I started with short-code generation and spent probably too long debating counter-based base62 vs hashing.
Start by clarifying requirements and scale, then design the core URL shortening service with a focus on high read throughput and low latency. Next, design the analytics pipeline to capture click events and process them for real-time and batch aggregations, ensuring scalability and fault tolerance.
Pro tip: Discuss trade-offs between consistency and availability for analytics data, and propose a lambda architecture (batch + stream) to balance accuracy and freshness. Also, mention how to handle hot keys and data skew in the analytics store.
Ask about expected QPS, read/write ratio, latency requirements, and analytics granularity (real-time vs. batch). Estimate storage needs for URLs and click events.
Choose a key generation strategy (e.g., base62 encoding of a distributed ID or hash). Design a highly available and scalable datastore for mappings, using caching for hot URLs.
Capture click events via redirect service, publish to a message queue (e.g., Kafka) for durability and decoupling. Process events with stream processors (e.g., Flink) for real-time aggregates and store raw events for batch processing.
Use a time-series database or a columnar store for aggregated metrics. Design schema to support queries for total clicks, clicks over time, and clicks by region. Consider pre-aggregation for performance.
Discuss partitioning, replication, and handling failures. Trade-offs: consistency vs. latency for analytics, cost of storage vs. query speed, and complexity of real-time vs. batch.
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This is basically the crux of the whole problem and I undercooked it.
Start by clarifying the requirements: the redirect must be as fast as possible, and the counter increment can be eventually consistent. Then propose an asynchronous, decoupled approach where the redirect response is sent immediately and the increment is handled out-of-band, such as via a message queue or in-memory buffer with periodic flushing.
Pro tip: Mention that you would use a fire-and-forget pattern with a bounded queue and backpressure handling to avoid losing increments under high load, and that you'd monitor queue depth and increment lag as key metrics.
Confirm that the redirect latency is critical and that the counter can be eventually consistent. Ask about expected traffic volume and acceptable delay for counter updates.
Propose that the redirect handler only enqueues an increment event (e.g., to a message queue like Kafka or an in-memory buffer) and immediately returns the redirect response.
Select a suitable async mechanism: message queue for durability, or in-memory aggregation with periodic flush for lower overhead. Consider trade-offs between latency, durability, and complexity.
Discuss how to handle queue failures, backpressure, and scaling consumers. Mention idempotency and deduplication if needed, and monitoring for queue depth and lag.
Conclude by summarizing the trade-offs: added complexity and potential data loss vs. minimal latency impact. Emphasize that this design meets the core requirement of no added latency.
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Counter plus base62 gives you predictable length and no collisions by design, hashing is stateless but you need collision detection.
Start by clarifying requirements (scale, uniqueness, length, custom aliases) and then compare the two approaches across key dimensions: uniqueness guarantees, scalability, predictability, and operational complexity. Conclude with a recommendation based on the specific use case, such as using a distributed counter for predictable short codes and hashing for simplicity at scale.
Pro tip: Mention that Snapchat likely needs to handle billions of URLs, so you'd consider sharding the counter or using a pre-generated key pool to avoid bottlenecks. Also, discuss how to handle collisions in the hashing approach with a retry mechanism or salt.
Ask about expected scale (URLs per day), desired short code length, whether custom aliases are needed, and if codes should be unpredictable.
Describe how a global counter (e.g., using Redis or ZooKeeper) generates sequential IDs, which are then base62-encoded to produce short codes. Mention sharding or range allocation to scale.
Describe how a hash function (e.g., MD5, SHA-256) is applied to the long URL, and a portion of the hash is base62-encoded. Discuss collision handling via retries or appending a counter.
Contrast the two: counter gives unique, predictable, short codes but requires coordination; hashing is stateless and simple but may have collisions and longer codes. Discuss scalability, performance, and operational overhead.
Choose an approach based on requirements. For Snapchat, a distributed counter with sharding might be preferable for uniqueness and predictability, but hashing could be used if simplicity is key.
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Pretty straightforward part of the question.
Start by clarifying the redirect use case and scale, then propose a multi-layer caching strategy combining edge caching with CDN and origin-level caching. Discuss trade-offs between cache TTL, consistency, and latency, and how to handle cache invalidation for dynamic redirects.
Pro tip: Mention that redirects are often immutable, so you can set long TTLs and use cache purging via API for updates, which drastically reduces origin load and latency. Also, consider using HTTP 301 for permanent redirects to enable aggressive caching by browsers and CDNs.
Ask about the nature of redirects (e.g., short links, vanity URLs), expected QPS, geographic distribution, and consistency requirements. This determines caching strategy and TTLs.
Leverage CDN to cache redirect responses at edge locations. Use appropriate cache headers (Cache-Control, Expires) and HTTP status codes (301 for permanent, 302 for temporary) to control caching behavior.
Use a distributed cache (e.g., Redis) at the origin to store redirect mappings, reducing database load. Ensure cache invalidation strategy (e.g., TTL, write-through, or pub/sub) is in place.
Discuss how to handle updates or deletions of redirects. Use CDN purge APIs, versioned URLs, or short TTLs for mutable redirects. Consider eventual consistency trade-offs.
Set up monitoring for cache hit ratios, latency, and origin load. Use analytics to adjust TTLs and caching layers for optimal performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.