Developing Kubernetes Controllers: An Insightful Guide
Understanding the Challenges of Developing Kubernetes Controllers
Many companies using Kubernetes eventually find themselves in the pursuit of developing custom controllers. The appeal lies in the ability to provision resources declaratively, but challenges arise when controllers are developed without a solid understanding of Kubernetes APIs and best practices, leading to unreliable implementations, especially when deployed in production environments.
Essential Practices for Building Effective Controllers
1. Design CRDs Like Kubernetes APIs
Generating a Kubernetes CustomResourceDefinition (CRD) might be quick with tools like controller-gen, but transitioning from a poorly designed API can take months. To avoid pitfalls, developers should thoroughly understand Kubernetes API conventions and study existing APIs. Common mistakes include misunderstanding the spec vs. status fields, incorrectly embedding child objects, and neglecting field semantics such as defaulting and validation.
2. Single-Responsibility Controllers
Controllers should adhere to single-responsibility principles, akin to Kubernetes core controllers. This clarity in function allows for easier reasoning and integration with other systems, following the UNIX philosophy for well-defined inputs and outputs.
3. Structuring the Reconcile() Method
The Reconcile() method is central to controller functionality. Large projects often employ common controller shapes to standardize the reconciliation process, helping to prevent bugs and enforce consistency. It’s beneficial to emulate such frameworks, as seen in projects like Cluster API, to master the reconciliation flow.
4. Reporting Status and Conditions
API objects managed by controllers should reflect their status through dedicated fields. For instance, LinkedIn APIs include a status.conditions field for high-level condition management, aiding both machine and human understanding of resource states.
5. Utilizing observedGeneration
The observedGeneration field is crucial for understanding whether condition data reflects the latest object configuration. This mitigates issues of stale data, allowing for accurate status assessments.
6. Understanding Cached Clients
Controllers often read from cached clients to improve efficiency, but this can lead to inconsistencies without proper handling, as cache might not reflect recent write operations. It is recommended to configure controllers to avoid unintentional cache creation and manage resources intentionally.
7. Achieving Fast and Offline Reconciliation
Efficient controllers minimize API calls and operate offline when the system state meets the desired configuration. Avoid unnecessary updates to external systems for unchanged resources to enhance reliability and scalability.
8. Reconcile Return Values
Understanding the ctrl.Result and error returns is essential. Errors should propagate naturally, while the Requeue flag should be used judiciously to manage ongoing or time-based processes.
9. Workqueue and Resync Mechanics
Workqueues periodically reconcile all objects, providing robustness against missed events. Controllers should be engineered to handle these resyncs effectively, ensuring responsiveness and efficiency, especially in high-load scenarios.
10. Expectations Pattern
The expectations pattern involves tracking the outcomes of operations, acknowledging informers’ potential delayed updates. This prevents controllers from acting on incomplete or outdated data, ensuring logical consistency and expected behavior.
Conclusion
Building reliable Kubernetes controllers requires a deep understanding of Kubernetes internals, careful consideration of API design, and operational mechanics. Engaging with the Kubernetes community, studying existing projects like Cluster API, and following best practices can significantly enhance one’s capability in developing scalable and effective controllers.
About the Author
Ahmet Alp Balkan, a software engineer at LinkedIn, brings insights from extensive experience with Kubernetes infrastructure. His career spans roles at Twitter, Google Cloud, and Microsoft Azure, with active contributions to the Kubernetes ecosystem.