10 Kafka Topic Best Practices
Kafka is a powerful tool, but there are best practices to follow to get the most out of it. Here are 10 of the most important ones.
Apache Kafka is a distributed streaming platform that forms the backbone of many data-driven architectures today. It is used for everything from building real-time streaming data pipelines to powering real-time streaming applications.
Kafka topics are a key part of the Kafka architecture and act as the foundation upon which all data is built. As such, it is important to design Kafka topics with care and to follow best practices.
In this article, we will discuss 10 Kafka topic best practices that every Kafka developer should know. By following these best practices, you can avoid common pitfalls and ensure that your Kafka topics are designed for success.
1. Use a single topic per application
If you have multiple applications producing to the same topic, it can be difficult to determine which application is responsible for a given message. This can lead to confusion and finger-pointing when things go wrong.
Additionally, if you have multiple applications consuming from the same topic, they will all compete for messages. This can lead to one application falling behind and not processing messages in a timely manner.
By using a single topic per application, you can avoid these problems. Each application will have its own dedicated topic, making it easy to determine which application is responsible for a given message. Additionally, each application will consume from its own dedicated topic, so there will be no competition for messages.
2. Make the number of partitions equal to the number of consumers in a consumer group
If you have more partitions than consumers, some of your consumers will be idle most of the time. This is wasted capacity.
On the other hand, if you have more consumers than partitions, some of your consumers will be constantly busy while others are idle. This can lead to uneven load distribution and decreased performance.
Therefore, to get the best performance from your Kafka cluster, make sure the number of partitions is equal to the number of consumers in each consumer group.
3. Set appropriate retention for your topics
Kafka is a distributed system, which means that data is replicated across multiple servers. The default replication factor in Kafka is three, which means that each piece of data is stored on three different servers.
If one of those servers goes down, the other two still have a copy of the data. This is why it’s important to set an appropriate retention for your topics. If you don’t, you run the risk of losing data if one of your servers goes down and isn’t able to recover.
The default retention in Kafka is seven days, but this may not be appropriate for all data. For example, if you’re storing financial data, you may want to increase the retention to ensure that you have a backup in case of a disaster.
It’s also important to consider the size of your data when setting retention. If you have large amounts of data, you may need to increase the retention to make sure that all of your data is replicated.
4. Avoid using key when possible
When you use key, all messages with the same key will be sent to the same partition. This can lead to out-of-order message processing, which is often undesirable.
It’s better to let Kafka handle message ordering by itself. This way, you’ll get the benefits of parallel processing and improved performance.
5. Choose the right replication factor
The replication factor is the number of times each message is copied and stored on different servers. So, if you have a replication factor of 3, that means each message is copied and stored on 3 different servers.
If you have a high replication factor, that means your data is more safe from being lost because there are more copies of it. However, it also means that your system will be slower because it takes longer to copy and store the data on multiple servers.
Therefore, you need to strike a balance between having a high replication factor so your data is safe, but not too high where it starts to impact performance. The best way to do this is to start with a lower replication factor and then increase it if you find that you’re losing data.
6. Prefer compacted topics over TTL-based cleanup
TTL-based cleanup is based on the assumption that all messages in a topic will be consumed within a certain time frame. This is often not the case, especially for topics with high message throughputs. If messages are not consumed within the TTL, they will be removed from the topic and will be unavailable for consumption.
Compacted topics, on the other hand, keep all messages in the topic regardless of whether they have been consumed or not. This ensures that all messages are available for consumption, even if they are delayed.
It is important to note that compacted topics use more storage than TTL-based topics. However, the trade-off is worth it for the increased availability of messages.
7. Be careful with deletion of topics
If you delete a topic that is being used by your applications, it can cause data loss and disrupt service. It is therefore recommended that you only delete topics that are no longer needed.
Additionally, when you delete a topic, any existing consumers will be unable to consume any new messages that are published to that topic. Therefore, it is also recommended that you stop consuming from a topic before deleting it.
8. Use unique names for internal topics
When you have multiple internal topics with the same name, it can be difficult to keep track of which data is going where. This can lead to confusion and errors when trying to process or analyze the data.
By using unique names for internal topics, you can avoid these problems. Not only will it be easier to keep track of your data, but you’ll also be able to more easily identify any issues that may arise.
9. Don’t use too many topics
If you have too many topics, it can be difficult to manage them all. You might end up with topics that are no longer being used, or you might have multiple topics that are serving the same purpose. This can lead to confusion and make it difficult to keep track of what data is where.
It’s important to find a balance between having too many topics and too few. If you have too few topics, you might end up with data that doesn’t fit well into any of your existing topics. On the other hand, if you have too many topics, you might end up with topics that are barely used or that are duplicates of other topics.
The best way to find the right balance is to start with a small number of topics and then add more as needed. You can always split up a topic into multiple smaller topics if necessary.
10. Monitor your Kafka cluster
Kafka is a distributed system, and so there are many moving parts that need to be monitored. For example, you need to monitor the health of your brokers, the topics and partitions, and the consumers.
You also need to monitor Kafka’s performance. This includes monitoring things like latency, throughput, and message size.
There are many tools available to help you monitor your Kafka cluster. For example, Apache Ambari provides an easy way to install and manage Kafka as well as other Hadoop-related components.
Kafka Manager is another tool that can help you monitor and manage your Kafka cluster. It provides a web interface for managing Kafka, and it can also help you monitor the health of your brokers, topics, and partitions.
Monitoring your Kafka cluster is essential for ensuring its stability and performance. By using the right tools, you can proactively identify and fix problems before they cause outages or impact your customers.