Discover the best practices for managing Kubernetes at scale, including methodologies and tools.
Kubernetes is often considered a complex technology with a relatively steep learning curve. These problems are multiplied when you start to deploy Kubernetes workloads at scale.
Effectively managing Kubernetes at scale is essential so you can get the most out of the platform. Kubernetes is designed to run expansive applications in production. However, the challenges and complications involved aren’t always obvious up front, meaning you might overlook problems that prevent cluster usability. Fortunately, there are ways to address this complexity.
In this article, you’ll learn some of the best practices for managing Kubernetes at scale, including methodologies and tools you can use when working with Kubernetes deployments of any size. These will help you to successfully run large-scale applications in your clusters.
Running Kubernetes “at scale” often has a subtly different meaning across organisations. The concept of scale usually applies to large applications and clusters. The practices discussed below help you manage services that are split into several pods and that run across multiple Kubernetes nodes. You could even have more than one Kubernetes cluster to control, adding another layer of complexity.
Although Kubernetes is designed for these kinds of distributed workloads, running them successfully is more involved than starting individual pods in single-node clusters. When you’re deploying applications at scale, you need to consider the overall stability of the resulting environment, as well as the consistency with which you can create and maintain each component.
Cluster management at scale also touches on adjacent challenges. When you’ve got clusters spanning a fleet of nodes, there are usually entire engineering teams dedicated to supporting them. Your infrastructure needs to handle cross-disciplinary collaboration without creating security risks due to overprivileged user access.
The difficulties associated with Kubernetes at scale can impact each organisation differently, depending on how many clusters you have, the ways in which they’re accessed, and the requirements of your individual workloads.
The most common issue is the ever-growing maintenance burden. The more assets you have (whether they're apps, nodes, or clusters) the more work you’ll have in keeping them all running reliably. This can eventually become unmanageable in larger organisations.
Large rosters of Kubernetes resources increase the risk of errors during day-to-day admin activities. You could forget important connections between clusters or workloads, unintentionally causing downtime or regressions. Upgrades can take longer to complete, and it’s harder for new team members to learn your Kubernetes landscape.
Even routine tasks like listing your deployed applications can become taxing when you’ve got several clusters to interrogate. Organisations that organically grow their use of Kubernetes can end up using several tools to achieve the same goal. This unmanaged approach to scaling is unwieldy, inefficient, and increasingly difficult to unpack and migrate away from.
The best way to manage Kubernetes at scale is to take a holistic and intentional approach. Be deliberate in your strategy and clearly document the tools you use, how they integrate, and what steps operators should take to achieve common tasks.
Acknowledging the complexities of scale early on is important. Try involving different stakeholders—such as developers, operators, and project managers - to understand their pain points and what they need to achieve using the system.
Deliberate management is more likely to stay effective over time. Focus on the fundamental must-haves for controlling your clusters, instead of micromanaging the details early on. The following are four significant high-level principles to consider.
Inconsistency tends to spawn complexity. Consolidating management activities around a set of standard tools is an effective way to promote consistency, making it less likely you’ll run into the challenges outlined above.
When workloads are consistent, team members can anticipate their requirements even if they haven’t worked with a particular component before. This also helps operators identify when errors have occurred in the cluster. Adopting standardised templates and tools makes it more obvious when drift occurs, so teams can more quickly restore the cluster to the desired specification.
To achieve consistency, you should use the same procedure each time you create a cluster, deploy a workload, or modify an existing component. These sequences should be documented somewhere that’s accessible to all your operators, ensuring each stakeholder performs tasks in the same way. This helps to centralise knowledge, guaranteeing resiliency if a particular team member leaves the business.
Kubernetes deployments should ultimately satisfy the needs of the developers and operators responsible for your services. These team members usually benefit from being given autonomy. For example, developers who can directly retrieve logs from production Kubernetes clusters might be able to spot and resolve bugs more quickly. Empowering team members to perform tasks on their own terms is often referred to as user enablement.
Unfortunately, scale tends to bring challenges in this area. Orchestrating permissions for hundreds of users across thousands of Kubernetes resources is an administrative burden that can carry a substantial risk of error.
You can facilitate user enablement at scale by using automated tools to roll out centralised policies across your environments. Integrating with existing authentication services and identity providers is one way to keep roles synchronised in each situation. However you implement your strategy, the most important part is ensuring users have self-service access to data as they need it, without compromising the ability of administrators to monitor and revoke permissions.
Security becomes more critical as the scale of your deployments grows. More resources means more endpoints to protect, raising the risk of oversight and compromise. Threats can come from multiple angles, such as a private service that’s unintentionally made public, or forgotten credentials that are leaked outside your organisation.
Even when allowing for user enablement, you should always lock your cluster down as thoroughly as possible. Follow the principle of least privilege —in which users receive only the bare minimum set of permissions needed for their work—to reduce the impact of lost or stolen authentication tokens.
Regular security audits are important so you can find and address live vulnerabilities. When you’re operating clusters at scale, it’s unrealistic for audits to be carried out by humans. Automated tools give you more accurate results, including real-time detection of emerging threats. Security is still a human concern, though: another aspect to consider is the awareness of developers and operators. Providing training on common attack routes and their mitigations is one of the most effective long-term defences.
A comprehensive monitoring solution is essential for any large-scale Kubernetes environment. You need visibility into the workloads in your cluster, their health statuses, and the overall performance of your Kubernetes installation. Without these insights, you’re left guessing the cause when slowdowns or bugs occur.
As new workloads deploy to Kubernetes, the number of running containers and services only increases. The sheer component count means administrators can lose track of what’s deployed and where it’s running. An effective monitoring solution should enable you to answer these questions, offering an overall view of your entire digital landscape.
Cloud-native observability platforms like Prometheus, Grafana, Jaeger, and Fluentd extract data from your Kubernetes clusters so you can keep tabs on metrics, log messages, and other important events. Aggregating this information into a centralised location helps you gauge how your resources are used, uncovering opportunities to apply enhancements or scale back to avoid waste.
Wayfinder by Appvia is a single platform that can help you achieve the four best practices covered above. Wayfinder is a dedicated system for managing Kubernetes at scale, designed to simplify administration and provide powerful controls to users.
Wayfinder works with Kubernetes clusters deployed on all major public clouds, including Azure Kubernetes Service (AKS), Amazon Elastic Kubernetes Service (EKS), and Google Kubernetes Engine (GKE). It abstracts away the differences between them, leading to a naturally consistent and repeatable experience.
The system also incorporates self-service controls for teams. Individual users can interact with the resources they need, reducing the bottlenecks that occur when admins need to approve individual access requests. Users are still constrained by centralised policies applied to your Wayfinder instance.
Wayfinder addresses security concerns too, maintaining safety over the life of your cluster. It includes an automated role-based access control (RBAC) solution that grants appropriate permissions to each user while respecting the principle of least privilege. This helps administrators avoid manual application of RBAC rules, which is often burdensome and error-prone.
There are multiple issues you can face when you run Kubernetes at scale. From duplicated tools to unstable environments, an unplanned approach to scaling often causes frustration and makes it hard to sustain growth.
You can mitigate these problems by focusing on the four tenets of consistency, user enablement, security, and monitoring. These enable you to effectively oversee your Kubernetes clusters, assess where resources are allocated, and provide team members with the information they need so that you can scale your projects with confidence.
