13-Step Kubernetes Reliability Checklist

What Is Kubernetes Reliability?

Kubernetes reliability refers to the platform’s ability to consistently perform its intended functions under prescribed conditions. Practically, it means ensuring that kubernetes orchestrates containerized applications in a dependable manner, maintaining operational stability even under varied loads. Reliability is crucial in environments where services need to be continually available and performant, avoiding disruptions that can affect user experience and business operations.

Reliability in kubernetes involves multiple layers, including hardware resiliency, network stability, and kubernetes configurations. These components work together to ensure that applications run smoothly, and any disruptions are quickly mitigated. Understanding and implementing key aspects of reliability helps organizations leverage kubernetes to its full extent, supporting critical services with minimal downtime or performance degradation.

This is part of a series of articles about Kubernetes monitoring

Common Reliability Risks in Kubernetes

Missing CPU and Memory Requests and Limits

Not setting appropriate CPU and memory requests and limits poses a significant risk to kubernetes reliability. When pods lack defined requests and limits, they can cause resource contention, affecting the stability and performance of all workloads in the cluster. This can lead to performance degradation, where critical applications may not receive the necessary resources, resulting in outages or degraded service quality.

It’s essential to set these requests and limits to ensure fair resource distribution among pods. This configuration helps kubernetes efficiently manage resources, enabling the scheduler to make informed decisions that support cluster stability. By defining clear resource boundaries, you can prevent noisy-neighbor scenarios and maintain consistent application performance.

Missing Liveness, Readiness, and Startup Probes

Liveness, readiness, and startup probes are pivotal in maintaining kubernetes reliability. Missing these probes can result in undetected application failures leading to prolonged outages. Liveness probes check if the application is running correctly, allowing kubernetes to restart failed containers. Readiness probes determine if a pod is ready to receive traffic, preventing service interruptions due to unresponsive endpoints.

Startup probes ensure applications are fully initialized before being marked available, preventing false starts. When these probes are not configured, kubernetes lacks critical insights into application states, increasing the risk of serving broken applications or facing downtime without automatic recovery.

No Availability Zone Redundancy

Running kubernetes clusters without availability zone redundancy is a risk that can significantly undermine reliability. Single-zone deployments are vulnerable to failures that could lead to complete service outages. For enhanced reliability, clusters should be distributed across multiple availability zones. This distribution provides resiliency by ensuring that if one zone fails, others can pick up the load.

Availability zone redundancy helps protect against data center and hardware failures, providing a more effective failover mechanism. It also supports load balancing during periods of high demand across zones to prevent resource bottlenecks. Implementing redundancy requires careful planning around network latencies and data replication.

Pods in CrashLoopBackOff and ImagePullBackOff

Another common reliability risk is pods entering CrashLoopBackOff and ImagePullBackOff states. Pods in CrashLoopBackOff are stuck in a cycle of continuous reboots often due to configuration errors, dependency issues, or incorrect startup commands. This state indicates an application failure that must be resolved promptly to restore service reliability and functionality.

Similarly, ImagePullBackOff occurs when a kubernetes node repeatedly fails to pull an image from a registry. This can lead to service downtime if image access issues aren’t resolved since pods cannot start without their required images. Causes may include incorrect image paths, tag issues, or network connectivity problems. Addressing these conditions involves debugging configuration issues and verifying image accessibility.

Unschedulable Pods and Errors

Unschedulable pods can pose challenges to reliability, occurring when nodes lack sufficient resources to accommodate new pods. This leads to services not starting, thus impacting application availability and performance. Addressing unscheduled pods requires adequate resource planning and proactive monitoring to avoid resource depletion that disrupts service continuity.

Strategies such as efficient resource reservation and scaling can help mitigate these issues. Implementing cluster autoscaling can dynamically adjust resources to match demand, reducing unscheduled events. Regular auditing of node capacity and ensuring accurate resource requests and limits help maintain reliability by ensuring that the kubernetes scheduler can efficiently place pods on the nodes with available capacity.

Related content: Read our guide to Kubernetes management

Kubernetes Reliability Checklist

Here are the main steps you can take to improve Kubernetes reliability. They are divided into four categories: Resource management, health checks, high availability, and monitoring/alerting.

Resource Management Best Practices

Effective resource management is crucial for maintaining kubernetes reliability. This entails setting appropriate CPU and memory requests and limits, using pod disruption budgets, and applying topology spread constraints.

1. Setting Appropriate CPU and Memory Requests and Limits

To maintain kubernetes reliability, it’s vital to set appropriate CPU and memory requests and limits for containers. This practice ensures that each container receives the necessary resources to function without interfering with others. Proper requests and limits facilitate efficient scheduler operation, which prevents resource contention and maintains stable application performance.

Requests define the minimum resource allocation, ensuring critical services have the resources needed to operate effectively. Limits prevent a single container from monopolizing host resources, providing a balanced environment for all workloads. Setting these appropriately requires understanding application resource demands, allowing for efficient resource utilization and improved cluster reliability.

2. Avoiding CPU Throttling and OOMKilled Errors

Avoiding CPU throttling and OOMKilled (out of memory killed) errors is vital to maintaining kubernetes reliability. CPU throttling occurs when a container is using more CPU than allowed by its limit, reducing performance. OOMKilled errors happen when a container exceeds its memory allocation, forcing kubernetes to terminate it, leading to service disruptions.

To prevent these issues, closely monitor resource usage and adjust limits to match application demands. Implement autoscaling to dynamically adapt resources based on load, reducing the risk of throttling and memory exhaustion. Effective resource management involves continuous monitoring and adjusting configurations to maintain optimal performance and stability in kubernetes environments.

3. Implementing Pod Disruption Budgets

Implementing pod disruption budgets (PDBs) ensures high reliability by controlling the number of disruptions that can occur to pods during routine operations. PDBs define the allowable disruptions while performing maintenance tasks, such as upgrades or node scaling, helping to maintain application availability by guaranteeing a number of replicas stay available.

By specifying a minimum number of pods to keep running, PDBs protect against service downtimes during planned interventions. They are essential in environments with stateful applications or those that require high availability for user-facing services. Implementing PDBs requires understanding of service dependencies and capacity planning to balance between maintenance needs and service continuity.

4. Using Pod Topology Spread Constraints

Pod topology spread constraints enhance kubernetes reliability by distributing pods across nodes to avoid resource contention and ensure balanced workloads. These constraints minimize the risk of a single point of failure by spreading replicas evenly across physical resources, which improves fault tolerance and performance across the cluster.

Implementing these constraints involves specifying rules that guide the kubernetes scheduler in placing pods on nodes. This ensures that critical workloads aren’t confined to a limited set of nodes, reducing the impact of any node failure. Effective use of topology spread constraints maintains robust application performance and enhances overall reliability by optimizing resource distribution and redundancy.

Implementing Health Checks

Health checks in kubernetes are vital for ensuring cluster reliability by constantly monitoring the status of applications. They help kubernetes detect and recover from failures automatically.

5. Configuring Liveness Probes

Configuring liveness probes is crucial to ensure kubernetes applications are healthy and running as expected. Liveness probes detect when an application is stuck or malfunctioning, prompting kubernetes to restart the container. This proactive measure helps maintain reliability by automatically resolving state issues that could degrade service performance.

Liveness probes are set through periodic executions of commands, HTTP checks, or TCP connections. If a check fails, kubernetes can terminate and restart the container, often without affecting the overall service. Properly configured liveness probes thus assist in preventing prolonged disruptions by quickly rectifying application health anomalies.

6. Configuring Readiness Probes

Readiness probes ensure that applications are ready to handle service requests, essential for maintaining reliability in kubernetes. They help identify if an application is in a state to receive traffic. If not, the pod is marked as unavailable, preventing traffic routing to it and avoiding potential service outages due to non-responsive instances.

Configuring readiness probes involves defining criteria based on application state, such as database connectivity or service dependencies. Proper setup ensures that only ready instances receive traffic, reducing the risk of client-facing failures. Readiness probes play a crucial role in traffic management and load balancing.

7. Configuring Startup Probes

Startup probes address the issue of properly initializing complex applications, ensuring they start before liveness or readiness probes are activated. These probes delay other health checks until application startup is complete, preventing premature restarts. Proper configuration of startup probes supports operational reliability by accounting for extended boot times without interfering with normal operations.

Setting startup probes involves specifying criteria or conditions consistent with application initialization requirements. They cater to slow-starting applications by providing sufficient time for dependencies and internal configurations to stabilize before service traffic begins. Implemented correctly, these probes maintain reliable application startup, reducing false positive failures during the initialization phase.

Ensuring High Availability

Ensuring high availability in kubernetes is essential for uninterrupted service delivery. Strategies include multi-replica deployments, distribution across availability zones, and effective cluster autoscaling. 

8. Multi-Replica Deployments

Multi-replica deployments are crucial for achieving high availability in kubernetes environments. By running multiple replicas of application pods, you ensure redundancy, meaning if one pod fails, others continue to serve traffic. This approach minimizes the risk of downtime, maintaining service availability even during scheduled maintenance or unexpected failures.

Managing multi-replica deployments involves configuring Deployments and ReplicaSets, ensuring an adequate number of replicas based on scaling requirements. It’s important to periodically review the number of replicas to align with demand changes, enhancing fault tolerance. By maintaining multiple replicas distributed across nodes, high availability and application reliability are significantly improved.

9. Distributing Pods Across Availability Zones

Distributing pods across availability zones enhances reliability by balancing loads and providing failover options. This strategy ensures that applications remain operational even if a zone experiences issues. Distributing pods across zones involves configuring node affinity and anti-affinity rules, which guide the scheduler in placing pods on nodes.

This distribution reduces the risk of total service downtimes, providing a buffer against regional outages. Ensuring geographically balanced deployments can mitigate potential network latency and load issues. Effective distribution requires a comprehensive understanding of application architecture and dependencies to ensure continuity.

10. Cluster Autoscaling

Cluster autoscaling dynamically adjusts resource allocation based on demand in kubernetes environments, crucial for maintaining high reliability. This approach automatically scales nodes up or down, ensuring that resource needs are met without human intervention. Implementing effective autoscaling strategies ensures application stability during peak loads or scaling down during low demand periods.

To configure autoscaling, define thresholds that create triggers for scaling actions. This involves setting up the Cluster Autoscaler and configuring Horizontal Pod Autoscalers to adjust resource allocations automatically. Regularly reviewing scaling policies ensures that they remain effective in accommodating workload changes, providing optimal performance and minimizing resource wastage.

Monitoring and Alerting in Kubernetes

Monitoring and alerting is crucial to maintain visibility of issues in Kubernetes clusters and carry out timely incident response.

11. Setting Up Monitoring Tools

Setting up monitoring tools in kubernetes involves deploying systems like Prometheus and Grafana, which collect and visualize performance metrics. These tools provide insights into cluster health, application performance, and resource utilization, essential for proactive reliability management. Properly configured monitoring ensures detailed visibility that helps identify and resolve issues swiftly.

Deploying these tools includes setting up exporters, dashboards, and defining alert rules. Exporters extract metrics from various kubernetes components, while dashboards visualize the data for easy interpretation. Alerts are configured to notify relevant teams of potential problems. Establishing robust monitoring is crucial for maintaining high availability, allowing teams to address performance bottlenecks and ensure smooth operation.

12. Defining Key Metrics and Alerts

Defining key metrics and alerts involves selecting crucial performance indicators and setting thresholds for notifications. Metrics may include CPU and memory usage, pod health, and node availability. Setting proper alert thresholds ensures timely notification of performance anomalies, allowing quick response and resolution to maintain reliability in kubernetes environments.

Properly defined alerts trigger actions when system behavior deviates from normal operation. This may involve configuring alert rules in tools like Prometheus, notifying teams through integrated communication platforms. Ensuring these metrics align with operational priorities and reliability goals is crucial for effective monitoring, contributing to optimized resource allocation and maintaining stable cluster performance.

13. Regular Health Analysis of Clusters

Regular health analysis of clusters is vital for sustaining kubernetes reliability. This involves periodic assessment of resource usage, performance trends, and infrastructure health. Analyzing these factors helps preemptively detect potential issues, enabling teams to take corrective actions before they affect service availability and performance.

Conducting health analysis requires comprehensive data collection and evaluation of key performance indicators over time. This may involve regular audits, performance reviews, and updating configurations to optimize resource usage. By maintaining an ongoing evaluation practice, organizations can enhance cluster reliability, ensuring applications run efficiently and aren’t affected by latent issues.

Improving Kubernetes Reliability with Komodor

Komodor is the Continuous Kubernetes Reliability Platform, designed to democratize K8s expertise across the organization and enable engineering teams to leverage its full value.

Komodor’s platform empowers developers to confidently monitor and troubleshoot their workloads while allowing cluster operators to enforce standardization and optimize performance. Specifically when working in a hybrid environment, Komodor reduces the complexity by providing a unified view of all your services and clusters.

By leveraging Komodor, companies of all sizes significantly improve reliability, productivity, and velocity. Or, to put it simply – Komodor helps you spend less time and resources on managing Kubernetes, and more time on innovating at scale.

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