How would you implement a sliding window aggregation in Spark Structured Streaming?

Sliding window: window(timeColumn, windowDuration, slideDuration) where slideDuration < windowDuration creates overlapping windows. Example: df.withWatermark("event_time", "10 minutes").groupBy(window(col("event_time"), "1 hour", "10 minutes"), col("user_id")).count(). Why watermark: Late data would grow state unbounded; watermark drops events older than (max_event_time - delay) and allows state cleanup. Scalability: State grows with (unique keys × windows); for high cardinality, consider approximate aggregations or truncate state. Output modes: Append only emits final results when watermark passes; Update emits partial results; Complete emits full state (use sparingly). Cost implication: Sliding windows with small slides (e.g., 1min slide, 1hr window) create many overlapping windows—state and compute scale with 1/slide. Architectural choice: Tumbling (slide=window) is cheapest; sliding trades cost for smoother curves. Best practice: Align watermark delay with late-arrival SLA; monitor state store size.