Скачать или смотреть Solving the KTable-KTable foreign-key join Issue in Apache Kafka Streams

Learn how to fix the problem of missing messages in Kafka Streams when using KTable-KTable joins with multiple partitions. Follow our guide for a smoother integration.
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Understanding the KTable-KTable Foreign-Key Join Issue

In modern streaming applications that utilize Apache Kafka, one common issue developers face is the incomplete output when performing KTable-KTable foreign-key joins, especially when dealing with multiple partitions in Kafka topics. A user reported that while experimenting with their application, they found that when their output topic was set to one partition, everything flowed smoothly. However, things took a turn for the worse when they increased the number of partitions, leading to a troubling drop in output messages. This article explores the causes behind this issue, and we offer a structured solution to ensure seamless processing in your Kafka Streams applications.

The Problem: Missing Messages

The user testing their application observed the following:

With 1 partition, 100% of messages from the source topic were processed successfully.

With 2 partitions, less than 50% of messages made it to the output topic.

With 10 partitions, the output dropped to less than 10% of the expected messages.

This discrepancy indicates that messages are being lost in the joining process. It was speculated that these messages got "stuck" within the intermediate topics created during the KTable foreign-key join, despite there being no visible error messages indicating an issue.

The Root Cause

The core problem lies in how Kafka distributes data across partitions. When using a single partition, every message arrives in a systematic and orderly fashion. However, with multiple partitions:

Data might end up in different partitions based on its key.

This hampers the join operation, which expects messages with the same key to reside in the same partition. If they don’t, the join fails on a per-partition basis.

The Solution: Ensuring Proper Partitioning

1. Key-Based Partitioning

To tackle the message loss during the KTable-KTable join, developers must ensure that data in their input topics is partitioned according to their keys. While a foreign-key join doesn't necessarily need both input topics to be co-partitioned, each topic itself must be partitioned by its key for effective joining.

2. Implementing the Workaround: map().toTable()

As a workaround, the user discovered that by changing their approach from KTable to KStream and calling toTable(), it resolved their issue successfully. Here's the breakdown of this implementation:

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

Summary of Steps

Consume your first topic as a KStream instead of a KTable.

Transform it as necessary before invoking toTable(), which triggers repartitioning based on keys.

Perform the left join with your second topic, ensuring that the join works as expected.

Final Thoughts

The above workaround helped address the issue of missing messages when performing foreign-key joins in Kafka Streams. Business users can benefit from understanding the importance of partitioning by key and adapting their processing strategies accordingly. By following these practices, your Kafka applications can become more efficient, reliable, and effective in processing streaming data.

With this knowledge, you can now confidently tackle issues relating to KTable-KTable joins and ensure that all your messages reach their destination without getting stuck along the way.