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I started with the API surface (POST a long URL, GET to redirect) and that part went fine.
Start by clarifying requirements and scale, then propose a high-level design with a focus on the core URL mapping and redirection flow. Dive into key components like ID generation, storage, and caching, and discuss trade-offs for scalability and reliability.
Pro tip: Emphasize how you would handle 100x traffic spikes and ensure low-latency redirects, as Microsoft values scalable and performant systems. Also, proactively mention monitoring and analytics to show you think beyond basic functionality.
Ask questions to understand functional (e.g., custom aliases, expiration) and non-functional (e.g., latency, availability, scale) requirements. Establish assumptions like read-heavy workload and 100M new URLs per day.
Sketch the core components: API servers, database, cache, and ID generator. Explain the flow: client sends long URL, service returns short URL; redirection uses 301/302.
Discuss ID generation (e.g., base62 encoding of a counter or hash), database schema (key-value store), and caching strategy (Redis) for hot URLs. Address collision handling and custom aliases.
Explain how to scale: sharding, replication, load balancing, and CDN for redirects. Discuss trade-offs between consistency and availability, and how to handle failures.
Summarize key trade-offs (e.g., SQL vs NoSQL, 301 vs 302) and mention monitoring, analytics, and potential extensions like rate limiting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: expected scale, read/write ratio, latency constraints, and whether codes need to be unpredictable. Then compare each strategy across dimensions like scalability, collision risk, complexity, and predictability, and conclude with a recommendation based on the given constraints.
Pro tip: Mention that the choice often depends on whether you need globally unique, non-sequential codes and that a hybrid approach (e.g., base-62 encoding of a distributed ID like Snowflake) can balance trade-offs.
Ask about scale (QPS, storage), latency needs, whether codes must be unique, short, and unpredictable, and if there are regulatory constraints.
For hashing, discuss collision probability, handling (e.g., linear probing, rehashing), and unpredictability; for base-62 auto-increment, note simplicity, predictability, and single point of failure; for distributed counters, cover coordination overhead, scalability, and potential bottlenecks.
Evaluate each on scalability, collision risk, complexity, predictability, and failure modes. Use a table or structured comparison to highlight differences.
Based on the requirements, recommend a strategy (or hybrid) and explain why it best fits, acknowledging any remaining trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a key-value store pretty quickly since the access pattern is almost entirely point lookups by short code.
Start by clarifying the requirements of the URL mapping service, such as read/write ratio, scale, latency, and consistency needs. Then compare key-value stores and relational databases against those requirements, and propose a schema for the chosen option, explaining trade-offs. Conclude with a recommendation that balances performance, scalability, and simplicity.
Pro tip: Emphasize that the choice depends on access patterns: if the primary operation is lookup by key, a key-value store is often optimal, but if you need complex queries or transactions, a relational database is better. Mention that a hybrid approach (e.g., using a relational DB for metadata and a KV store for mappings) can be effective at scale.
Ask about expected scale (e.g., millions of URLs), read/write ratio, latency requirements, and consistency needs. This shows you don't jump to solutions without understanding the problem.
Discuss key-value stores (e.g., Redis, DynamoDB) for their simplicity, speed, and horizontal scalability, versus relational databases (e.g., SQL Server, PostgreSQL) for their ACID guarantees, indexing, and query flexibility.
For a key-value store, the schema is a simple mapping: short URL key -> long URL value. For a relational database, propose a table with columns like id, short_code, long_url, created_at, and indexes on short_code.
Highlight trade-offs: KV stores offer low latency and easy scaling but lack complex queries; relational DBs provide transactions and joins but may require sharding for scale. Mention caching and read replicas as potential optimizations.
Based on the requirements, recommend one option or a hybrid approach, and justify it. For example, if the service is read-heavy and needs ultra-low latency, choose a KV store; if you need analytics or complex reporting, choose a relational DB.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scale and access patterns (e.g., QPS, read/write ratio, latency SLA) to frame the caching design. Then propose a multi-tier cache architecture (client, CDN, application-level, distributed cache) with appropriate eviction policies and hot-key handling. Finally, discuss consistency, invalidation, and monitoring to ensure efficiency and reliability.
Pro tip: Emphasize that hot short codes are often skewed (e.g., 1% of codes get 90% of traffic), so design for extreme skew by replicating hot keys across cache nodes and using local caches to reduce network hops.
Ask about expected QPS, read/write ratio, latency targets, and data size to determine caching strategy and technology choices.
Propose layers: client-side caching (HTTP cache headers), CDN for edge caching, application-level in-memory cache (e.g., Caffeine), and a distributed cache (e.g., Redis) for shared state.
Detect hot keys via monitoring, replicate them across multiple cache nodes, use local caches with short TTLs, and consider key sharding or request coalescing to prevent overload.
Choose eviction policies (LRU, LFU) based on access patterns, and decide on cache invalidation strategies (TTL, write-through, write-behind) balancing consistency and performance.
Instrument cache hit ratio, latency, and hot key detection; use metrics to adjust TTLs, cache sizes, and replication factors continuously.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I said read replicas in multiple regions and push the most popular mappings to CDN edge nodes so the redirect happens without hitting origin.
Start by clarifying the requirements: global user distribution, acceptable latency targets, and types of redirects (e.g., HTTP 301/302, edge rules). Then propose a multi-layered architecture: use a CDN with edge compute (e.g., Azure Front Door) to perform redirects at the edge, and deploy origin services in multiple regions with geo-routing to minimize latency. Discuss trade-offs between consistency, cost, and complexity, and how to measure and optimize latency.
Pro tip: Emphasize that redirects should be handled at the edge (CDN) to avoid round-trips to origin, and mention using anycast or latency-based routing to direct users to the nearest edge. Also, highlight the importance of caching redirect rules and using short TTLs for dynamic decisions.
Ask about user distribution, latency SLOs, redirect types (permanent vs temporary), and whether redirects depend on user context (e.g., geo, device).
Propose using CDN edge compute (e.g., Azure Front Door, Cloudflare Workers) to evaluate redirect rules and issue responses at the edge, minimizing latency.
Deploy origin services in multiple regions with geo-routing (e.g., Azure Traffic Manager) to handle dynamic redirect logic when edge cannot decide, ensuring low latency.
Use caching for static redirect rules, monitor latency with real user measurements, and iterate on rule placement and TTLs to balance freshness and performance.
Address consistency vs. latency, cost of edge compute vs. origin, and complexity of managing distributed rules; propose a hybrid approach.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Rate limiting per IP and per user token, reject or queue requests above a threshold.
Start by clarifying the threat model and scale requirements, then propose a layered defense combining rate limiting at the edge and application levels with asynchronous malware URL scanning. Emphasize trade-offs between security, latency, and user experience, and describe how you would monitor and iterate on the system.
Pro tip: Show you understand that abuse prevention is an arms race: design for observability and rapid iteration, and mention how you'd use canary deployments and A/B testing to tune thresholds without breaking legitimate users.
Ask about expected traffic volume, types of abuse (e.g., scraping, spam, malware), and acceptable false positive rates. Identify key assets and endpoints to protect.
Propose a multi-tier rate limiting approach: global, per-user, per-IP, and per-endpoint limits using algorithms like token bucket or sliding window. Discuss where to enforce (API gateway, service mesh, application) and how to handle distributed counters (e.g., Redis).
Describe an asynchronous pipeline: when a URL is submitted, enqueue a scan job using services like Microsoft Defender or VirusTotal, and return a pending status. Cache results and use webhooks or polling to update status.
Explain how to balance security with latency and availability: e.g., fail-open vs. fail-closed, graceful degradation, and circuit breakers. Discuss how to avoid blocking legitimate users via allowlists and adaptive thresholds.
Outline metrics to track (e.g., rate limit hits, scan queue depth, false positives) and how to use them to tune rules. Mention logging, dashboards, and automated alerts for anomalies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I suggested async event streaming so the redirect itself stays low latency and click events get processed in the background.
Start by clarifying requirements like scale, latency, and analytics needs, then propose a high-level architecture covering ingestion, processing, storage, and serving. Walk through each component, justifying technology choices and addressing trade-offs such as cost, scalability, and data accuracy.
Pro tip: Emphasize idempotency and exactly-once processing to avoid double-counting clicks, and discuss how to handle late-arriving data with watermarks or batch reprocessing.
Ask about expected QPS, latency requirements, data retention, and what metrics are needed (e.g., real-time vs. batch). This ensures the design meets actual needs.
Outline the end-to-end flow: click event generation, ingestion, processing, storage, and query/serving layers. Mention key components like load balancers, message queues, stream processors, and databases.
Detail how events are collected (e.g., HTTP redirects with logging) and ingested (e.g., Kafka). Describe stream processing for real-time aggregation and batch processing for historical analysis.
Explain storage choices for raw events (e.g., data lake) and aggregated metrics (e.g., time-series DB or OLAP). Discuss how to serve queries for dashboards or APIs.
Address partitioning, replication, fault tolerance, and monitoring. Discuss trade-offs between consistency, availability, and cost.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the service's core functionality and user actions, then make reasonable assumptions about user base size, activity patterns, and data characteristics. Use a structured estimation approach to calculate storage and QPS, breaking down into components and validating with sanity checks.
Pro tip: Always state your assumptions explicitly and round numbers to powers of 10 for easier mental math; interviewers care more about your reasoning process than exact numbers.
Ask questions to understand what the service does, its key features, and the primary user actions that generate data and queries. For example, is it a social network, a file storage service, or a messaging app?
Assume a realistic user base size (e.g., 100 million users) and estimate daily active users (DAU), actions per user per day, and peak traffic multipliers. Consider growth and regional distribution if relevant.
Break down data into types (e.g., user profiles, content, logs), estimate average size per item, and multiply by volume. Include replication and backup overhead. Sum to get total storage.
Estimate average QPS from daily actions, then apply peak factor (e.g., 2-3x) to get peak QPS. Consider read vs. write ratios and different query types.
Validate numbers against known benchmarks (e.g., Twitter QPS, storage per user) and summarize key figures. Discuss implications for system design (e.g., sharding, caching).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.