Streaming systems manage out-of-order data by employing techniques designed to maintain the integrity and order of the data as it is processed. Out-of-order data occurs frequently in streaming architectures due to network delays, variations in processing speeds, or when multiple sources send data simultaneously. To handle this, streaming systems often implement buffering and timestamping. Buffers temporarily hold incoming data until enough of it has arrived to fill in any gaps. Timestamping involves assigning a time identifier when data is created, allowing the system to rearrange messages based on their timestamps for correct ordering during processing.
A common method used in many streaming systems, such as Apache Kafka or Apache Flink, is the concept of windowing. Windowing allows the system to group incoming records into defined time intervals—known as windows—so that all data within a specific timeframe can be processed together. This approach helps accommodate small delays and out-of-order events within tolerable limits. For instance, if a stream processor receives data from a sensor every few seconds but due to network latency, some messages arrive late, it can still process those messages if they fall within the appropriate time window, ensuring that results still reflect all relevant data.
Another effective strategy is to utilize watermarks. Watermarks act as indicators of the progress of event time in a stream. Whenever data is processed, the watermark allows the system to determine how much data can still be expected to arrive for prior timestamps. If data arrives after its corresponding watermark, it can be dropped or processed differently, depending on the application logic. While this helps manage out-of-order data effectively, developers must define appropriate handling policies to deal with late data without losing critical information or compromising system performance.