Creating a Stream in ksqlDB to Detect Changes Across Multiple Tables

Discover how to use `ksqlDB` to create a stream that detects changes across three different tables with our detailed guide.
---
This video is based on the question https://stackoverflow.com/q/75405887/ asked by the user 'Farhan Islam' ( https://stackoverflow.com/u/8157160/ ) and on the answer https://stackoverflow.com/a/75415407/ provided by the user 'andresmonc' ( https://stackoverflow.com/u/10803013/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Is it possible to create a stream in ksqlDB to emit an ID that detects changes from 3 different tables that are joined together?

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Creating a Stream in ksqlDB to Detect Changes Across Multiple Tables

In the world of modern data processing, a recurring challenge developers face is detecting changes across multiple tables efficiently. If you've ever wondered whether it's possible to create a stream in ksqlDB that emits an ID when there are changes in any of the three related tables, you are not alone. Let's dive into the details of this problem and explore a robust solution to achieve the desired result.

Understanding the Requirement

Imagine you have three tables as follows:

Table A

id: Unique identifier

b_id: Foreign key pointing to Table B

c_id: Foreign key pointing to Table C

field_abc: A specific field in Table A

field_xyz: Another field in Table A

Table B

id: Unique identifier

foo: A specific field in Table B

Table C

id: Unique identifier

bar: A specific field in Table C

Your goal is to create a stream that emits the ID from Table A whenever any of the fields (field_abc in Table A, foo in Table B, or bar in Table C) undergoes a change. This need is crucial for triggering events or updates in downstream systems.

The Challenge with ksqlDB

Unfortunately, ksqlDB has certain limitations when it comes to streams directly emitting changes across multiple tables. Originally, it might seem impossible to achieve the functionality of emitting an ID based on changes in three tables simultaneously. However, there is a relevant workaround that taps into the capabilities of ksqlDB in a creative way.

The Solution: Creating a Custom Query with a Connector

To circumvent the limitation, the solution involves creating a custom query using a connector that establishes a three-way join across the tables. By combining the necessary fields, you can effectively monitor changes across the tables and emit the relevant IDs. Here’s how you can implement this solution:

Steps to Create the Source Connector

Define the Connector Configuration: Below is a sample configuration to create a source connector that will monitor changes across the tables:

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

Explanation of the Query

GREATEST Function: This function is used to determine the latest modification timestamp across the three tables.

LEFT JOIN: This ensures that all records from Table A are included, even if there are no corresponding records in Table B or Table C.

Subquery: The subquery combines the necessary columns and applies the logic to check for the most recent update.

Final Thoughts

By employing this custom query approach, you can effectively create streams or tables from the merged data that will emit the IDs of Table A whenever any of the fields in the three tables change. This method not only enhances your data processing capabilities but also ensures that your applications remain responsive to changes in underlying data.

With the growing landscape of data interactions, being able to monitor changes efficiently across multiple tables can significantly improve the performance and reliability of your data architecture. Up next, consider how this integration could be optimized for scalability as your dataset continues to grow.