Achieving Nested JSON Output with JDBC Kafka Connect for Multiple Tables

Описание: Learn how to configure JDBC Kafka Connect to produce nested JSON and arrays from multiple database tables efficiently.
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Building a Nested JSON with JDBC Kafka Connect and Multiple Tables

In today's world of data processing, integrating multiple data sources into a cohesive format is crucial for many applications. One of the common challenges you may face involves producing a nested JSON structure from multiple database tables while using Kafka Connect. This guide will guide you through a practical example and outline the steps to achieve nested JSON outputs using JDBC Kafka Connect.

The Problem

Let’s start by outlining the scenario we are working with. We have two tables in a database: contacts and phones. The contacts table stores information about individuals, while the phones table stores multiple phone numbers associated with each contact.

You're aiming to deserialize this data into a nested JSON structure like the following:

Expected JSON Output

[[See Video to Reveal this Text or Code Snippet]]

The challenge lies in the fact that Kafka Connect's JDBC source connector does not natively support producing nested structures or array types directly from the database tables.

The Solution

To achieve the desired nested JSON structure, you will need to implement a few steps involving a database view and a stream processing library. Here’s how to go about it:

Step 1: Create Database Tables and Populate Them

You’ll first want to create the structure and populate the tables properly. Below is a reminder of what the SQL for your tables looks like:

[[See Video to Reveal this Text or Code Snippet]]

Then, insert data into these tables:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Define a View for Data Joining

Since the JDBC connector does not support arrays, you can define a view that results from a join operation between your contacts and phones tables. For example:

[[See Video to Reveal this Text or Code Snippet]]

This view can be queried by Kafka Connect, and it would output a flat structure that includes multiple records per contact.

Step 3: Configure Kafka Connect

Once you configure Kafka Connect to connect to the database view (contact_details), Kafka Connect will produce individual records for each phone entry associated with the same contact, generating a key/value pair for the contact.

Step 4: Process Data in Kafka Streams or KSQL

After obtaining these individual records, you can use either Kafka Streams or KSQL to aggregate them back into a nested format. Here, you will "group by" the contact_id to consolidate phone records into a single entry for each contact.

You can implement stream processing logic to blend these flat records into the desired nested JSON structure.

Conclusion

Creating a nested JSON output for complex data structures using Kafka Connect can initially seem daunting, but by breaking it down into manageable steps — from establishing the right database relations to utilizing Kafka Streams or KSQL for aggregation — this task becomes a well-structured endeavor. The method discussed involves leveraging views and stream processing libraries, ultimately resulting in a clean and organized JSON format suitable for modern applications.

Keep this approach in mind next time you work with JDBC Kafka Connect and need to synthesize data from multiple sources!