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If late data fall within this threshold, the query gets updated eventually ( right image in the figure below). The state of an aggregate will then be maintained by Spark until max eventTime - delayThreshold > T, where max eventTime is the latest event time seen by the engine and T is the starting time of a window. It takes two Parameters, a) an event time column (must be the same as the aggregate is working on) and b) a threshold to specify for how long late data should be processed (in event time unit). WithWatermark(eventTime: String, delayThreshold: String): Dataset You can enable it by simply adding the withWatermark -Operator to a query: Since Spark 2.1, watermarking is introduced into Structured Streaming API. Therefore watermarking is a useful concept to constrain the system by design and prevent it from exploding at runtime. And that is not practical in a real world scenario, unless the system has unlimited resources (resp. But this means that the state of all aggregates must kept indefinitely, which causes the memory usage to grow to indefinitely too. In case a late event occurs, the query can then be reprocessed. To deal with this problem, the state of an aggregate must be preserved. For the sake of fault tolerance it’s therefore necessary to handle such Out-of-Order data. Because of that, it’s not guaranteed that data will arrive in a Stream Processing Engine in the order they were created. In distributed and networked systems, there’s always a chance for disruption - nodes going down, sensors are loosing connection and so on and so forth. Flooding watermarks But, Why Should I Care?

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