Elasticsearch is a distributed search and analytics engine. It uses indexing to make searching through large amounts of data high-speed.
Managing Elasticsearch clusters can be complex, significantly when scaling them. Thankfully, there are a few strategies and techniques that you can use to manage your Elasticsearch clusters.
For example, a container storage and data management layer that enforces io profiles optimized for Elasticsearch and storage pool types that use SSDs will help. Additionally, you can use Persistent Volume claims to scale storage size on the fly quickly.
Sizing your Nodes
Increasing the number of nodes in an Elasticsearch cluster can be an effective way to improve performance. However, sizing your nodes is a complex and dynamic process.
Rather than focusing on the number of nodes, it is essential to consider various factors. These factors include scalability, cost, availability, and performance.
Kubernetes is a container orchestration system that automates containerized applications’ deployment, scaling, and management. It provides built-in capabilities for load balancing, health checks, and auto-healing.
It also allows for telemetry and service discovery. The underlying infrastructure is a set of containers and pods connected by networking. Each container is backed by an image from the Amazon Elastic Container Registry (ECR).
The control plane component is responsible for adjusting the state of the cluster to match a desired configuration. It consists of a controller node, which interacts with the Kubernetes API and controls the task assignments, and a scheduler that monitors idle pods.
Storage and networking are handled by plugins that run on the nodes. These plugins can directly manage storage and network operations or utilize cloud-provider solutions like Google Cloud Platform or AWS Cloud Storage.
When a user requests persistent storage, they create an ongoing volume claim. These claims are much like container images and require CPU resources.
Managing the Cluster
Kubernetes offers various tools and features to simplify scaling Elasticsearch clusters. These include Persistent Volumes (PVs), stateful sets, and deployments.
Using Kubernetes to deploy your cluster can help reduce deployment time and improve the availability of your data. It also simplifies maintenance, upgrades, and backups.
Before configuring, you should check Elasticsearch on Kubernetes best practices and ensure enough memory is available. This will allow you to efficiently sort and aggregate data.
Once you have your Elasticsearch nodes deployed, you can begin managing the cluster. This includes sizing your nodes, creating an Elasticsearch service, and setting up networking to distribute traffic amongst the nodes.
You can use Elastic Cloud on Kubernetes (ECK) to make it easier to manage your cluster. ECK is built as a Kubernetes operator, making it easy to deploy Elasticsearch and Kibana on Kubernetes.
When you create your cluster, ECK sets up a controller node in the primary zone and two data nodes in each area. It will then monitor the health of your group and update its configuration if necessary.
You can add or remove nodes to accommodate growth when you scale your cluster. You can also increase or decrease the number of shards in the index. Bits increase search performance by distributing the data across multiple nodes.
Deploying Elasticsearch
Elasticsearch is an open-source, scalable search engine that is easy to configure, manage and scale. It also forms the core of the ELK Stack, a popular technology stack that can perform log analysis, SIEM as a Service, and data visualization.
It can be deployed as a cluster in Kubernetes using ECK, a framework that provides general management capabilities for Kubernetes. These capabilities include managing multiple Kubernetes clusters, upgrading Kubernetes and the Elastic stack, monitoring groups, detecting capacity expansion or reduction, changing cluster configurations, backing up Elasticsearch, and dynamically expanding local storage (including Elastic Local Volume).
There are three types of nodes in an Elasticsearch cluster: Controller, Data, and Client. Each type of node performs a specific task in the group.
The controller node handles cluster-wide operations like adding and removing nodes, creating indices, and choosing new controller nodes as needed. The data node stores the data and executes queries on it, and the client node forwards cluster requests to the controller node and data-related requests to the data nodes.
Besides being highly scalable, Elasticsearch offers several features to make it more robust and resilient to hardware failures. For example, it provides data redundancy by distributing the documents across several shards or replicas of the index.
In addition, it has a graphical user interface called Kibana that lets you create dashboards and perform real-time analytics. This graphical user interface is available for free. There is also a Helm chart that enables you to deploy Kibana and Elasticsearch on Kubernetes with ease.
Monitoring the Cluster
When you scale elastic search clusters with Kubernetes, monitoring the entire collection and its resources is essential. This helps you determine whether your group is functioning correctly and at a suitable capacity and how it uses cloud resources.
Monitoring tools and reports provide insights and visibility into your data, allowing you to optimize your cluster health, performance, and security configurations. They also offer alerting capabilities, which can be used to notify your team about events that may impact your business.
Some monitoring tools monitor specific cluster and application metrics, while others provide more comprehensive views of a cluster’s state. The most comprehensive tools give a high-level view of all aspects of your group, from nodes and pods to containers and applications.
Among the most important metrics to monitor are those that reflect overall resource utilization at multiple layers of your cluster, including nodes, pods, and containers. They also help you understand when resources are reaching their limits, which can significantly impact your applications.
For example, it is essential to monitor node-level memory usage and if pods are running on the same node. This will help you to know if you need to add more nodes or if you should evict pods from an already-constrained node before they reach their limits.
Other important cluster metrics include API request latency, measured in milliseconds. This metric can help you track if user-initiated requests to the API server need to catch up. This can indicate a problem with the API server, affecting your end users’ experience and causing you to incur additional costs.
