Sliding windows allow flexibility in aggregating incoming streams with different intervals and overlaps. Implement sliding windows in Flink for data aggregations.
- [Instructor] In this video, I will show you … how to use sliding windows for Flink streaming. … We have already seen tumbling windows … in the basic streaming operations example. … So we won't be reviewing it again. … We continue with the windowing operations class … in chapter three by consuming data from Kafka. … For this example, we create a sliding window … of 10 seconds that slides by five seconds each time. … For each window, we print the current start timestamp … of the window, a count, the minimum and maximum … timestamp for events for that window. … We will window the entire stream, … and we will not partition it by any key. … We start off by using a map function … to build a Tuple with the current timestamp, a count of one, … and then the minimum and maximum timestamps in the event. … To begin with, they would be the same. … We will use reduce later to aggregate on this. … We specify the return types. … We then create a timeWindowAll to indicate a window … across the entire stream without partitioning. …
- 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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