10 Spring Boot Kafka Best Practices

Apache Kafka is a popular distributed streaming platform that acts as a messaging queue or an enterprise messaging system. It is widely adopted for its scalability, reliability, and performance. Spring Boot is a popular Java-based framework used to build production-grade web applications and services.

In this article, we will discuss 10 best practices for using Spring Boot and Apache Kafka together. We will look at how to configure and deploy Kafka in a Spring Boot application, and how to use Kafka to send and receive messages. We will also discuss how to monitor and troubleshoot Kafka applications.

1. Use a single topic per application

Using a single topic per application allows you to easily track and monitor the data that is being sent and received. It also makes it easier to debug any issues that may arise, as all of the messages related to an application are in one place. Additionally, using a single topic helps keep your Kafka cluster organized and efficient, as there will be fewer topics overall.

2. Prefer compacted topics over log compaction

Compacted topics are more efficient than log compaction because they store only the latest value for a given key. This means that if you have multiple messages with the same key, only the most recent one will be stored in the topic. Log compaction, on the other hand, stores all of the values for a given key, which can lead to unnecessary storage overhead and slower performance.

Additionally, compacted topics make it easier to implement event sourcing patterns since they guarantee that the state of the system is always up-to-date. This makes them ideal for applications where data needs to be kept consistent across different services.

3. Avoid using Kafka as a database

Kafka is designed to be a distributed streaming platform, and it’s not meant to store data for long periods of time. It’s best used as an event-driven system that can process large amounts of data quickly and efficiently.

If you need to store data for longer than a few hours or days, then you should use a database such as MySQL or MongoDB instead. Kafka is great for processing real-time events, but it’s not the right tool for storing data over extended periods of time.

4. Keep the number of partitions low

When you have too many partitions, it can lead to a lot of overhead in terms of managing the data. This is because each partition needs its own consumer thread and Kafka broker instance. Having too many partitions also increases the chances of having unbalanced workloads across your cluster, which can cause performance issues.

Therefore, it’s important to keep the number of partitions as low as possible while still allowing for enough throughput. The best way to do this is by monitoring your system and adjusting the number of partitions accordingly.

5. Don’t use auto-commit

When auto-commit is enabled, the consumer will commit offsets automatically after a certain amount of time or when a certain number of messages have been processed. This can lead to data loss if the application crashes before the offset is committed.

Instead, you should use manual commits and make sure that your code handles any exceptions that may occur during processing. You should also ensure that all offsets are committed in an atomic fashion so that no message is lost in case of failure. Finally, it’s important to monitor the lag between the consumer and producer to ensure that everything is running smoothly.

6. Always handle exceptions in your consumer code

Kafka is a distributed system, and as such, it’s possible for messages to be lost or corrupted. If your consumer code doesn’t handle exceptions properly, then you could end up with data loss or corruption in your application. To prevent this from happening, make sure that all of your consumer code handles any potential exceptions gracefully. This means catching any errors that may occur and either retrying the message or logging an error so that you can investigate further. Doing this will ensure that your Kafka-based applications remain reliable and resilient.

7. Use idempotent producers

Idempotent producers guarantee that messages are only sent once, even if the producer is restarted or fails. This ensures that no duplicate messages are sent and that all messages are delivered in order. Without idempotency, there’s a risk of message loss or duplication, which can lead to data inconsistency and other issues.

Using an idempotent producer also helps improve performance by reducing the number of requests sent to Kafka. By ensuring that each message is only sent once, you reduce the amount of network traffic and processing time required for each request.

8. Tune your consumers carefully

Kafka consumers are responsible for consuming messages from topics and processing them. If the consumer is not tuned properly, it can lead to performance issues such as slow message consumption or even data loss. To ensure optimal performance, you should tune your Kafka consumers by setting the right configuration parameters such as batch size, max poll records, etc. Additionally, you should also monitor the consumer metrics regularly to identify any potential bottlenecks.

9. Test your applications with real data

When you’re developing applications with Spring Boot Kafka, it’s important to make sure that your code is working as expected. Testing with real data allows you to ensure that the application behaves correctly when faced with different types of input and scenarios. This helps you identify any potential issues before they become a problem in production.

Additionally, testing with real data can help you optimize performance by identifying bottlenecks or areas where improvements can be made. By running tests with real data, you can also gain insights into how users interact with your application and use this information to improve user experience.

10. Monitor your applications and cluster

Kafka is a distributed system, and as such it’s important to monitor the health of your applications and cluster. This includes monitoring for errors, latency, throughput, and other metrics that can help you identify potential issues before they become problems.

You should also use tools like Kafka Manager or Burrow to track consumer lag and ensure that messages are being processed in a timely manner. Additionally, setting up alerts for when certain thresholds are exceeded can help you quickly respond to any issues that arise.