From the course: Apache Flink: Real-Time Data Engineering
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Time attributes in Flink - Flink Tutorial
From the course: Apache Flink: Real-Time Data Engineering
Time attributes in Flink
- [Instructor] In this video, I will discuss the various options available for time based processing in Flink. Timestamps play an important role in realtime stream processing. This is especially true for windowing where we need to set start and end times for windows. Please note that in a processing pipeline, there are multiple sources which may contribute to the same incoming data stream. And multiple Flink task nodes that process them in pattern. The source of the timestamp used by windowing plays a critical role in determining the outcomes of windowing. There is latency between the source nodes where they events are generated and the processing nodes where they are finally processed. There can also be latency differences between various sources. So, even generated at the same time on two sources can arrive with the processing node at different times. To solve the problem, Flink provides three different time…
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