ElasticSearch on Kubernetes

What is Elasticsearch?

Elasticsearch is a data store which stores data within indexes. It is a distributed, real-time, and scalable search engine that enables structured, full-text, and analytical search. It is ideal for searching and storing across large volumes of text data, such as logs. It can also be used to search for numerous types of documents.

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What is Kubernetes?

Kubernetes is an open-source platform that executes a group of worker and master nodes that enables teams to scale, manage, deploy, and automate containerized workloads like Elasticsearch. Kubernetes is capable of handling many large-scale applications, that includes stateful applications like databases or search platforms. Kubernetes relies on the shoulders of giants like Google, which first designed the software after using similar technology to carry out production workloads for more than a decade.

ElasticSearch on Kubernetes:

Suppose you are building an Elasticsearch stack like ELK to be highly efficient, scalable and secure, and you want to follow today's DevOps models. In that case, Kubernetes is an excellent starting point. It enables the organizations in simplifying the operations like restarting, monitoring, scaling and upgrading that are developed on the Kubernetes platform. Although Kubernetes provides many features, it is not a miracle solution for what we need to run Elasticsearch.

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Worker Configuration: As Elasticsearch shorts and aggregates the data, it needs heavy memory. So, Kubernetes worker nodes must have enough memory for executing Kubernetes data management tools as well as Elasticsearch. The best way for deploying the stateful set that demands a particular amount of memory is to have worker nodes labelled for the memory-intensive workloads.

Reliable container storage as well as data management: Elasticsearch can be hard to write, meaning we need a sufficiently flexible data storage and management layer for meeting these requirements but which is also closely associated with your Kubernetes cluster for easy use. Applying optimized io profiles to Elasticsearch as well as storage pool types using SSDs may contribute to performance. In addition, the use of container storage and data management layer, which requires the location of data for the persistent volume of your Pod complaints, even in case of failover, will guarantee the best possible setup.

Data security: Elasticsearch offers integrated security functions that include RBAC at the application level, in-flight data encryption and audit. However, we need to consider whether the data on the drive is secure and what users have access to handle these support stores. To be more secure, organizations need to protect their data at the application level and secure the data layer through encryption and access control.

Protection of data and disaster recovery: Elasticsearch has the ability to receive and store this information in indexes. These indexes may have data retention policies established according to the configuration of the curator. Still, this is only part of the battle. Select a data management and container storage solution that offers appropriate data replication, disaster recovery, backups, and offsite backups for recovering the failure of the node or site and obtain full availability and protection.

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Is Kubernetes an appropriate choice for Elasticsearch?

Kubernetes was initially designed and built to carry out transient workloads, that is, stateless applications and various work. The databases were never designed to work on Kubernetes or on containers generally. StatefulSets that make deployments stateful work are an add-on later, although they work and work great. Avoid the use of a database or datastore on Kubernetes:

• An additional abstraction layer has its performance penalties.
• Running datastores and databases on vanilla Virtual Machines allows you to select the correct sizes without any waste of resources. In Kubernetes, there is still a portion of the virtual machine executing the node which is not in use.
• It is much easier to resize machines and disks when using virtual machines.
• This additional abstraction layer will make it much more difficult to manage large clusters.
• As mentioned earlier, Kubernetes was not originally designed for stateful applications.
• Orchestration, automation, continuous updates, etc., for Virtual Machine, based Elasticsearch clusters can be readily resolved with the help of Pulumi, Terraform, Ansible and some other similar tools.

In few cases where performing Elasticsearch on Kubernetes can be very helpful indeed, and it is recommended that:

• We use Kubernetes for everything and don't know any other way of deploying. Here, the reuse of existing infrastructure is a significant factor.
• Moreover, all we need is a very small cluster that probably does not justify the management of any other tool or deployment method. However, for a large group, learning and using some other tool or method will be useful to save the headache from management on Kubernetes.
• If the system is to be deployed as a SaaS, on a public cloud but simultaneously on site for your customers, with the help of the same configuration and deployment.  

One of the major benefits of running Elasticsearch above Kubernetes is the resilience of the cluster, especially when restarting nodes. Anyway, if all the data pods are programmed on the same or the same nodes, this advantage diminishes considerably and can even result in the lack of available data pods.

Elastic has released the basic ECK feature under the basic Free-forever level to make those proprietary features available to all users, regardless of where Elastic products are deployed. The Elastic Enterprise subscription also allows users to access more advanced features.

Conclusion: 

In this blog, we have learnt about Elasticsearch, Kubernetes and seen whether Kubernetes is an Appropriate Choice For Elasticsearch or not. I hope you found this information helpful.

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