Deploying Cassandra on Kubernetes – A Step-By-Step Guide

Berry Mathew

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Deploying Cassandra on Kubernetes - A Step-By-Step Guide

Cassandra is a distributed database built for redundancy and scalability. Its flexible architecture, and powerful features allow it to run on multiple machines in a ring, ensuring that data is always accessible.

This guide will walk you through deploying Cassandra on Kubernetes using a StatefulSet and a dynamic, persistent volume. The resulting cluster will be self-healing and able to grow or shrink without disrupting the customer experience.

Install Cassandra on Kubernetes

Cassandra is a distributed database designed for redundancy and scalability. It uses a shared-nothing architecture, so each node has its memory and storage run in its container. It provides fault tolerance and allows you to scale your Cassandra cluster without losing data. Kubernetes (K8S) is a container-based platform for running, managing, and scaling distributed applications. It is a container-based platform that provides a management layer for cloud-based infrastructure. K8S provides a stateful set abstraction that makes it easy to manage and scale stateful applications such as Cassandra.

K8S and Cassandra support elasticity by adding or removing nodes from a cluster based on demand. Cassandra also has a replication strategy that lets you replicate data across multiple nodes for disaster recovery and high availability.

Running Cassandra on K8S requires you to be familiar with the open-source Kubernetes cluster command line tool kubectl and the cloud-based instance of the open-source platform you use for your development environment (GKE, EKS, or PKS). Once a working k8s cluster is connected to your local machine, you can apply cass-operator configuration YAML files.

The YAML files contain the definitions that tell k8s how to run your Cassandra cluster. They include information about the database, such as the cluster name, storage class, and data center. K8s uses this configuration to create a class-operator deployment object added to the StatefulSet that manages the Cassandra pods in your cluster.

Monitor Cassandra

Cassandra is a distributed database system that blends NoSQL’s ease of use with the reliability of mature open-source software. It has high scalability, no single point of failure, and a multi-cloud deployment agnosticism that allows optimal performance no matter where your data is hosted. The primary function is to manage large amounts of data using ordinary servers effectively.

When deployed on Kubernetes, Cassandra can run as a stateful service or a persistent cluster. A stateful service is designed to hold continuous data across server outages, while an ongoing group offers greater availability and durability than a stateless application.

Using Cassandra on Kubernetes requires monitoring its performance to ensure your business can meet client demand. A critical performance metric is the rate Cassandra can respond to read requests. A low read rate may indicate a bottleneck and require adding additional capacity to the cluster. You should also monitor the number of exceptions thrown by Cassandra. These errors can indicate that data needs to be backed up properly, resulting in lost information or unrecoverable data loss.

To monitor Cassandra, you can use native and third-party tools. One option is to create a service that performs a DNS lookup between Cassandra pods in the same cluster. To do this service, run the following command in a minikube container:

Configure Cassandra

Cassandra is a highly available distributed database that merges the ease of use with the reliability of a mature open-source project. Its architecture enables data and applications to be deployed across multiple machines while maintaining a unified identity. This granular control allows software developers to optimize the platform for ideal performance, availability, and security.

In contrast to relational databases that rely on a centralized architecture and table-based data, Cassandra offers a more flexible model that supports NoSQL querying languages and scales horizontally across clusters. The distributed structure provides numerous advantages to businesses, such as reducing costs by eliminating the requirement for multiple copies of data. Additionally, it effectively prevents any potential downtime due to a single data center outage.

To set up a Cassandra cluster, the config file for each node contains a section called “- seeds.” It is a comma-delimited list of internal IP addresses where the nodes will reside within your cluster. When a new node joins the set, it reads this seed list and bootstraps the discovery of its neighbors.

Then, you’ll create a headless service in Kubernetes to handle all incoming connections to the Cassandra pods. To complete this task, kindly navigate to your configuration files directory. 

Deploy Cassandra

Cassandra is a NoSQL database system built to handle massive amounts of data. It was first developed for Facebook and was then open-sourced and became an Apache project (now maintained by the Americal non-profit Apache Software Foundation) in 2008. This solution is versatile and can be deployed on-premises, in the cloud, or combination. It is scalability, and distributed design allows it to expand across multiple data centers. It also has a built-in redundancy feature, so if one node fails, the data is still available on other nodes for retrieval at a later time.

To use Cassandra with Kubernetes, you must ensure that the data and application operations can operate close together without experiencing latency or waiting for data to move between different servers. It can be achieved by using a data operator.

A Cassandra data operator is a unique Kubernetes object that simplifies how Cassandra and other databases work with the platform. It helps remove some of the complexities of running a Cassandra cluster on Kubernetes, such as manual maintenance. Instead, a Cassandra operator manages the configuration and placement of Cassandra nodes in your set. It means you can avoid manual deployment and configuration tasks and free up time to focus on more critical studies.

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