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Learn how to effectively manage blocking operations and exception handling within Kafka Streams using Spring Boot. Discover best practices for ensuring message reliability without data loss.
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Mastering Kafka Streams: How to Handle Blocking Operations in the map() Method

When building applications with Apache Kafka and Spring Boot, one common challenge developers encounter is the need to perform blocking operations within the Kafka Streams map() method. Specifically, if you need to wait for a CompletableFuture to complete while ensuring that messages aren't lost or acknowledged upon failure, it can become quite tricky. In this post, we'll break down a practical solution to handle these scenarios effectively.

Understanding the Problem

In a typical Kafka Streams application, the mapValues() method allows you to transform each record in the stream. However, when you're dealing with asynchronous operations using CompletableFuture, calling .get() can throw exceptions like InterruptedException and ExecutionException. This can cause issues, especially if you require strict message processing guarantees and prefer to not send failed messages to a dead letter topic.

Here’s a sample of what might happen in your code:

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If completableFuture.get() throws an exception, the chained method might get executed, which is not desirable in your case. You want to ensure the Kafka message is not acknowledged if an error occurs.

Solution: Using Branching in Kafka Streams

A powerful feature in Kafka Streams is the ability to create branches for stream processing. This allows you to isolate records that succeed in processing and those that fail, providing better control over the flow of data.

Step 1: Branching the Input Stream

You can utilize the split() method to diverge the processing based on success or failure in your asynchronous operation. Here's an outline of how to implement this:

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In this code snippet:

We attempt to fetch the result from CompletableFuture.

If it succeeds, we divert the record to the good-records branch.

If any exception occurs, the record will end up in the defaultBranch.

Step 2: Processing the 'Good' Records

Once you have separated the records, you can perform further operations only on the successful records:

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This ensures that only records that successfully complete the future will continue through the stream processing pipeline.

Addressing Acknowledgment of Messages

While this solution efficiently filters out unsuccessful records, it does not inherently prevent the acknowledgment of messages in the case of exceptions. This is an essential concern since often, retaining every message is critical for the application's integrity.

Introducing Retries and Handling Failures

A potential approach to handle this is to implement retries. Here’s a conceptual strategy:

Set a retry limit on your operation.

If all retries fail, you could consider processing logic that defines what to do after exhausting all possible retries, such as logging the failure.

However, be aware that failing all retries without having a mechanism such as a Dead Letter Topic (DLT) could lead to message loss, which you've indicated is unacceptable for your use case.

Conclusion

Handling blocking operations within Kafka Streams can seem daunting, but with the effective use of branching and understanding the lifecycle of your messages, you can create robust applications that handle failures gracefully without losing data. While this post has laid out a foundational approach for managing CompletableFuture calls within the map() method, remember to tailor the logic to fit your specific needs and operational requirements.

In summary:

Use branching to manage the flow of records based on their proc