I started with the read/write ratio and worked outward from there, which felt right.
Start by clarifying requirements and scale (e.g., read-heavy, 100M URLs, 10K QPS), then design the core components: a key generation service, a datastore for mappings, and a redirect service. Discuss trade-offs in alias generation (hash vs. counter), storage choices (SQL vs. NoSQL), and how to handle expiration and analytics without impacting redirect latency.
Pro tip: Emphasize that redirects must be extremely low-latency and highly available, so consider caching and a separate analytics pipeline to avoid synchronous writes on the critical path. Also, mention that custom aliases require a uniqueness check and may need a separate namespace or reservation system.
Ask about expected traffic (read/write ratio), URL volume, latency requirements, and whether analytics need to be real-time. This shapes decisions on storage, caching, and architecture.
Outline the key services: alias generation, URL mapping storage, redirect service, and analytics collector. Define APIs for shortening and redirecting.
Compare approaches: base62 encoding of a distributed counter (e.g., using ZooKeeper or Snowflake) vs. hashing (MD5/SHA) with collision handling. Discuss custom alias support and expiration.
Decide between SQL (strong consistency, easy uniqueness) and NoSQL (scalability, eventual consistency). Propose a cache (e.g., Redis) for hot URLs to reduce latency and database load.
Design an asynchronous pipeline for click analytics (e.g., Kafka + stream processing) to avoid impacting redirects. For expiration, use TTL in the datastore or a background cleanup job.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through SQL vs NoSQL tradeoffs and landed on a key-value store with range-based or hash partitioning.
Start by clarifying the workload characteristics (read-heavy, write-heavy, latency requirements) and then propose a database that fits, such as a distributed key-value store like Cassandra or DynamoDB. Explain partitioning strategies like consistent hashing or range partitioning, and discuss trade-offs around hot spots, rebalancing, and query patterns.
Pro tip: Mention that URL mappings are typically immutable after creation, which allows for aggressive caching and simplifies partitioning. Also, consider using a hash of the short URL as the partition key to ensure even distribution.
Ask about read/write ratio, latency, consistency needs, and expected growth to tailor your choice.
Select a distributed NoSQL database (e.g., Cassandra, DynamoDB) for scalability and high availability, or a sharded relational database if strong consistency is needed.
Use consistent hashing or range partitioning on a key like short URL hash to distribute data evenly across nodes.
Discuss techniques like salting, pre-splitting, or using a composite key to avoid hot partitions, and how to handle node additions.
Weigh consistency vs. availability, latency vs. durability, and operational complexity of the chosen solution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: read-heavy workload, latency targets, and consistency needs. Then propose a multi-tier cache (e.g., in-memory LRU for hot URLs, distributed cache like Redis for warm URLs) with an eviction strategy that balances hit rate and memory. Justify your choice with trade-offs and mention monitoring for adaptation.
Pro tip: Tie your eviction strategy to the workload's access pattern: if hot URLs follow a power-law distribution, LFU or LRU-K often outperform plain LRU. Also, mention that you'd measure hit rate and eviction rate to validate the choice.
Ask about read/write ratio, latency SLOs, memory budget, and consistency requirements. This shapes cache size, eviction policy, and tiering.
Propose a multi-level cache: local in-memory (e.g., Caffeine) for ultra-hot URLs, and a distributed cache (e.g., Redis) for the rest. Explain how requests flow through tiers.
Select an eviction policy based on access patterns: LRU for recency, LFU for frequency, or LRU-K for both. Discuss trade-offs and why it fits hot URLs.
Explain how to handle updates: TTLs, write-through/write-behind, or explicit invalidation. Mention trade-offs between consistency and performance.
Describe metrics (hit rate, eviction rate, latency) and how you'd use them to tune cache size or switch eviction policies dynamically.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Token bucket per IP, sliding window as an alternative.
Start by clarifying the requirements: expected traffic, abuse scenarios, and acceptable trade-offs. Then propose a layered rate limiting strategy, such as token bucket per user/IP with Redis, and discuss how to handle distributed enforcement and graceful degradation. Finally, cover monitoring, alerting, and iterative tuning based on metrics.
Pro tip: Mention that rate limiting should be applied at multiple levels (edge, service, and user) and that you'd use a combination of algorithms like token bucket for bursts and sliding window for precision, showing you understand real-world trade-offs.
Ask about expected request volume, abuse patterns (e.g., bots, scrapers), and whether the endpoint is public or authenticated. Discuss trade-offs between strictness and user experience.
Select an algorithm like token bucket, leaky bucket, fixed window, or sliding window based on burst tolerance and precision needs. Use a fast, shared store like Redis for distributed counters.
Decide where to enforce limits: at the API gateway (e.g., Kong, Envoy), in the service itself, or both. Consider per-user, per-IP, and global limits, and how to handle authenticated vs. anonymous users.
Return HTTP 429 with Retry-After header and clear error messages. Implement fallbacks like caching or queuing if the rate limiter fails, and ensure the system remains available under attack.
Track metrics like request rates, 429 counts, and latency. Set up alerts for anomalies and use data to adjust limits and algorithms over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: what click data is needed, acceptable latency for analytics, and scale. Then design an asynchronous pipeline where the redirect path only emits a lightweight event (e.g., to a queue) and returns immediately, while separate consumers process and store the analytics data. Emphasize decoupling, fault tolerance, and trade-offs between accuracy and latency.
Pro tip: Mention that you'd use a fire-and-forget approach with client-side or edge logging to avoid any blocking I/O, and discuss how you'd handle failures (e.g., retries with idempotency) to prevent data loss without impacting user experience.
Ask about expected click volume, required analytics freshness (real-time vs batch), and acceptable data loss. Confirm that redirect latency is critical and must remain unaffected.
Keep the redirect handler minimal: validate the request, emit an event to a highly available, low-latency queue (e.g., Kafka, Kinesis, or a local buffer), and immediately return the redirect response. Avoid synchronous writes to databases or external services.
Consume events from the queue asynchronously, process them (e.g., aggregate click counts), and store in a suitable analytics store (e.g., time-series DB, data warehouse). Use stream processing for real-time counts or batch for cost efficiency.
Implement retries, dead-letter queues, and idempotent processing to handle failures. Scale consumers horizontally and partition the queue to handle high throughput.
Acknowledge trade-offs: eventual consistency vs real-time, cost vs latency, and complexity. Propose monitoring for queue depth, processing lag, and redirect latency to detect issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.