Windows provide the ability to split streams and perform aggregations. In this video, learn about windowing concepts and how it is implemented in Apache Flink.
- [Instructor] Windowing allows grouping of stream events … into buckets based on time for the purpose … of aggregation and analytics. … Windowing can be done on the entire stream … or on a key by key basis. … In this video, I will walk you through the types … of windows available in Flink. … The first type of window is a Tumbling Window. … A Tumbling Window is an equal sized, continuous, … and non-overlapping window. … Here is an example of how a Tumbling Window looks like … for a 10 second window interval. … In the data stream, all events from zero second … to 10 second, fall into Window 1, … even from the 10 second to the 20th second, … fall into Window 2, … the same process repeats for the entire stream. … This is the default type of window … which we have used in our earlier examples … with the Time Window function. … Sliding Windows are overlapping windows. … It has two parameters, the window interval … and the sliding offset. … This is an example of a Sliding Window … of interval 10 seconds that slides by five seconds. …
- 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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