persistence and complex data types, Memcached is simpler and faster for pure key-value caching

B Memcached supports transactions, Redis does not

C Redis cannot scale horizontally, Memcached can

D Memcached supports data persistence, Redis does not

💡 Hint

Think about features and use cases of Redis and Memcached.

Redis supports persistence and complex data types like lists and sets, making it versatile. Memcached is simpler and optimized for fast key-value caching but lacks persistence and complex data structures.

🧠 Conceptual

advanced

Cache invalidation strategies in distributed caching

Which cache invalidation strategy is best suited for a distributed cache to maintain data freshness without overwhelming the backend database?

A Write all updates only to the cache and never to the database

B Always delete cache entries immediately after every database update

C Never expire cache entries and rely on manual cache clearing

D Use time-to-live (TTL) expiration combined with lazy loading on cache miss

💡 Hint

Consider balancing freshness and load on the database.

TTL expiration ensures cache entries expire after a set time, and lazy loading reloads data on cache miss. Immediate deletion can cause high load, and never expiring risks stale data. Writing only to cache breaks data consistency.

❓ estimation

expert

Estimating cache capacity for a high-traffic system

Your system expects 10 million active users, each with an average session size of 2 KB. You want to cache 30% of active sessions simultaneously in Redis. Estimate the minimum memory required for the Redis cache, considering 20% overhead for metadata and replication.

A Approximately 7.2 GB

B Approximately 4.8 GB

C Approximately 12 GB

D Approximately 3.6 GB

💡 Hint

Calculate total data size, then add overhead.

30% of 10 million users = 3 million sessions. Each session 2 KB → 6 GB raw data. Adding 20% overhead → 6 GB * 1.2 = 7.2 GB minimum memory.

Practice

1. What is the primary purpose of using a distributed cache like Redis or Memcached in a system?

easy

A. To reduce database load by storing frequently accessed data in memory

B. To permanently store all application data for backup

C. To replace the database entirely for all data storage needs

D. To increase the size of the database by adding more servers

Solution

1. Step 1: Understand the role of distributed cache

   Distributed caches store frequently accessed data in fast memory to reduce the number of database queries.

2. Step 2: Identify the main benefit

   This reduces the load on the database and speeds up data retrieval for repeated requests.

3. Final Answer:

   To reduce database load by storing frequently accessed data in memory -> Option A

4. Quick Check:

   Distributed cache reduces DB load = C [OK]

Hint: Cache stores frequent data to reduce DB hits [OK]

Common Mistakes:

• Thinking cache permanently stores all data
• Confusing cache with database backup
• Assuming cache replaces the database

2. Which of the following is the correct way to set a key-value pair in Redis using its command syntax?

easy

A. SET key value

B. GET key value

C. STORE key value

D. PUT key value

Solution

1. Step 1: Recall Redis command for setting data

   Redis uses the command SET key value to store a value under a key.

2. Step 2: Verify other options

   GET retrieves data, STORE and PUT are not valid Redis commands.

3. Final Answer:

   SET key value -> Option A

4. Quick Check:

   Redis set command = SET key value [OK]

Hint: Remember Redis uses SET to store key-value [OK]

Common Mistakes:

• Confusing GET with SET
• Using non-existent commands like STORE or PUT
• Mixing Redis commands with SQL syntax

3. Consider this simplified Redis usage in code:

cache.set('user:1', 'Alice')
name = cache.get('user:1')
print(name)

What will be printed?

medium

A. None

B. 'Alice'

C. user:1

D. Error

Solution

1. Step 1: Analyze the cache set operation

   The key 'user:1' is set with the value 'Alice' in the cache.

2. Step 2: Analyze the cache get operation

   Retrieving 'user:1' returns the stored value 'Alice', which is then printed.

3. Final Answer:

   'Alice' -> Option B

4. Quick Check:

   Set then get returns stored value = 'Alice' [OK]

Hint: Get returns value set by set command [OK]

Common Mistakes:

• Expecting key name instead of value
• Assuming None if not careful with key format
• Thinking it causes an error without context

4. A developer tries to use Memcached but notices cache misses even after setting keys. Which of these is a likely cause?

medium

A. The database is down, so cache cannot be used

B. Memcached does not support key-value storage

C. The cache keys are expiring too quickly due to a low TTL setting

D. Memcached requires SQL queries to retrieve data

Solution

1. Step 1: Understand Memcached key expiration

   Memcached keys have a TTL (time to live). If TTL is too low, keys expire quickly causing cache misses.

2. Step 2: Eliminate incorrect options

   Memcached supports key-value storage, does not depend on database status, and does not use SQL queries.

3. Final Answer:

   The cache keys are expiring too quickly due to a low TTL setting -> Option C

4. Quick Check:

   Low TTL causes cache misses = A [OK]

Hint: Check TTL settings if cache misses occur [OK]

Common Mistakes:

• Thinking Memcached lacks key-value support
• Assuming database status affects cache directly
• Confusing Memcached with SQL databases

5. You are designing a high-traffic web app that uses Redis as a distributed cache. To ensure data consistency when multiple servers update the same cache key, which approach is best?

hard

A. Use Memcached instead because it handles concurrency automatically

B. Allow all servers to update cache keys without coordination

C. Disable caching for keys that are frequently updated

D. Use Redis transactions or Lua scripts to perform atomic updates

Solution

1. Step 1: Identify concurrency challenges in distributed cache

   When multiple servers update the same key, race conditions can cause inconsistent data.

2. Step 2: Choose atomic update methods in Redis

   Redis supports transactions and Lua scripts to ensure updates happen atomically, preventing conflicts.

3. Step 3: Evaluate other options

   Allowing uncoordinated updates risks inconsistency; disabling caching reduces performance; Memcached lacks built-in atomic scripting.

4. Final Answer:

   Use Redis transactions or Lua scripts to perform atomic updates -> Option D

5. Quick Check:

   Atomic updates ensure consistency = D [OK]

Hint: Use Redis atomic commands for safe concurrent updates [OK]

Common Mistakes:

• Ignoring concurrency causing stale data
• Thinking Memcached handles concurrency automatically
• Disabling cache unnecessarily reduces speed