In working with our customers, we find that success with Kafka starts by making sure that your platform is optimized. Since there’s so much potential within the platform, ensuring you’re getting all you can out of it is key. Here are 4 best practices—which we dive deeper into in another post—we suggest when it comes to optimizing Kafka:
1. Upgrade to the latest version.
Using an outdated version of Kafka can lead to long-running rebalances as well as rebalance algorithm failures. Making sure you’re using the most up to date version of Kafka can prevent those balance issues and ensure you’re getting the most out of the framework.
2. Understand how to improve data throughput rates.
Kafka has settings to control how data moves throughout the stack. Understanding and tuning these settings is the first step towards improving your data throughput rates and getting the most from your Kafka architecture.
3. Stick to random partitioning when writing to topics, unless architectural demands call for otherwise.
Kafka supports randomized writes. As you tune Kafka, you might be tempted to dictate where data is written. However, randomized writes will yield better performance in most cases.
4. Adjust consumer socket buffers to achieve high-speed ingest while maintaining data integrity.
To adjust consumer socket buffers, more capable networks can support higher buffer sizes. For example, 10Gbps networks may warrant socket buffers up to 16MB.