Elasticsearch is an open-source tool that ingests application data, indexes it then stores it for analytics.
Since it gathers large volumes of data while indexing different data types, Elasticsearch is often considered write-heavy. To manage such dynamic volumes of data, Kubernetes can make it easier to configure, manage, and scale Elasticsearch clusters. Kubernetes also simplifies the provisioning of resources for Elasticsearch using Infrastructure-as-Code configurations, abstracting cluster management, and increasing the automation and repeatability of your deployments.
The Elasticsearch operator by Elastic, called Elastic Cloud on Kubernetes, or ECK, has many capabilities to assist in the ongoing operations of Elastic on Kubernetes including:
Mayadata supports multiple operators to run Elasticsearch on Kubernetes
OpenEBS together with Elasticsearch gives a complete logging solution. Many OpenEBS users have shared their experience of using OpenEBS for local storage management in Kubernetes for Elasticsearch, including the Cloud Native Computing Foundation, ByteDance (TikTok), and Zeta Associates (Lockheed Martin).
While Kubernetes alone cannot store data generated by a cluster, persistent volumes can be used to sustain it for future use. To help with this, OpenEBS provisions local persistent volumes or LocalPV and allows for data to be stored on physical disks.
Even if the node fails or rebooted during upgrades, persistent volumes from OpenEBS continue to be highly available.
OpenEBS provides in-depth monitoring and storage for logs. You can check metrics and monitor Elasticsearch instances using Prometheus and Grafana.
OpenEBS allows you to take backup of Elasticsearch to any object storage and restore it to the same or any Kubernetes cluster.
In this blog we look at running Elasticsearch on OpenEBS in an easy way
This step by step solution guide explains the steps and important considerations for deploying Elasticsearch clusters on Kubernetes using OpenEBS Persistent Volumes.