Watermark: Defines max lateness (e.g., 10 min). Events older than (max_event_time - watermark) are dropped. State: Kept for aggregations within watermark; beyond that, state is purged to avoid unbounded growth. Output modes: Append (only finalized results), Update (changed...
Red Flag: Saying 'use watermark' without discussing state growth or dropped data handling. Pro-Move: 'We use 10-min watermark aligned to p99 latency; late events go to DLQ for batch backfill'—shows end-to-end design.
This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Bitwise, Incedo, Swiggy. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (spark, window) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
Watermark: Defines max lateness (e.g., 10 min). Events older than (max_event_time - watermark) are dropped. State: Kept for aggregations within watermark; beyond that, state is purged to avoid unbounded growth. Output modes: Append (only finalized results), Update (changed rows), Complete (full result). Why: Trade-off between latency and correctness—tighter watermark = less state, more dropped late data. Looser = more state, more complete results. Scalability: State scales with unique keys × window count; oversized watermark = OOM risk. Cost: Longer watermark = more state = more memory. Best practice: Set watermark from SLA (e.g., 99% of events arrive within 5 min). Use DLQ for dropped events when audit is needed.
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