🎧 Optimizing Apache JVMs for Apache Kafka

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Java Virtual Machines (JVMs) impact Apache Kafka® performance in production. How can you optimize your event-streaming architectures so they process more Kafka messages using the same number of JVMs? Gil Tene (CTO and Co-Founder, Azul) delves into JVM internals and how developers and architects can use Java and optimized JVMs to make real-time data pipelines more performant and more cost effective, with use cases.

Gil has deep roots in Java optimization, having started out building large data centers for parallel processing, where the goal was to get a finite set of hardware to run the largest possible number of JVMs. As the industry evolved, Gil switched his primary focus to software, and throughout the years, has gained particular expertise in garbage collection (the C4 collector) and JIT compilation. The OpenJDK distribution Gil's company Azul releases, Zulu, is widely used throughout the Java world, although Azul's Prime build version can run Kafka up to forty-percent faster than the open version—on identical hardware.

Gil relates that improvements in JVMs aren't yielded with a single stroke or in one day, but are rather the result of many smaller incremental optimizations over time, i.e. "half-percent" improvements that accumulate. Improving a JVM starts with a good engineering team, one that has thought significantly about how to make JVMs better. The team must continuously monitor metrics, and Gil mentions that his team tests optimizations against 400-500 different workloads (one of his favorite things to get into the lab is a new customer's workload). The quality of a JVM can be measured on response times, the consistency of these response times including outliers, as well as the level and number of machines that are needed to run it. A balance between performance and cost efficiency is usually a sweet spot for customers.

Throughout the podcast, Gil goes into depth on optimization in theory and practice, as well as Azul's use of JIT compilers, as they play a key role in improving JVMs. There are always tradeoffs when using them: You want a JIT compiler to strike a balance between the work expended optimizing and the benefits that come from that work. Gil also mentions a new innovation Azul has been working on that moves JIT compilation to the cloud, where it can be applied to numerous JVMs simultaneously.


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