In an earlier post, How to Build an Autocomplete Feature with Elasticsearch, we showed how to build a basic autocomplete that looks for all documents in the index. This feature is good for the generic autocomplete feature, but it is not enough if your index has a lot of product categories, for example. Therefore, in this post we’ll explore context-based autocompletion, which will help you implement intelligent filtering based on categories and geo points. Let’s get started!

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We cannot over-emphasize that the ELK stack is a great solution to ship, search, and analyze logs, system metrics, statistics, and other types of insight-driven data. You can utilize various components of the ELK stack such as Kibana to monitor what is happening in your cluster/s, host and applications getting instant insights guiding your business decisions.

However, what options do we have for monitoring Elasticsearch itself? To make Elasticsearch serve request fast and ensure the health of the cluster, we need a good monitoring solution that helps identify issues as they arise. Fortunately, there are a lot of free monitoring tools available for the Elasticsearch, including Elasticsearch Kopf , Big Desk, or Whatson.

In this article, we’ll review one of the best web-based monitoring tools for Elasticsearch — ElasticHQ. This plugin has been chosen as the built-in monitoring solution by Qbox for its hosted Elasticsearch 6.2.1 clusters.

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Bulk indexing in Elasticsearch is an important topic to understand because you might occasionally need to write your own code to bulk index custom data. In addition, experience with bulk indexing is important when you need to understand performance issues with an Elasticsearch cluster.

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In Part II of the article, we’ll focus on Qbox plugins that provide various third-party integrations including SQL, Neo4j graph platform, and Couchbase Transport, and we will examine language plugins that enhance querying and analysis of the text in Korean, Chinese, Polish, Hebrew, and several other languages.

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