As you might already know from the previous Elasticsearch aggregation series, both metrics and buckets aggregations work on the numeric fields in the document set directly.

In contrast to this, pipeline aggregations, which we discuss in this article, work on the output produced by other aggregations transforming the values already computed by them. A pipeline aggregation, hence, works on the intermediary values not present in the original document set. This makes pipeline aggregation very useful for calculating complex statistical and mathematical measures like cumulative sum, derivatives, and moving averages among others.

In the first part of this series, we'll discuss two basic types of pipeline aggregations and show examples of such common Elasticsearch pipelines as a sum and cumulative sum, min and max, avg bucket, and derivative pipeline aggregations. Let's get started!

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Wordpress is a very popular CMS that is used all over the internet for a variety of use cases. Wordpress traditionally uses MySQL for the database and for its search functionality.

A good alternative is to use Elasticsearch to extend Wordpress’s search functionality.

In this article, we cover how to install ElasticPress, a plugin that can be used to exploit Elasticsearch’s powerful search capabilities with Wordpress.

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Not yet enjoying the benefits of a hosted ELK-stack enterprise search on Qbox? Discover how easy it is to manage and scale your Elasticsearch environment.

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Qbox dashboard offers a variety of useful features such as cluster monitoring, backups, cloning, viewing alerts, etc. Our Kubernetes-backed AWS users can now easily access Elasticsearch logs from their dashboards.

Qbox Download Logs Feature

In order to get your Elasticsearch logs, select "Download Logs" under the "Manage" drop-down of your cluster. The logs will be downloaded in the tar format.

With this blog post we begin a comprehensive overview of Elasticsearch metrics aggregations that focuses on Elasticsearch numeric metrics aggregations -- a subset of metrics aggregations that produces numeric values. There are two types of these aggregations in Elasticsearch: single-value aggregations, which output a single value, and multi-value aggregations, which generate multiple metrics.

In the first part of our metrics aggregations series, we'll discuss such single-value metrics aggregations as average and weighted average, min, max, and cardinality. The only multi-value aggregation type discussed in this article is extended stats aggregation. To help you understand how these aggregations work, we'll accompany each description with the corresponding visualization in the Kibana dashboard. Let's get started!

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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 are excited to announce that Elasticsearch 6.3.2 is now available for cluster provisioning on Qbox.io. This is a continuation of our efforts to integrate the latest Elasticsearch versions into Qbox offerings. (We previously made Elasticsearch 6.2.1 clusters available on our platform on March 28, 2018.)

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