When I moved my blog from WordPress to Jekyll, I was faced with the problem of letting users search content in it. I didn’t give it much thought and used Google Custom Search Engine. In this post, I’d like to review possible options to search static sites, and review each of them.

In this post, I’d like to explore a sample data set using Kibana. This requires some data to start with: let’s index some tweets. It’s quite straightforward to achieve that by following explanations found in my good friend David’s blog post and wait for some time to fill the index with data. Basic metric Let’s start with something basic, the number of tweets indexed so far. In Kibana, go to Visualize  Metric, then choose the twitter index.

Some weeks ago, I wrote a post detailing how to send JMX metrics from a Spring Boot app to Elasticsearch by developing another Spring Boot app. Getting to create such an app is not always the right idea but developers are makers - software makers, and developing new apps is quite alluring to them. However, in the overall scheme of things, this means time is not only spent in development, but also for maintenance during the entire lifetime of the app.

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).

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.