Are you planning to deploy Kubernetes on Google Cloud? That’s great, but before you get started, make sure you avoid some common mistakes that many teams make. These tips will save you time, headaches and resources.

Mistake 1: Favoring theoretical functionality over developer friendliness

Many teams are starting to design their clusters based on the potential of Kubernetes. This is a mistake. In a general DevOps team, underestimating the overhead and developer friendliness of a system leads to lack of adoption and a delayed development cycle. This hinders the flow and creativity of developers.

To limit this, start designing software with the human user in mind. Instead of designing functionality, you design an interface and apply the “no need” principle. Trust that you can be creative in developing solutions when they are truly needed. Remember that simplicity is key for future customizations.

It’You won’t need it“Principle (YAGNI) means that when developing software, it is best to start with the simplest solution and avoid adding unnecessary features or complexity as much as possible.

Trust that you can be creative and develop additional solutions in the future when they are truly needed, rather than burdening your system with unnecessary things from the start. This fits with the broader vision of flexible development. The goal is to remain flexible so that user feedback can be integrated into development rather than over-developing and planning in advance.

Mistake 2: Misunderstanding the purpose of Kubernetes

Some teams are more inclined to adopt Kubernetes because it is a familiar system rather than choosing the best system for their specific purpose. This leads to errors like relying on some node or some podswhich destroys the reliability of Kubernetes through redundancy.

Misunderstanding the idea of ​​setting up a new cluster for each project in Kubernetes: At first glance, this seems like a very simple setup, but it makes activities like system upgrades more difficult.

For maintenance, complexity management, and reliability reasons, it is usually better to have a large multi-cluster cluster node for greater reliability and a lower risk of system failure. A desired separation of all needs can thereby be achieved Namespaces can be used efficiently and over a good network Service networks. Simply put: two clusters require two updates, two clusters require two node If both nodes fail, this could result in a complete system failure. While a cluster contains four node is it possible that two node Error occurs and the system remains operational.

Additionally, teams should always consider simpler system designs, such as: serverlessFunctionality.

Mistake 3: Improper monitoring and limited resources

Monitoring and setting appropriate limits for your resources is critical to efficient Kubernetes usage. Many teams make the mistake of using too many resources, which leads to unnecessary costs. While others play around, pod evictions and system bottlenecks occur.

The solution lies in calibration through experiments and monitoring.

Mistake 4: Neglecting autoscaling and flexibility

This exposes the common mistake of not implementing horizontal pod autoscaling or GCP autopilot. This leads to unnecessary costs during off-peak times and system bottlenecks during busy periods. To solve this problem, enable autoscaling and autopilot so that Kubernetes automatically adds pods or nodes as needed.

Mistake 5: Insufficient security measures and assuming that namespaces can protect your Kubernetes environment

Think, that namespace It is a misunderstanding that only secure separation between environments within a cluster is guaranteed. traffic in between Namespaces remains possible and a single permeable one pod in a forgotten one namespace can affect the entire cluster. Network policies can make it more difficult pods but without encryption and authentication of data traffic within a cluster, attacks such as spoofing and snooping remain possible. So, to secure a cluster that hosts multiple systems, threat sessions should always consider the need for such a system Service Mesh.

Mistake 6: Use Kubernetes for stateful applications unless necessary

Companies often use a Kubernetes cluster pods for caching. When a Caching Pod If the device exceeds the limits of its resources, it will be shut down Cache lost. It is of course possible to use StatefulSets, but often a managed service like Memorystore is better suited for caching needs. The same applies to databases. Trust us, it happens. Relying on Kubernetes for databases is suboptimal compared to choosing any other Civic Solution like VMs or managed services like Cloud SQL based on it.

Mistake 7: Excessive use of Kubernetes instead of managed services

This brings us to another common mistake. Although GKE is a useful enabler of Kubernetes (k8), it still requires significant maintenance and detailed maintenance knowledge is both costly and scarce. To optimize resources, it is very helpful to avoid unnecessary Kubernetes maintenance altogether. Managed services such as Cloud Run and Pub/Sub are often equally useful but significantly more efficient alternatives from an HR perspective.

Mistake 8: Unnecessary splitting of microservices

Imprudent division into microservices can lead to complexity and maintenance issues, as well as network latency costs. Not everything that can be separated needs to be separated.

It is crucial to think in functional components and strive for simplicity. Separation should only occur if it improves functionality; otherwise there will be reduced functionality, more difficult maintenance and increased overhead.

To solve this problem, an architectural approach is required. In addition to your regular sequence diagrams and architectural overview, think about functional boundaries by creating a handy overview in a Venn diagram to illustrate boundaries around and relationships between functional components. When subcomponents are naturally grouped, it may be more beneficial to leave them in a system.

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