Streaming systems handle late-arriving data through several strategies that ensure the timely processing of incoming events while still accounting for occasional delays. One common approach is to use watermarks, which are special markers that indicate the point in time up to which processing can proceed. When an event arrives, the system compares it to the watermark. If the event's timestamp is older than the watermark, it can be safely considered late. Depending on the specific rules set for late data, the system might either discard the data, apply specific handling techniques, or place it in a separate processing queue for further evaluation.
Another method is event time processing. In this approach, systems evaluate events based on their timestamps rather than the order in which they arrive. This allows the system to dynamically handle out-of-order events by defining a window of time during which events are considered together. For example, in a stream processing framework like Apache Flink, developers can configure sliding or tumbling windows that aggregate events over a defined time range. Late events can still be processed, provided they fall within the window’s allowed lateness, which can also be configured based on application needs. If an event arrives after the window has already closed, it can either be discarded or processed based on custom logic.
Lastly, many streaming systems incorporate retries or buffering for late data. When an event arrives late, the system may temporarily hold it in a buffer or queue, allowing for later processing. This is particularly useful in systems that aim to maintain high availability and do not want to lose potentially valuable data. For example, if a financial transaction event arrives after some critical calculations have been performed, it might be reprocessed once it is determined to be valid and timely according to the established business rules. This helps ensure that the final results reflect all pertinent data, even if some events arrive later than expected.