How do streaming systems handle out-of-order data?

Streaming systems handle out-of-order data by using event time processing, watermarks, and windowing strategies to ensure accurate results despite delays. Unlike processing time (when data enters the system), event time reflects when an event actually occurred, which is critical for correctness. For example, a system analyzing user activity logs must group events by their timestamps, even if network delays cause late arrivals. To manage this, streaming frameworks like Apache Flink or Apache Beam track event time using watermarks—a mechanism that estimates when all data up to a certain timestamp has likely been received. This allows the system to proceed with calculations while accounting for minor delays.

Watermarks work by setting a threshold that determines how long to wait for late data. Suppose a window aggregates events from 2:00 PM to 2:05 PM. A watermark might signal that events up to 2:05 PM have been processed by 2:07 PM (allowing a 2-minute buffer). Any data arriving after the watermark is considered “late” and requires special handling. Windowing strategies, such as fixed (tumbling) or sliding windows, define how events are grouped. For instance, a session window could automatically close after 10 minutes of inactivity. Systems also use triggers to emit partial results (e.g., hourly summaries) or update outputs when late data arrives, ensuring flexibility without blocking indefinitely.

To handle data that arrives beyond the watermark’s threshold, systems employ allowed lateness configurations and side outputs. For example, Google Dataflow lets developers specify a grace period (e.g., 1 hour) during which late data can still update prior results. Data outside this period is routed to a separate stream for manual inspection or logging. State management ensures the system retains window data until the watermark and grace period expire, after which it’s cleaned up to avoid memory leaks. Tools like Apache Kafka can also buffer out-of-order events before ingestion. These mechanisms balance accuracy with performance, enabling real-time insights even in unpredictable environments.