Design a Distributed Caching System (Memcached)
Explain the architecture of a dedicated caching layer. Discuss data partitioning, client-side vs. server-side caching, and cache-aside vs. read-through strategies.
Why Interviewers Ask This
Interviewers at Google ask this to evaluate your ability to design scalable, high-performance systems under constraints. They specifically test your understanding of consistency models, data partitioning strategies like consistent hashing, and failure handling in distributed environments. The goal is to see if you can balance latency against complexity while ensuring the system remains available during node failures.
How to Answer This Question
1. Start by clarifying requirements: define throughput, latency targets, and whether eventual or strong consistency is needed. 2. Propose a high-level architecture diagram including clients, load balancers, and cache nodes. 3. Detail data partitioning using consistent hashing to handle scaling and rebalancing efficiently. 4. Discuss caching strategies: compare client-side DNS-based routing versus server-side proxying, and explain when to use cache-aside versus read-through patterns. 5. Address failure scenarios like node crashes and network partitions, mentioning replication factors and eviction policies like LRU. Conclude by summarizing trade-offs between complexity and performance.
Key Points to Cover
- Explicitly mention consistent hashing for dynamic scaling
- Distinguish clearly between cache-aside and read-through implementation details
- Address replication factors and fault tolerance mechanisms
- Define specific eviction policies like LRU for memory management
- Explain how client-side libraries reduce server-side complexity
Sample Answer
To design a Memcached-like system for Google-scale traffic, I first clarify that we need low-latency reads with high availability. We will use a client-side library that implements consistent hashing to map keys to speci…
Common Mistakes to Avoid
- Focusing only on the code without discussing the underlying distributed architecture principles
- Ignoring the implications of data consistency when nodes fail or partition
- Suggesting a centralized coordinator which creates a bottleneck and single point of failure
- Overlooking the need for replication and how it impacts write latency
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