Using OpenNLP and GraalVM to Detect Sentences in Kubernetes While Comparing Helidon and Spring Boot’s Metrics

We have resorted to using Java for our data science endeavours to handle data processing rather than relying on the computer language Python. However, when compared to the typical default package of JDK + JVM that is made accessible by Oracle or OpenJDK, GraalVM, an industry-leading high-performance JVM, provides several benefits that are not shared by the other two. So, we are using GraalVM as our default JDK and Apache OpenNLP for NLP-related tasks. Apache OpenNLP’s framework now incorporates widely used classifiers like maximum entropy, Naive Bayes, and perceptron, amongst others. This toolkit was created entirely in the Java programming language, and it helps with a broad number of activities that are generally connected with natural language processing (NLP). For example, OpenNLP is being used here so that we can recognise sentences. OpenNLP was incorporated into industry-leading frameworks such as Helidon and Spring Boot, and their performance and CPU and memory utilisation were tested with the help of the tool Apache JMeter on varying user thread loads in a containerised Kubernetes environment. We concluded that Spring Boot would be the preferable alternative if we desire a low CPU consumption and a quicker jar startup time. In addition, it has a large user base, making it simpler to start with and troubleshoot any problems that can arise with the code. Helidon is a new cloud-native framework in the industry and has made many promises. Helidon resulted in a very tiny jar and a modest size for the docker image. It also has a low error rate during user load and uses less memory, which are both significant features to be considered.