Kubernetes in Production: Requirements and Critical Best Practices

What Is Kubernetes in Production? 

Kubernetes in production refers to deploying and managing containerized applications using Kubernetes at a scale suitable for live, user-facing workloads. It involves more complex considerations than development environments, including reliability, security, scalability, and monitoring. 

Production-level deployments require a solid infrastructure setup, comprehensive monitoring tools, and strategies to ensure high availability and disaster recovery. 

Running Kubernetes in production involves orchestrating containers across multiple host machines, managing the lifecycle of applications, and ensuring that resources are used efficiently. It demands attention to details like network configuration, storage solutions, and security policies. 

This is part of a series of articles about Kubernetes architecture

In this article:

What Is Needed to Run Kubernetes in Production? 

Here’s an overview of the requirements for running Kubernetes in production environments.

Infrastructure Requirements

To run in production, Kubernetes needs a robust and scalable infrastructure. This includes having enough machines or cloud instances to ensure redundancy and high availability. It also requires a network configuration that supports inter-container communication and external access, as well as compatibility with the relevant storage solutions. 

The infrastructure must be able to scale easily with demand without compromising performance or security. Compatibility with the runtime environment, whether it’s on-premises data centers or public clouds (e.g., AWS, Google Cloud, Azure), is also important.

Cluster Configuration and Management

Efficient cluster configuration and management involves setting up master and worker nodes correctly, optimizing pod scheduling, and configuring the right network policies for secure communication between services. Automated tools and scripts for cluster deployment and management help maintain consistency and reduce human errors.

Additional considerations include keeping the Kubernetes software up to date, managing resource limits, and applying best practices for scalability and fault tolerance. Using Kubernetes services, such as auto-scaling and self-healing, ensures that the applications remain available and performant under varying loads.

Storage and Persistent Data Management

For applications requiring persistent data, Kubernetes needs to integrate with storage systems that support dynamic provisioning, snapshots, and backups. Using persistent volumes (PVs) and persistent volume claims (PVCs), Kubernetes can manage container storage needs flexibly, allowing applications to maintain state across restarts and redeployments.

The storage solution must meet the performance and availability requirements of the application. This could be a cloud-based storage service, network-attached storage (NAS), or block storage.

Security Measures

Security in production Kubernetes environments encompasses network policies, pod security policies, role-based access control (RBAC), and secrets management. Implementing strict controls over who can access the cluster and what actions they can perform is critical to prevent unauthorized access and potential breaches.

Additional considerations include encrypting data at rest and in transit, scanning images for vulnerabilities, and using namespaces to isolate workloads. Regular updates and patches to the Kubernetes platform and applications further mitigate security risks.

Monitoring and Logging

Comprehensive monitoring and logging are essential for troubleshooting and maintaining operational efficiency in production environments. Tools like Prometheus for monitoring and Fluentd for logging provide insights into the health and performance of applications, as well as the underlying infrastructure.

Effective monitoring involves tracking key metrics such as CPU, memory usage, and network throughput, while centralized logging enables quick identification and resolution of issues. Alerts based on predefined thresholds help in proactively addressing problems before they affect users.

Learn more in our detailed guide to Kubernetes monitoring 

Backup and Disaster Recovery

Implementing backup and disaster recovery strategies ensures data protection and high availability. Regularly scheduled backups of the cluster state and persistent data protect against data loss, while replication across multiple zones or regions guards against regional outages.

Disaster recovery planning involves defining recovery point objectives (RPO) and recovery time objectives (RTO) to minimize downtime and data loss during outages. Automated failover and redundancy mechanisms enable a quick recovery and continuity of service.

Key Challenges You Might Face When Running Kubernetes in Production 

Here are some of the challenges involved in running Kubernetes in production environments.

Complexity

Kubernetes’ Its flexible and powerful architecture comes with a steep learning curve and requires expertise in container orchestration, networking, and security. This complexity can lead to misconfigurations, performance issues, and security risks.

Managing a Kubernetes cluster involves numerous components and interdependencies. Keeping the system running smoothly demands continuous monitoring, updates, and adjustments.

Cost Management

Managing costs in a Kubernetes environment is challenging due to the dynamic nature of containerized workloads and the underlying infrastructure costs. Effective cost management involves optimizing resource allocation, scaling resources based on demand, and selecting the appropriate instance types or VM sizes.

Implementing policies for resource requests and limits, utilizing spot instances or preemptible VMs for non-critical workloads, and monitoring for unused or underutilized resources can help control costs. Tools like Kubernetes autoscaling and cost-monitoring platforms assist in achieving cost efficiency.

Kubernetes Upgrades and Compatibility

Upgrading Kubernetes clusters can be a complex task. Carrying out the upgrade independently can be labor intensive, and even if the upgrade is carried out automatically by a cloud provider, there might be compatibility issues with existing applications. The intricacies of interdependent components, third-party tools, and different resource types require thorough testing before an upgrade. Maintaining an accurate inventory of your cluster’s applications and dependencies ensures you can anticipate possible upgrade impacts.

To navigate these challenges, it’s crucial to stay informed about deprecations and new features in upcoming releases. Make use of staging or testing environments that replicate your production cluster for upgrade rehearsals. This allows you to identify and fix issues in a controlled setting before applying them to live environments.

Secrets Management

Properly managing secrets, such as passwords, tokens, and keys, within Kubernetes is challenging but crucial. Secrets need to be securely stored, accessed, and managed to protect sensitive information and ensure the security of applications. Native Kubernetes secrets are not encrypted by default, requiring additional steps to secure them.

Integration with external secrets management systems like HashiCorp Vault or AWS Secrets Manager can provide enhanced security features. However, configuring and managing these integrations adds complexity and requires careful planning and implementation.

Log Management

In a distributed system like Kubernetes, log management becomes complex due to the volume of logs generated by numerous pods and services. It’s important to centralize, index, and analyze these logs for debugging and monitoring the health of applications and the cluster.

Tools like Fluentd, Elastic Stack (ELK), or Splunk can help aggregate logs in a centralized location, but setting up and maintaining these systems in a scalable and cost-effective manner requires effort and expertise. Automated log rotation and retention policies are also useful to manage storage costs and compliance requirements.

Best Practices for Kubernetes in Production 

Here are some of the measures that organizations can take to ensure the success of their Kubernetes deployments in production environments.

1. Implement Infrastructure as Code (IaC)

Using IaC for Kubernetes infrastructure ensures consistency, reproducibility, and automation. Tools like Terraform, Ansible, and CloudFormation enable defining and managing infrastructure through code, which reduces manual errors and simplifies deployment processes.

IaC facilitates collaboration among teams, version control of infrastructure changes, and efficient scaling of resources. Automating the provisioning and management of Kubernetes clusters through IaC enhances operational efficiency and security.

2. Implement High Availability (HA)

High availability is crucial for production Kubernetes environments to avoid single points of failure and ensure the continued availability of applications to end-users. This involves deploying Kubernetes masters in a multi-master setup, utilizing multiple worker nodes across different availability zones or regions, and implementing load balancing.

HA configurations help in achieving fault tolerance and reducing downtime during outages, maintenance, or upgrades. Cloud provider features and third-party tools can simplify the setup and management of HA Kubernetes clusters.

3. Automate Deployments with CI/CD Pipelines

Adopting CI/CD pipelines for Kubernetes deployments automates the process of building, testing, and deploying applications, leading to faster release cycles and reduced human errors. Tools like Jenkins, GitLab CI, and Spinnaker integrate seamlessly with Kubernetes, supporting containerized workflows and dynamic environments.

CI/CD pipelines also encourage best practices like version control, automated testing, and blue-green or canary deployments. This automation aids in scaling DevOps practices and improving application quality.

4. Implement Role-Based Access Control (RBAC)

RBAC helps manage access to Kubernetes resources securely. It allows fine-grained control over who can access what resources and perform specific actions within the cluster. RBAC policies ensure that only authorized personnel can make changes, enhancing security.

Using RBAC in conjunction with namespaces isolates workloads and reduces the risk of unauthorized access or impact to other parts of the cluster. Regular audits of RBAC policies and roles can help maintain an optimal security posture.

5. Monitor Rigorously and Centralize Logs

Comprehensive monitoring and centralized logging enable visibility into the performance and health of Kubernetes clusters and applications. Utilizing tools like Prometheus for monitoring and Fluentd or Elastic Stack for logging helps detect issues early and troubleshoot efficiently.

Setting up dashboards and alerts based on key metrics and logs ensures that teams can quickly respond to anomalies or performance issues. Regular reviews and optimization of monitoring and logging practices help enhance operational insight and incident response capabilities.

6. Leverage Persistent Volumes for Stateful Applications

Stateful applications in Kubernetes require persistent storage to save data across pod restarts and redeployments. Persistent volumes (PVs) and persistent volume claims (PVCs) provide a way to abstract storage needs and integrate with various backend storage options.

Selecting the appropriate storage class and properly configuring PVs and PVCs ensure that stateful applications can manage data reliably and efficiently. Regular backups and testing of data persistence mechanisms are important for data integrity.

Learn more in our detailed guide to Kubernetes persistent volume 

7. Plan Kubernetes Upgrades to Minimize Downtime 

IN advance of a Kubernetes upgrade, create a staging environment that mirrors the production cluster. This allows you to conduct test upgrades safely, ensuring your applications and workloads are compatible with the new version. Review the Kubernetes release notes for potential breaking changes and deprecations. Address deprecated features and APIs in your cluster to ensure continued functionality.

Start by upgrading individual components like the control plane (masters) first while maintaining a backup. Proceed with worker nodes after verifying that the control plane is stable. When possible, utilize rolling updates to incrementally update worker nodes, ensuring workloads are redistributed smoothly across nodes. Carefully monitor for errors and system health throughout the upgrade to detect and resolve issues promptly.

Automating Kubernetes Infrastructure with Spot by NetApp

Spot Ocean from Spot by NetApp frees DevOps teams from the tedious management of their cluster’s worker nodes while helping reduce cost by up to 90%. Spot Ocean’s automated optimization delivers the following benefits:

  • Container-driven autoscaling for the fastest matching of pods with appropriate nodes
  • Easy management of workloads with different resource requirements in a single cluster
  • Intelligent bin-packing for highly utilized nodes and greater cost-efficiency
  • Cost allocation by namespaces, resources, annotation and labels
  • Reliable usage of the optimal blend of spot, reserved and on-demand compute pricing models
  • Automated infrastructure headroom ensuring high availability
  • Right-sizing based on actual pod resource consumption  

Learn more about Spot Ocean today!