Why Distributed caching (Redis, Memcached) in HLD? - Purpose & Use Cases

The Big Idea

What if your website could serve millions instantly without crashing?

The Scenario

Imagine a busy online store where every user request asks the main database for product details. When hundreds of users visit at once, the database gets overwhelmed, causing slow page loads and frustrated customers.

The Problem

Relying only on the main database means every request waits for the same slow process. This causes delays, crashes, and unhappy users. Manually trying to speed this up by copying data everywhere is confusing and error-prone.

The Solution

Distributed caching stores frequently used data in fast, easy-to-access places like Redis or Memcached. This means most requests get answers quickly without bothering the slow database, making the whole system faster and more reliable.

Before vs After

✗ Before

result = database.query('SELECT * FROM products WHERE id=123')

✓ After

result = cache.get('product_123') or database.query('SELECT * FROM products WHERE id=123')

What It Enables

It enables lightning-fast responses and smooth user experiences even when millions of people use the system at the same time.

Real Life Example

Big websites like Amazon use distributed caching to quickly show product info and prices without waiting for the database every time you click.

Key Takeaways

Manual database calls slow down under heavy load.

Distributed caching stores data closer to users for quick access.

This improves speed, reliability, and user satisfaction.

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