How to Incorporate Caching Strategies During a System Design Interview

Caching – storing frequently used data in a fast storage layer like memory – is a crucial technique in system design.

It greatly improves performance by reducing how often the database or other back-end services must be accessed . In system design interviews, including caching in your design shows you can build for high performance at scale.

Key Caching Concepts to Know

• Cache Hit vs. Cache Miss: A cache hit means the requested data was found in the cache and returned quickly. A cache miss means the data wasn’t in cache, so the system must fetch it from the source .

• Cache Eviction Policies: When the cache is full, an eviction policy decides which item to remove. Common policies include LRU (Least Recently Used), LFU (Least Frequently Used), and FIFO (First-In First-Out).

• Write-Through vs. Write-Back vs. Write-Around: These terms describe how writes interact with the cache:

  • Write-through: On update, write to the cache and database simultaneously (cache stays up-to-date; writes are slower).

  • Write-back: On update, write to the cache first and to the database later (faster writes; risk of data loss if the cache fails before the DB update).

  • Write-around: On update, write directly to the database (skip cache). The data enters the cache only when it’s read later (good if writes are rarely read).

Learn more about caching concepts.

Where to Use Caching in a System Design

Consider adding caching in these places in your design:

• Database Query Caching: Cache results of expensive database queries in memory so that future requests can be served quickly without repeatedly hitting the database.

• Content Delivery Network (CDN) Caching: Use a CDN to cache static assets (images, videos, CSS/JS files) on servers around the world. This brings content closer to users, reducing latency and offloading traffic from your origin server.

• Application-Level Caching: Within your application, store frequently used or expensive-to-compute data in memory (e.g. an in-memory cache). This avoids redundant computation and speeds up responses for repeat requests.

• Distributed Caching: Use a distributed cache system (like Redis or Memcached) that all servers share. This provides a common fast data store in a multi-server setup and ensures all app instances see the same cached data.

Understand caching strategies.

How to Incorporate Caching in a System Design Interview

When presenting your design, follow these steps to integrate caching:

1. Identify Bottlenecks: Pinpoint where the system might slow down under load (e.g. frequent database reads, expensive calculations, external API calls). These high-latency or heavy-load operations are prime candidates for caching.

2. Choose a Caching Strategy: Decide what to cache and where. For example, cache database query results in memory, and cache static files on a CDN. Pick an approach that fits the data usage pattern (if data is read often and rarely changes, it’s ideal to cache).

3. Plan Cache Consistency: Explain how you will keep the cache in sync with the source of truth. For instance, set expiration times (TTL) on cache entries or invalidate/update the cache when the underlying data changes. This prevents serving stale data.

4. Address Scalability & Failures: Describe how the cache layer scales and what happens if it fails. You might use a cluster of cache servers to handle growth and avoid a single point of failure. Also note that if the cache is unavailable, the system will fall back to the database (slower, but still functional).

Comparison of Caching Strategies

Here’s a quick comparison of different caching approaches and when to use them:

Real-World Caching Examples

• Netflix: Netflix uses a CDN to cache videos on servers at ISPs. Popular shows stream from these local caches instead of Netflix’s central servers. Learn how to design a video streaming service.

• Facebook: Facebook uses a distributed cache tier (Memcached) to serve content quickly and reduce database load. Learn how to design Facebook Messenger.

• Amazon: Amazon’s platform caches results of frequent product searches, so popular queries are served from cache instead of querying the database each time Learn how to design e-commerce system.

Common Caching Mistakes to Avoid

• Stale Data: Not invalidating or refreshing cache entries can lead to outdated data being served. Plan how cached data gets updated when the source changes.
• Single Point of Failure: Relying on one cache server is risky – if it goes down, your database could get overwhelmed. Use replication or clustering to avoid a single point of failure.
• Over-Caching: Don’t cache everything; focus on high-value data (frequently accessed or expensive operations).

Best Practices in Interviews

• Explain Why: When you add a cache in your design, state what problem it solves (e.g. “to reduce database load for common reads”).

• Justify Your Choices: If you pick a particular caching approach or tool, relate it to the system’s requirements (for instance, using Redis because multiple servers need a shared cache).

• Mention Trade-offs: Acknowledge trade-offs like consistency vs. speed, and explain how you’ll mitigate them (use short TTLs to limit stale data).

Final Thoughts

Caching is an effective way to improve a system’s performance and scalability. Be sure to handle consistency (expiration and invalidation) carefully. Used correctly, caching can turn a good design into a great one.