Mistake 1: Designing for Theoretical Functionality Over Developer-Friendliness
Many teams set out to architect their clusters based on the unnecessary potential that Kubernetes offers. This is a mistake. In a general DevOps team, under-considering the overhead and developer friendliness of a system, leads to a lack of adoption and a slowed-down development cycle, hindering the flow and creativity of developers.
To mitigate this, start by designing software with the human user in mind. Rather than designing functionality, design an interface and apply the “you aren’t gonna need it” principle. Trust that you can be creative in building solutions when they are actually needed. Remember, simplicity is key for future adjustments.
Information I: The “you aren’t gonna need it” (YAGNI) principle advises you to start with the simplest possible solution when designing software and avoid adding unnecessary features or complexity. Trust that you can be creative and build additional solutions in the future if and when they are actually needed, rather than burdening your system with unnecessary bloat from the start. This fits the broader view of agile development – which aims at remaining flexible, in order to integrate user feedback during development rather than over-engineering and planning in advance.
Mistake 2: Misunderstanding the Purpose of Kubernetes
Some teams adopt Kubernetes simply because it’s a well-known system, rather than selecting the best system for their specific purpose. This leads to mistakes like relying on single nodes or single pods., which defeats Kubernetes’reliability through redundancy.
Misunderstanding the idea of setting up a new cluster for each project in Kubernetes. At first glance, doing so may seem like an elegantly simple setup but it actually makes activities like system upgrades more challenging For maintenance, complexity management, and for reliability, it is usually better to have a single large cluster with many nodes for increased reliability and decreased chances of system failure. A desired separation of concerns can be achieved through the effective use of namespaces and proper networking with service meshes. In simplest terms: two clusters require two updates, two clusters with two nodes lead to complete system failure if both nodes go down, while one cluster with four nodes allows for two nodes to be down while keeping the system operational.
By extension, teams should always consider even simpler system designs like serverless functionality.
Mistake 3: Improper Monitoring and Resource Limits
Monitoring and setting appropriate resource limits are critical for efficient Kubernetes usage. Many teams make the mistake of over-provisioning resources, leading to unnecessary costs, while others make “wild guess” assessments, resulting in pod evictions and system bottlenecks.
The solution lies in calibration through experimentation and monitoring.
Mistake 4: Neglecting Autoscaling and Flexibility
This reveals the common mistake of not implementing horizontal pod autoscaling or GCP’s autopilot. This leads to unnecessary costs during low-traffic periods and system bottlenecks during high-traffic periods. To solve this, enable autoscaling and autopilot to allow Kubernetes to automatically add pods or nodes based on demand.
Mistake 5: Inadequate Security Measures: thinking namespacing can secure your Kubernetes environment
Thinking that namespacing alone provides secure separation between environments within a cluster is a misconception. Traffic between namespaces remains possible, and a single porous pod in a forgotten namespace can implicate the whole cluster. Networking policies can make it harder for pods to contact each other, but without encrypting and authenticating traffic within the cluster attacks such as spoofing and snooping remain possible. Hence, to secure a cluster that hosts multiple systems, threat sessions must always consider the need for a service mesh.
Mistake 6: Using Kubernetes for Stateful Applications Unless Necessary
, On a Kubernetes cluster, companies often use pods for caching. When a caching pod exceeds resource limits it is evicted and its cache is lost. It is of course possible to use StatefulSets but it is often better to just utilize a managed service like Memorystore for caching needs. The same holds for databasing. Believe us, it happens. Relying on Kubernetes for databases is not optimal compared to choosing a more natively stateful solution like VMs or managed services like Cloud SQL that rely on them.
Mistake 7: Overusing Kubernetes Instead of Managed Services
This brings us to another general mistake. Although GKE is a useful facilitator of Kubernetes (k8), it still requires substantial maintenance and detailed maintenance expertise is both costly and scarce. To optimize resources, it’s very helpful to avoid unnecessary Kubernetes maintenance altogether. Managed services such as Cloud Run and Pub/Sub are often as effective but much more human resource-efficient alternatives.
Mistake 8: Splitting Microservices Unnecessarily
Splitting into microservices without careful consideration can introduce complexity and maintenance challenges, along with networking latency costs. Not everything that can be separated should be. It’s crucial to think in terms of functional components and strive for simplicity. Separation should only be done if it enhances functionality; otherwise, it results in reduced functionality, decreased maintainability, and increased overhead. Mitigating this issue requires an architectural approach. On top of your regular sequence diagrams and architectural overview, think about functional boundaries by creating a functional overview in a Venn diagram to illustrate boundaries around and relationships between functional components. When subcomponents naturally cluster together, it may be more beneficial to keep them within a single system.
