Challenging in selecting good metrics for monitoring:-
Choosing good metrics for Kubernetes monitoring can be challenging due to the need to determine relevant metrics for specific applications, manage the overwhelming volume of available data, understand complex interdependencies, lack of standardization, and balance scalability and performance impact.
The best Practices for Kubernetes Monitoring Metrics are:-
1. CPU Metrics:
– CPU Utilization: Measures the percentage of CPU capacity used by pods or containers.
– CPU Load: Monitors the average number of processes in the CPU’s run queue, indicating CPU congestion.
– CPU Usage: Tracks the CPU usage percentage by individual pods or containers.
– CPU Frequency: Measures the CPU clock speed, indicating the processing power available.
– CPU Throttling: Monitors the number of CPU throttling events, which occur when a container exceeds its CPU limit.
2. Memory Metrics:
– Memory Utilization: Measures the percentage of memory capacity used by pods or containers.
– Memory Load: Monitors the average number of processes waiting for memory, indicating memory congestion.
– Memory Usage: Tracks the memory usage percentage by individual pods or containers.
– Memory Pressure: Measures the memory pressure on nodes or containers, indicating potential resource constraints.
– Memory Capacity: Tracks the total memory capacity of nodes or containers.
– Memory Allocation: Monitors the allocated memory by individual pods or containers.
3. Network Metrics:
– Network Throughput: Measures the amount of data transmitted per unit of time, indicating network performance.
– Network Latency: Monitors the time taken for a packet to travel from source to destination, indicating network responsiveness.
– Network Packet Loss: Tracks the percentage of lost network packets, indicating network reliability.
– Network Congestion: Monitors network congestion levels, indicating potential network bottlenecks.
– Network Errors: Tracks the number of network errors or packet drops, indicating network stability.
– Network Bandwidth: Measures the available network bandwidth, indicating network capacity.
4. Storage Metrics:
– Storage Capacity: Tracks the storage capacity of persistent volumes, indicating available storage space.
– Storage Latency: Measures the time taken to read from or write to storage, indicating storage performance.
– Storage Read/Write Performance: Monitors the read and write performance of storage, indicating data transfer speeds.
– Storage Errors: Tracks the number of storage-related errors, indicating potential issues with storage systems.
– Storage Utilization: Measures the percentage of storage capacity used by persistent volumes.
5. Pod Metrics:
– Pod Creation Time: Measures the time taken to create pods, indicating deployment speed.
– Pod Execution Time: Tracks the time taken for pods to complete their execution, indicating performance efficiency.
– Pod Memory/CPU Usage: Monitors the memory and CPU usage of individual pods, indicating resource consumption.
– Pod Uptime: Tracks the duration for which pods have been running, indicating stability and reliability.
– Pod Restarts: Measures the number of times pods have been restarted, indicating potential issues.
– Pod Failures: Tracks the number of pod failures, indicating application reliability.
6. Deployment Metrics:
– Deployment Time: Measures the time taken to deploy applications, indicating deployment efficiency.
– Deployment Success Rate: Monitors the success rate of deployments, indicating application stability.
– Deployment Failure Rate: Tracks the failure rate of deployments, indicating potential issues.
– Deployment Rollbacks: Measures the number of deployment rollbacks, indicating potential deployment problems.
– Deployment Rollout Time: Monitors the time taken for deployments to roll out, indicating deployment speed.
7. Autoscaling Metrics:
– Autoscaling Events: Tracks the events triggering autoscaling actions, indicating workload changes.
– Autoscaling Thresholds: Monitors the threshold values for autoscaling, indicating when scaling should occur.
– Autoscaling Requests: Measures the number of autoscaling requests, indicating the frequency of scaling actions.
– Autoscaling Success Rate: Tracks the success rate of autoscaling actions, indicating the effectiveness of scaling.
– Autoscaling Failure Rate: Measures the failure rate of autoscaling actions, indicating potential issues with scaling algorithms.