Builtin Watermark Generators

As described in Generating Watermarks, Flink provides abstractions that allow the programmer to assign their own timestamps and emit their own watermarks. More specifically, one can do so by implementing the WatermarkGenerator interface.

In order to further ease the programming effort for such tasks, Flink comes with some pre-implemented timestamp assigners. This section provides a list of them. Apart from their out-of-the-box functionality, their implementation can serve as an example for custom implementations.

Monotonously Increasing Timestamps

The simplest special case for periodic watermark generation is the when timestamps seen by a given source task occur in ascending order. In that case, the current timestamp can always act as a watermark, because no earlier timestamps will arrive.

Note that it is only necessary that timestamps are ascending per parallel data source task. For example, if in a specific setup one Kafka partition is read by one parallel data source instance, then it is only necessary that timestamps are ascending within each Kafka partition. Flink’s watermark merging mechanism will generate correct watermarks whenever parallel streams are shuffled, unioned, connected, or merged.

Java

WatermarkStrategy.forMonotonousTimestamps();

Scala

WatermarkStrategy.forMonotonousTimestamps()

Python

WatermarkStrategy.for_monotonous_timestamps()

Fixed Amount of Lateness

Another example of periodic watermark generation is when the watermark lags behind the maximum (event-time) timestamp seen in the stream by a fixed amount of time. This case covers scenarios where the maximum lateness that can be encountered in a stream is known in advance, e.g. when creating a custom source containing elements with timestamps spread within a fixed period of time for testing. For these cases, Flink provides the BoundedOutOfOrdernessWatermarks generator which takes as an argument the maxOutOfOrderness, i.e. the maximum amount of time an element is allowed to be late before being ignored when computing the final result for the given window. Lateness corresponds to the result of t - t_w, where t is the (event-time) timestamp of an element, and t_w that of the previous watermark. If lateness > 0 then the element is considered late and is, by default, ignored when computing the result of the job for its corresponding window. See the documentation about allowed lateness for more information about working with late elements.

Java

WatermarkStrategy.forBoundedOutOfOrderness(Duration.ofSeconds(10));

Scala

WatermarkStrategy.forBoundedOutOfOrderness(Duration.ofSeconds(10))

Python

WatermarkStrategy.for_bounded_out_of_orderness(Duration.of_seconds(10))