Running Reactive Spring Boot on GraalVM in Docker

GraalVM is an open source polyglot VM which makes it easy to mix and match different languages such as Java, Javascript and R. It has the ability (with some restrictions) to compile code to native executables. This of course offers great performance benefits. Recently, GraalVM Docker files and images have become available. See here.

Since Spring Boot is a popular Java framework and reactive (non blocking) RESTful services/clients implemented in Spring Boot are also interesting to look at, I thought; lets combine those and produce a Docker image running a reactive Spring Boot application on GraalVM.

I've used and combined the following

• Building a Reactive RESTful Web Service
• Spring Boot with Docker and Running Spring Boot in a Docker container on OpenJDK, Oracle JDK, Zulu on Alpine Linux, Oracle Linux, Ubuntu
• Oracle's GraalVM Docker images
• (my very own) Ubuntu Development VM (requires VirtualBox, Vagrant)

As a base I've used the code provided in the following Git repository here. In the 'complete' folder (the end result of the tutorial) is a sample Reactive RESTful Web Service and client.

R and the Oracle database: Using dplyr / dbplyr with ROracle in Windows 10

R uses data extensively. Data often resides in a database. In this blog I will describe installing and using dplyr, dbplyr and ROracle on Windows 10 to access data from an Oracle database and use it in R.

R: Utilizing multiple CPUs

R is a great piece of software to perform statistical analyses. Computing power can however be a limitation. R by default uses only a single CPU. In almost every machine, multiple CPUs are present, so why not utilize them?

Machine learning: Getting started with random forests in R

According to Gartner, machine learning is on top of the hype cycle at the peak of inflated expectations. There is a lot of misunderstanding about what machine learning actually is and what it can be done with it.

Machine learning is not as abstract as one might think. If you want to get value out of known data and do predictions for unknown data, the most important challenge is asking the right questions and of course knowing what you are doing, especially if you want to optimize your prediction accuracy.

In this blog I'm exploring an example of machine learning. The random forest algorithm. I'll provide an example on how you can use this algorithm to do predictions. In order to implement a random forest, I'm using R with the randomForest library and I'm using the iris dataset which is provided by the R installation.