![]() ![]() For a Python application, the prometheus_client library also allows to expose measurements - worker.py,.By integrating Micrometer with a Java Spring Boot application, it is possible to expose the measurements of its services - HasherHandler.java,.In a previous article, I explain how to install a complete Kubernetes, Prometheus and Grafana stack - Locally install Kubernetes, Prometheus, and Grafana,.Well, that’s not much and it already exists: A Java application compilable in Bytecode and native.Prometheus and Grafana to collect and display these measurements,.Different processing measurements from microservices.A Kubernetes cluster to run our containers,.To implement this solution, we will need: What is missing for a more realistic evaluation?.Let’s deploy the native version of the application.An update of this article is available at JVM vs Native - Configuring Java Containers in Kubernetes ![]() The configuration of containers is essential when it comes to measuring memory and CPU consumption. Playing on the number of containers in order to vary the load of the system.Transposing the code into Java language under the Spring Boot / WebFlux frameworks and using Spring Native for the build in Bytecode or in native,.It seemed like a good example to serve as a basis for this comparison by: The efficiency of the system is measured by the number of treatments performed per second. It uses different Python and Ruby applications which interact by means of Docker containers. Recently, I came across a very interesting course, containers and orchestration, by Jérôme Petazzoni. Secondly, we must also ask ourselves what we are going to measure. To compare the execution of a Java application between its Bytecode (JVM) and native (GraalVM) versions, you must first decide on its architecture and the framewoks to use. ![]()
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