Summary
Tuning Kafka can feel like guesswork. The JVM significantly impacts Kafka performance, as Kafka brokers and client applications are built on Java and run within a JVM. The JVM directly controls how Kafka executes workloads at scale
In this post you will learn:
- Tweaking partitions, replica fetch sizes, and I/O threads return negligible gains
- ITAM professionals must carefully consider their readiness to upgrade to the new LTS version of Java
- Azul Platform Prime performs 20-50% faster than vanilla OpenJDK, without any code changes.
You want your applications to operate with speed. Performance. Reliability. Certainty. But tuning Kafka can feel like guesswork. Tweaking partitions, replica fetch sizes, and I/O threads rewards you with negligible gains. Finding the “right” configuration at scale can seem impossible… and frustrating.
“While Apache Kafka offers immense power, achieving optimal performance isn’t automatic,” Google Cloud Strategic Cloud Engineer Abhi Sharma and Data Architect Blake DuBois note in a 2025 article, which lists steps to benchmark your managed services for Kafka deployments. Running benchmarks and tuning to improve performance is an inexact, time-intensive exercise.
And besides… what if the answer wasn’t in Kafka anyway?
How the JVM affects Kafka performance
The JVM significantly impacts Kafka performance, as Kafka brokers and client applications are built on Java and run within a JVM. The JVM directly controls how Kafka executes workloads at scale. There are several critical areas to consider:
- Garbage collection (GC) pauses: Kafka relies on the JVM’s garbage collector to manage memory. High GC times can lead to “stop the world” pauses, where application threads are temporarily halted, reducing throughput and increasing latency. Tuning JVM heap size and choosing an appropriate garbage collector for low-latency applications like Kafka are crucial.
- JVM choice: Different JVM implementations, such as OpenJDK and specialized JVMs like Azul Platform Prime, offer varying performance characteristics. Specialized JVMs often focus on reducing GC pauses and improving overall execution speed, which can lead to higher Kafka throughput and lower, more predictable latencies.
- Memory management: The JVM’s memory management directly affects Kafka’s efficiency. Proper heap sizing, considering both initial and maximum heap sizes, is important to prevent frequent or excessively long GC pauses.
- Compiler optimizations: The JVM’s just-in-time compiler performs runtime optimizations that can significantly improve Kafka’s execution speed. Specialized JVMs often incorporate advanced compiler optimizations to further enhance performance.
- Resource utilization: The efficiency of the JVM can impact CPU and memory resource utilization. A well-tuned JVM can achieve higher throughput with fewer resources, potentially reducing infrastructure costs.
Kafka performs better with Azul
Azul Platform Prime includes several features at no additional cost that can improve your application’s performance.
- Azul’s Continuously Concurrent Compacting C4 Garbage Collector can run 2-5 times more transactions through your infrastructure — without pauses, jitters, or timeouts.
- The Falcon Compiler ensures that Java developers and Java-based businesses can extract maximum performance from server hardware.
- Azul Platform Prime performs 20-50% faster than vanilla OpenJDK, without any code changes.
The Azul Performance Engineering Lab has carefully bookmarked Kafka performance.
- Customers tell us that out-of-the box Kafka delivers a 20% improvement in raw speed with Azul Platform Prime’s Falcon Compiler.
- Azul improves throughput and responsiveness by 45% and eliminates garbage collection pauses without changing a single line of code.
- Platform Prime customers can meet Kafka SLA targets with fewer cloud instances, fewer .servers, and less performance tuning
Join us for a free webinar and learn more about Kafka
Join Azul Senior Products Manager Jiří Holuša and Vice President of Product Management Jonhn Ciccarelli in a live webinar, How to Boost Kafka Performance in One Day. They will show how the JVM directly impacts Kafka’s efficiency and how the right JVM can deliver measurable improvements. In this session, they will cover:
- Flamegraphs and dive into assembly code to reveal where performance improvements come from
- How running Kakfa on Azul Platform Prime can reduce latency by up 40%, while handling the same load
- Actionable insights into how the right JVM can boost performance of the cluster to improve SLAs while reducing infrastructure spend.
