Start by clarifying requirements and scale, then design a system that decouples data ingestion from API serving. Use a scheduled ingestion service to fetch hourly data from the provider, store it in a low-latency data store, and serve it via a read-optimized API. Ensure the 10-minute staleness constraint by monitoring provider publish times and triggering fetches accordingly.
Pro tip: Emphasize that the 10-minute staleness is relative to the provider's publish time, not the hour boundary, so you need to detect when new data is available (e.g., via polling or webhooks) and fetch immediately. Also, consider caching at the edge to reduce latency and load on your data store.
Confirm the number of locations (2,000), data update frequency (hourly), and the staleness constraint (10 minutes from provider publish time). Ask about expected API traffic, read/write ratio, and consistency needs.
Use a scheduled job (e.g., cron or cloud scheduler) that polls the provider for new data. Since data is hourly, poll frequently (e.g., every minute) to detect new publishes and fetch within the 10-minute window. Alternatively, use webhooks if supported.
Store the latest temperature per location in a low-latency store like Redis or a relational database with proper indexing. Keep historical data in a separate store if needed. Ensure writes are idempotent and handle failures with retries.
Expose a simple REST endpoint (e.g., GET /temperature?location=...) that reads from the data store. Use caching (e.g., CDN or in-memory cache) to reduce latency and load. Ensure the API returns the most recent data and includes a timestamp for staleness checks.
Implement monitoring for data freshness (alert if data exceeds 10 minutes), handle provider failures with retries and fallback to last known good data, and ensure high availability of the API with load balancing and auto-scaling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with a pretty standard GET endpoint and versioning in the path.
Start by clarifying the use case and constraints, then propose a RESTful resource model that supports both lookup methods via separate endpoints or query parameters. Define the response schema with temperature, location, and freshness metadata, and discuss error handling, caching, and versioning.
Pro tip: Consider using a single endpoint with query parameters for flexibility, but be explicit about the trade-offs between path-based and query-based lookups. Also, include a 'last_updated' timestamp and a 'source' field in the metadata to build trust with consumers.
Ask about expected clients, scale, latency requirements, and whether the API is public or internal. This informs decisions on caching, authentication, and versioning.
Model temperature as a resource with a unique identifier. Decide on endpoints: e.g., GET /temperatures/{id} for internal ID and GET /temperatures?lat={lat}&lon={lon} for coordinates.
Include fields: temperature (value and unit), location (id, lat, lon, name), and metadata (timestamp, source, freshness). Use a consistent envelope for errors and success.
Add Cache-Control headers and ETags based on freshness. Include a 'last_updated' field and consider a 'stale' flag if data is older than a threshold.
Define error responses for invalid coordinates, unknown IDs, and rate limiting. Mention versioning strategy (e.g., URL path or header) for future changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The backfill piece is where I got a bit lost.
Start by clarifying requirements and constraints (e.g., scale, latency, provider APIs) before diving into the design. Then walk through the pipeline end-to-end, covering detection, fan-out, retries, idempotency, and missed hours, while highlighting trade-offs and failure handling. Conclude by discussing monitoring, alerting, and how you would validate the design.
Pro tip: Emphasize idempotency and exactly-once semantics as the backbone of your design, and proactively discuss how you'd handle provider-specific quirks like rate limits or inconsistent timestamps. This shows you've thought about real-world integration challenges beyond the happy path.
Ask about scale (providers, locations, events per hour), latency expectations, provider API capabilities (webhooks vs. polling), and data consistency needs. This ensures your design targets the right trade-offs.
Explain how you detect new publishes (e.g., webhooks, polling with cursor, change data capture) and how you fan out to locations (e.g., message queue, pub/sub, sharded workers). Discuss ordering and partitioning strategies.
Describe retry policies with exponential backoff and jitter, dead-letter queues, and idempotency keys to deduplicate processing. Mention how you ensure exactly-once or at-least-once semantics with idempotent writes.
Outline strategies for detecting and recovering missed hours, such as watermarking, reconciliation jobs, or backfill APIs. Discuss how you avoid duplicate processing during backfill.
Cover observability (metrics, logs, tracing), alerting on failures or lag, and how you'd test and iterate on the pipeline. Mention capacity planning and cost considerations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Write-through cache with a TTL slightly under 10 minutes was my answer.
Start by clarifying the data access patterns and consistency requirements, then propose a layered caching strategy with TTLs and event-driven invalidation to meet the 10-minute freshness SLA. Explain how the API enforces freshness by checking timestamps or versions before serving cached data, falling back to the source if stale.
Pro tip: Emphasize that the freshness SLA is a business requirement, so you should discuss monitoring and alerting on cache hit ratios and staleness metrics to ensure compliance. Also, mention that you'd validate the strategy with load testing and chaos experiments to handle edge cases like cache stampedes.
Ask about data volatility, read/write ratios, and acceptable latency to tailor the caching strategy. Confirm that the 10-minute SLA applies to all data or specific entities.
Propose a multi-level cache (e.g., in-memory, Redis) with TTLs set below 10 minutes (e.g., 5 minutes) to ensure freshness. Use write-through or write-behind patterns for updates.
Combine time-based expiration with event-driven invalidation (e.g., pub/sub on data changes) to proactively refresh or evict stale entries. Consider versioning to handle concurrent updates.
Before serving cached data, the API checks the entry's timestamp or version against the SLA. If stale, it fetches fresh data from the source, updates the cache, and returns the fresh data.
Track cache hit ratio, staleness, and invalidation latency. Set alerts for SLA breaches and continuously refine TTLs and invalidation triggers based on metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I did some back-of-envelope math on the spot.
Start by clarifying the system's scope and key components, then walk through scaling strategies for each layer (e.g., load balancing, caching, sharding) while providing rough estimates for QPS and storage based on assumptions. Emphasize trade-offs and justify your choices with reasoning about traffic patterns and data growth.
Pro tip: Always state your assumptions explicitly (e.g., daily active users, average requests per user) before giving estimates; this shows structured thinking and allows the interviewer to correct you if needed. Also, mention monitoring and auto-scaling as part of the solution to handle spikes dynamically.
Ask questions to understand the system's current scale, expected traffic patterns, and data characteristics. State assumptions about user base, request types, and growth projections.
Calculate rough QPS by estimating daily active users, requests per user per day, and peak-to-average ratio. Estimate storage by considering data size per record, retention period, and replication factor.
Analyze each component (web servers, databases, caches) for potential bottlenecks. Propose horizontal scaling, caching, sharding, and asynchronous processing as appropriate.
Discuss techniques like auto-scaling, load balancing, rate limiting, and queueing to handle sudden spikes. Mention the importance of monitoring and alerting.
Recap the proposed architecture and highlight trade-offs (e.g., cost vs. performance, consistency vs. availability). Suggest iterative improvements based on metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I talked about alerting on the age of the newest record per location and setting a threshold alert before you actually breach the SLA.
Start by defining what freshness SLOs mean for the system and how they are measured, then walk through a layered monitoring strategy that distinguishes between upstream failures and partial outages. Finally, describe graceful degradation as a set of fallbacks that preserve core functionality and user experience while alerting the right teams.
Pro tip: Emphasize that alerting should be actionable and tied to error budgets—avoid alert fatigue by alerting on symptoms (SLO burn) rather than every upstream blip. Also, mention that graceful degradation should be tested regularly via chaos engineering or game days.
Clearly specify what 'freshness' means (e.g., data age, update latency) and how it's measured (e.g., time since last successful update). Establish SLO targets and error budgets to guide alerting thresholds.
Monitor at multiple levels: infrastructure (CPU, network), application (processing latency, queue depths), and business (data freshness). Use synthetic checks and real user monitoring to detect partial outages.
Alert on SLO violations using burn rates to catch both fast and slow degradations. Differentiate alerts for upstream failures (e.g., dependency health checks) and partial outages (e.g., regional failures) with appropriate severity and routing.
Define fallback behaviors such as serving stale data with a warning, degrading non-critical features, or switching to a backup source. Ensure degradation is automatic and reversible, and communicate status to users.
Regularly test failure scenarios through chaos experiments and game days. Review incidents to refine SLOs, alerts, and degradation strategies, ensuring they remain aligned with business needs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Ran a bit short on time here so this was more of a quick sketch.
Start by clarifying the platform's critical user journeys and business impact, then propose a multi-region active-active or active-passive architecture with explicit RPO/RTO targets derived from those needs. Walk through the trade-offs between cost, complexity, and resilience, and explain how you'd validate the design with chaos engineering and regular DR drills.
Pro tip: Anchor your RPO/RTO targets to concrete business metrics (e.g., 'a 5-minute RPO for contact data means at most 5 minutes of lost updates, which we can tolerate during a regional failover') and mention that you'd revisit them as the platform scales—this shows you understand that DR is a continuous process, not a one-time setup.
Ask about the platform's critical services, data sensitivity, compliance needs, and acceptable downtime. Establish whether the goal is active-active or active-passive, and identify any budget or latency constraints.
Propose specific RPO/RTO values per service tier (e.g., RPO < 1 min for transactional data, RTO < 15 min for core APIs) and justify them with business impact analysis. Explain how these targets drive replication and failover strategies.
Describe data replication (synchronous vs. asynchronous), traffic routing (DNS, global load balancers), and failover mechanisms. Cover stateless services, stateful data stores, and how to handle consistency vs. availability trade-offs.
Explain how you'd monitor replication lag, automate failover, and run regular DR drills. Include chaos engineering practices to validate resilience and ensure runbooks are up to date.
Summarize the cost, complexity, and performance implications of your design. Mention how you'd iterate—e.g., starting with active-passive and moving to active-active as needs grow—and how you'd measure success.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.