Designing a Distributed Cache: Redis and Memcached at Scale

When designing high-performance systems, one of the most critical components is caching. A well-designed distributed caching system can dramatically improve application responsiveness and reliability, providing sub-millisecond response times and reducing pressure on backend databases. But scaling a cache across distributed systems is no trivial task—it involves addressing challenges such as data consistency, replication, cache eviction policies, and the ever-present threat of "hot keys."

In this blog post, we’ll dive deep into designing a distributed cache using Redis and Memcached at scale. We’ll explore key concepts like consistent hashing, replication strategies, cache eviction policies, and handling cache stampede. By the end, you’ll have a solid understanding of how to design a robust caching layer—critical knowledge for your next system design interview.

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🔥 Why Distributed Caching Matters

Imagine you’re working on a high-traffic service, like Netflix, where millions of users query recommendations at any given moment. Without caching, every request hits the database, leading to overwhelming load, increased latency, and poor scalability. A distributed cache solves this by storing frequently accessed data closer to the application, enabling sub-millisecond response times.

But scaling a cache across distributed systems introduces complexity:

• How do we ensure data consistency across nodes?
• What happens if a node fails?
• How do we handle scenarios where one key becomes disproportionately popular (i.e., a hot key)?

These are the kinds of questions senior engineers must answer during system design interviews.

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🗺️ Key Components of Distributed Caching Systems

1. Consistent Hashing

Consistent hashing is a fundamental technique for distributing keys across cache nodes. Instead of mapping keys directly to servers, consistent hashing uses a hash ring that provides load balancing and node failure tolerance.

How it works:

• Hash both the cache servers and the keys using the same hash function.
• Place the servers on a logical ring, and map each key to the nearest server on the ring.
• When a server is added or removed, only a subset of keys are remapped, minimizing disruption compared to traditional hashing methods.

Example:

Let’s say we are using five Redis nodes, and a new node is added to the cluster. With consistent hashing, only the keys mapped to the new node need to be redistributed, ensuring minimal cache invalidation.

Diagram:

[Hash Ring Diagram]
- Nodes: A, B, C, D
- Keys distributed across the ring
- Adding Node E minimally affects redistribution

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2. Cache Eviction Policies

Caches have finite storage, so determining which data to evict when the cache fills up is crucial. Common eviction strategies include:

• Least Recently Used (LRU): Evicts the least recently accessed item.
• Least Frequently Used (LFU): Evicts the item accessed the least number of times.
• Random Replacement: Evicts a random item (simpler but less optimal).
• Time-Based Expiry: Items expire after a fixed duration.

Redis Example:

Redis supports configurable eviction policies such as volatile-lru, allkeys-lru, and noeviction. For instance, allkeys-lru evicts the least recently used item regardless of TTL.

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3. Replication Strategies

Replication ensures high availability in distributed systems. When designing a distributed cache, replication strategies are critical for fault tolerance and data durability.

Common strategies:

1. Master-Slave Replication: A master node handles writes, while slave nodes replicate data for reads. This reduces read latency but introduces potential consistency issues.
2. Multi-Master Replication: All nodes can handle reads and writes. This improves availability but requires conflict resolution mechanisms.
3. Quorum-Based Replication: Data is replicated to a subset of nodes, and operations succeed when a majority of nodes agree.

Real-World Example:

Twitter uses replication in its caching systems to ensure high availability for trending topics. By replicating hot keys across multiple caching nodes, they mitigate the risk of downtime during high-traffic events.

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4. Handling Hot Keys

A hot key is a cache key that is accessed disproportionately compared to others. If unaddressed, hot keys can result in uneven load distribution, overwhelming individual cache nodes.

Strategies to handle hot keys:

• Key Sharding: Split hot keys into multiple sub-keys to distribute the load.
• Replication: Replicate hot keys across multiple nodes for load balancing.
• Write-Through Cache: Use a write-through strategy to ensure database hits for hot keys are evenly distributed.

Real-World Example:

Netflix mitigates hot key issues for popular content (e.g., trending movies) by replicating these keys across multiple Redis nodes and using consistent hashing to balance the load.

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🚨 CAP Theorem and Cache Stampede

CAP Theorem in Distributed Caching

The CAP theorem states that distributed systems can only guarantee two of three properties: Consistency, Availability, and Partition Tolerance. For caching systems:

• Consistency: Ensures clients always receive up-to-date data.
• Availability: Ensures the system always responds, even during failures.
• Partition Tolerance: Ensures the system operates despite network partitions.

Caching systems often prioritize availability and partition tolerance at the expense of strict consistency. This trade-off is acceptable for use cases where stale data is tolerable (e.g., session data or analytics).

Interview Tip:

Highlight how the CAP theorem applies to caching systems during your interview. Discuss scenarios where consistency might be sacrificed for better performance.

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Cache Stampede Problem

A cache stampede occurs when multiple clients simultaneously request a missing key, overwhelming the backend database or cache. For example, during a sudden traffic spike, a cache miss for a popular key can trigger thousands of expensive database queries.

Solutions:

1. Locking Mechanisms: Use a lock to ensure only one client queries the database and updates the cache while others wait.
2. Request Coalescing: Aggregate duplicate requests for the same key into a single backend query.
3. Lazy Cache Population: Populate the cache asynchronously after serving stale data.

Real-World Example:

Uber addresses cache stampedes for surge-pricing calculations by implementing request coalescing in its distributed caching architecture.

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🎯 Common Interview Pitfalls and How to Avoid Them

1. Ignoring Edge Cases: Failing to address node failures, hot keys, or cache stampedes can undermine your design.

   • Tip: Always discuss fallback mechanisms and scalability challenges.

2. Overengineering: Adding unnecessary complexity (e.g., multi-master replication for ephemeral data) can confuse interviewers.

   • Tip: Focus on simplicity and justify design decisions.

3. Skipping Metrics: Neglecting performance metrics like cache hit ratio or replication latency shows a lack of real-world perspective.

   • Tip: Include observability and monitoring strategies in your design.

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🗣️ Interview Talking Points and Frameworks

Framework for Discussing Distributed Caching Design:

1. Problem Definition: Clearly define the caching use case—what kind of data is being cached and why.
2. Architecture Overview: Discuss the cache system architecture (e.g., Redis cluster with consistent hashing).
3. Scalability: Explain how the system scales horizontally and handles node failures.
4. Data Consistency: Highlight trade-offs between consistency, availability, and partition tolerance.
5. Optimization Strategies: Address cache eviction, hot keys, and stampede mitigation techniques.
6. Monitoring: Describe how you’ll measure cache performance (e.g., hit ratios, latency).

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📌 Key Takeaways

1. Consistent hashing is essential for scalable cache distribution.
2. Eviction policies like LRU and LFU help manage finite cache storage.
3. Replication strategies ensure fault tolerance and high availability.
4. Hot key mitigation prevents uneven load distribution.
5. CAP theorem trade-offs are critical for caching systems.
6. Cache stampede solutions ensure database resilience during traffic spikes.

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🚀 Next Steps for Interview Preparation

1. Practice System Design Questions: Build distributed cache designs for services like Netflix, Uber, or Instagram.
2. Dive into Redis and Memcached: Experiment with clustering and replication configurations.
3. Master Observability Tools: Learn to monitor cache performance using tools like Prometheus and Grafana.
4. Mock Interviews: Use the frameworks provided to practice articulating your designs.

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Caching is one of the most exciting and challenging topics in system design interviews. By mastering distributed caching concepts and understanding their practical implications, you’ll set yourself apart as a senior engineer ready to tackle real-world scalability challenges.

Good luck, and happy interviewing!

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This blog post is designed for Markdown rendering and includes placeholders for diagrams (e.g., [Hash Ring Diagram]). You can create these diagrams using tools like **Lucidchart**, **Draw.io**, or **Excalidraw** to visually enhance your blog content.