Cache Invalidation

System Design Fundamentals: Caching - Cache Invalidation

Caching is a crucial technique for improving system performance by reducing latency and load on origin servers. However, simply caching data isn't enough. Maintaining data consistency between the cache and the origin server is paramount. This is where Cache Invalidation comes in. Incorrectly handled cache invalidation can lead to stale data being served, causing incorrect application behavior.

This document outlines the fundamentals of cache invalidation strategies.

The Problem: Stale Data

When data changes at the origin server, the cached copy becomes stale. Serving stale data can have various consequences:

• Incorrect Results: Users see outdated information.
• Business Logic Errors: Decisions based on stale data can lead to flawed outcomes (e.g., over-selling an item).
• User Experience Issues: Inconsistent data across different parts of the application.

Cache Invalidation Strategies

Here's a breakdown of common cache invalidation strategies, categorized by their complexity and effectiveness:

1. Time-To-Live (TTL) - Simplest Approach

• How it works: Each cached item is assigned a TTL. After the TTL expires, the cache entry is considered invalid and is either evicted or refreshed on the next request.
• Pros:
  • Easy to implement: Requires minimal logic.
  • Guaranteed eventual consistency: Data will eventually become fresh.
• Cons:
  • Potential for stale data: Data might be stale for the entire TTL duration, even if the origin data changed immediately after caching.
  • Difficult to tune: Choosing the right TTL is challenging. Too short, and you lose the benefits of caching. Too long, and you risk serving stale data.
  • Doesn't react to changes: TTL is passive; it doesn't know if the underlying data has changed.
• Use Cases: Data that doesn't change frequently, or where eventual consistency is acceptable (e.g., infrequently updated configuration settings).

2. Expiration-Based Invalidation

• How it works: Similar to TTL, but more granular. Instead of a fixed duration, expiration can be based on specific events or conditions.
• Pros:
  • More flexible than simple TTL.
  • Can be tied to data modification events.
• Cons:
  • Still relies on pre-defined rules and doesn't react to immediate changes.
  • Requires more complex logic than TTL.
• Use Cases: Data with predictable update patterns (e.g., daily reports).

3. Change-Based Invalidation (Write-Through/Write-Back with Invalidation)

These strategies focus on invalidating the cache when the origin data is modified.

• Write-Through Cache:
  • How it works: Every write operation goes to both the cache and the origin server simultaneously. This ensures the cache is always up-to-date.
  • Pros: Strong consistency.
  • Cons: Higher write latency (as you're waiting for both operations to complete). Can increase load on the origin server.
• Write-Back Cache:
  • How it works: Writes are initially made to the cache. The cache then asynchronously writes the changes to the origin server. The cache is marked as "dirty" until the write is propagated.
  • Pros: Lower write latency. Reduced load on the origin server.
  • Cons: Data loss risk if the cache fails before the write is propagated. More complex to implement. Requires careful handling of cache failures.
• Invalidation Messages:
  • How it works: When data is updated in the origin server, a message (e.g., using a message queue like Kafka or RabbitMQ) is sent to the cache servers to invalidate the corresponding entry.
  • Pros: More responsive than TTL. Can achieve near real-time invalidation.
  • Cons: Requires a reliable messaging system. Can be complex to implement, especially in distributed systems. Potential for message loss or delays.

4. Event-Based Invalidation (Webhooks, Database Triggers)

• How it works: The origin server actively notifies the cache when data changes.
  • Webhooks: The origin server sends an HTTP POST request to the cache when data is modified.
  • Database Triggers: Database triggers can be configured to send invalidation messages when data is updated.
• Pros: Highly responsive. Near real-time invalidation.
• Cons: Requires the origin server to be able to send notifications. Reliability depends on the notification mechanism. Can be complex to set up and maintain.

5. Versioned Cache Keys

• How it works: Instead of caching based on the data itself, cache keys include a version number. When the data changes, the version number is incremented, effectively invalidating the old cache entry.
• Pros: Simple and effective. Guarantees consistency.
• Cons: Requires managing version numbers. Can be less efficient if version numbers change frequently.
• Use Cases: Data that is updated infrequently but needs strong consistency.

6. Cache Tagging/Dependency Tracking

• How it works: Associate tags with cached items. When data changes, invalidate all cache entries associated with the relevant tags.
• Pros: Efficiently invalidates related cache entries.
• Cons: Requires careful tag management. Can be complex to implement.
• Use Cases: Data with complex dependencies (e.g., a product page that depends on inventory, price, and description).

Considerations for Distributed Caches

In a distributed caching environment (e.g., using Redis Cluster, Memcached), invalidation becomes more challenging:

• Consistency: Ensuring all cache nodes are updated consistently.
• Network Latency: Invalidation messages can take time to propagate across the network.
• Partial Failures: Some cache nodes might fail to receive invalidation messages.

Strategies to address these challenges:

• Gossip Protocol: Cache nodes periodically exchange invalidation information with each other.
• Two-Phase Commit: Ensures that invalidation messages are applied to all cache nodes before acknowledging the operation. (More complex, higher latency)
• Eventual Consistency with Retries: Accept eventual consistency and implement retry mechanisms for invalidation messages.

Choosing the Right Strategy

The best cache invalidation strategy depends on several factors:

• Data Volatility: How frequently does the data change?
• Consistency Requirements: How important is it to serve the latest data?
• System Complexity: How much complexity are you willing to accept?
• Performance Requirements: What is the acceptable latency for read and write operations?
• Scalability: How well does the strategy scale to handle increasing data volumes and traffic?

Best Practices

• Monitor Cache Hit Rate: Track the cache hit rate to identify potential invalidation issues.
• Implement Logging and Alerting: Log invalidation events and set up alerts for failures.
• Test Thoroughly: Test your cache invalidation strategy under various load conditions.
• Consider a Hybrid Approach: Combine different strategies to achieve the best balance between consistency, performance, and complexity. For example, use TTL for infrequently updated data and change-based invalidation for frequently updated data.

This document provides a foundational understanding of cache invalidation. The specific implementation details will vary depending on your application's requirements and the caching technology you choose.