Stream Processing

An unbounded event stream has no natural end at which a complete answer can be computed. Processing systems use windows, state and timing rules to define when a result is emitted and whether it may change. Event time describes when an event occurred; processing time describes when the system handles it. Watermarks can express progress assumptions for late data, but do not make arbitrary delays disappear. Stateful joins and aggregations require recovery mechanisms. Exactly-once processing has a defined system boundary and conditions; it should not be generalized to external side effects that do not participate in the same protocol.

The practitioner defines event schemas, keys, windows and acceptable lateness from the task's needs. They plan state retention, checkpoints and replay behavior, then test duplicates and out-of-order inputs. Useful artifacts include timing examples that show when outputs become final and what happens to late corrections. The implementation should monitor lag and state growth, not merely job uptime. Sink behavior matters during recovery, so the team verifies whether repeated output updates are safe and how an unavailable destination affects ongoing ingestion.

Low latency and complete results can conflict when events arrive late. Large state or skewed keys can exhaust resources, and watermarks based on unrealistic assumptions can discard important records. Stream processing is unnecessary when a periodic batch meets the decision's needs more simply. Quality checks must specify timing and delivery semantics, because a correct total eventually produced may still be unsuitable for a decision that needed it earlier.