August 3, 2023
15 min read
In this post, Rajesh Tilwani (Co-Founder of Humalect) covers a variety of strategies for preventing and managing incidents with Kubernetes.
Creating just any infrastructure on Kubernetes is not enough. There are so many basic configurations you could apply and create the infrastructure for your application for the time being and it might work just fine.
The incident responses won’t always remain 100% reliable. You will run into newer potholes, and that’s okay.
To make a system at least 99.99% reliable, you need to implement Kubernetes (K8s) best practices for better incident management, response issue remediations, and preventive measures.
In this post, we’ll cover a variety of strategies for preventing and managing incidents with Kubernetes. But before we dive deep into incident management in Kubernetes, let’s go over the role of Kubernetes in containerized applications.
Kubernetes, often abbreviated as K8s, aims to automate the management process of containerized applications. At its core, Kubernetes is a powerful container orchestration system used to automate the deployment and management of containerized applications.
Containers encapsulate applications and their dependencies, making them ideal for the dynamic and diverse infrastructure of modern applications. It provides a sophisticated set of features and components that allow seamless autoscaling, high availability, and resource optimization.
It abstracts the underlying infrastructure, enabling developers to focus on writing code and delivering features rather than worrying about the intricacies of deployment and infrastructure management.
Unlike traditional architectures which involve the development/operations team configuring a server to get it up and running, Kubernetes acts as an orchestrator where you just provide instructions based on the requirements, and everything else related to infrastructure is handled by Kubernetes.
This way, the development team can focus on the application development, instead of worrying about infrastructure. Now that you get the hang of Kubernetes, how does incident management play a vital role in Kubernetes?
Kubernetes is known for building scalable and reliable cloud infrastructure, but without incident management best practices in place you’re missing opportunities to leverage K8s to its full potential.
Incident management involves detecting and resolving incidents to maintain application reliability and minimize user impact. K8s clusters play a vital role in incident management by providing several features and tools to handle incidents effectively.
It has predefined techniques for auto-healing. If a pod crashes, Kubernetes automatically restarts it or creates a new one, based on the desired state.
Let’s assume you had a version deployed and live on your Kubernetes environment (v1) and you have another environment that you deploy (v2).
For some reason, your v2 application turns out faulty and unresponsive in the backend and encounters multiple errors. You need to immediately revert the changes of the application v1 to v2.
Without Kubernetes, this would result in an enormous amount of downtime which yields a major business impact/revenue loss.
Kubernetes handles these scenarios automatically without human intervention, as the Kubernetes master is responsible for keeping your application up and running with minimal to no downtime and ensuring that the system follows all the reliability principles.
HPA adjusts the number of replicas (pods) based on CPU utilization or other custom metrics. This feature ensures that applications can handle varying loads and scale up or down as needed, preventing performance issues and outages.
HPA becomes handy, especially for a system that emphasizes more on scaling.
You can set the amount of infrastructure you want to allocate for each pod in Kubernetes. This is called resource limits and requests for containers, which prevents resource contention and ensures a good amount of resource allocation across the cluster.
Kubernetes can be integrated with many open-source monitoring tools like Prometheus and Grafana. These tools enable real-time monitoring of cluster health, health checks, resource usage, and application performance.
By setting up alerts based on these metrics such as CPU and memory, teams can proactively identify potential incidents and respond promptly.
In the context of Kubernetes, incidents refer to defective events or failures that impact the working of containerized applications and the overall health. These incidents can be caused by various factors such as software bugs, configuration errors, resource constraints, or external dependencies' failures.
Common Kubernetes incidents include pod crashes, node failures, networking issues, excessive resource utilization, and application performance degradation. When incidents occur, they can lead to service disruptions, downtime, and potential user impact.
Incident management in Kubernetes involves promptly detecting, investigating, and resolving issues to ensure the application's reliability and availability. Kubernetes' robust incident management capabilities contribute to its position as a go-to platform for deploying and managing modern applications in dynamic and ever-changing environments.
Although the Kubernetes way of deploying applications reduces the number of errors or failures in the production environment, it cannot offer 100% uptime (by default).
It is important to understand the concept of SLAs in the deployment process. No system promises to give 100% SLA. This is because there can be multiple factors that contribute to failures.
Understanding the different possible errors that could occur can help us eliminate them in the future. Let’s explore the different errors that could occur in Kubernetes to make our systems efficient and accurate.
These are critical because your applications run in the form of pods. If your pods aren’t up and running, your applications won’t be accessible. To keep the pods healthy, you can use strategies such as replica sets to incorporate high availability.
Kubernetes master node then checks the required number of replicas of the application and ensures to maintain the configuration.
Every component of Kubernetes runs on nodes. There are two types of nodes - Master nodes and Worker nodes. Master nodes, as the name suggests, are used to run the components of Kubernetes master components. Worker nodes run the applications you containerize and deploy to your live environments.
Nodes act like virtual machines and, as you know, machines require constant maintenance. Most of the maintenance is done by Kubernetes, however, you need to validate if patches are getting updated into the machines and if the utilization of node capacity is handled properly. Else, this will lead to the crashing of nodes and thus, the pods.
This might not always impact your application if you configure your cluster to use more nodes together so that even if one node goes down, you have other nodes on which your pods can run. So consider planning your cluster accordingly keeping these failures in mind.
Misconfigured settings for applications, services, or networking can result in incidents that affect the functionality of the entire cluster.
Every application has dependencies over many packages and other components/APIs. Incidents can arise due to failures in external services or dependencies that applications rely on for proper functioning.
When containerizing applications, you store those in container registries such as docker registries, Azure container registries, etc. If the container image specified in a pod cannot be pulled from the container registry, it results in image pull failures.
Causes for these issues can be due to various reasons like misconfiguration on the application code, network restrictions between infrastructure components, identity, and access management issues, and many more.
Looking out for such potential problems is the key and half of such issues are automatically managed and you may not even notice it.
In this section, we will talk about how to prepare for incident response in Kubernetes and some best practices to follow when doing it.
Leveraging Kubernetes effectively during incidents is important in order to maintain the stability and reliability of your applications and infrastructure of your cluster setup. Here are some best practices for incident response in Kubernetes:
Implement robust monitoring and alerting solutions to detect incidents promptly. Utilize tools like Prometheus, Grafana, or Kubernetes-native monitoring solutions to collect and analyze metrics, logs, and events.
Set up alerts based on predefined thresholds to notify the incident response team when specific issues occur.
Leverage Kubernetes' built-in self-healing capabilities to automate incident resolution where possible. Kubernetes can automatically restart failed pods, reschedule them to healthy nodes, and replace unresponsive containers.
This automated approach reduces manual intervention during incidents, leading to faster resolution times.
Implement HPA to automatically adjust the number of replicas based on CPU or custom metrics. This ensures that your applications have the necessary resources to handle increased loads, reducing the risk of resource-related incidents.
When deploying new versions of your application, use Kubernetes' rolling update feature to ensure a smooth transition while minimizing service disruption. Additionally, Kubernetes' rollback capability allows you to revert to the previous stable version quickly if an incident occurs during the update process.
Employ Kubernetes Network Policies to control the communication between pods and enforce security measures. By defining and implementing network policies, you can reduce the risk of networking incidents and isolate potentially vulnerable components.
Use ConfigMaps and Secrets to manage configurations and sensitive data separately from the application code. Proper configuration management reduces the risk of misconfigurations and enhances security posture.
After resolving incidents, conduct a thorough post-incident analysis to identify the root cause and contributing factors. Use the analysis to update incident response procedures, improve preventive measures, and enhance overall cluster resilience.
Document the incident, response actions, and lessons learned for future reference.
By following these Kubernetes incident management best practices, companies can effectively leverage Kubernetes during incidents, ensuring swift and efficient resolution, minimizing downtime, and maintaining the overall stability and reliability of their Kubernetes environments.
Incident identification and triage are different aspects of incident response in Kubernetes. These steps involve detecting incidents, categorizing them, and initiating the appropriate response.
Beyond traditional monitoring, consider implementing proactive monitoring for application health and performance. Implement health checks using probes within your application code to ensure that Kubernetes can detect and handle unhealthy pods automatically once deployed.
Utilize Kubernetes-native features like Horizontal Pod Autoscaler (HPA), and Kubernetes events to automatically detect incidents such as pod crashes, node crashes, resource constraints, or scaling problems.
When an incident is triggered, categorize it based on its impact, severity, and nature. Common categories may include security breaches, application failures, resource-related issues, networking problems, etc.
Document relevant information about the incident, including timestamps, affected resources, initial observations, and alert details. This documentation will be valuable during the post-incident analysis and reporting.
If the incident requires immediate attention or exceeds predefined response time objectives, escalate the alert to the Incident Commander (IC) or higher management for awareness and further action.
Initiate preliminary response actions based on the incident category and severity level. For example, containment measures, isolating affected resources, or initiating automatic recovery procedures.
💡 You can use Rootly automate incident tasks like creating a Slack channel, notifying internal responders and customers, generating post-mortem timelines and templates, and more to reduce manual effort during incidents.
After identifying and triaging an incident, the next action is on mitigating the impact and finding solutions to resolve the issue. How does Kubernetes help with this?
Let’s find out!
Incident mitigation in Kubernetes is all about making sure that the impact of an incident is contained as soon as possible.
Here are a few steps to prevent possible incidents from impacting the uptime of your application and reduce all possible failures.
When an incident happens, your team might not be fully aware of what’s causing it. It could have happened due to some dependency over other applications. But there is a good chance that it might impact other healthy applications dependent or routed to it.
So, the first step would be to follow some isolation steps to contain the failure from spreading on more critical applications. Here’s a quick win before we dive into different ways to isolate impacted resources just after a Kubernetes incident has occurred.
💡 Disable scheduling on your nodes to perform operations anytime during maintenance activity. This is known as Node Draining and Cordoning.
Isolation involves identifying the affected resources, such as Kubernetes pods or nodes, and taking immediate action to prevent their impact from spreading to other parts of the cluster.
By containing the incident, SREs can minimize the overall disruption to the system and limit the number of affected users or applications.
SREs use Kubernetes namespaces to logically isolate applications and resources from one another. Each namespace creates a boundary within which resources are organized, helping to prevent the incident's impact from crossing namespace boundaries.
Additionally, network policies can be employed to control the communication between pods within and across namespaces, further reinforcing the isolation of resources.
SREs can utilize pod affinity and anti-affinity rules to control the scheduling of pods across nodes or zones.
By setting pod affinity, pods can be co-located on specific nodes based on labels, enhancing performance and resource utilization.
On the other hand, pod anti-affinity ensures that identical pods are spread across different nodes or zones, preventing single points of failure and enhancing fault tolerance.
While Kubernetes provides several built-in mechanisms for isolation, there may be situations where SREs need fine-grained control to contain the incident effectively.
In such cases, manual intervention might involve disabling problematic services, modifying network policies, or adjusting resource settings.
If the Kubernetes incident is related to resource constraints or high traffic, scale the affected application or service horizontally by increasing the number of replicas or vertically by allocating more resources (CPU, memory) to the pods.
There is another mechanism for vertical autoscaling as well which comes in handy when you don’t know how much resources to allocate.
Let’s look at some of the mechanisms included in scaling as a preventive measure to handle Kubernetes incidents.
Horizontal Pod Autoscaler (HPA) or Vertical Pod Autoscaler (VPA)
Horizontal Pod Autoscaler is a built-in Kubernetes feature that automatically scales the number of pod replicas based on CPU utilization or custom metrics defined by the user.
When an incident leads to a sudden surge in traffic or resource demands, HPA can dynamically add more replicas to the application to accommodate the increased load. This ensures that the application can handle the heightened workload without degrading performance.
Incidents like sudden traffic spikes or unexpected bursts in application requests can impact the responsiveness and availability of the service.
With HPA/VPA in place, Kubernetes can automatically increase the number of replicas to distribute the incoming requests across multiple pods, ensuring that each pod handles a proportionate share of the load.
As a result, your applications can better cope with the increased demand, thus, reducing the risk of service degradation or unavailability.
Ensuring Resource Availability
Resource constraints can lead to incidents, such as out-of-memory errors or CPU throttling.
By configuring HPA to monitor and respond to resource utilization, Kubernetes can automatically scale the number of replicas to ensure that there are sufficient resources available for each pod. This effectively prevents resource-related incidents and maintains the overall stability of the cluster.
Bursty Workloads Handling
Certain applications experience bursty workloads, where resource demands vary drastically over time.
Scaling with HPA enables Kubernetes to adjust the number of replicas based on real-time resource usage, meeting the fluctuating workload requirements efficiently. This mitigates the risk of performance bottlenecks and ensures consistent application performance.
Manual Scaling and Kubernetes Cluster Autoscaler
While HPA is an automated approach to scaling, SREs can also manually scale resources when necessary. They can increase or decrease the number of replicas manually based on anticipated incidents or known patterns of resource consumption.
Additionally, Kubernetes provides Cluster Autoscaler, which automatically adjusts the size of the underlying infrastructure by adding or removing nodes from the cluster as needed. This dynamic infrastructure scaling ensures sufficient capacity to handle incidents and sudden increases in resource demands.
Scaling Down Gracefully
Once an incident is resolved, Kubernetes can automatically scale down the number of replicas when the workload decreases.
This ensures efficient resource utilization and cost optimization, allowing the cluster to return to its standard configuration when the incident is no longer affecting the system.
The first part was about containing the impact of an incident, once it has happened. In this section, we will take a look at steps in order to prevent the recurrence of such incidents.
Conduct a detailed investigation to identify the root cause of the incident. Analyze logs, events, and metrics to understand what triggered the issue which is easily available when you integrate monitoring into your cluster.
This is why it is recommended to use monitoring and alerting which would make the job of RCA much easier.
If the incident is caused by application bugs or issues in the code, developers should work on fixing the problems and implementing code changes.
Ensure all your infrastructure components are up to date. This will prevent half of the issues that would occur and the defects that already have occurred.
Optimize application code and resource utilization to improve performance and prevent resource-related incidents. If the incident is related to security vulnerabilities, apply security patches to fix the issues and improve the overall security of the system.
Consider making infrastructure improvements, such as upgrading Kubernetes components or optimizing the cluster setup, to enhance stability and reliability.
Several incidents occurred at the beginning of the usage of clusters at companies like Azure, Netflix, and GitLab. Let’s look at them to understand how problems occur and how we can learn to manage them more efficiently.
In September 2020, Microsoft Azure experienced a huge outage that affected several services along with Azure Kubernetes Service (AKS). It was caused by a DNS configuration error that resulted in the loss of DNS resolution for a large number of Azure services.
Incident Response & Management
Microsoft's incident response team promptly identified the issue and worked on the fix. They communicated with customers, providing timely updates on the situation. Post-incident, Microsoft conducted a detailed analysis of the root cause and implemented measures to prevent similar incidents in the future.
Incident: GitLab, a popular web-based Git repository manager, experienced a major data deletion incident in January 2017. During a database migration, a human error resulted in the accidental deletion of production data, impacting user data, projects, and repositories.
Incident Response & Management
GitLab's incident response team quickly responded, leveraging their backup strategy to restore most of the data. They were transparent with users about the incident and its impact.
GitLab improved its backup and recovery methods, implemented validation checks, and established a bug bounty program to encourage community contributions.
Netflix, known for its advanced use of container technologies, highlighted how it integrated Spinnaker (a continuous deployment tool) with Kubernetes for incident management.
Spinnaker allowed Netflix to automate the deployment process and easily roll back changes in case of incidents, reducing downtime and minimizing user impact.
By implementing automated deployment and rollback mechanisms, Netflix achieved faster incident resolution and enhanced application reliability.
These case studies are examples that showcase the importance of proactive incident management and other techniques we discussed earlier.
They emphasize the significance of transparent communication, robust backup and recovery strategies, automated deployment and rollback mechanisms, and the continuous improvement of incident response procedures.
Successful incident management requires a combination of technical expertise, clear communication, and a commitment to learning from past incidents to enhance future response capabilities.
Rajesh Tilwani is the co-founder of Humalect (a DevOps Automation Platform) and a DevOps engineer with extensive experience deploying and scaling production-grade applications in multi-cloud and hybrid-cloud environments.
Curious how companies like Figma, Tripadvisor, and 100s of others use Rootly to manage incidents in Slack and unlock instant best practices? Book a free, personalized demo today.
{{subscribe-form}}
.png)
.png){kind=icon}
.png){kind=icon}
.png)
.png)
.jpg)
.jpg)
