Kubernetes has simplified managing the workload of developers, with the benefits gained by containerization coupled with the powerful orchestration functionalities of Kubernetes. However, with the ever-increasing complexity of software, there may be instances where you have to experience performance bottlenecks even with Kubernetes. In this post, we will see how to effectively handle complex workloads in a Kubernetes cluster.
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Managing Node Resources
One of the simplest methods to deal with complex workloads is to throw more computing resources at them. While this method may seem a crude approach, it might be the only option if your workloads demand more resources.
The best way to approach this method is to create dedicated nodes with higher-performance hardware that can be used for specific workloads. Cluster administrators can use features like Kubernetes node affinity to target where pods will get scheduled. Then these pods will get created in those dedicated nodes, while all the other workloads will be distributed elsewhere. It also helps to keep expenditure at a more manageable level without having to scale up every node in the cluster.
Storage is another aspect in a Kubernetes cluster that is often overlooked. Insufficient capacity and lower-performing storage devices can cause performance degradation if your workload is storage-bound. Changing the underlying device type to something more suited for your workload, like a read or write optimized disk or a higher speed device, helps mitigate storage-related issues.
Migrating to a Distributed Architecture
Even though increasing resources is a good way to handle complex tasks, it can be cost-prohibitive and sometimes not the best way to increase performance. Besides, running an application as a single container might not be the most
efficient way to manage a complex workload. Thus, breaking down the application into multiple containers and shifting to a microservices-based architecture will allow users to separate and distribute the workloads more effectively.
This process undoubtedly requires considerably more time and resource investments than increasing resources. However, the advantages gained by this approach far outweigh any complexities faced during the migration process. If you are just starting your development process, you can directly target a microservices-based architecture to eliminate any migration woes down the line. The main advantage of this type of architecture is its ability to break down an application into smaller individual services, allowing users to scale in and out individual services as needed. Moreover, it helps to isolate complex workloads to specific services without overburdening the entire application and target resources for that service.
Correctly Utilize DaemonSets, Jobs, and CronJobs
Kubernetes has several built-in resources that can be used to manage workloads. Among them, DaemonSets can be used to create Pods that will be automatically added to nodes. It can also be used to run specific workloads like storage daemons and log collectors that can offload some tasks out of your applications and run cluster-wide at each node. These Daemonsets can be further customized using multiple Daemonsets for a single type of demon with different CPU and memory configurations or different DaemonSet configurations for different hardware combinations in the nodes.
Meanwhile, Jobs and Cron Jobs in Kubernetes enable users to run standalone tasks or a specific task in a recurring schedule, respectively. A Job will be ideal for a single execution batch processing task like the initial load of a dataset. There, the Pod related to the Job will carry out the data loading task and automatically terminate and clean up after itself. Cronjobs in Kubernetes acts similar to a traditional Cronjob, the difference being Cron Jobs executing a container. This feature can be utilized to offload functions such as generating reports or backing up data from the primary application to the Cronjob container. Both these mechanisms enable developers to manage a workload more efficiently by decoupling some tasks to be run as Kubernetes jobs or containers and even run multiple jobs parallel.
Implementing GitOps to Managing Deployments
Complex workload refers to complex application architecture, including a large number of resources like Pods, network services, and volumes spread across the cluster. This complexity can lead to situations where even a minor change to a single resource can have a cascading effect, leading to service disruptions. Thus, manually managing deployments is not scalable when dealing with complex workloads. This is where a proper continuous delivery tool comes in handy to manage the deployments within the cluster and keep track of all the changes, dependencies, resources, etc.
Furthermore, you can combine the continuous delivery tools with GitOps practices to enable a trackable and reversible deployment workflow. Version controlling and managing deployment configurations via a Git repository enables users to easily version-control, track, and manage all changes. The continuous delivery tool will refer to these configuration files and carry out the deployments automatically. It not only offers better visibility over the entire cluster but allows troubleshooting any problems detected in the environment easily. Since all the resources within the cluster are properly tracked, the chances of unused resources impacting the overall cluster performance will be eliminated.
Conclusion: Facilitating Complex Workloads
As mentioned above, there is no single way to deal with complex workloads in Kubernetes. While increasing available resources may be simpler, it can lead to unexpected increases in cluster expenditure. On the other hand, migrating an existing workload to a different architecture is better suited for a distributed environment. However, it will also be a time-consuming and resource-intensive process. Thus, it will not be an option in some cases, and it may be easier to create a new application rather than rewriting the application to a more modern standard.
So the best approach will be a combination of both methods. Developers can start by offloading some work to Jobs and CronJobs and then utilize DaemonSets to run constituents of workloads across the cluster. After that, they can look at the application level to see if further optimizations can be done. While this process will require a deeper dive into the exact workload, it can bring more benefits, as inadequate optimizations are one of the key reasons for performance bottlenecks. Finally, consider increasing the underlying hardware resources if all the possible avenues are exhausted.
