Mastering Cache Architecture: Key Considerations for Scalable Systems

Caching is one of the most essential techniques used in backend architecture to improve latency and enhance user experience. By temporarily storing frequently accessed data, cache reduces the need for repeated computations or database access. But, implementing cache architecture effectively requires careful thought. In this article, we will explore vital considerations when designing cache systems.

Estimating Cache Size

Before building a caching system, it is important to determine how much data to store in the cache. A commonly used principle here is the 80/20 rule: 20% of objects are responsible for 80% of the accesses.

Example:

• Assume you have 10 million daily active users
• Each user’s metadata is 500KB
• If you decide to cache 20% of the data:

500KB * 10M * 0.2 = 100GB

Given that most modern cloud platforms support up to 512GB of RAM on a single VM, you could manage this cache on a single machine. However, in some scenarios, caching all the data might be justified to minimize latency—but such decisions must be scenario-specific.

Cache Clustering for Scalability

If your cache can't fit on a single VM, the next step is to implement a cache cluster. Typically, a hash function is used to distribute keys across nodes.

Simple Hashing Example:

• Key: Request ID
• Hash Function: mod

This approach is simple, but presents a major issue: scaling pain.

Problem:

• Start with 3 cache nodes → data distributed evenly
• Add a 4th node → hash mappings change drastically
• Result: Significant data movement required (e.g., 8 keys must be remapped)

Solution: Consistent Hashing

To minimize data migration during scaling, consistent hashing is introduced. This method ensures minimal redistribution of keys when nodes are added or removed. For a deeper dive, you can refer to Consistent Hashing on Wikipedia.

Cache Replacement Policies

Since it's often impractical to cache everything, a replacement policy is needed to decide what to remove when cache reaches capacity. Here are popular strategies:

• FIFO (First-In-First-Out): Evicts the oldest stored data.
• LFU (Least Frequently Used): Removes data accessed least often.
• LRU (Least Recently Used): Discards the data not accessed for the longest time.

Recommendation: LRU is generally the most effective and commonly used in real-world applications.

Cache Update Policies

How and when cache is updated in sync with the database matters significantly. Here are three common strategies:

1. Write-Through

• Update DB and cache simultaneously
• Ensures strong consistency
• Downside: Slower write performance (not ideal for write-heavy systems)

2. Write-Around

• Write only to DB, invalidate the cache
• Cache is populated on the next read
• Risk: Multiple cache misses initially

3. Write-Back

• Write only to cache first, and update DB later
• Improves write latency and throughput
• Risk: Data loss if cache fails before DB is updated

Final Thoughts

Cache exists at every layer—from CPU and RAM, to databases and front-end browsers. When architecting a cache system:

• Estimate cache size effectively using access patterns
• Use clustering and consistent hashing for scalable systems
• Implement smart replacement policies to retain high-value data
• Choose appropriate update strategies based on workload (read vs. write heavy)

With these considerations, you can build a resilient, scalable, and high-performance cache architecture that boosts overall system efficiency.

Thank you for reading! Feel free to share this with your team or revisit it when architecting your next scalable system.