Flink Parallel Dataflows

tags: Flink Parallel Dataflows Redistributing streams (as between map() and keyBy/window above, as well as between keyBy/window and Sink) change the partitioning of streams. Each operator subtask sends data to different target subtasks, depending on the selected transformation. Examples are keyBy() (which re-partitions by hashing the key), broadcast(), or rebalance() (which re-partitions randomly). In a redistributing exchange the ordering among the elements is only preserved within each pair of sending and receiving subtasks (for example, subtask[1] of map() and subtask[2] of keyBy/window)....

tags: Flink Parallel Dataflows One-to-one streams (for example between the Source and the map() operators in the figure above) preserve the partitioning and ordering of the elements. That means that subtask[1] of the map() operator will see the same elements in the same order as they were produced by subtask[1] of the Source operator.