Joins based on specific keys provide the ability to match data between multiple data streams and generate new insights. Use window joins in Flink for merging a file and a Kafka data stream.
- [Instructor] Flink allows to perform … SQL-like horizontal joins … on two data streams in the same window. … Within a given window, … the two data streams act like SQL tables. … Join conditions can be specified, … and it results in a Cartesian product of the matching rows. … The example code is available, … in the windows joins class under Chapter Three. … In this example we will use two audit trial streams. … The five streams, … as well as the Kafka streams simultaneously. … We will then join both the streams … based on the user, with a window of five seconds. … For each matching record, we will … output the username and the count. … Let's explore the code now. … We will set up and consume the CSV file stream … into the file trial 'obj' object. … Then we set up and consume a Kafka topic stream, … into the Kafka trial 'obj' object. … We have reviewed the sets of code in our previous examples. … Now let's get to the join function. … We use the join function on the first data stream, …
- 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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