Using proper timestamps for windowing is important to generate accurate analytics. Review various options for using time 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 attributes … that can be used for processing. …
- Streaming with Apache Flink
- Using the DataStream API for basic stream processing
- Working with process functions
- Windowing and joins
- Setting up event-time processing
- State management in Flink
Skill Level Advanced
1. Apache Flink
2. DataStream API
4. Event Time Processing
5. State Management
6. Use Case Project
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