How to Optimize Java Serverless Functions in Kubernetes using Quarkus

How to Optimize Java Serverless Functions in Kubernetes using Quarkus

This tutorial explains how to optimize Java performance to run Serverless functions on Kubernetes using Quarkus. Achieve faster startup and a smaller memory footprint to run serverless functions on Kubernetes.

Achieve faster startup and a smaller memory footprint to run serverless functions on Kubernetes.

A faster startup and smaller memory footprint always matter in  Kubernetes due to the expense of running thousands of application pods and the cost savings of doing it with fewer worker nodes and other resources. Memory is more important than throughput on containerized microservices on Kubernetes because:

  • It's more expensive due to permanence (unlike CPU cycles)
  • Microservices multiply the overhead cost
  • One monolith application becomes N microservices (e.g., 20 microservices ≈ 20GB)

This significantly impacts serverless function development and the Java deployment model. This is because many enterprise developers chose alternatives such as Go, Python, and Nodejs to overcome the performance bottleneck—until now, thanks to Quarkus, a new Kubernetes-native Java stack. This article explains how to optimize Java performance to run serverless functions on Kubernetes using Quarkus.

kubernetes java serverless quarkus

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