By Rez Moss | Tuesday, June 10, 2025

Kubernetes Events provide crucial insights into cluster operations, but as clusters grow, managing and analyzing these events becomes increasingly challenging. This blog post explores how to build custom event aggregation systems that help engineering teams better understand cluster behavior and troubleshoot issues more effectively.

The challenge with Kubernetes events

In a Kubernetes cluster, events are generated for various operations - from pod scheduling and container starts to volume mounts and network configurations. While these events are invaluable for debugging and monitoring, several challenges emerge in production environments:

  1. Volume: Large clusters can generate thousands of events per minute
  2. Retention: Default event retention is limited to one hour
  3. Correlation: Related events from different components are not automatically linked
  4. Classification: Events lack standardized severity or category classifications
  5. Aggregation: Similar events are not automatically grouped

To learn more about Events in Kubernetes, read the Event API reference.

Real-World value

Consider a production environment with tens of microservices where the users report intermittent transaction failures:

Traditional event aggregation process: Engineers are wasting hours sifting through thousands of standalone events spread across namespaces. By the time they look into it, the older events have long since purged, and correlating pod restarts to node-level issues is practically impossible.

With its event aggregation in its custom events: The system groups events across resources, instantly surfacing correlation patterns such as volume mount timeouts before pod restarts. History indicates it occurred during past record traffic spikes, highlighting a storage scalability issue in minutes rather than hours.

The benefit of this approach is that organizations that implement it commonly cut down their troubleshooting time significantly along with increasing the reliability of systems by detecting patterns early.

Building an Event aggregation system

This post explores how to build a custom event aggregation system that addresses these challenges, aligned to Kubernetes best practices. I've picked the Go programming language for my example.

Architecture overview

This event aggregation system consists of three main components:

  1. Event Watcher: Monitors the Kubernetes API for new events
  2. Event Processor: Processes, categorizes, and correlates events
  3. Storage Backend: Stores processed events for longer retention

Here's a sketch for how to implement the event watcher:

package main import ( "context" metav1 "k8s.io/apimachinery/pkg/apis/meta/v1" "k8s.io/client-go/kubernetes" "k8s.io/client-go/rest" eventsv1 "k8s.io/api/events/v1" ) type EventWatcher struct { clientset *kubernetes.Clientset } func NewEventWatcher(config *rest.Config) (*EventWatcher, error) { clientset, err := kubernetes.NewForConfig(config) if err != nil { return nil, err } return &EventWatcher{clientset: clientset}, nil } func (w *EventWatcher) Watch(ctx context.Context) (<-chan *eventsv1.Event, error) { events := make(chan *eventsv1.Event) watcher, err := w.clientset.EventsV1().Events("").Watch(ctx, metav1.ListOptions{}) if err != nil { return nil, err } go func() { defer close(events) for { select { case event := <-watcher.ResultChan(): if e, ok := event.Object.(*eventsv1.Event); ok { events <- e } case <-ctx.Done(): watcher.Stop() return } } }() return events, nil }

Event processing and classification

The event processor enriches events with additional context and classification:

type EventProcessor struct { categoryRules []CategoryRule correlationRules []CorrelationRule } type ProcessedEvent struct { Event *eventsv1.Event Category string Severity string CorrelationID string Metadata map[string]string } func (p *EventProcessor) Process(event *eventsv1.Event) *ProcessedEvent { processed := &ProcessedEvent{ Event: event, Metadata: make(map[string]string), } // Apply classification rules processed.Category = p.classifyEvent(event) processed.Severity = p.determineSeverity(event) // Generate correlation ID for related events processed.CorrelationID = p.correlateEvent(event) // Add useful metadata processed.Metadata = p.extractMetadata(event) return processed }

Implementing Event correlation

One of the key features you could implement is a way of correlating related Events. Here's an example correlation strategy:

func (p *EventProcessor) correlateEvent(event *eventsv1.Event) string { // Correlation strategies: // 1. Time-based: Events within a time window // 2. Resource-based: Events affecting the same resource // 3. Causation-based: Events with cause-effect relationships correlationKey := generateCorrelationKey(event) return correlationKey } func generateCorrelationKey(event *eventsv1.Event) string { // Example: Combine namespace, resource type, and name return fmt.Sprintf("%s/%s/%s", event.InvolvedObject.Namespace, event.InvolvedObject.Kind, event.InvolvedObject.Name, ) }

Event storage and retention

For long-term storage and analysis, you'll probably want a backend that supports:

  • Efficient querying of large event volumes
  • Flexible retention policies
  • Support for aggregation queries

Here's a sample storage interface:

type EventStorage interface { Store(context.Context, *ProcessedEvent) error Query(context.Context, EventQuery) ([]ProcessedEvent, error) Aggregate(context.Context, AggregationParams) ([]EventAggregate, error) } type EventQuery struct { TimeRange TimeRange Categories []string Severity []string CorrelationID string Limit int } type AggregationParams struct { GroupBy []string TimeWindow string Metrics []string }

Good practices for Event management

  1. Resource Efficiency

    • Implement rate limiting for event processing
    • Use efficient filtering at the API server level
    • Batch events for storage operations

  2. Scalability

    • Distribute event processing across multiple workers
    • Use leader election for coordination
    • Implement backoff strategies for API rate limits

  3. Reliability

    • Handle API server disconnections gracefully
    • Buffer events during storage backend unavailability
    • Implement retry mechanisms with exponential backoff

Advanced features

Pattern detection

Implement pattern detection to identify recurring issues:

type PatternDetector struct { patterns map[string]*Pattern threshold int } func (d *PatternDetector) Detect(events []ProcessedEvent) []Pattern { // Group similar events groups := groupSimilarEvents(events) // Analyze frequency and timing patterns := identifyPatterns(groups) return patterns } func groupSimilarEvents(events []ProcessedEvent) map[string][]ProcessedEvent { groups := make(map[string][]ProcessedEvent) for _, event := range events { // Create similarity key based on event characteristics similarityKey := fmt.Sprintf("%s:%s:%s", event.Event.Reason, event.Event.InvolvedObject.Kind, event.Event.InvolvedObject.Namespace, ) // Group events with the same key groups[similarityKey] = append(groups[similarityKey], event) } return groups } func identifyPatterns(groups map[string][]ProcessedEvent) []Pattern { var patterns []Pattern for key, events := range groups { // Only consider groups with enough events to form a pattern if len(events) < 3 { continue } // Sort events by time sort.Slice(events, func(i, j int) bool { return events[i].Event.LastTimestamp.Time.Before(events[j].Event.LastTimestamp.Time) }) // Calculate time range and frequency firstSeen := events[0].Event.FirstTimestamp.Time lastSeen := events[len(events)-1].Event.LastTimestamp.Time duration := lastSeen.Sub(firstSeen).Minutes() var frequency float64 if duration > 0 { frequency = float64(len(events)) / duration } // Create a pattern if it meets threshold criteria if frequency > 0.5 { // More than 1 event per 2 minutes pattern := Pattern{ Type: key, Count: len(events), FirstSeen: firstSeen, LastSeen: lastSeen, Frequency: frequency, EventSamples: events[:min(3, len(events))], // Keep up to 3 samples } patterns = append(patterns, pattern) } } return patterns }

With this implementation, the system can identify recurring patterns such as node pressure events, pod scheduling failures, or networking issues that occur with a specific frequency.

Real-time alerts

The following example provides a starting point for building an alerting system based on event patterns. It is not a complete solution but a conceptual sketch to illustrate the approach.

type AlertManager struct { rules []AlertRule notifiers []Notifier } func (a *AlertManager) EvaluateEvents(events []ProcessedEvent) { for _, rule := range a.rules { if rule.Matches(events) { alert := rule.GenerateAlert(events) a.notify(alert) } } }

Conclusion

A well-designed event aggregation system can significantly improve cluster observability and troubleshooting capabilities. By implementing custom event processing, correlation, and storage, operators can better understand cluster behavior and respond to issues more effectively.

The solutions presented here can be extended and customized based on specific requirements while maintaining compatibility with the Kubernetes API and following best practices for scalability and reliability.

Next steps

Future enhancements could include:

  • Machine learning for anomaly detection
  • Integration with popular observability platforms
  • Custom event APIs for application-specific events
  • Enhanced visualization and reporting capabilities

For more information on Kubernetes events and custom controllers, refer to the official Kubernetes documentation.