As containers have taken hold as the standard method of developing and deploying cloud-native applications, many organizations are adopting Kubernetes as the solution they use for container orchestration. A recent Cloud Native Computing Foundation (CNCF) survey showed that 96% of respondents were using or evaluating Kubernetes and 93% of respondents are using Linux containers in production environments. In other words, containers and Kubernetes are becoming prevalent, particularly in emerging technology hubs such as Africa, where many are pursuing Kubernetes deployments, 73% of which are in production. And it’s not only emerging companies adopting these technologies, but large companies as well – frequently even more than smaller ones. But does that mean that these companies are following the basics of Kubernetes best practices? Sometimes…
All too often, organizations rush Kubernetes adoption without fully understanding the complexities inherent in deploying it successfully. Many teams are just beginning to understand the control plane, what the scheduler does, how nodes work in Kubernetes and what the kubelet does, what daemonsets are, and how Kube changes the software development lifecycle. Understanding Kubernetes clusters, which metrics to track with Kube instrumentation and how to do that tracking, and troubleshooting to discover the root cause of an issue all remain a challenge for most organizations.
Whether you’re still considering when and how to implement Kubernetes to deploy SaaS solutions and other apps and services, or you already have K8s in place, it’s never too late to apply Kube best practices by adopting a monitoring system and establishing monitoring metrics to help you create processes, clarify tasks, and set your priorities. The CNCF guides many open source projects to make it easy to adopt Kube and optimize cloud native architectures.
Prometheus is an open-source systems monitoring solution that collects and stores metrics as time series data and provides alerting. Monitoring tools and monitoring solutions are critical to helping DevOps teams troubleshoot issues in distributed systems. So, let’s take a step back and look at the top five Kubernetes practices that you need to focus on today to help you maximize the long-term value K8s can provide.
Security is always a critical component of technology, and Kubernetes is no exception. One common misunderstanding people have is in thinking that K8s is secure by default. That sounds great, but it simply isn’t true. Kubernetes manages how stateless microservices run in a cluster by balancing velocity and resilience, which can enable developers more flexibility in how they deploy software. However, those benefits don’t come without security risks if you don’t have the right governance and risk controls in place in your Kubernetes deployments.
When your K8s deployment is running smoothly, you may think that everything is also configured correctly. Unfortunately, over-permissioning is an easy way to get something you’re struggling with to work. Giving root access can solve a lot of challenges, while also exposing your organization to a denial-of-service (DoS) attack or security breach. In fact, misconfigurations areone of the most challenging concepts in Kubernetes environments. Even minor misconfigurations, particularly containers running with root-level access, are increasingly becoming a vulnerability that cyberattackers look for. These security configurations are not set by default in Kubernetes, they are settings that your security team must establish and then enforce through automation and policies.
Most organizations adopt Docker containers and container orchestration solutions because they are inherently efficient in terms of infrastructure utilization. Containerized environments, quite simply, allow you to run multiple applications per host — each within its own Docker container. That helps you reduce the overall number of compute instances you need and therefore also reduce your infrastructure costs without sacrificing functionality or application performance.
Kubernetes dynamically adapts to your workload’s resource utilization and allows automatic scaling (using a Horizontal Pod Autoscaler or HPA) and cluster scaling (using Cluster Autoscaler) to provide scalability. Kubernetes allows you to set resource requests and limits on your workloads so you can maximize infrastructure utilization but also ensure that your application performance is smooth. Sounds great, right? Only if you set your Kubernetes resource limits and requests correctly. If your memory limits are too low, K8s will kill your application for violating its limits, but if you set your limits too high, you’ll over-allocate resources — in other words, you’ll pay more than you need to. Figuring out the right resource limits and requests is challenging, both for new adopters of Kubernetes and for organizations that have been using Kube for years.
Reliability is always going to be the goal, but achieving Kubernetes reliability is a complex undertaking. It takes skill to optimize Kubernetes, particularly if you are using technology that predates cloud-native applications and configuration management tools, which don’t always offer the most reliable cloud-native experience. Many organizations continue to use older solutions and layer Kubernetes on top of that, but this makes optimization, reliability, and scalability even harder to achieve, particularly as your business scales. An excellent way to ensure the reliability of your clusters is to shift to using Infrastructure as Code (IaC), which helps you to reduce human error, increase consistency and repeatability, improve auditability, and make disaster recovery easier. It can also help you resolve performance issues in your Kubernetes workloads.
One common approach to adopting Kubernetes is to pilot it with a single application, which is an excellent way to get started. But once your organization commits to using Kube across multiple applications, development teams, and operation teams, it becomes challenging to manage cluster configuration for workloads that are inconsistently deployed. When your teams don’t have guardrails on how to deploy applications and services, you’ll quickly find that you have discrepancies in configurations across your containers and clusters. These discrepancies can be challenging to identify, correct, and keep consistent. It’s quite difficult to manually identify these misconfigurations.
To manage multi-cluster environments, you need to establish Kubernetes policies to enforce consistent security, efficiency, and reliability configurations. While policies can enable best practices across the board, some may be specific to your organization or environment. A best practices document seems like a good way to manage these policies, but it’s likely to fall by the wayside fast. Adopting Kubernetes policy enforcement tools can help you prevent common misconfigurations from being released, enable IT compliance, and empower your teams to ship with confidence — because they know that guardrails are in place to enforce your policies.
Kubernetes monitoring configurations are frequently an afterthought — many organizations don’t think about setting them up until something goes wrong. But optimizing Kubernetes monitoring and alerting can help you ensure that your infrastructure and applications are up and running, which requires you to use the right tools to optimize your monitoring capabilities. Observability goes hand in hand with monitoring, because the ability to observe your system in real-time helps you correlate data to report on the health of a system, monitor key metrics, debug production environments, and stay ahead of outages. Kubernetes monitoring tools, particularly ones that offer a Kubernetes dashboard that tracks Kubernetes pods, Kubernetes nodes, and Kubernetes services are part of a comprehensive monitoring strategy. A few critical Kubernetes metrics to track in a Kubernetes platform include:
Memory usage
Namespaces
Resource metrics
Resource usage
Resource utilization
Memory allocation
CPU usage
Tools like Grafana can help you with visualization of application metrics and application performance. Using open source tools can help you avoid lock-in to a single cloud provider, such as AWS, Azure, Google Cloud Platform, and others. Integrations can help end-users improve kube usage and reduce latency to optimize scalability, minimize restarts, and improve user experience. Monitoring the Kubernetes API server enables teams to have visibility into the communication between the cluster components.
For most teams, that means you need to think about what needs to be monitored and why — identifying what Kubernetes monitoring best practices look like for your organization. Understanding which configurations are risky or wasting resources, identifying security and compliance risks early, and uncovering misconfigurations before deployment can help you resolve issues early and prevent many possible problems.
Security, cost optimization, reliability, policy enforcement, and Kubernetes monitoring and alerting are complicated. While Kubernetes offers many capabilities that organizations are increasingly adopting and taking advantage of, they also require your deployments to work well at the deployment level and the cluster level alike. It can be hard for many teams adopting Kube, or even those that already have it in place, to know where to start when it comes to implementing these Kubernetes best practices.
Kubernetes can enable your organizations to increase the utility and productivity of your containers and build cloud-native applications that can run anywhere. To maximize your Kubernetes implementation, it’s essential to ensure that you’re following these five Kubernetes best practices. With the right technology and Kubernetes guardrails in place, you’ll be able to deliver on the promise of building scalable, efficient cloud-native applications that can run reliably and securely anywhere, independent of cloud-specific requirements.
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