In this blog, we will be creating an index in detail, which ranges from static index creation for the creation of simple indices, to dynamic template creation for creating multiple indices.
Note: This blog describes resizing of classic Elasticsearch clusters no longer offered to new Qbox users. The information provided below is deprecated for new Qbox users with Supergiant AWS clusters.
Auto-scaling has been a frequent-request feature since the inception of the Qbox service because auto-scaling with Elasticsearch isn’t as easy as is commonly thought.
Horizontal scaling up is trivial, of course, and is one of the primary benefits of this technology. Automatic scaling down is typically more troublesome and-if not done carefully-rebalancing/reindexing carry an intrinsic computational overhead that dramatically affects performance. Meanwhile, our nodes-by-the-compute-hour model makes vertical scaling a potentially expensive prospect.
Taking all of this into consideration, today we announce a vertical resizing feature addition that is available on your dashboard. Now you can easily resize vertically by migrating to bigger VMs that have more resources. This gives you more flexibility beyond the horizontal scaling that is already available (adding nodes to an existing cluster).
We set up for this post, Part 3 in our Kibana tutorial blog series, by working through the basics of the setup and usage our Kibana analytics dashboard in Using Kibana and Elasticsearch to Examine Twitter Trends(Part 1) and in Tutorial: Simple Analysis using Kibana (Part 2).
<p”>Here, we’ll take a look at some of the advanced analytics features in Kibana, including </p”>