Navigating Kubernetes: First Look at Workload Considerations and Best Practices

Picture of Stefan Tsankov
Stefan Tsankov
DevOps and Cloud Engineer
Reading time: 3 mins.
Last Updated: 03.01.2024

Table of Contents


Opposite of my Hardcore Kubernetes blog series, the Navigating Kubernetes one starting with this blog post will instead focus on general overview, outlines and guidance as embarking on the Kubernetes journey can be overwhelming, with ongoing debates about what is and isn’t suitable for Kubernetes management. In this blog post, we will explore the nuances of the choices while also delving deep into some of the best practices for configuring and managing your Kubernetes environment.

Kubernetes: A Versatile Platform Beyond Just Containers

Kubernetes has evolved to be more than just a platform for running containers. Its real power manifests in its ability to manage a diverse range of resources, whether or not they reside within the same cluster. An example is Argo CD, which can manage resources from AWS EC2 instances to Azure database schemas, showcasing Kubernetes’ adaptability.

Navigating Kubernetes Picture

Backend Applications and APIs: Laying the Groundwork

To comprehend Kubernetes’ utility, it’s pivotal to explore backend applications and APIs. Systems often build APIs on top of backend applications, leading to a proliferation of APIs. Implementing an API Gateway or transitioning to an event-driven architecture becomes imperative. In such architectures, components operate in a decoupled manner, communicating via events.

The Untapped Potential of Kubernetes Controllers

A common misconception is that Kubernetes is only suitable for stateless applications. However, the introduction of new controllers means that a plethora of workloads, including databases, can be efficiently managed by Kubernetes, dispelling such notions.

The Only Real Limitation: Data

Kubernetes’ primary limitation lies in data representation. Workloads that can’t be encapsulated as data might not be suitable candidates. However, most functionalities can be converted into a declarative format such as YAML, broadening Kubernetes’ capabilities.

Examples and Considerations

To discern if a workload aligns with Kubernetes, inquire:

Can the workload be depicted as data?

Is there an available Custom Resource Definition (CRD) and a controller for this workload?

Affirmative answers indicate Kubernetes’ capability to manage the workload efficiently.

Diving into Best Practices for Kubernetes Management

1. Infrastructure as Code (IaC):

IaC is a cornerstone for cloud infrastructure management. Tools like Terraform and Pulumi enable deploying Kubernetes clusters along with networking, load balancers, DNS configuration, and an integrated Container Registry. By specifying infrastructure declaratively as code, deployments become consistent, reliable, and repeatable, delivering stable environments promptly and at scale.

2. Monitoring & Centralized Logging

Implementing monitoring solutions like Prometheus and Grafana is vital for observing both the platform and applications. Effective monitoring can preempt issues such as expired certificates or node memory overcommit causing an outage. Centralized logging solutions like Fluentd or Filebeat channel logs to platforms like ElasticSearch, ensuring traceability and standard monitoring for all applications without additional developer effort.

3. Centralized Ingress Controller with SSL Certificate Management:

A centralized Ingress Controller, such as Nginx, optimizes traffic management. Coupled with cert-manager, it automates HTTPS certificates management using Let’s Encrypt, wildcard certificates, or a private Certification Authority. This ensures that all incoming traffic is automatically encrypted and directed to the correct Kubernetes pods, freeing developers from manual configurations.

4. Role-Based Access Control (RBAC):

Implementing RBAC is pivotal for securing Kubernetes access. Integrating with IAM solutions like Keycloak, Azure AD, or AWS Cognito enables centralized authentication and authorization. By defining roles and groups, users access resources based on their team or role, adhering to the principle of Least Privilege.

5. GitOps Deployments:

GitOps platforms like ArgoCD and Flux facilitate declarative state management. Git becomes the single source of truth for the Kubernetes state, ensuring that all changes are traceable and automated. Manual changes in production are immediately rolled back, ensuring consistency.

6. Secret Management:

Proper secret management ensures secure injection of secrets into containers. Utilizing central vaults like Azure Key Vault, Hashicorp Vault, or AWS Secrets Manager, with an operator like External Secrets Operator, ensures that secret references are stored in GIT and are accessible only as per necessity.

In Conclusion: Synthesizing Best Practices and Workload Suitability

Establishing a managed Kubernetes cluster is simple, but optimizing it necessitates expertise. By adhering to best practices like IaC, monitoring, RBAC, GitOps deployments, and secret management, one ensures a foundation of stability and security.

In essence, almost anything can run in or be managed by Kubernetes if it can be represented as data and there’s a corresponding CRD and controller. Kubernetes’ uniform API standardizes interactions, making it a versatile choice for a diverse range of applications.

Understanding these principles ensures a smooth Kubernetes journey, avoiding potential pitfalls and ensuring efficient workload management. Future discussions will delve into other considerations such as ServiceMesh, Security scanning, and end-to-end traceability.

Thank you for reading, and stay tuned for more insights in the next installment!

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