10 Kafka Topic Design Best Practices

Apache Kafka is a powerful distributed streaming platform that enables you to build robust message pipelines and event-driven applications. A key part of Kafka’s design is its topics. Topics are the core abstraction in Kafka and they are used to structure your data.

In this article, we will discuss 10 best practices for designing Kafka topics. By following these best practices, you can design Kafka topics that are scalable, reliable, and easy to maintain.

1. Use a single topic per entity

If you have multiple entities in a single topic, it’s difficult to keep track of which messages belong to which entity. This can lead to confusion and data loss.

It’s also difficult to update the schema for a single entity if it’s spread across multiple topics. For example, let’s say you have a customer entity with two fields: name and address. If you want to add a new field, such as phone number, you would need to update the schema in all of the topics that contain customer data.

By using a single topic per entity, you can avoid these problems.

2. Make the key meaningful

When a message is produced to a Kafka topic, it is assigned a key. The key is used by the Kafka broker to determine which partition the message will be written to. If all messages have the same key, they will all go to the same partition. This can lead to hot spots and performance issues.

To avoid this problem, make sure that the key is meaningful. For example, if you are producing messages that represent events that happened at a certain time, use the timestamp as the key. This will ensure that messages are evenly distributed across partitions.

3. Avoid using too many partitions

If you have too many partitions, your Kafka cluster will be bogged down managing all of the metadata associated with those partitions. This can lead to performance issues and even outages.

Additionally, if you have too many partitions, it can be difficult to manage them all effectively. You may end up with some partitions that are underutilized while others are overutilized.

Ideally, you should aim for around 10-20 partitions per topic. This will strike a good balance between having enough partitions to provide good performance and not having so many that they become unmanageable.

4. Choose your partitioning strategy wisely

Partitioning is how Kafka distributes data across the brokers in a cluster. There are two main strategies for partitioning: key-based and round robin.

Key-based partitioning is the more efficient of the two because it allows Kafka to send messages with the same key to the same partition, which means that all the messages for a given key will be processed by the same consumer.

Round robin partitioning is less efficient because it can’t make use of this optimization. However, it has the advantage of being much simpler to implement.

The best partitioning strategy for your use case will depend on your specific requirements. If you need maximum efficiency, key-based partitioning is the way to go. If you need simplicity, round robin partitioning may be a better choice.

5. Keep in mind that Kafka is not a database

Kafka is a streaming platform that can be used for a variety of purposes such as building real-time streaming data pipelines that reliably get data between systems or building a centralized log for all system activity.

However, because Kafka is not a database, it does not have features such as transactions, primary keys, or indexes. This means that when designing your Kafka topics, you need to think about how the data will be queried and ensure that the topic structure facilitates this.

For example, let’s say you are building a system that needs to track user activity on a website. A common way to do this would be to create a Kafka topic for each user where each message represents an action taken by that user.

While this approach would work, it would be very difficult to query the data in this topic to answer questions such as “What percentage of users clicked on this button?” or “How many users visited this page?”.

A better approach would be to design a Kafka topic that contains messages representing user actions with the relevant information such as the user id, the page they were on, and the time of the action. This would make it much easier to query the data and answer these types of questions.

When designing your Kafka topics, always keep in mind that Kafka is not a database and design your topics accordingly.

6. Monitor your topics

Kafka is a distributed system, which means there are many moving parts that can fail. By monitoring your topics, you can quickly identify and fix problems before they cause major issues.

There are many ways to monitor Kafka, but one of the most popular is using open-source tools like Prometheus and Grafana. These tools can help you track key metrics like message throughput, latency, and consumer lag.

Monitoring your Kafka topics will help you avoid outages, improve performance, and keep your data safe.

7. Think about compaction and retention policies

If you have a topic with multiple partitions, each partition will have its own offset (a position in the log). When you enable compaction for a topic, Kafka will keep only the latest record for each key in the topic. This means that older records with the same key will be removed from the log.

This is useful if you only care about the most recent value for each key in the topic. For example, if you have a topic for user profiles, you might only care about the most recent profile update for each user. In this case, you would enable compaction for the topic.

However, there are some cases where you might not want to enable compaction. For example, if you have a topic for financial transactions, you might want to keep all records in the log, even if they have the same key. In this case, you would not enable compaction.

Similarly, you need to think about retention policies when designing your topics. By default, Kafka will retain all records in a topic indefinitely. However, you can configure a retention policy so that Kafka will automatically delete old records.

For example, you might configure a topic to retain records for 7 days. After 7 days, any records in the topic will be automatically deleted.

Retention policies are important because they help you control the size of your Kafka cluster. If you have a topic that retains records forever, your Kafka cluster will eventually become full and you’ll have to add more brokers. On the other hand, if you have a topic with a short retention policy, you can keep your Kafka cluster small.

To sum up, when designing Kafka topics, you should think about whether or not to enable compaction and what kind of retention policy to use.

8. Consider security

Kafka was designed with security in mind from the beginning. The Kafka community has continued to build out security features, and the platform now offers a robust set of capabilities for authenticating, authorizing, and encrypting data in transit.

When designing your topics, it’s important to consider how you will secure your data. Do you need to encrypt data in transit? Do you need to authenticate clients? What authorization policies do you need to put in place?

Answering these questions early on will help you design topics that are secure from the start.

9. Plan for data migration

As your data volume and velocity increases, you’ll eventually need to migrate your data from one Kafka cluster to another. This could be for a number of reasons, such as upgrading to a new version of Kafka or adding more capacity to your existing cluster.

Whatever the reason, it’s important to plan for data migration in your topic design. This means designing your topics in a way that makes it easy to move data from one cluster to another.

One way to do this is to use a naming convention for your topics that includes the name of the Kafka cluster. For example, you could have a topic called “cluster1-topic1” and another called “cluster2-topic1”. This would make it easy to identify which topics need to be migrated when moving data from one cluster to another.

Another way to plan for data migration is to use replication. This means having multiple copies of your data on different Kafka clusters. This way, if you need to migrate data from one cluster to another, you can simply stop writing to the source cluster and start writing to the destination cluster. Your data will still be available during the migration process.

10. Test, test, test!

As with any software design, it’s important to test your topics before putting them into production. This will help ensure that your topics are performing as expected and will give you confidence that they can handle the load of a real-world deployment.

There are a few different ways to test Kafka topics. One approach is to use a tool like Apache JMeter to simulate traffic and load testing. This can be helpful for testing the performance of your topics under different conditions.

Another approach is to use a tool like Kafka Monitor to monitor the health of your topics in real time. This can be useful for catching issues early on and ensuring that your topics are always running smoothly.

Whatever approach you choose, make sure to test your topics thoroughly before putting them into production.