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This article is the beginning of our Elastic Stack article series, that explains the Elastic Stack for Beginners and curious people. I use football data as the basis for our demonstrations and examples for three reasons. The first reason is that the football (soccer) season starts again this weekend. I live in the city of the current Swiss Football champion (Bsc Young Boys). I want them to succeed again this season. The final reason is my little nephew. On my summer vacation visit, my nephew asked me what I do for a living in Switzerland: Software Engineering and Architecture for Distributed Computing. That was too abstract or vague for him. Kids are naturally curious. He is a smart kid, so he rephrases the question of what kind of products or tools I use for work. It came down to the Elastic Stack.

This article describes how to deploy pods (applications) on the desired node or nodes. OpenShift uses K8S (Kubernetes) to do that, so we will also cover the K8S basics about that.

Do you know how to download the latest version of a Java Application from a JFrog Artifactory repository? This blog post gives you one solution. To work it requires at least the Artifactory Pro version. The Artifactory API enables us to search for last modified items/artifacts.

In Information Technology Housekeeping refers to standard routines whereby a computer system is cleaned up after usage (e.g. freeing resources such as disk space). This housekeeping might include such activities as removing or archiving logs that the system has made as a result of the user’s activities, or deletion of temporary files which may otherwise take up space.

Jekyll powers this blog. There are several possibilities to accomplish comments on static pages. I opted for utterances, that automatically creates in a public GitHub repository an issue for the particular blog post. To make comments on a blog article, you need a GitHub account. That might be exclusive to non-technical readers, but my blog articles are mostly technical. Enjoy commenting and follow the thumb rule:

In our previous articles, we introduce the techniques to create a suggestion for typeahead searches. In this article, we do an example of how to utilise it in public transportation. In Switzerland, Public Transportation is extraordinary. It is prevalent, the service and overall reliability are extraordinary. With public transportation in Switzerland, you can nearly arrive at any destination in Switzerland comfortably.

In the previous articles, we look into Prefix Queries and Edge NGram Tokenizer to generate search-as-you-type suggestions. Suggesters are an advanced solution in Elasticsearch to return similar looking terms based on your text input. Movie, song or job titles have a widely known or popular order. In this article, we are going to complete with a hands-on example.

In the previous article, we look into the possibilities of prefix queries to create suggestions based on existing data to enhance the search experience. We experience how fast and straightforward it could help us in the beginning. We also learned that it has some drawbacks like latency and duplicates if the data-set grows more significant over time. In this article, we are going to overcome the problems with Edge NGram Tokenizer.

You know searches from Google or DuckDuckGo. As you start typing the search engines, give you some autocomplete suggestions.