← Attentive Interview Insights
Start by clarifying requirements and scale, then design a service that ingests messages, maps them to companies, and fans out to subscribers. Discuss data models, delivery guarantees, and scalability, and outline an algorithm for processing the message log efficiently.
Pro tip: Emphasize idempotency and failure handling—real-world broadcasting must handle retries and duplicate messages without spamming subscribers. Also, mention how you'd monitor delivery success and latency.
Ask about scale (number of companies, subscribers, messages per second), delivery guarantees (at-least-once, exactly-once), and latency expectations. Confirm if messages are processed in real-time or batch.
Define structures for company-subscriber mapping (e.g., hash map or database table) and message log (e.g., queue or time-series store). Consider indexing for efficient lookups.
Describe how messages are ingested, validated, and routed to the correct company's subscribers. Discuss batching, parallelism, and backpressure handling.
Explain how to ensure reliable delivery: retries, dead-letter queues, idempotency keys, and acknowledgment mechanisms. Discuss trade-offs between different delivery semantics.
Propose scaling strategies (sharding, partitioning by company, horizontal scaling) and monitoring metrics (throughput, latency, error rates). Mention how to handle hot companies with many subscribers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty natural extension once you have the polling model.
Start by clarifying the requirements: what is the expected query frequency, data volume, and latency tolerance? Then propose an extension of the polling mechanism to accept a time range, discussing trade-offs between different implementation strategies (e.g., client-side aggregation vs. server-side range queries) and how to handle large ranges efficiently.
Pro tip: Demonstrate awareness of real-world constraints by mentioning how you would handle very large time ranges (e.g., pagination, sampling, or pre-aggregation) and how you would monitor and alert on query performance to avoid degrading the system.
Ask about the expected frequency of range queries, the typical size of the range, data volume, and latency requirements. This ensures you design a solution that meets actual needs.
Consider how data is stored (e.g., time-series database, relational DB) and whether existing indexes support efficient range queries. Discuss if new indexes or partitioning are needed.
Propose how the polling API would change to accept t1 and t2, and how the server would process the request. Consider options like returning raw data, aggregated results, or a stream.
Discuss strategies to handle large ranges: pagination, limit/offset, time-based chunking, caching, or pre-computed aggregates. Mention trade-offs between completeness and latency.
Cover edge cases like empty ranges, very large ranges, and out-of-order data. Explain how you would monitor query performance and set up alerts for slow queries.
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 entities and use cases (e.g., messages, topics, schedules) and non-functional requirements like scale and latency. Then propose a RESTful API with clear resource naming, HTTP methods, and status codes, covering broadcast, subscribe, unsubscribe, and schedule operations. Discuss trade-offs such as synchronous vs. asynchronous processing, idempotency, and pagination.
Pro tip: Emphasize idempotency and error handling for each operation—interviewers often look for how you handle retries and partial failures in distributed systems. Also, mention versioning and backward compatibility to show you think about long-term API evolution.
Ask about scale, latency, consistency, and client types to tailor the API design. Identify core resources and operations needed.
Map operations to RESTful resources (e.g., /broadcasts, /subscriptions, /schedules) with appropriate HTTP methods and status codes.
Detail payloads for each operation, including required fields, validation rules, and response formats (e.g., JSON).
Cover idempotency, pagination, rate limiting, authentication, and error handling. Discuss async patterns for long-running operations like scheduling.
Compare design choices (e.g., REST vs. gRPC, sync vs. async) and justify decisions based on requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through a relational approach vs a key-value store.
Start by clarifying the access patterns and requirements for each data type, then propose a storage model that balances write throughput, read latency, and cost. Justify your choices with trade-offs and mention how they align with Attentive's scale and real-time needs.
Pro tip: Show awareness of operational concerns like retention policies, cost per GB, and query flexibility—these often matter more than raw performance in production. Also, mention how you'd evolve the storage model as data grows, demonstrating foresight.
Ask about write volume, read patterns, retention period, and query needs for logs and subscriber lists. This ensures your recommendation is grounded in actual use cases.
Recommend a write-optimized, scalable store like a distributed log (e.g., Kafka) for ingestion and a columnar store (e.g., ClickHouse, BigQuery) for analytics. Explain why this handles high throughput and efficient querying.
Suggest a relational database (e.g., PostgreSQL) for strong consistency and complex queries, or a NoSQL store (e.g., DynamoDB) for scale if access patterns are simple. Discuss indexing and sharding strategies.
Highlight trade-offs: e.g., logs favor availability and partition tolerance (AP), while subscriber lists may need consistency (CP). Mention cost, operational complexity, and query flexibility.
Explain how the models can evolve (e.g., tiered storage for logs, caching for subscribers) and how they integrate with other systems (e.g., stream processing, CDC).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Last follow-up and by this point my brain was a bit fried.
Start by clarifying requirements such as scale, timing precision, and failure handling. Then propose a high-level architecture with a scheduling service, a durable job store, and a worker pool, and dive into trade-offs like polling vs. event-driven, consistency, and scalability.
Pro tip: Emphasize idempotency and at-least-once delivery with deduplication, as missed or duplicate broadcasts are costly. Also, discuss how to handle time zone and DST complexities, showing attention to real-world edge cases.
Ask about scale (number of scheduled broadcasts, QPS), timing precision (exact vs. approximate), and reliability guarantees (at-least-once, exactly-once).
Outline components: API for scheduling, persistent store for jobs, scheduler that triggers jobs, and workers that execute broadcasts. Consider using a message queue for decoupling.
Discuss how to efficiently find due jobs: polling database vs. priority queue vs. time-wheel. Address scalability with sharding and distributed locks.
Explain how to ensure jobs are not lost: persistence, retries with exponential backoff, dead-letter queues, and idempotent execution.
Compare options (e.g., cron vs. one-time, push vs. pull) and discuss monitoring, alerting, and future features like recurring broadcasts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.