Overview

Example:

Flink real-time consumes user order data from the Kafka source table, associates the product ID with the dimension table through Redis to obtain the product category, calculates the sales amount of different categories of products, and writes the calculation results into the RDS (Relational Database Service, such as MySQL) result table.

Table information is as follows:

• Source table: Order data table, including user ID, product ID, order ID, order amount, and other information.
• Dimension table: User information table, including product ID and product category information.
• Result table: Statistics of order sales amount data by product category.

The job first reads real-time order data from the order data source table, associates the order data stream with the product category information dimension table, then aggregates and calculates the total order amount, and finally writes the statistical results into the result table.

In this example, the order table serves as the driving source table input, the product category information table serves as the static dimension table, and the statistical result table serves as the final output of the job.