Streamlining Your Elasticsearch Deployment With Kubernetes

Brian Eugen
By Brian Eugen 7 Min Read
7 Min Read

Kubernetes is a container orchestration platform that automates deployment, scaling, and management. It’s built-in scaling, health checks, and auto-healing mechanisms help handle outages and demand peaks.

It also allows users to run stateful applications like databases without changing config files on a disk. For instance, Elasticsearch uses persistent volume claims and stateful sets.


Kubernetes makes it easier to deploy and scale Elasticsearch clusters at a large scale. It is an open-source container orchestrator that runs a set of worker and controller nodes.

Kubernetes provides unparalleled scalability because it allows resources as needed to meet changing demands without investing in additional infrastructure. Moreover, it supports the automation of deployments and scaling operations using tools.

The platform also allows development teams to develop applications as microservices that communicate via API calls. This will enable developers to code and test in parallel and deploy them independently.

Having containers on a single platform allows enterprises to migrate their operations to new environments and platforms without making significant code changes. It gives a level of flexibility essential for enterprises to thrive in today’s environment.

Scaling an Elasticsearch cluster on Kubernetes can be done in one of two ways: directly using deploy elasticsearch on Kubernetes or automatically with the Elasticsearch Kubernetes Operator. This operator is the best way to scale Elasticsearch clusters on Kubernetes because it combines the power of containers with Elasticsearch’s robust scaling capabilities.

This operator will scale Elasticsearch based on your specific metrics, keeping a good load balance across indices and shards. It will maintain the number of bits per index you uploaded initially and adjust the total number of nodes in the Elasticsearch cluster automatically if required.


Kubernetes makes it easy to deploy a highly scalable and secure Elasticsearch cluster. It also supports a wide range of DevOps practices, so you can easily automate tasks such as upgrades and scale.

A stateful Elasticsearch cluster needs data to persist between service restarts, and a distributed architecture is essential. Kubernetes handles this with a well-known pattern known as sharding.

Unlike traditional applications, stateful services need persistent storage that will not be lost if the service shuts down. Additionally, stateful applications must be configured to handle rollbacks, as changes to the stored data can cause problems.

This is where a centralized logging solution can help troubleshoot and analyze data quickly. One popular logging solution is Elasticsearch which can help you quickly sort through and analyze log data produced by your Pods.

To get started, you’ll need to access a Kubernetes cluster. You can choose from Google Kubernetes Engine (GKE) or any other cloud provider. You’ll also need to install a kubectl command-line tool to manage your cluster.

Once you’ve accessed your cluster, the next step is to deploy Elasticsearch and Filebeat. These open-source tools collect Pod logs and store them in Elasticsearch, which can be accessed with Kibana. Once completed, you’ll have a fully functional Elasticsearch cluster running on Kubernetes.


Kubernetes provides built-in automation of many everyday operations, including upgrades, scaling, restarts, and monitoring. This makes it a good choice for Elasticsearch, as it automates much of the repetition and inefficiencies that can occur when doing everything manually.

Deployment is done through clusters of nodes, with each node running a containerized workload. The control plane manages the nodes, which can also route traffic between them.

A node can control the cluster, store data, or perform other tasks, depending on its role. It can be a controller node, which runs a single Elasticsearch instance, or a data node, which splits indices into shards and replicates information between them.

Each node should have a minimum of 4GB of memory to run Elasticsearch. This is because Elasticsearch shorts and aggregates data, so it needs a lot of memory.

Another important consideration is the type of worker node to use. It is best to label worker nodes for the stateful sets that require heavy memory. This ensures that the node has enough memory to run both Kubernetes data management tools and Elasticsearch.

Once your nodes are set up, you can deploy your Elasticsearch cluster. You can do this using a Helm chart or manually configure your nodes. The latter option is recommended if you want complete control over your nodes and to ensure they are correctly configured.


Kubernetes provides a range of security options for your Elasticsearch cluster, including access controls, encryption, and monitoring. These options are flexible and easy to implement to build a secure deployment for any application.

Stateful services require persistent storage that won’t be lost in the event of a service failure. In addition, these services need to operate in a distributed way.

To achieve this, Elasticsearch clusters use a configuration that combines master, data, and client nodes in a Kubernetes pod-based cluster. The Controller node handles cluster-wide management and design.

The Data nodes store indices and perform search operations. The Client nodes forward cluster requests to the Controller node and data-related requests to Data nodes.

This architecture is a good choice for high availability and fault tolerance. The data nodes split indices into shards and replicate information between them so that the data is not lost if one node goes down.

Lastly, Elasticsearch clusters must have an encryption policy that uses Transport Layer Security (TLS). This allows you to protect sensitive data and prevent attackers from tampering with your indices or replicating data.

In addition, you must ensure that all containers are built to run as a non-root user. This helps reduce security vulnerabilities and post-exploitation activities that can cause crashes or abnormal behavior. It also encourages developers to build container applications that function correctly without root privileges.

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Brian Eugen is a tech-savvy wordsmith with a knack for captivating readers through his expertly crafted tech blog articles. His passion lies in dissecting the intricacies of technology, particularly in the realms of Android, Windows, internet, social media, gadgets, and reviews. With a deep understanding of the latest trends and a talent for simplifying complex concepts, His articles offer readers valuable insights and up-to-date information. His expertise in writing and genuine love for all things tech make him a trusted source in the digital landscape.
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