Apache Kafka is a powerful tool. It allows for the creation of real-time, high-throughput, low-latency data streams that are easily scalable. When optimized, Kafka creates other benefits, such as resistance to machine/node failure occurring inside the cluster and persistence of both data and messages on the cluster. This is why Kafka optimization is so important.
Optimization of your Kafka framework should be a priority. However, it can be hard to know how exactly to optimize Kafka. That’s why we’re bringing you four Kafka best practices you can implement to get the most out of the framework.
Here are four basic Kafka optimization tips:
- Upgrade to the latest version of Kafka.
- Understand data throughput rates.
- Implement random partitioning.
- Adjust consumer socket buffers.
Your Kafka deployment can be a challenge because there are many layers to the distributed architecture and many parameters that can be tweaked within those layers.
For example, normally, a high-throughput publish-subscribe (pub/sub) pattern with automated data redundancy is a good thing. But when your consumers struggle to keep up with your data stream, or if they fail to read the messages because these messages disappear way before the consumers get to them, then work needs to be done to support the performance needs of the consuming applications.
But these basic four practices should be the foundation of your Kafka optimization. Read on to dive deeper into these methods.
Best Practices for Kafka Optimization
Achieving and maintaining a Kafka deployment requires continuous monitoring. Kafka is a powerful real-time data streaming framework. Failure to optimize results in slow streaming and laggy performance.
Kafka optimization is a broad topic that can be very deep and granular, but here are four highly utilized Kafka best practices to get you started:
1. Upgrade to the latest version of Kafka.
This might sound blindingly obvious, but you’d be surprised how many people use older versions of Kafka. A really simple Kafka optimization move is to upgrade and use the latest version of the platform. You have to determine if your customers are using older versions of Kafka (ver. 0.10 or older). If they are, they should upgrade immediately.
Kafka changes slightly with each update. Released in April of 2021, the latest Kafka release provided an early access version of KIP-500, enabling users to run Kafka brokers even without Apache ZooKeeper. This eliminated the need for an internal Raft implementation. Other changes included support for more partitions per cluster, more seamless operation, and tighter security.
2. Understand data throughput rates.
Optimizing your Apache Kafka deployment is an exercise in optimizing the layers of the platform stack. Partitions are the storage layer upon which throughput performance is based.
The data-rate-per-partition is the average size of the message multiplied by the number of messages-per-second. Put simply, it is the rate at which data t