It’s fair to say that Kubernetes has done wonders for containerization, becoming a crucial component of organizations’ digital transformation strategies.

Kubernetes goes some way to easing the burden of managing various container clusters, while its ease-of-use minimizes the time taken to get containers up and running.

Yet getting to grips with Kubernetes can still be remarkably complex: developers buckle under the weight of 3000-line configuration files and week-long debugging sessions. In fact, according to research from the Cloud Native Computing Foundation, complexity topped the list of developer’s challenges in deploying and using containers.

Enter Autopilot, the fully-managed Kubernetes offering from Google that includes auto-scaling, auto-upgrades, maintenance, and automated Day 2 operations. With Autopilot, Google aims to tackle the complexities of running Kubernetes, extending management beyond the control plane to the nodes themselves. In essence, it’s a fully “hands-off” way of managing Kubernetes environments.

From Manual to Automated to Autonomous

Google Autopilot represents the next phase of digitization.

Technology has largely followed a set pattern: starting off with manual processes, through to automated ones – those that operate without external control or intervention, and finally, truly autonomous operations – those that operate independently of any outside input.

Autopilot fits firmly within the final category, aiming to make many automated tasks in IT operations completely autonomous. Autopilot eliminates and simplifies Day 2 operations of node management, removing the need for developers and engineers to monitor the health of their nodes or calculate the amount of compute capacity that their workloads require. All of which frees up IT departments from time-consuming administrative tasks, helping them to accelerate innovation.

This point is particularly important: As businesses adapt to the rigors of digital transformation, the greater agility that autonomous processes will afford will become critical to meeting the ever-changing needs of customers.

Couchbase + Autopilot: A Perfect Partnership

Given that Couchbase is among Kubernetes’ earliest adopters and innovators, Autopilot has proven to be a natural fit for our own strategy.

Ever since the launch of our first Autonomous Operator for Kubernetes in 2018, our goal has been to transform database management from a manual process to a truly autonomous one. Just as Autopilot eliminates and simplifies Day 2 operations of node management, Couchbase Autonomous Operator manages all of Day 2 operations of using Couchbase clusters and their nodes, all while implementing best practices for operating and securing them.

In other words, Autopilot complements the aims of Autonomous Operator perfectly, and we anticipate that as Kubernetes users become more familiar with Autopilot, their experiences will help us evolve Autonomous Operator even further.

Couchbase is on the same journey as Google to tackle the complexities of running a distributed database on Kubernetes. The Couchbase Autonomous Operator now includes advanced features such as auto-scaling Couchbase services, automated rolling/bulk upgrade, high availability, and automated Day 2 operations.

Auto-Scaling Couchbase Services

Couchbase’s auto-scaling monitors your cluster and automatically adjusts capacity to maintain steady and predictable performance based on the pre-defined thresholds for all Couchbase services. As a result, organizations can provide a consistent experience with no unexpected costs during peak times – which might otherwise occur with unchecked scaling or in the absence of cluster auto-scaling.

High Availability across Distributed Infrastructure

Leveraging Kubernetes labels, the Autonomous Operator can automatically schedule pod creation across failure domains (cloud availability zones) and ensure that they get added to the correct Couchbase server groups for rack/zone awareness.

Combined with cross data center replication (XDCR) and security (TLS) support, the Autonomous Operator can automatically and securely recover a Couchbase cluster, even after the largest of physical infrastructure failures, all while remaining available to your customers and applications.

Automated Day-2 Operations

The Couchbase Autonomous Operator eliminates and simplifies Day-2 operations of database management, such as self-healing, security, backup, and data replication. In addition, it fully manages one or more Couchbase deployments so that you don’t need to worry about the operational complexities of running Couchbase.

Not only does the Operator automatically administer your Couchbase cluster, but it also manages the cluster according to Couchbase best practices. All of which frees up IT departments from time-consuming administrative tasks, helping them to accelerate innovation.

Not Just a Nice-to-Have

Although many technologies remain firmly within the “manual” or “automated” phases, changing patterns of customer demand will soon force them to evolve. After all, digital transformation has accelerated substantially over the last 12 months. This pace looks set to continue.

Developers, engineers and the wider IT department will find it increasingly difficult to balance their day-to-day responsibilities with new innovation requirements. There’s never been a greater need for technology that removes as much of the administrative burden as possible, which makes both Couchbase Autonomous Operator and Google Autopilot the standard for others to emulate.

Author

Posted by Anil Kumar, Director Product Management, Couchbase Server

Anil Kumar is the Director of Product Management at Couchbase. Anil’s career spans more than 15+ years building software products across various domains, including enterprise software, mobile services, and voice and video services. He is a hands-on product leader responsible for Couchbase Server, Couchbase Cloud, and Kubernetes product lines, including evangelizing the product strategy and vision with customers, partners, developers, and analysts. Before joining Couchbase, Anil spent several years working at Microsoft Redmond in the Entertainment division and most recently in the Windows and Windows Live division. Anil holds a master’s degree in computer science from the University of Toronto (Canada) and a bachelor’s in information technology from Visvesvaraya Technological University (India).

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