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.

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. 

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!

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!

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.)

Container environments are very dynamic and fluid. New containers are stopped and started all the time due to scaling, rescheduling to new nodes, and updating. Solutions for monitoring containers should thus be flexible, too. For example, we should be able to respond to container start/stop events by launching or stopping some monitoring services and modules. In this way, we can dynamically respond to the changing state of the container environment. 

Starting with version 6.1, Metricbeat introduced support for the Autodiscover feature that allows tracking Docker and Kubernetes APIs to respond to container start and stop events. For example, if a new Apache HTTP container is launched, Autodiscover would automatically enable the Apache module with specific metric sets and channel Apache events to the specified output. Without this feature, we would have to launch all Filebeat or Metricbeat modules manually before running the shipper or change a configuration when a container starts/stops. Autodiscover solves this problem well. Let's see how to set up it with Metricbeat and send Docker container metrics directly to Elasticsearch.