How did you determine the necessary resources for the service in Kubernetes?

Determining the necessary resources for a service in Kubernetes is an important step to ensure stable operation and efficient use of infrastructure.

Main steps:

1. Load analysis — study how much CPU and memory the service consumes under different scenarios (load testing, profiling).
2. Real-time monitoring — run the service with minimal resources and collect metrics (e.g., using Prometheus).
3. Setting requests and limits — based on collected data, define the minimum guaranteed resources (requests) and maximum (limits) so that Kubernetes can efficiently schedule pods.
4. Resource reservation — consider peak loads and buffer for growth.
5. Auto-scaling — configure Horizontal Pod Autoscaler (HPA) for dynamic adjustment of the number of replicas based on load.

Example in Kubernetes manifest:

resources:
  requests:
    cpu: "500m"
    memory: "256Mi"
  limits:
    cpu: "1"
    memory: "512Mi"

In a Go service, profiling with pprof can be added to collect data on resource consumption.

Thus, the approach is based on empirical data and monitoring, not guesses.