I started with requirements which felt right, but I spent too long on the functional side and the interviewer had to nudge me toward non-functionals.
Start by clarifying requirements and scale, then propose a high-level architecture with a durable job store, distributed workers, and a scheduler. Dive into reliability mechanisms like at-least-once delivery, idempotency, and failure recovery, and discuss trade-offs between consistency and availability.
Pro tip: Emphasize idempotency and deduplication as key to handling retries without side effects, and mention how you'd monitor job execution and alert on failures to ensure no job loss.
Ask about scale (jobs per second, total jobs), latency requirements, job types (one-time vs recurring), and reliability guarantees (at-least-once, exactly-once).
Propose components: a job store (e.g., database or distributed log), a scheduler service, a pool of workers, and a coordination layer (e.g., ZooKeeper or etcd).
Explain how to ensure no job loss: persistent storage, replication, acknowledgments, retries with exponential backoff, and dead-letter queues.
Discuss partitioning jobs, horizontal scaling of workers, leader election for scheduler, and handling worker failures via heartbeats and job reassignment.
Compare at-least-once vs exactly-once semantics, discuss idempotency, and mention monitoring, metrics, and alerting for operational visibility.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: what types of jobs (batch, streaming, cron), expected query patterns (point lookups, time-range scans, scheduling), and scale. Then propose a schema with core fields (job_id, status, schedule_time, etc.) and discuss indexing strategies (B-tree for range queries, composite indexes for common filters) and partitioning for scalability.
Pro tip: Mention that you would consider using a time-series database or a specialized scheduler like Quartz, but also explain how to achieve it with a relational database, showing depth. Also, discuss the trade-offs between read and write optimization, and how to handle updates to job schedules.
Ask about job types, expected query patterns (e.g., find jobs in a time range, find next job to run), scale (number of jobs, QPS), and consistency needs.
List essential fields: job_id (UUID), type, status (pending, running, completed, failed), schedule_time (timestamp), created_at, updated_at, payload (JSON), priority, retry_count, etc.
For time-range queries, create an index on schedule_time (B-tree). For filtering by status and time, use a composite index (status, schedule_time). Consider partial indexes for active jobs.
Discuss partitioning by time (e.g., monthly) or by status to manage large volumes. Consider sharding by job_id or schedule_time if needed.
Compare SQL vs NoSQL, mention time-series DBs, and explain how to handle updates (e.g., rescheduling) without hot spots.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: define 'near real-time' (e.g., sub-second latency), expected QPS, and data scale. Then propose a two-part solution: an API design that accepts N and returns jobs efficiently, and a storage/query strategy using a time-partitioned index (e.g., time-bucketed tables or a time-series database) with caching for hot windows. Discuss trade-offs between consistency, latency, and cost, and how to handle variable N and high QPS.
Pro tip: Mention that you would precompute and cache the results for common N values (e.g., 1h, 6h, 24h) and use a sliding window approach to avoid recomputing from scratch, which shows you think about practical optimizations at scale.
Ask about expected QPS, data volume, latency SLA, and whether N is arbitrary or from a set of common values. Also clarify if the dashboard needs exact or approximate results.
Define a REST endpoint like GET /jobs?start=now&end=now+Nh with pagination and filtering. Consider using a cursor-based pagination and returning a compact payload to reduce bandwidth.
Propose a time-partitioned storage (e.g., hourly partitions in a relational DB or time-series DB) with an index on scheduled_time. For high QPS, use a distributed cache (e.g., Redis) to store precomputed results for frequent N values.
Use range queries on the time index, and for variable N, dynamically compute the time window. For high QPS, consider read replicas, sharding by time or job ID, and caching with TTL based on data freshness.
Compare consistency vs. latency (e.g., eventual consistency with cache vs. strong consistency). Discuss how to handle spikes, cache invalidation, and cost implications of precomputation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.