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Stripe·Software Engineer·Onsite - System Design / Architecture·Staff

Staff
May 2026

Summary

Stripe EM interview with a system design question on distributed caching. Not a lot of context to go on but the problem itself is a classic that somehow still trips people up.

Questions Asked (1)

Q1

Design a distributed LRU cache system.

System DesignTechnical Trade-offsAlgorithms & Data Structures
Author's notes

This is the kind of question where you think you know it cold and then you open your mouth and realize you've been fuzzy on the details for years.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (scale, consistency, latency, eviction policy) and then propose a high-level architecture with a distributed cache layer, consistent hashing for partitioning, and replication for fault tolerance. Dive into the LRU eviction mechanism per node, discuss trade-offs between local and global LRU, and address consistency and failure handling.

Pro tip: Emphasize that a true global LRU is impractical in a distributed system due to coordination overhead; instead, propose a per-node LRU with a consistent hashing ring and discuss how to approximate global LRU using techniques like segmented LRU or frequency-based admission.

1. Clarify Requirements and Constraints

Ask about expected scale (QPS, data size), latency requirements, consistency needs (strong vs eventual), and eviction policy specifics (strict LRU vs approximate).

2. High-Level Architecture

Propose a distributed cache cluster with consistent hashing to partition keys across nodes, and replication for fault tolerance. Mention a coordinator or client-side hashing for routing.

3. LRU Implementation per Node

Describe how each node implements LRU using a hash map and doubly linked list, and discuss concurrency control (e.g., sharding within a node, locks, or lock-free approaches).

4. Handling Distribution and Consistency

Explain how eviction works across nodes: per-node LRU vs global LRU trade-offs. Discuss cache coherence, replication strategies (e.g., primary-backup), and how to handle node failures (e.g., rebalancing).

5. Trade-offs and Optimizations

Discuss trade-offs: memory overhead, latency vs consistency, and potential optimizations like segmented LRU, TTL, or write-through/write-back policies.

Key Points to Mention

  • Consistent hashing for even distribution and minimal rehashing on node changes
  • Replication and failover strategies to ensure availability
  • Per-node LRU with local eviction vs global LRU approximation
  • Concurrency control within a node (e.g., sharding the cache, using read-write locks)
  • Handling hot keys and load balancing (e.g., key splitting or replication)
  • Monitoring and metrics for cache hit ratio and eviction rates

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.