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Harden a URL-Caching HTTP Service for Production

ShopifySoftware Engineer · Technical Screen
MediumSoftware EngineerTechnical ScreenSoftware Engineering Fundamentals

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This problem tests the ability to scale and harden a simple in-memory cache service, focusing on operational stability, concurrency control, and safe deployment patterns.

Problem Statement

You are maintaining an in-memory HTTP service that acts as a proxy cache for URL content. Currently, it is a single-node Python process using a simple dictionary to store URL -> HTML_Content mappings. It lacks production-grade safeguards.

Your task is to outline two distinct, iterative phases to transform this into a resilient production service. You must address the following:

  1. Concurrency & Stampedes: The current implementation blocks threads and crashes under high load when multiple requests hit the same missing URL (the "cache stampede" problem).
  2. Operations & Observability: How do you implement logging and metrics without leaking sensitive user data from the cached URLs?
  3. Deployment Safety: Design a strategy to migrate from this single-node in-memory cache to a distributed cache (e.g., Redis) without downtime or massive cache-miss spikes.

Requirements for your Design:

  • Provide a high-level architecture for both the 'Stabilization' phase and the 'Distributed' phase.
  • Describe the specific status codes and error handling patterns you would introduce for external network failures versus cache-local failures.
  • Explain how you would safely roll out the transition from local-memory to distributed storage.

Constraints

  • The system must remain highly available during the transition.
  • You must demonstrate how to avoid overloading the origin server when the cache transitions to empty or fails.

Hints

Hint 1

Consider using locks or request coalescing (e.g., singleflight) to ensure only one request fetches a given URL at a time when the cache is empty.

Hint 2

For telemetry, suggest techniques like hashing URLs or stripping query parameters before recording logs to maintain privacy.

Hint 3

For the migration, look into a 'shadow-write' or 'dual-read' pattern where the application writes to both the old and new storage systems before decommissioning the local memory.

Follow-up questions

  • How would you handle cache eviction policies if the memory footprint of the new storage system grows beyond limits?
  • If the origin server becomes unresponsive, how should the cache behave? Would you serve stale content, return an error, or try to reconnect?
  • How would you design an automated rollback plan if the new distributed cache implementation causes a high latency spike immediately after deployment?