Your data in Elasticsearch is valuable. Therefore the need for backups is provided by the snapshot and restore mechanism in Elasticsearch.

Today we are tackling a common problem in IT. You have data that contain unique ids. Many use cases in the Elastic Stack require additional data.

Since Kibana v6.4.0 there are significant improvements and enhancements. Under all these enhancements, a hidden gem and long-awaited feature is finally available: the Static Lookup Field Formatter.

In a previous blog post I demonstrated how the vector datatype in Elasticsearch can be used to search words by their semantic meaning. In this post I will show how a reverse image search for paintings can be implemented using the same methods. Given a photo of a painting, we will use Elasticsearch to find other paintings which look similar.

For the ones started their journey with Elasticsearch before version 5.x sometimes upgrading to the newer versions like 6.x or 7.x bring many challenges. From data type changes to the index structure changes and deprecations, from Transport to REST client and so one. One of those changes that would influence your system, is the scoring algorithm evolution. In this blog, we demonstrate the impact on relevance and scoring if the Elasticsearch changes its algorithms under the hood between major versions. We will show it practically, using a small dataset. If the scoring feature of Elasticsearch…