The adoption of Log4j overshadows all other java logging frameworks. With Log4j 2, Apache gave us a next-generation Asynchronous Logger based on the famous LMAX Disruptor library. Yes, we scale!

Therefore, it’s a pity that currently there is no official Logstash 2.x plugin for Log4j2. Unofficially? There is https://github.com/jurmous/logstash-log4j2, but unless you’re a Ruby expert, it would take considerable effort to compile and install it correctly. In this article we did that for you and present a small demo on docker-compose.

Apache Kafka is a very popular message broker, comparable in popularity to Logstash.

More and more companies build streaming pipelines to react on, and publish events.
Kafka gains accelerated adoption for event storage, distribution, and Elasticsearch for projection. My friend Hannes and I call it a perfect match, so we  gathered during a quiet christmas holiday to implement a connector between the two.

All code is available on Github and runs on Docker Compose.

An optimal search engine knows the user’s request before he types.

A “recommender system” gathers relations between people and things in order to propose the information a user wants.

With this article we propose some simple strategies to implement recommender systems with Elasticsearch.

All examples in this article can be executed with an Elasticsearch 2.x installation, using the Kibana Sense console. They are easily adaptable for other approaches, for example, curl commands.

When building vertical search engines, for example for collecting recipes, prices or addresses, the first step is to crawl the web for information.

In this tutorial you will learn how to configure the Nutch web crawler to feed data into Elasticsearch.