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The new ELK stack 6.6.0 was officially released by Elasticsearch on January 29, 2019, and it offers a lot of groundbreaking features and enhancements for Elasticsearch, Kibana, Logstash, APM, and Beats.

We’ve already tested Elasticsearch 6.6.0 with the brand new Kibana and are excited to share our experience with such valuable features as Index Lifecycle Management and Remote Cluster management. In this article, we’ll summarize these and other major new features for Elasticsearch, Kibana, and Elastic APM and will give you a glimpse of some cool stuff you can now do with your Elasticsearch indices in Kibana 6.6.0. Let’s get started!

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  • Deploying Supergiant Capacity Service for more efficient management of cluster resources. Supergiant Capacity Service used to provision Kubernetes nodes for Elasticsearch Pods substantially improves the stability of user deployments. We've experienced fewer issues with inability to provision Elasticsearch clusters after deploying Supergiant Capacity Service.

In Progress

  • Adding Elasticsearch 6.4 to the list of available versions. This release offers several new features (e.g., WeightedAvg metric aggregation), and you can learn about them here.
  • Adding the Learn-to-Rank plugin to the provisioner. The Elasticsearch Learn-to-Rank plugin leverages Machine Learning (ML) to improve search relevance ranking.
  • Security Improvements. We are currently working on email verification and user verification before being able to log into Qbox.


  • Add Hunspell dictionaries to the Qbox dashboard. Hunspell is a spell checker and morphological analyzer originally designed for the Hungarian language. It is a good analyzer solution for languages with rich morphology and complex word compounding and character encoding.

In this article, we’ll continue our overview of Elasticsearch bucket aggregations, focusing on significant terms and significant text aggregations. These aggregations are designed to search for interesting and/or unusual occurrences of terms in your datasets that can tell much about the hidden properties of your data. This functionality is especially useful for the following use cases:

  • Identifying relevant documents for the user queries containing synonyms, acronyms, etc. For example, the significant terms aggregation could suggest documents with “bird flu” when the user searches for H1N1.
  • Identifying anomalies and interesting occurrences in your data. For example, by filtering documents based on location, we could identify the most frequent crime types in particular areas.
  • Identifying the most significant properties of a group of subjects using the significant terms aggregation on integer fields like height, weight, income, etc.

It should be noted that both significant terms and significant text aggregations perform complex statistical computations on documents retrieved by the direct query (foreground set) and all other documents in your index (background set). Therefore, both aggregations are computationally intensive and should be properly configured to work fast. However, once you master them with the help of this tutorial, you’ll acquire a powerful tool for building very useful features in your applications and getting useful insights from your datasets. Let’s get started!

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