Hi, We have a single Flink job that works on data from multiple data sources. These data sources are not aligned in time and also have intermittent connectivity lasting for days, due to which data will arrive late
We attempted to use the event time and watermarks with parallel streams using keyby for the data source In case of parallel streams, for certain operators, the event time clock across all the subtasks of the operator is the minimum value of the watermark among all its input streams. Reference: https://ci.apache.org/projects/flink/flink-docs-release-1.8/dev/event_time.html#watermarks in-parallel-streams While this seems to be a fundamental concept of Flink, are there any plans of having event time clock per operator per subtask for such operators? This is causing us, not to use watermarks and to fallback on processing time semantics or in the worst case running the same Flink job for each and every different data source from which we are collecting data through Kafka Thanks, Sush
