Nicolas Fränkel

Last week, I wrote about how one could start developing one’s Logstash plugin coming from a Java developer background. However, with the acquisition of Packetbeat, Logstash now has help from Beats to push data to Elasticsearch. Beats are developed in Go, another challenge for traditional Java developers. This week, I tried porting my Logstash Reddit plugin to a dedicated Beat. This post documents my findings (spoiler: I found it much easier than with Ruby). Setting up the environment On

I recently became interested in Logstash, and after playing with it for a while, I decided to create my own custom plugin for learning purpose. I chose to pull data from Reddit because a) I use it often and b) there’s no existing plugin that offers that.

This week’s post aims to describe how to send JMX metrics taken from the JVM to an Elasticsearch instance. Business app requirements The business app(s) has some minor requirements. The easiest use-case is to start from a Spring Boot application. In order for metrics to be available, just add the Actuator dependency to it: <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-actuator</artifactId> </dependency&

Given the trend around microservices, it has become mandatory to be able to follow a transaction across multiple microservices. Spring Cloud Sleuth is such a distributed tracing system fully integrated into the Spring Boot ecosystem. By adding the spring-cloud-starter-sleuth into a project’s POM, it instantly becomes Sleuth-enabled and every standard log call automatically adds additional data, such as spanId and traceId to the usual data. 2016-11-25 19:05:53.221 INFO [demo-app,b4d33156

As a Java developer, when you are first shown how to run the JVM in debug mode, attach to it and then set a breakpoint, you really feel like you’ve reached a step on your developer journey. Well, at least I did. Now the world is going full microservice and knowing that trick means less and less in it everyday. This week, I was playing with Logstash to see how I could send all of an application exceptions to an Elasticsearch instance, so I could display them on a Kibana dashboard for analy

Most Spring developers know about the BeanPostProcessor and the BeanFactoryPostProcessor classes. The former enables changes to new bean instances before they can be used, while the latter lets you modify bean definitions - the metadata to create the bean. Commons use-cases include: Bootstrapping processing of @Configuration classes, via ConfigurationClassPostProcessorResolving ${…​} placeholders, through PropertyPlaceholderConfigurerAutowiring of annotated fields, setter methods and

As a consultant, I’ve done a lot of Java projects in different 'enterprise' environments. In general, the Continuous Integration stack - when there’s one, is comprised of: Github Enterprise or Atlassian Stash for source version control, Jenkins as the Continuous Integration server, sometimes but rarely Atlassian Bamboo, Maven for the build tool, JaCoCo for code coverage, or even Artifactory as the artifacts repository - once I had Nexus. Recently, I started to develop Kaadin, a Kotlin

In Android, every tutorial teaching you the basics describe how to design screen through XML files. It’s also possible to achieve the same result with Java (or any JVM-based language). Android screen design is not the only domain where XML and Java are valid options. For example, Spring configuration and Vaadin screen design allow both. In all those domains, there’s however a trade-off involved: on one hand, XML has a quite rigid structure enforced by an XML-schema while Java gives po

Microservices are not a trend anymore. Like it or not, they are here to stay. Yet, there’s a huge gap before embracing the microservice architecture and implementing them right. As a reminder, one might first want to check the many fallacies of distributed computed. Among all requirements necessary to overcome them is the ability to follow one HTTP request along microservices involved in a specific business scenario - for monitoring and debugging purpose. One possible implementation of it

Kotlin’s extension functions are a great way to add behavior to a type sitting outside one’s control - the JDK or a third-party library. For example, the JDK’s String class offers the toLowerCase() and toUpperCase() methods but nothing to capitalize the string. In Kotlin, this can be helped by adding the desired behavior to the String class through an extension function: fun String.capitalize() = when { length < 2 -> toUpperCase() else -> Character.toUpperC