Joining Flink Tables using the Apache Flink® Table API | Apache Flink® Table API
Get started: https://cnfl.io/apache-flink-table-ap... | In this video, we'll have a look at some of the joins supported by the Apache Flink® Table API including the basic inner join, outer join, and full outer join, but also temporal joins such as an interval join.
The Apache Flink Table API provides the ability to work with streaming data similar to how we work with relational database tables. This includes the ability to join tables together to provide more detailed views.
However, joining data streams has complexities which can cause the Flink internal state to grow significantly, if you aren't careful.
Much like with many other Table API operations, joins in Flink work best when there is a natural time window in which the join should be applied. This is where tools like an interval join can help to optimize how the internal state is being maintained.
RELATED RESOURCES
►Joins in the Flink Table API: https://nightlies.apache.org/flink/fl...
►Your Guide to the Apache Flink® Table API: An In-Depth Exploration: https://cnfl.io/4kOYjmQ
►Join Queries in Confluent Cloud for Apache Flink (SQL): https://cnfl.io/41Qd9AR
CHAPTERS
00:00 - Intro
00:39 - Why do we need to join data in Flink?
01:11 - How does an inner join work in the Flink Table API?
01:42 - How can we avoid ambiguous field names when joining Tables?
02:19 - How can we limit the fields returned by the join?
02:40 - Why can simple joins be resource intensive?
03:52 - How can we adjust the time-to-live for the job state?
04:15 - What is an interval join, and why is it more state efficient?
05:44 - Closing
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