How can you handle cache consistency issues?

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ICSM Computer · Answer

Handling cache consistency issues is crucial to ensure data accuracy while leveraging the performance benefits of caching. Here are some strategies to maintain consistency between Redis and the primary database:
1. Cache Expiry (TTL - Time-To-Live)
Set a TTL on cache entries so that outdated data is automatically evicted.Helps in ensuring fresh data is fetched periodically.
Best For: Scenarios where slight staleness is acceptable, such as caching frequently changing API responses.
2. Cache Invalidation Strategies
These strategies ensure that the cache is updated or removed when the underlying data changes.
a. Write-Through Cache
Data is written to both the database and cache at the same time.Ensures the cache always has the latest data.Slightly slower due to double writes.
Best For: Reads and writes with equal importance.
b. Write-Behind (Asynchronous Updates)
Data is written to Redis first and then asynchronously updated in the database.Improves write performance but risks losing data if the process crashes.
Best For: High-write applications where occasional data loss is acceptable.
c. Cache Aside (Lazy Loading)
The application first checks the cache.If the data is not found (cache miss), it fetches it from the database, stores it in Redis, and returns it.The cache is updated only when needed.
Best For: Read-heavy applications where stale data is acceptable.
d. Read-Through Cache
Similar to cache-aside but abstracts caching logic within a caching layer.Fetching logic is handled automatically.
Best For: Simplified cache management with libraries handling expiration.
3. Database Change Notifications (Event-Driven Updates)
Use Redis Pub/Sub or Message Queues to notify cache updates when data changes.Example: When an update happens in the database, send a message to a Redis channel to clear or update the cache.
Best For: Real-time applications that require cache updates upon data changes.
4. Versioning (Cache Stampede Prevention)
Store a version identifier (timestamp or hash) with the cached data.Clients request the version from the database to check if the cache is outdated before using it.
Best For: Preventing outdated data in distributed cache systems.
5. Distributed Locks for Critical Updates
Use Redis distributed locks (SET NX + EX) to prevent race conditions where multiple processes update the cache simultaneously.Example: Use RedLock Algorithm to ensure safe concurrent updates.
Best For: High-concurrency applications with frequent cache updates.
6. Background Cache Refresh (Refresh-Ahead)
Before cache expiration, a background job updates the cache with fresh data.Helps reduce cache misses and delays.
Best For: Performance-critical applications where cache misses are expensive.
7. Dual Writes with Transactional Support
Use Redis Transactions (MULTI, EXEC) or a distributed transaction system to update both the database and cache atomically.Ensures consistency in critical updates.
Best For: Banking and financial systems where data integrity is critical.
Which One Should You Use?
If stale data is acceptable → Cache-aside with TTL.If data must always be fresh → Write-through cache.If real-time updates are needed → Event-driven updates (Redis Pub/Sub).If high concurrency is a concern → Distributed locks (RedLock).

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How can you handle cache consistency issues?

ICSM Computer

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1 Answers

1. Cache Expiry (TTL - Time-To-Live)

2. Cache Invalidation Strategies

a. Write-Through Cache

b. Write-Behind (Asynchronous Updates)

c. Cache Aside (Lazy Loading)

d. Read-Through Cache

3. Database Change Notifications (Event-Driven Updates)

4. Versioning (Cache Stampede Prevention)

5. Distributed Locks for Critical Updates

6. Background Cache Refresh (Refresh-Ahead)

7. Dual Writes with Transactional Support

Which One Should You Use?

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