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I started with capacity math which felt right, 500M writes a month, 100:1 read ratio, worked out to something like 20k redirects per second average.
Start by clarifying requirements and scale (e.g., read/write ratio, QPS, latency, storage) to set the stage. Then present a high-level architecture covering API, encoding, storage, and caching, followed by a low-level design detailing the encoding algorithm, database schema, and key components. Conclude by discussing trade-offs and potential optimizations.
Pro tip: Proactively discuss trade-offs between different encoding strategies (e.g., base62 vs. hash-based) and how they affect collision handling and scalability. Also, mention how you would handle custom aliases and analytics, as these are common in real-world systems like Bitly.
Ask questions to understand functional and non-functional requirements: expected QPS, read/write ratio, latency, storage, and features like custom aliases or analytics. Estimate scale to inform design decisions.
Outline the main components: API gateway, application servers, database, cache, and analytics. Describe the flow: client sends long URL, service generates short URL, stores mapping, and returns short URL; redirects look up the mapping.
Detail the encoding algorithm (e.g., base62 of auto-increment ID or hash). Discuss database schema (e.g., short_key, long_url, creation_date, user_id) and choice of database (SQL vs. NoSQL) based on scale and consistency needs.
Explain how to scale: caching frequently accessed URLs, using a distributed counter for ID generation, sharding the database, and employing a CDN for redirects. Address read-heavy nature with read replicas.
Discuss trade-offs: base62 vs. hash (collision vs. predictability), SQL vs. NoSQL, cache eviction policies. Mention optional features like custom aliases, expiration, and analytics, and how they impact design.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Asked about scale, TTL on links, and whether we needed analytics.
Start by acknowledging that clarifying questions are essential to avoid building the wrong system. Then, structure your questions around key dimensions like functional requirements, scale, constraints, and trade-offs, explaining why each matters. Finally, tie your questions back to how they would influence your design decisions.
Pro tip: Ask questions that uncover non-functional requirements and business context, not just technical details. This shows you think like a product engineer who considers user impact and cost, which is highly valued at Microsoft.
Ask what the system should do, who the users are, and what the core features are. This ensures you build the right thing and avoid scope creep.
Ask about expected user base, request volume, data size, latency requirements, and throughput. These drive architectural choices like sharding, caching, and replication.
Ask about budget, timeline, technology stack, compliance, and consistency vs. availability preferences. These constraints shape feasible solutions and prioritization.
Ask about data sources, formats, retention policies, and external system integrations. This affects data modeling, storage, and API design.
Summarize your understanding and confirm which requirements are most critical. This aligns your design with stakeholder expectations and reduces risk.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the interview got interesting.
Start by clarifying the requirements (scale, latency, collision tolerance, security). Then compare each strategy on key dimensions like performance, scalability, collision handling, and operational complexity. Conclude with a recommendation based on the tradeoffs and mention hybrid approaches.
Pro tip: Emphasize that the choice depends on the specific constraints; for example, hashing is simple but collisions require resolution, while a counter is collision-free but can be predictable and a single point of failure. Mention that pre-generated keys offer the best of both but add infrastructure overhead.
Ask about expected scale (URLs per second), latency requirements, collision tolerance, and security needs (e.g., unpredictability).
Briefly explain how hashing, global counter, and pre-generated key service work, including their basic mechanics.
Compare strategies on performance, scalability, collision handling, predictability, and operational complexity.
Choose a strategy based on the clarified requirements and justify why it fits best, possibly suggesting a hybrid approach.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
62 to the 7th power is something like 3.5 trillion.
Start by explaining that the minimum short code length is determined by the smallest integer L such that 62^L >= N, where N is the expected number of unique codes. Then walk through the calculation using logarithms: L = ceil(log_62(N)), and discuss practical considerations like collision avoidance and future growth.
Pro tip: Mention that in real systems you should add a safety margin (e.g., 10-20% extra capacity) and consider that base-62 is case-sensitive, which can cause issues with human transcription; sometimes base-58 (excluding ambiguous characters) is preferred despite slightly longer codes.
Clarify the expected number of unique short codes needed (N) and whether it's for total URLs, concurrent users, or another metric. Also consider growth over time.
Explain that base-62 uses digits 0-9, lowercase a-z, and uppercase A-Z, giving 62 possible characters per position. The total number of unique codes of length L is 62^L.
Find the smallest integer L such that 62^L >= N. This can be done by taking the logarithm: L = ceil(log(N) / log(62)). Provide an example, e.g., for 1 billion codes, L = ceil(log(1e9)/log(62)) = 6.
Discuss adding a safety margin (e.g., 10-20%) to account for unused codes, collisions, or future growth. Recalculate L if necessary.
Mention trade-offs: shorter codes are user-friendly but risk collisions; longer codes are safer but less memorable. Also consider alternative encodings like base-58 to avoid ambiguous characters.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
301 is permanent so browsers and CDNs cache it, meaning repeat visits never hit your servers.
Start by defining the semantic difference between 301 (permanent) and 302 (temporary) redirects, then discuss the implications for caching, SEO, and client behavior. Emphasize that the choice depends on the permanence of the redirect and consider modern alternatives like 307/308 for method preservation.
Pro tip: Mention that 301 redirects are cached aggressively by browsers, which can cause issues if the redirect is later reversed; also note that 308 preserves the HTTP method, unlike 301, which may change POST to GET in some clients.
Clearly state that 301 indicates a permanent redirect, while 302 indicates a temporary redirect. Explain that this affects how clients and search engines treat the redirect.
Explain that 301 responses are cacheable by default and may be cached indefinitely by browsers, potentially causing stale redirects. 302 responses are not cached by default, allowing for flexibility.
Describe how search engines treat 301 as a signal to transfer page rank and update indexes to the new URL, while 302 suggests the original URL should remain indexed.
Note that 301 and 302 may change the request method (e.g., POST to GET) in some clients, whereas 307 and 308 preserve the method. Mention that 308 is the permanent version of 307.
Summarize when to use each: use 301 for permanent moves (e.g., domain changes), 302 for temporary (e.g., maintenance), and consider 307/308 when method preservation is critical.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Write to a queue asynchronously after sending the redirect response.
Start by clarifying the requirements: what analytics are needed, acceptable latency, and scale. Then propose an asynchronous, decoupled architecture where the redirect path only enqueues a lightweight event (e.g., to a message queue) and returns immediately, while a separate consumer processes and stores the analytics. Emphasize that the redirect path must remain fast and reliable, so analytics should be best-effort and not block the user.
Pro tip: Mention that you would monitor the redirect path's latency and have a kill switch to disable analytics if it degrades performance, showing you prioritize user experience over data collection.
Ask about the expected traffic volume, acceptable latency overhead, and what specific analytics are needed (e.g., per-link clicks, unique users, referrers).
Keep the redirect path minimal: validate the link, enqueue an analytics event asynchronously, and return the redirect response. Use a fast, non-blocking mechanism like a message queue or in-memory buffer.
Select a scalable message queue (e.g., Kafka, Azure Event Hubs) and a consumer service that processes events and writes to a analytics store (e.g., Azure Data Explorer, Cosmos DB).
Make the enqueue operation best-effort with minimal impact on latency; if the queue is unavailable, drop the event rather than failing the redirect. Use retries and dead-letter queues in the consumer.
Instrument the redirect path to track latency and error rates. Set up alerts and a kill switch to disable analytics if latency increases. Continuously optimize the enqueue operation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the part I felt least prepared for.
Start by explaining that sharding strategy and key generation are tightly coupled decisions. Describe how you would choose a sharding scheme (e.g., range, hash, directory) based on access patterns and growth projections, and then explain how your key generation (e.g., UUID, Snowflake, composite keys) supports that scheme while avoiding hotspots and enabling efficient scaling. Conclude with trade-offs and how you would handle resharding.
Pro tip: Emphasize that you would design for resharding from day one—e.g., using consistent hashing or a directory service—and that you would monitor shard load to proactively split before hotspots become critical. This shows you think about operational maturity, not just initial design.
Ask about data volume, read/write ratio, query patterns, and latency requirements to determine if sharding is even necessary and what scheme fits best.
Evaluate range, hash, or directory-based sharding based on access patterns, and explain how each affects scalability and hotspot risk.
Select a key scheme (e.g., UUID, Snowflake, composite) that ensures even distribution, avoids hotspots, and allows efficient routing to shards.
Describe how you would handle adding/removing shards, migrating data with minimal downtime, and monitoring shard health.
Acknowledge trade-offs like complexity vs. scalability, and mention alternatives like vertical scaling or using managed services.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.