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Distributed Cache

Scale caching across multiple servers with distributed cache architectures.

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Distributed Cache

Why Distributed?

Single server cache: Server 1 has cached data
Multiple servers: Server 2 does not have it (cache miss!)

Solution: Shared distributed cache (Redis)

Consistency Challenges

Challenge Description
Stale data Different servers see different data
Cache stampede Many requests for expired key
Split brain Cache partition failure

Key Points

  • Understanding Distributed Cache is essential for production systems
  • Always consider scalability and maintainability
  • Test thoroughly before deploying to production
  • Monitor performance and set up alerting

Common Patterns

  1. Validation: Always validate input at the boundary
  2. Error Handling: Use structured error responses
  3. Logging: Log key events for debugging
  4. Testing: Unit, integration, and load tests
  5. Documentation: Keep docs updated with code changes

Cache Best Practices

Strategies

  • Cache-Aside: Application manages cache
  • Write-Through: Sync write to cache and DB
  • Write-Behind: Async write to DB
  • Read-Through: Cache loads from DB

Invalidation

  • Time-based TTL
  • Event-based invalidation
  • Version-based keys
  • Tag-based grouping

Monitoring

  • Hit rate > 80% is good
  • Monitor eviction rates
  • Track cache size
  • Alert on anomalies

Key Points

  • Understanding Distributed Cache is essential for production systems
  • Always consider scalability and maintainability
  • Test thoroughly before deploying to production
  • Monitor performance and set up alerting

Common Patterns

  1. Validation: Always validate input at the boundary
  2. Error Handling: Use structured error responses
  3. Logging: Log key events for debugging
  4. Testing: Unit, integration, and load tests
  5. Documentation: Keep docs updated with code changes

Practice Problems

0/3solved
Implement Distributed Cache

Design and implement a solution for Distributed Cache in a backend system. Consider scalability, error handling, and production readiness.

Solution
// Distributed Cache implementation
// Key aspects: validation, error handling, logging, testing

public class DistributedCache {
    // Production-ready implementation
}
Distributed Cache Edge Cases

Identify and handle edge cases for Distributed Cache. What happens under high load, with invalid input, or during failures?

Solution
// Edge case handling:
// 1. Null/empty input -> validation
// 2. High load -> rate limiting, queuing
// 3. Failures -> retries, circuit breaker
// 4. Concurrent access -> locks, idempotency
Distributed Cache Testing Strategy

Write a testing strategy for Distributed Cache. Include unit tests, integration tests, and performance tests.

Solution
// Test plan:
// - Unit: 80% coverage target
// - Integration: API contracts
// - Performance: latency, throughput
// - Chaos: failure injection

Quiz

1. Distributed cache solves?

Question 1 options

2. Cache stampede is?

Question 2 options

3. What is the primary purpose of Distributed Cache?

Question 3 options

4. What is a common mistake when implementing Distributed Cache?

Question 4 options

Flashcards

Question

Distributed cache purpose?

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Answer

Share cached data across multiple servers

Question

Cache stampede?

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Answer

Many requests for expired key simultaneously

Question

What is Distributed Cache?

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Answer

Distributed Cache is a key concept in backend development.

Question

When to use Distributed Cache?

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Answer

Use Distributed Cache when building production systems that require reliability, scalability, and maintainability.

Question

Distributed Cache best practices

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Answer

Follow SOLID principles, write clean code, test thoroughly, document decisions, and monitor in production.

Revision Notes

Key Takeaways

  • 1.Distributed cache shares data across servers
  • 2.Redis is common distributed cache
  • 3.Challenges: consistency, stampede, split brain
  • 4.Use consistent hashing for distribution

Interview Tips

  • •Design distributed caching
  • •Handle consistency challenges

Cheat Sheet

Distributed Cache

  • Shared cache across servers
  • Redis: common choice
  • Challenges: consistency, stampede
  • Use consistent hashing