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:

  1. Upgrade to the latest version of Kafka.
  2. Understand data throughput rates.
  3. Implement random partitioning.
  4. 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.

Kafka optimization

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 travels through the partition. Desired throughput rates dictate the target architecture of the partitions.

Here’s a key Kafka optimization tip: To improve throughput, you can scale up the minimum amount of data fetched in a request. This results in fewer requests. The messages are then delivered in larger batches. This is critical especially when there is a low volume of data being produced. Extensive knowledge of Kafka throughput metrics will help users fully optimize their Kafka systems in this scenario.

3. Stick to random partitioning when writing to topics, unless architectural demands call for otherwise.

Solutions architects would prefer each partition to support similar amounts of data and throughput rates. In reality, data rates vary over time as do the raw number of producers and consumers.

The performance challenge presented by the variability is the potential for consumer lag, AKA consumer read rates falling behind producer write rates. As Kafka environments scale, random partitioning is an effective way to ensure you don’t introduce artificial bottlenecks unnecessarily attempting to apply static definitions to a moving performance target.

Partition leadership is usually the product of simple elections via metadata maintained with the Zookeeper. Leadership election does not, however, take into account the performance of the individual partitions.

There are proprietary balancers that can be leveraged depending on your Kafka distribution. But short of such tooling, random partitioning provides the most hands-off path to balanced performance.

This is why random partitioning is one of the key Apache Kafka best practices we recommend. It evenly distributes the load for consumers. As a result, scaling the consumers becomes significantly easier. This is effectively what happens when you use the default partitioner while not manually identifying a specific partition or a message key. Random partitioning works best for stateless or “embarrassingly parallel” services.

The takeaway? Stick to random partitioning when writing to topics, unless architectural demands demand otherwise.

4. Adjust consumer socket buffers to achieve high-speed ingest.

In the older Kafka versions, the parameter receive.buffer.bytes is set to 64kB as its default. In the newer Kafka versions, the parameter is socket.receive.buffer.bytes, with 100kB as the default.

What does this mean for Kafka optimization? For high-throughput environments, these default values are way too small, thus insufficient. This is very much the case when the network’s bandwidth-delay product between the broker and the consumer is bigger than that of LAN (local area network).

Threads slow down and become limited when there aren’t enough disks. Among the most important Apache Kafka best practices is to increase the size of the buffers for network requests. Doing so will help you improve throughput.
If your network is running on 10 Gbps or higher and has latencies of 1 millisecond or more, you’re advised to tune your socket buffers to 8 or 16 MB. If memory is an issue, consider 1 MB.

Explore More Ways to Optimize Kafka Performance

Optimizing your Apache Kafka deployment is an ongoing job, but these four best practices for Kafka should be a solid start. The performance optimization tips mentioned above are just some of the optimization approaches users can implement to improve Kafka performance.

Kafka is becoming more and more popular for application developers, IT professionals, and data managers. And for good reasons. For more on Kafka, check out our other blog post discussing the best practices for Kafka when applied to specific areas of application development and data management.

Take a free 15-day trial to see what Big Data success looks like

Pepperdata products provide complete visibility and automation for your big data environment. Get the observability, automated tuning, recommendations, and alerting you need to efficiently and autonomously optimize big data environments at scale.