Anti-Patterns helps you identify the problems and point out bad-practices in the implementation. we make sure to constantly link each anti-pattern with the appropriate solution. You will be able to truly check your own deployment procedure and correct any problems without having to hunt down additional information using this method.
To prevent these issues, a fundamental understanding of Kubernetes' architecture and operation is required. In this blog-series, we'll take a look at some of the more typical blunders made during Kubernetes installations, explain how they occur, and offer some advice for how to correct them or avoid them in the first place.
Putting the configuration inside container images: Storing configuration inside container images may lead to a security risk as the configuration could be accessed by anyone with access to the image. It also makes it difficult to update configuration without rebuilding the entire image.
Connecting applications to Kubernetes features/services for no apparent reason: Over-reliance on Kubernetes features or services without a clear reason can result in unnecessary complexity and potential performance issues.
Mixing infrastructure and application deployment: Deploying infrastructure and application code together may lead to issues with scaling and versioning. It's best to separate them and deploy them separately.
Deploying without memory and CPU limits: Failing to set resource limits for containers can lead to performance issues and can impact the stability of the entire cluster.
Using health probes wrongly: Incorrect usage of health probes can result in applications being killed or restarted unnecessarily, leading to potential downtime.
The absence of Helm (and not understanding what Helm brings to the table): Helm provides a package manager for Kubernetes, enabling easier deployment and management of applications. Failing to understand its benefits can result in unnecessary complexity and potential issues.
Being unable to comprehend the application's performance due to a lack of deployment metrics: Without deployment metrics, it's difficult to understand how the application is performing and to optimize it for better performance.
Being unprepared for secret situations; handling secrets on an as-needed basis: Failure to prepare for secrets situations can lead to potential security breaches or downtime. It's best to have a plan in place for managing secrets.
Looking to commit fully to Kubernetes (even with databases and stateful loads): Kubernetes may not be the best option for stateful loads or databases. It's important to consider other options for these workloads.
Manual kubectl edit/patch deployments: Manually editing or patching deployments can result in errors and make it difficult to track changes, leading to inconsistent deployments.
Kubernetes deployments with latest-tagged containers: Using latest-tagged containers can lead to versioning issues and make it difficult to reproduce previous deployments, leading to unexpected behavior or downtime.
Kubectl debugging: Debugging with kubectl can be time-consuming and difficult, and alternative solutions such as logging and tracing should be considered.
Misunderstanding Kubernetes network concepts: Misunderstanding Kubernetes networking can result in network-related issues and make it difficult to manage networking configurations.
Utilizing fixed staging environments rather than dynamic environments: Fixed staging environments can lead to bottlenecks and make it difficult to scale deployments or test new features.
Combining clusters of production and non-production: Combining production and non-production clusters can result in security and performance issues, and should be avoided to ensure proper separation of environments.
The above points highlight common anti-patterns in Kubernetes implementation that can result in security issues, performance problems, and difficulty in managing deployments. The list includes issues such as mixing infrastructure and application deployment, deploying without resource limits, and misusing health probes. It also emphasizes the importance of using tools such as Helm for deployment management and understanding network concepts in Kubernetes. Proper management of secrets, avoiding fixed staging environments, and separating production and non-production clusters are also crucial for successful Kubernetes implementation.