Caching in a Typical Architecture: A Multi-Layered Approach

Caching in a Typical Architecture

Caching in a Typical Architecture: A Multi-Layered Approach

Data is cached at multiple levels across a system, from the front end to the back end, to improve performance, scalability, and reliability. This guide explains the different caching layers in a standard architecture.

Multiple Layers of Caching

1. Client Apps

HTTP responses can be cached locally by the browser, reducing unnecessary network requests. When a request is made for the first time, the server responds with data and an expiry policy in the HTTP headers.

  • Future requests for the same data are served from the browser cache, improving response times.
  • Service Workers enable offline caching and background synchronization.

2. Content Delivery Network (CDN)

CDNs cache static web resources like images, JavaScript files, and CSS in geographically distributed servers.

  • Clients retrieve data from the nearest CDN node, reducing latency.
  • CDNs also support dynamic content acceleration.

3. Load Balancer

Load balancers can cache resources to minimize redundant requests to backend servers. They also ensure fault tolerance in distributed architectures.

4. Messaging Infrastructure

Message brokers (e.g., Kafka, RabbitMQ) store messages on disk before consumers process them.

  • Kafka caches messages in clusters to ensure efficient event-driven processing.

5. Services (Application Layer Caching)

Services use multiple cache layers to reduce database queries.

  • CPU-level caching stores frequently accessed data in memory.
  • A second-level cache stores data beyond the CPU cache.

6. Distributed Caching

Redis and Memcached store key-value pairs in memory, improving read/write performance.

  • Used for session storage, API rate limiting, and real-time analytics.

7. Full-Text Search Indexing

Search engines like Elasticsearch index a copy of the data for document and log searches.

8. Database Caching Layers

Databases employ multiple caching mechanisms:

  • Write-Ahead Log (WAL): Logs transactions before committing them.
  • Buffer Pool: Allocates memory for query results.
  • Materialized Views: Stores precomputed query results.
  • Transaction Log: Records database updates.
  • Replication Log: Tracks database replication states.

Security Concern: Data Persistence and Privacy

With data cached at so many levels, an important question arises:

How can we ensure that sensitive user data is completely erased from the system when required?

Possible Solutions:

  • Cache Invalidation Policies: Ensuring sensitive data is never cached indefinitely.
  • Memory Scrubbing: Securely clearing in-memory caches.
  • GDPR Compliance: Implementing data erasure mechanisms.
  • Log Anonymization: Preventing PII from being stored.
  • Secure Database Deletion: Using cryptographic erase methods.

Final Thoughts

While caching is essential for performance, data governance and security must be carefully managed to prevent unauthorized access or unintended persistence of sensitive information.

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