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.

Are we looking for a new home? We also move very soon in to our new office location in Bern. In my previous article about medical use cases, I shortly introduced the capability of geographical distance queries. In this post, we examine its impact on Elasticsearch scoring.

With the frequency of patch releases being available for Pivotal Cloud Foundry (PCF), it becomes inevitable to automate their roll out in any professional operations setup. The patch releases often contain security fixes that harden the deployment, but also lot's of bug fixes that are highly useful to roll out. Typically, operators need to not only upgrade a single Cloud Foundry environment, but have to operate and patch multiple deployments of Cloud Foundry.