Github user koeninger commented on a diff in the pull request: https://github.com/apache/spark/pull/15702#discussion_r86066774 --- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala --- @@ -536,6 +535,41 @@ class Dataset[T] private[sql]( } /** + * :: Experimental :: + * Defines an event time watermark for this [[Dataset]]. A watermark tracks a point in time + * before which we assume no more late data is going to arrive. + * + * Spark will use this watermark for several purposes: + * - To know when a given time window aggregation can be finalized and thus can be emitted when + * using output modes that do not allow updates. + * - To minimize the amount of state that we need to keep for on-going aggregations. + * + * The current event time is computed by looking at the `MAX(eventTime)` seen in an epoch across + * all of the partitions in the query minus a user specified `delayThreshold`. Due to the cost + * of coordinating this value across partitions, the actual watermark used is only guaranteed + * to be at least `delayThreshold` behind the actual event time. In some cases we may still + * process records that arrive more than `delayThreshold` late. + * + * @param eventTime the name of the column that contains the event time of the row. + * @param delayThreshold the minimum delay to wait to data to arrive late, relative to the latest + * record that has been processed in the form of an interval + * (e.g. "1 minute" or "5 hours"). --- End diff -- Should this make it clear what the minimum useful granularity is (ms)?
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