The Production-Ready Kubernetes Service Checklist

Kubernetes has emerged as a powerful tool for managing and orchestrating containerized applications. It provides scalability and availability and manages workloads so you can focus on your software's core functionality. However, moving your application from a test environment to a production environment is not straightforward.

The purpose of this article is to list the checks we use before pushing an application to production.

Production-Ready Infrastructure

Running Kubernetes in production requires an infrastructure designed for high availability and resilience. Here are some key considerations.

High Availability With Multiple Master Nodes

• To prevent downtime if one master node goes down, use at least three spread across availability zones. The control plane components, such as the API server, scheduler, and controllers, should be replicated.
• Configure a load balancer in front of the master nodes. The load balancer will distribute requests across the masters and eliminate a single point of failure.
• Enable automated failover in your cloud provider or Kubernetes setup. If a master node fails, a new one can be automatically spawned to replace it.

Appropriate Node Sizing

• Size nodes according to your expected workload resource demands. Undersizing leads to insufficient capacity during spikes. Oversizing wastes resources.
• For nodes running critical system pods like ingress controllers and metrics servers, allocate more CPU and memory to provide headroom.
• Use auto-scaling groups and cluster autoscaler to add nodes automatically when certain thresholds are hit. This allows elastic scaling up and down.

Private Networking

• Place the Kubernetes cluster within a private subnet with no internet access. This reduces the attack surface area.
• API server access can be locked down to known IP ranges and secured further with authentication and authorization policies.
• Use private networking between nodes for intra-cluster communication. This prevents eavesdropping or tampering with traffic.

Security Measures

Security is critical for any production resources. Here are some key security measures to implement.

Role-Based Access Control (RBAC)

• Use Kubernetes RBAC policies to limit user access to only what is needed. Restrict broad permissions like cluster-admin.
• Create roles for developers, ops teams, etc., with narrowly scoped permissions.
• Continuously review and refine RBAC policies as teams need to evolve.

Network Policies

• Leverage network policies to restrict pod-to-pod and pod-to-external communication.
• Set default deny policies and selectively allow traffic as needed.
• Use namespace-level policies for broad security. Use pod-level policies for fine-grained control.

Encryption

• Enable etcd encryption at rest to secure Kubernetes secrets and sensitive data.
• Consider using a third-party service like Vault by HashiCorp to manage secrets.
• Encrypt data in transit using mTLS between Kubernetes components.
• Use a reverse proxy like Nginx for SSL/TLS termination at the edge.

Auditing

• Enable audit logging to track all API requests and user actions.
• Forward audit logs to an SIEM for monitoring and analysis.
• Alert on suspicious activities like high-risk RBAC permissions.

Scanning and Monitoring

• Continuously scan Kubernetes for misconfigurations using tools like kube-bench.
• Monitor clusters for threats and anomalies with solutions like Sysdig Falco.
• Remediate issues immediately to minimize risk exposure.

Efficient Logging and Monitoring

When operating Kubernetes in production, having robust logging and monitoring in place is critical for maintaining high availability and quickly troubleshooting issues. Here are some key elements to implement.

Cluster, Node, and Pod Monitoring

• Monitor CPU, memory, disk, and network usage for the Kubernetes cluster, nodes, and pods. This allows you to catch resource shortages or bottlenecks before they cause outages. Popular tools include Prometheus and Grafana.
• Track pod uptimes and restart counts. Frequent restarts may indicate instability.
• Set alerts for nodes down, key pods evicted, or pods restarting frequently. Get notified quickly when issues occur.

Log Aggregation

• Use a log aggregation tool like Elasticsearch, Fluentd, or Datadog to centralize and index logs from across cluster components. This provides a single place to search logs.
• Enable log collection at the node and pod level. Capture application logs as well as Kubernetes system logs.
• Add metadata like pod names and namespaces to logs to trace issues.

Alerting

• Set up alerting rules triggered by log errors or usage metrics exceeding thresholds. For example, alert if CPU or memory usage spikes on a node.
• Configure different notification channels like email, Slack, or PagerDuty. Critical alerts should page on-call staff immediately.
• Document common alerts and recommended responses. This speeds up troubleshooting when alerts occur.
• Test alerts frequently to ensure notifications are working. Reliable alerting prevents outages from going unnoticed. With robust cluster monitoring, log aggregation, and alerting in place, operators gain deep visibility into the health of a Kubernetes cluster. Issues can be rapidly detected and debugged before they impact users.

Namespaces For Isolation

Kubernetes namespaces provide isolation between groups of applications, teams, or environments. Namespaces are an important part of a production-ready Kubernetes environment for the following reasons:

• Separate environments. Namespaces can separate development, staging, and production environments so they do not impact each other. For example, you can have a `dev` namespace for developers to test new features without affecting the applications in `prod`.
• Access control. Namespaces allow you to set permissions for who can access, modify, or delete resources within that namespace. For example, you may restrict access to production namespaces to a small team of admins, while opening dev namespaces to all developers.

Namespaces provide the foundation for multi-tenancy and access control in Kubernetes. Make sure to define proper namespaces aligned to your environments and access needs as you scale your clusters. Restrictive permissions on production namespaces are crucial to avoid unwanted changes that could cause downtime. Namespaces give you isolation and control over resources between teams and environments in a cluster.

Resource Quotas and Limits

In a shared Kubernetes cluster, it’s important to prevent any single application or team from using more than its fair share of resources. Resource quotas and limits allow you to restrict resource usage per namespace as well as per pod/container.

Setting namespace quotas ensures that a single team can’t create an unlimited number of pods, services, etc which could degrade performance for other teams. You can restrict total CPU, memory, number of pods, services, persistent volume claims and more per namespace.

Additionally, you can set resource limits per pod or container, restricting the maximum CPU and memory usage. This prevents any single pod from becoming a resource hog and stabilizes cluster performance.

With quotas and limits in place, you avoid scenarios where one rogue application can drain node resources, cause OOM kills, or otherwise impact other critical services running on the cluster. This improves overall stability and quality of service across teams.

Having guardrails through resource quotas and limits is a best practice for multi-tenant clusters handling production workloads. It ensures fair sharing of cluster resources between teams and applications.

Deployments and Rollbacks

Kubernetes deployments provide a declarative way to deploy containerized applications. With deployments, you define the desired state of your application, including details like image version, replicas, and configurations.

The Kubernetes control plane works to match the actual state of your application to the desired state. This declarative approach takes the guesswork out of deploying applications. You simply declare the desired state through a deployment manifest, and Kubernetes handles all the underlying details like starting containers, distributing them across nodes, monitoring health, and more.

One powerful benefit of Kubernetes deployments is the ability to roll out updates and automatically roll back on failures. When you update your deployment manifest with a new image version or config change, Kubernetes initiates a rolling update. It takes down old containers and brings up new ones based on the new spec, a few pods at a time. If any pod fails its startup health checks during the rollout, Kubernetes will stop the update and roll back to the previous stable version automatically.

This prevents bad updates from taking down your entire application. You can define startup probes and health checks to catch errors and flaws in your new versions. Overall, Kubernetes deployments give DevOps engineers a reliable way to push application changes frequently and confidently.

Health Checks and Auto-repairs

Kubernetes health checks, known as liveness and readiness probes, allow you to monitor the health of your applications and restart or redeploy containers when issues arise. This provides automated self-healing capabilities.

Liveness and Readiness

Liveness probes check if an application is running properly. If a liveness probe fails, Kubernetes will restart the container to restore service.

Readiness probes indicate when a pod is ready to receive traffic. If a readiness probe fails, the pod will be removed from the load balancers until it passes the probe and is ready again.

Configure liveness and readiness probes on your deployments to catch crashes and avoid sending traffic to unhealthy pods. Use HTTP checks or TCP socket checks for apps that provide endpoints, and execute commands for other apps. Set frequency and response thresholds wisely to balance reliability with overhead.

Self-Healing

The Kubernetes control plane continually monitors containers and hosts for failures. If a node goes down, pods are automatically scheduled on other available nodes.

For deployments and stateful sets, any pods that are evicted or crash are recreated on healthy nodes. Enable auto-scaling and multiple replicas in deployments for additional self-healing capacity.

The cluster can gracefully handle node failures and traffic spikes by spinning up additional pods on demand. Set resource requests and limits to prevent any single pod from overloading nodes.

With health checks and auto-healing capabilities, Kubernetes provides resilient self-managing infrastructure for production environments. Automate container restarts, replacements, and scaling to maximize application uptime.

Quality of Service (QoS)

Kubernetes provides capabilities to control the quality of service (QoS) that individual pods receive. This allows you to guarantee that a Pod gets a certain amount of compute resources, avoid noisy neighbor issues, and prioritize critical system services. Two main features help provide QoS:

• Pod priority. The priorityClassName field can be set on a Pod to assign it a priority class. Priority classes range from 0 to 1000000, with higher values indicating higher priority. By default, pods have no priority class and are treated equally. Setting priority ensures critical pods, like monitoring agents, will get scheduling priority over less important ones. Priority also affects preemption — lower priority pods will get preempted to make room for pending high-priority pods.
• Resource reservations resource. requests and limits should be configured for all containers in a Pod. The request amount reserves and guarantees the specified compute resources for that container. The limit sets a maximum usage threshold. By reserving resources for each container, you avoid Starvation Deadlocks and ensure a minimum share of cluster resources. Limiting usage per container prevents any single process from dominating capacity. Together, priority classes and resource reservations provide Pod-level QoS features to deliver critical business services reliably on Kubernetes.

Autoscaling

Kubernetes provides automatic scaling functionality to match the number of pods and nodes to the current workload demand. This allows the cluster to scale up during spikes in traffic and scale back down when demand decreases.

Horizontal Pod Autoscaler (HPA)

The Horizontal Pod Autoscaler (HPA) automatically scales the number of pods in a deployment or replica set based on observed CPU utilization or other select metrics. The HPA helps ensure adequate pods are available to handle load changes and prevents over-provisioning of idle pods when demand is low. 

To set up an HPA, define the minimum and maximum number of pod replicas, as well as the CPU utilization percentage that will trigger scaling. The Kubernetes controllers will then automatically scale the number of pods between those ranges based on the observed metric.

Cluster Autoscaler

While the HPA handles pod scaling, the Cluster Autoscaler specifically handles automatic node scaling in a cluster. It will automatically add or remove nodes based on pending pod resource requests. Much like the HPA, the Cluster Autoscaler helps ensure adequate nodes are available for new pods during spikes in demand. It also removes any unnecessary nodes when they are underutilized to optimize costs. 

The Cluster Autoscaler needs to be deployed separately in the cluster and pointed at the node groups it should autoscale. Thresholds like resource utilization and scale-in/scale-out delays can also be configured. Together, the HPA and Cluster Autoscaler provide comprehensive autoscaling functionality for pods and nodes. Configuring both helps create a truly self-managing Kubernetes cluster.

Backup and Disaster Recovery

To ensure business continuity, a production Kubernetes cluster needs robust backup and disaster recovery capabilities. Here are some key considerations:

• Cluster snapshots. Take regular snapshots of the Kubernetes cluster to capture the state of workloads and resources at a point in time. Store snapshots offsite for optimal data protection. Snapshotting allows for restoring the cluster to a previously known good state if something goes wrong.
• Offsite backup storage. In addition to snapshots, back up critical data and application configurations to a remote offsite storage location. This provides an extra layer of protection in case the primary cluster experiences a catastrophic failure or outage. Choose a secure and resilient offsite storage service designed for backup data.
• Multi-region clusters. For maximum redundancy, run Kubernetes across multiple regions or cloud providers. This protects against region-specific failures. Critical applications can be replicated in multiple regions for continuous availability. Global load balancing then directs traffic to the closest healthy cluster. A multi-region architecture significantly hardens Kubernetes resiliency. With comprehensive snapshotting, offsite backups, and multi-region clusters, Kubernetes can deliver robust recovery from outages, disasters, data loss, and more. Careful planning for backup and disaster recovery helps ensure that applications in Kubernetes will remain available.

Pod Topology Spread Constraints

The Pod Topology Spread Constraints refers to Kubernetes mechanisms used to control the distribution of replicas across different topologies within a cluster. Kubernetes allows you to define rules regarding how pods should be scheduled across different nodes or zones within a cluster to improve fault tolerance, availability, and performance.

Constraints like this can be life saviors when it comes to applications that require high availability and resilience to node or zone failures. It makes sure that pods are evenly distributed across different failure domains.

What About Your Checklist?

Having navigated our way through the essential considerations for a production-ready Kubernetes checklist, it’s equally important to reflect upon the unique needs of your project. As Kubernetes is highly versatile and adaptable, the specific requirements can vary greatly from one deployment to another.

This is where we would love to hear from you, our reader. If you’ve identified essential points in this journey that we didn’t cover, or you’ve got unique constraints you’re considering in your deployment strategy, please drop them in the comments below. Your insights might make this checklist more valuable for our community, potentially assisting many others in their own Kubernetes adventures. Looking forward to exchanging ideas!