In this lesson, you will learn how to expose and track custom metrics from your stateful services to ensure your data synchronization remains healthy and resource consumption stays within limits.
Beyond Basic Health Checks
While Kubernetes liveness and readiness probes are excellent for checking if a container is running, they don't tell you the whole story for stateful applications like databases or message brokers. For these, you need to track "internal" health, such as data replication lag, the number of active connections, or the synchronization status between nodes in your StatefulSet.
Exposing Custom Metrics
To monitor this data, your application needs to expose a metrics endpoint (typically in Prometheus format) that reports internal state. Since your StatefulSet assigns a predictable, unique identity to each pod (e.g., my-db-0, my-db-1), you can use a headless service to scrape these specific endpoints.
For instance, your application could expose a /metrics endpoint that outputs a custom gauge:
# Example of custom state metrics
db_replication_lag_seconds{pod="my-db-0"} 0.02
db_active_sync_connections{pod="my-db-0"} 12
By tracking db_replication_lag_seconds, you can detect if a specific member of your cluster is falling behind in data synchronization—a critical indicator that your stateful service is struggling, even if the pod itself reports as "Ready."
Tracking Resource Consumption
Because stateful workloads often manage high-performance volumes or local persistent storage, monitoring resource consumption is just as vital as data health. You should monitor:
- Disk I/O Wait times: High wait times often suggest that your storage backend cannot keep up with write volume.
- Memory usage of cache buffers: If memory usage spikes, your stateful application might be forced to swap to disk, drastically degrading performance.
By visualizing these custom metrics alongside standard Kubernetes CPU and memory usage, you gain a complete picture of whether your stateful application is healthy, synchronized, and performing optimally under the current load.
Key Takeaway: Monitor internal state metrics like replication lag and disk I/O alongside standard health probes to ensure your stateful applications are truly synchronized and performant.