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The core design isn't where you lose points, it's the scale numbers.
Start by clarifying requirements and constraints, then estimate scale to drive design decisions. Propose a high-level architecture covering URL generation, storage, redirection, and optional features, and dive into key components like database choice, caching, and analytics pipeline. Discuss trade-offs and justify decisions based on the 1M creates/day and 1B redirects/day scale.
Pro tip: Emphasize read-heavy optimization: redirects outnumber creates 1000:1, so focus on caching, CDN, and efficient key-value storage. Also, mention that analytics can be asynchronous to avoid impacting redirect latency.
Ask questions to understand functional and non-functional requirements, such as custom short URLs, expiration policies, analytics granularity, and latency/availability targets. Confirm scale assumptions and data retention needs.
Calculate storage and throughput: 1M creates/day ~ 12 writes/sec, 1B redirects/day ~ 12K reads/sec. Identify core components: URL generator, key-value store, cache, redirect service, and optional analytics pipeline.
Choose a short URL generation strategy (e.g., base62 encoding of a distributed ID or hash). Select a scalable key-value store (e.g., Cassandra, DynamoDB) for mappings, and design for high availability and partition tolerance.
Implement a redirect service that looks up the short URL in a cache (e.g., Redis) first, falling back to the database. Use HTTP 301/302 redirects and consider CDN for edge caching to reduce latency and load.
For expiry, use TTL in the data store or a background cleanup job. For analytics, log events asynchronously to a message queue and process them offline. Discuss trade-offs between consistency, latency, and cost.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Geo-routing plus CDN caching for redirects, regional read replicas for the DB.
Start by clarifying the system's requirements and constraints, then outline a multi-layered strategy that combines global distribution, intelligent routing, and caching. Emphasize trade-offs between latency, consistency, cost, and complexity, and tailor the solution to the specific workload characteristics.
Pro tip: Demonstrate awareness of Microsoft's global infrastructure (e.g., Azure regions, CDN, Front Door) and how to leverage it, but focus on architectural principles rather than specific product names. Also, quantify latency improvements where possible to show impact.
Ask about user distribution, latency targets, data consistency needs, budget, and existing infrastructure. This ensures the solution is tailored and avoids over-engineering.
Propose deploying the system in multiple regions close to users, using active-active or active-passive setups. Discuss data replication strategies and how to handle regional failures.
Use global load balancing (e.g., anycast, DNS-based) to route users to the nearest healthy endpoint. Leverage edge computing for static content and dynamic acceleration.
Introduce multi-layer caching (CDN, regional caches, in-memory) and consider read replicas or geo-distributed databases. Discuss consistency trade-offs (e.g., eventual consistency).
Acknowledge trade-offs like cost, complexity, and consistency. Explain how to monitor latency and iterate, using tools like distributed tracing and real-user monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is the one I actually liked answering.
Start by clarifying the requirements and constraints, then propose a layered solution that combines immediate scaling (e.g., horizontal scaling, load balancing) with asynchronous processing (e.g., queueing) to absorb the spike while keeping the synchronous API responsive. Emphasize trade-offs between consistency, latency, and cost, and mention monitoring and auto-scaling to handle future spikes.
Pro tip: Show that you understand the difference between scaling reads and writes: URL creation is a write-heavy operation, so focus on write scalability and idempotency to avoid duplicates. Also, mention that you would measure and set SLOs for latency and error rates to guide decisions.
Ask about expected latency, consistency requirements, and whether the spike is temporary or sustained. This helps tailor the solution.
Propose adding more API servers behind a load balancer and scaling the database (e.g., sharding, read replicas for reads, but writes need scaling).
Use a message queue (e.g., Kafka, SQS) to decouple request acceptance from URL creation, allowing the API to return quickly (e.g., 202 Accepted) and process writes asynchronously.
Implement idempotency keys to handle retries and avoid duplicate URL creations during the spike.
Set up auto-scaling based on metrics like QPS and queue depth, and have a fallback (e.g., rate limiting) to protect the system if overwhelmed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.