Implementing Prometheus Metrics for DNS Services: A Complete Observability Strategy

In modern network infrastructure, visibility is paramount. DNS services, being critical infrastructure components, require comprehensive monitoring to ensure reliability, performance, and security. This post explores implementing Prometheus metrics for DNS services, with practical examples from building a custom CoreDNS deployment for wireless infrastructure.

Monitoring

Implementing Prometheus Metrics for DNS Services: A Complete Observability Strategy

Introduction

In modern network infrastructure, visibility is paramount. DNS services, being critical infrastructure components, require comprehensive monitoring to ensure reliability, performance, and security. This post explores implementing Prometheus metrics for DNS services, with practical examples from building a custom CoreDNS deployment for wireless infrastructure.

The Observability Challenge in DNS Services

Traditional DNS Monitoring Limitations

  • Limited visibility: Basic up/down monitoring insufficient for production
  • Performance blind spots: No insight into query response times
  • Capacity planning difficulties: Unclear resource utilization trends
  • Security gaps: No monitoring of DNS-based attacks or anomalies
  • Troubleshooting complexity: Lack of detailed operational metrics

Modern Observability Requirements

# DNS service observability goals
observability:
 availability: 99.99%
 response_time: < 10ms (cached), < 100ms (recursive)
 query_success_rate: > 99.9%
 security_monitoring: real-time threat detection
 capacity_planning: predictive scaling metrics

Prometheus Integration Architecture

CoreDNS Prometheus Plugin Configuration

# Corefile configuration for metrics
.:53 {
 prometheus :11915
 forward . 8.8.8.8 8.8.4.4
 cache 30
 log
 errors
}

Key Configuration Elements: - Port 11915: Dedicated metrics endpoint separate from DNS port - Prometheus plugin: Native CoreDNS metrics export - Cache integration: Metrics on cache performance - Error tracking: Detailed error classification and counting - Logging correlation: Metrics aligned with log data

Container Metrics Exposure

FROM coredns/coredns:1.11.1 # Expose DNS port (53) and metrics port (11915)
EXPOSE 53/udp 53/tcp
EXPOSE 11915/tcp # Health check using metrics endpoint
HEALTHCHECK --interval=30s --timeout=3s \
 CMD wget -q --spider http://localhost:11915/metrics || exit 1

Design Principles: 1. Separate ports: DNS and metrics on different ports for security 2. Health checks: Metrics endpoint doubles as health check 3. Container-native: Metrics accessible from container orchestration 4. Security isolation: Metrics port not exposed externally

Core DNS Metrics Categories

Query Performance Metrics

# Query response time by type
coredns_dns_request_duration_seconds_bucket{job="project"} # Query rate by type and response code
rate(coredns_dns_requests_total{job="project"}[5m]) # Response code distribution
coredns_dns_responses_total{job="project"}

Key Performance Indicators: - Response time percentiles: P50, P95, P99 latency measurements - Query throughput: Requests per second by query type - Success rate: Percentage of successful DNS resolutions - Error classification: NXDOMAIN, SERVFAIL, timeout rates

Cache Performance Metrics

# Cache hit ratio
(
 rate(coredns_cache_hits_total{job="project"}[5m]) /
 rate(coredns_cache_requests_total{job="project"}[5m])
) * 100 # Cache size and capacity
coredns_cache_entries{job="project"}
coredns_cache_capacity{job="project"}

Cache Optimization Metrics: - Hit ratio: Percentage of queries served from cache - Cache size: Current number of cached entries - Eviction rate: How frequently cache entries are removed - Memory usage: Cache memory consumption patterns

Upstream Resolver Metrics

# Upstream query success rate
rate(coredns_forward_requests_total{rcode="NOERROR",job="project"}[5m]) /
rate(coredns_forward_requests_total{job="project"}[5m]) # Upstream response time
coredns_forward_request_duration_seconds{job="project"}

Upstream Monitoring: - Resolver health: Success rates for each upstream DNS server - Failover behavior: Automatic switching between upstream servers - Response time correlation: Impact of upstream latency on client experience - Load distribution: Query distribution across upstream servers

Advanced Metrics Implementation

Custom Metrics for Wireless Infrastructure

# Custom CoreDNS plugin configuration for wireless-specific metrics
.:53 {
 prometheus :11915 {
 # Custom metrics for wireless infrastructure
 enable_wireless_metrics true
 pgw_zone_tracking true
 subscriber_query_classification true
 }  # Wireless-specific plugins
 wireless_pgw {
 metrics_enabled true
 zone_classification true
 }  forward . 8.8.8.8 8.8.4.4 {
 health_check 5s
 max_fails 3
 }  cache 300 {
 success 9984 30
 denial 9984 30
 prefetch 1 60m 10%
 }  log {
 class denial error
 }
}

Wireless-Specific Metrics:

# PGW zone query distribution
coredns_wireless_pgw_queries_total{zone="private-pgw-east"} # Subscriber query classification
coredns_wireless_subscriber_queries_total{type="data", category="high_priority"} # Zone health metrics
coredns_wireless_zone_health{zone="private-pgw-east", status="healthy"}

Security Metrics

# DNS query anomaly detection
rate(coredns_dns_requests_total{qtype!~"A|AAAA|CNAME|MX"}[5m]) # Query size distribution (potential DNS amplification)
histogram_quantile(0.95, coredns_dns_request_size_bytes_bucket{job="project"}) # Client query patterns (potential DDoS)
topk(10, rate(coredns_dns_requests_total{job="project"}[5m]) by (client_ip))

Security Monitoring Capabilities: - Anomaly detection: Unusual query patterns or types - Attack pattern recognition: DNS amplification, DDoS indicators - Client behavior analysis: Suspicious query patterns - Threat intelligence: Integration with security feeds

Monitoring Dashboard Design

Grafana Dashboard Configuration

{
 "dashboard": {
 "title": "Wireless PWG DNS Service",
 "panels": [
 {
 "title": "Query Rate",
 "type": "graph",
 "targets": [
 {
 "expr": "rate(coredns_dns_requests_total{job=\"project\"}[5m])",
 "legendFormat": "{{qtype}} queries/sec"
 }
 ]
 },
 {
 "title": "Response Time Percentiles",
 "type": "graph", 
 "targets": [
 {
 "expr": "histogram_quantile(0.50, rate(coredns_dns_request_duration_seconds_bucket{job=\"project\"}[5m]))",
 "legendFormat": "P50"
 },
 {
 "expr": "histogram_quantile(0.95, rate(coredns_dns_request_duration_seconds_bucket{job=\"project\"}[5m]))",
 "legendFormat": "P95"
 }
 ]
 }
 ]
 }
}

Key Dashboard Panels

1. Service Health Overview

# Service uptime
up{job="project"} # Error rate
(
 rate(coredns_dns_responses_total{rcode!="NOERROR",job="project"}[5m]) /
 rate(coredns_dns_responses_total{job="project"}[5m])
) * 100

2. Performance Monitoring

# Query throughput
sum(rate(coredns_dns_requests_total{job="project"}[5m])) # Cache efficiency
(
 rate(coredns_cache_hits_total{job="project"}[5m]) /
 (rate(coredns_cache_hits_total{job="project"}[5m]) + 
 rate(coredns_cache_misses_total{job="project"}[5m]))
) * 100

3. Resource Utilization

# Memory usage
container_memory_usage_bytes{name=~".*project.*"} # CPU utilization
rate(container_cpu_usage_seconds_total{name=~".*project.*"}[5m]) * 100

Alerting Strategy

Critical Service Alerts

# Prometheus alerting rules
groups:
 - name: wireless_dns_alerts
 rules:
 - alert: DNSServiceDown
 expr: up{job="project"} == 0
 for: 1m
 labels:
 severity: critical
 annotations:
 summary: "DNS service is down"
 description: "Wireless PWG DNS service has been down for more than 1 minute"  - alert: HighDNSErrorRate
 expr: |
 (
 rate(coredns_dns_responses_total{rcode!="NOERROR",job="project"}[5m]) /
 rate(coredns_dns_responses_total{job="project"}[5m])
 ) * 100 > 5
 for: 2m
 labels:
 severity: warning
 annotations:
 summary: "High DNS error rate detected"  - alert: DNSResponseTimeHigh
 expr: |
 histogram_quantile(0.95, 
 rate(coredns_dns_request_duration_seconds_bucket{job="project"}[5m])
 ) > 0.1
 for: 5m
 labels:
 severity: warning
 annotations:
 summary: "DNS response time P95 > 100ms"

Performance Degradation Alerts

 - alert: CacheHitRateLow
 expr: |
 (
 rate(coredns_cache_hits_total{job="project"}[10m]) /
 rate(coredns_cache_requests_total{job="project"}[10m])
 ) * 100 < 80
 for: 5m
 labels:
 severity: info
 annotations:
 summary: "DNS cache hit ratio below 80%"  - alert: UpstreamResolverDown
 expr: |
 coredns_forward_healthcheck_failures_total{job="project"} > 3
 for: 1m
 labels:
 severity: warning
 annotations:
 summary: "Upstream DNS resolver failing health checks"

Metrics Collection Architecture

Prometheus Configuration

# prometheus.yml configuration for DNS metrics
global:
 scrape_interval: 15s
 evaluation_interval: 15s scrape_configs:
 - job_name: 'project'
 static_configs:
 - targets: ['dns-service:11915']
 scrape_interval: 5s
 scrape_timeout: 4s
 metrics_path: /metrics  # Additional labels for service identification
 relabel_configs:
 - source_labels: [__address__]
 target_label: __param_target
 - source_labels: [__param_target]
 target_label: instance
 - target_label: __address__
 replacement: dns-service:11915  # Metric filtering for performance
 metric_relabel_configs:
 - source_labels: [__name__]
 regex: 'coredns_dns_.*'
 action: keep

Service Discovery Integration

# Kubernetes service discovery for dynamic environments
scrape_configs:
 - job_name: 'kubernetes-dns-services'
 kubernetes_sd_configs:
 - role: service
 namespaces:
 names:
 - wireless-infrastructure  relabel_configs:
 - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scrape]
 action: keep
 regex: true
 - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_path]
 action: replace
 target_label: __metrics_path__
 regex: (.+)

Performance Optimization

Metrics Collection Efficiency

# Optimized metrics collection
prometheus:
 retention: "30d"
 storage:
 local:
 path: "/prometheus/data"
 retention: "30d"  # Reduce cardinality for high-volume metrics
 metric_relabel_configs:
 - source_labels: [client_ip]
 regex: '.*'
 replacement: 'aggregated'
 target_label: client_ip  # Sample reduction for detailed metrics
 sample_limit: 10000
 target_limit: 100

High-Cardinality Metric Management

# Use recording rules for expensive queries
groups:
 - name: dns_recording_rules
 interval: 30s
 rules:
 - record: wireless_dns:query_rate_5m
 expr: rate(coredns_dns_requests_total{job="project"}[5m])  - record: wireless_dns:error_rate_5m
 expr: |
 rate(coredns_dns_responses_total{rcode!="NOERROR",job="project"}[5m]) /
 rate(coredns_dns_responses_total{job="project"}[5m])  - record: wireless_dns:cache_hit_ratio_10m
 expr: |
 rate(coredns_cache_hits_total{job="project"}[10m]) /
 rate(coredns_cache_requests_total{job="project"}[10m])

Integration with External Systems

Consul Service Discovery Metrics

# CoreDNS configuration with Consul integration
.:53 {
 consul {
 endpoint http://consul.service.consul:8500
 }  prometheus :11915 {
 enable_consul_metrics true
 }
}

Consul Integration Metrics:

# Service discovery health
coredns_consul_services_discovered{job="project"} # Consul query performance
coredns_consul_query_duration_seconds{job="project"}

Log Correlation

# Structured logging with metrics correlation
log {
 format combined
 correlation_id_header "X-Request-ID"
 include_metrics_labels true
}

Capacity Planning and Scaling

Predictive Metrics

# Query volume trend analysis
predict_linear(wireless_dns:query_rate_5m[6h], 86400) # Memory usage growth prediction
predict_linear(container_memory_usage_bytes{name=~".*project.*"}[2h], 3600) # Cache utilization trending
deriv(coredns_cache_entries{job="project"}[1h])

Auto-scaling Integration

# Kubernetes HPA based on custom metrics
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
 name: wireless-dns-hpa
spec:
 scaleTargetRef:
 apiVersion: apps/v1
 kind: Deployment
 name: project
 minReplicas: 3
 maxReplicas: 10
 metrics:
 - type: Pods
 pods:
 metric:
 name: dns_queries_per_second
 target:
 type: AverageValue
 averageValue: "1000"

Troubleshooting with Metrics

Common DNS Issues and Metrics

1. High Response Times

# Identify slow query types
topk(5, 
 histogram_quantile(0.95, 
 rate(coredns_dns_request_duration_seconds_bucket{job="project"}[5m])
 ) by (qtype)
)

2. Cache Inefficiency

# Cache miss rate by query type
(
 rate(coredns_cache_misses_total{job="project"}[5m]) /
 rate(coredns_cache_requests_total{job="project"}[5m])
) by (qtype) * 100

3. Upstream Resolver Issues

# Upstream resolver response time comparison
avg(coredns_forward_request_duration_seconds{job="project"}) by (to)

Debug Metrics

# Enhanced debugging configuration
.:53 {
 debug {
 enable_metrics true
 detailed_timing true
 }  prometheus :11915 {
 enable_debug_metrics true
 histogram_buckets 0.001,0.01,0.1,1,10
 }
}

Security and Compliance

Metrics Security

# Secure metrics endpoint
prometheus :11915 {
 path /metrics
 allowed_ips 10.0.0.0/8,172.16.0.0/12,192.168.0.0/16
 basic_auth {
 username prometheus
 password_file /etc/secrets/metrics-password
 }
}

Audit Metrics

# Security-focused metrics queries
# Unusual query patterns
rate(coredns_dns_requests_total{qtype=~"TXT|ANY"}[5m]) # High-volume clients (potential DDoS)
topk(10, sum(rate(coredns_dns_requests_total{job="project"}[5m])) by (client)) # Failed authentication attempts
rate(coredns_auth_failures_total{job="project"}[5m])

Future Enhancements

AI-Powered Analytics

  • Anomaly detection: Machine learning for unusual pattern recognition
  • Predictive scaling: AI-driven capacity planning
  • Intelligent alerting: Context-aware alert prioritization
  • Performance optimization: Automated tuning recommendations

Enhanced Observability

  • Distributed tracing: Request flow visualization across services
  • Custom metrics: Business-specific KPIs and measurements
  • Real-time analytics: Stream processing for immediate insights
  • Correlation analysis: Cross-service impact analysis

Conclusion

Implementing comprehensive Prometheus metrics for DNS services transforms operational visibility from reactive troubleshooting to proactive optimization. The key success factors include:

  1. Comprehensive coverage: Monitor all aspects from queries to infrastructure
  2. Actionable alerts: Focus on metrics that drive operational decisions
  3. Performance optimization: Balance detail with collection efficiency
  4. Integration strategy: Connect metrics with logs, traces, and external systems
  5. Security awareness: Monitor for both performance and security implications

The investment in robust DNS metrics pays dividends through: - Improved reliability: Proactive issue identification and resolution - Better performance: Data-driven optimization opportunities - Enhanced security: Real-time threat detection and response - Capacity planning: Accurate forecasting for resource requirements - Operational excellence: Complete visibility into service behavior

As network infrastructure becomes increasingly complex, metrics-driven observability becomes not just beneficial but essential for maintaining reliable, secure, and performant DNS services in production environments.

About the Author: Jagannath S specializes in implementing comprehensive monitoring solutions for telecommunications infrastructure. Connect to discuss Prometheus metrics, DNS monitoring strategies, or network service observability.