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prometheus-configuration

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Prometheus Configuration

Complete guide to Prometheus setup, metric collection, scrape configuration, and recording rules.

Purpose

Configure Prometheus for comprehensive metric collection, alerting, and monitoring of infrastructure and applications.

When to Use

  • Set up Prometheus monitoring
  • Configure metric scraping
  • Create recording rules
  • Design alert rules
  • Implement service discovery

Prometheus Architecture

┌──────────────┐
│ Applications │ ← Instrumented with client libraries
└──────┬───────┘
       │ /metrics endpoint
       ↓
┌──────────────┐
│  Prometheus  │ ← Scrapes metrics periodically
│    Server    │
└──────┬───────┘
       │
       ├─→ AlertManager (alerts)
       ├─→ Grafana (visualization)
       └─→ Long-term storage (Thanos/Cortex)

Installation

Kubernetes with Helm

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo update

helm install prometheus prometheus-community/kube-prometheus-stack \
  --namespace monitoring \
  --create-namespace \
  --set prometheus.prometheusSpec.retention=30d \
  --set prometheus.prometheusSpec.storageVolumeSize=50Gi

Docker Compose

version: "3.8"
services:
  prometheus:
    image: prom/prometheus:latest
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
      - prometheus-data:/prometheus
    command:
      - "--config.file=/etc/prometheus/prometheus.yml"
      - "--storage.tsdb.path=/prometheus"
      - "--storage.tsdb.retention.time=30d"

volumes:
  prometheus-data:

Configuration File

prometheus.yml:

global:
  scrape_interval: 15s
  evaluation_interval: 15s
  external_labels:
    cluster: "production"
    region: "us-west-2"

# Alertmanager configuration
alerting:
  alertmanagers:
    - static_configs:
        - targets:
            - alertmanager:9093

# Load rules files
rule_files:
  - /etc/prometheus/rules/*.yml

# Scrape configurations
scrape_configs:
  # Prometheus itself
  - job_name: "prometheus"
    static_configs:
      - targets: ["localhost:9090"]

  # Node exporters
  - job_name: "node-exporter"
    static_configs:
      - targets:
          - "node1:9100"
          - "node2:9100"
          - "node3:9100"
    relabel_configs:
      - source_labels: [__address__]
        target_label: instance
        regex: "([^:]+)(:[0-9]+)?"
        replacement: "${1}"

  # Kubernetes pods with annotations
  - job_name: "kubernetes-pods"
    kubernetes_sd_configs:
      - role: pod
    relabel_configs:
      - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
        action: keep
        regex: true
      - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path]
        action: replace
        target_label: __metrics_path__
        regex: (.+)
      - source_labels:
          [__address__, __meta_kubernetes_pod_annotation_prometheus_io_port]
        action: replace
        regex: ([^:]+)(?::\d+)?;(\d+)
        replacement: $1:$2
        target_label: __address__
      - source_labels: [__meta_kubernetes_namespace]
        action: replace
        target_label: namespace
      - source_labels: [__meta_kubernetes_pod_name]
        action: replace
        target_label: pod

  # Application metrics
  - job_name: "my-app"
    static_configs:
      - targets:
          - "app1.example.com:9090"
          - "app2.example.com:9090"
    metrics_path: "/metrics"
    scheme: "https"
    tls_config:
      ca_file: /etc/prometheus/ca.crt
      cert_file: /etc/prometheus/client.crt
      key_file: /etc/prometheus/client.key

Reference: See assets/prometheus.yml.template

Scrape Configurations

Static Targets

scrape_configs:
  - job_name: "static-targets"
    static_configs:
      - targets: ["host1:9100", "host2:9100"]
        labels:
          env: "production"
          region: "us-west-2"

File-based Service Discovery

scrape_configs:
  - job_name: "file-sd"
    file_sd_configs:
      - files:
          - /etc/prometheus/targets/*.json
          - /etc/prometheus/targets/*.yml
        refresh_interval: 5m

targets/production.json:

[
  {
    "targets": ["app1:9090", "app2:9090"],
    "labels": {
      "env": "production",
      "service": "api"
    }
  }
]

Kubernetes Service Discovery

scrape_configs:
  - job_name: "kubernetes-services"
    kubernetes_sd_configs:
      - role: service
    relabel_configs:
      - source_labels:
          [__meta_kubernetes_service_annotation_prometheus_io_scrape]
        action: keep
        regex: true
      - source_labels:
          [__meta_kubernetes_service_annotation_prometheus_io_scheme]
        action: replace
        target_label: __scheme__
        regex: (https?)
      - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_path]
        action: replace
        target_label: __metrics_path__
        regex: (.+)

Reference: See references/scrape-configs.md

Recording Rules

Create pre-computed metrics for frequently queried expressions:

# /etc/prometheus/rules/recording_rules.yml
groups:
  - name: api_metrics
    interval: 15s
    rules:
      # HTTP request rate per service
      - record: job:http_requests:rate5m
        expr: sum by (job) (rate(http_requests_total[5m]))

      # Error rate percentage
      - record: job:http_requests_errors:rate5m
        expr: sum by (job) (rate(http_requests_total{status=~"5.."}[5m]))

      - record: job:http_requests_error_rate:percentage
        expr: |
          (job:http_requests_errors:rate5m / job:http_requests:rate5m) * 100

      # P95 latency
      - record: job:http_request_duration:p95
        expr: |
          histogram_quantile(0.95,
            sum by (job, le) (rate(http_request_duration_seconds_bucket[5m]))
          )

  - name: resource_metrics
    interval: 30s
    rules:
      # CPU utilization percentage
      - record: instance:node_cpu:utilization
        expr: |
          100 - (avg by (instance) (rate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)

      # Memory utilization percentage
      - record: instance:node_memory:utilization
        expr: |
          100 - ((node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes) * 100)

      # Disk usage percentage
      - record: instance:node_disk:utilization
        expr: |
          100 - ((node_filesystem_avail_bytes / node_filesystem_size_bytes) * 100)

Reference: See references/recording-rules.md

Alert Rules

# /etc/prometheus/rules/alert_rules.yml
groups:
  - name: availability
    interval: 30s
    rules:
      - alert: ServiceDown
        expr: up{job="my-app"} == 0
        for: 1m
        labels:
          severity: critical
        annotations:
          summary: "Service {{ $labels.instance }} is down"
          description: "{{ $labels.job }} has been down for more than 1 minute"

      - alert: HighErrorRate
        expr: job:http_requests_error_rate:percentage > 5
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "High error rate for {{ $labels.job }}"
          description: "Error rate is {{ $value }}% (threshold: 5%)"

      - alert: HighLatency
        expr: job:http_request_duration:p95 > 1
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "High latency for {{ $labels.job }}"
          description: "P95 latency is {{ $value }}s (threshold: 1s)"

  - name: resources
    interval: 1m
    rules:
      - alert: HighCPUUsage
        expr: instance:node_cpu:utilization > 80
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "High CPU usage on {{ $labels.instance }}"
          description: "CPU usage is {{ $value }}%"

      - alert: HighMemoryUsage
        expr: instance:node_memory:utilization > 85
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "High memory usage on {{ $labels.instance }}"
          description: "Memory usage is {{ $value }}%"

      - alert: DiskSpaceLow
        expr: instance:node_disk:utilization > 90
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "Low disk space on {{ $labels.instance }}"
          description: "Disk usage is {{ $value }}%"

Validation

# Validate configuration
promtool check config prometheus.yml

# Validate rules
promtool check rules /etc/prometheus/rules/*.yml

# Test query
promtool query instant http://localhost:9090 'up'

Reference: See scripts/validate-prometheus.sh

Best Practices

  1. Use consistent naming for metrics (prefix_name_unit)
  2. Set appropriate scrape intervals (15-60s typical)
  3. Use recording rules for expensive queries
  4. Implement high availability (multiple Prometheus instances)
  5. Configure retention based on storage capacity
  6. Use relabeling for metric cleanup
  7. Monitor Prometheus itself
  8. Implement federation for large deployments
  9. Use Thanos/Cortex for long-term storage
  10. Document custom metrics

Troubleshooting

Check scrape targets:

curl http://localhost:9090/api/v1/targets

Check configuration:

curl http://localhost:9090/api/v1/status/config

Test query:

curl 'http://localhost:9090/api/v1/query?query=up'

Reference Files

  • assets/prometheus.yml.template - Complete configuration template
  • references/scrape-configs.md - Scrape configuration patterns
  • references/recording-rules.md - Recording rule examples
  • scripts/validate-prometheus.sh - Validation script

Related Skills

  • grafana-dashboards - For visualization
  • slo-implementation - For SLO monitoring
  • distributed-tracing - For request tracing

Source

git clone https://github.com/wshobson/agents/blob/main/plugins/observability-monitoring/skills/prometheus-configuration/SKILL.mdView on GitHub

Overview

Set up Prometheus for comprehensive metric collection, storage, and monitoring of infrastructure and applications. This skill covers installation with Helm or Docker Compose, configuring scrape jobs, recording and alert rules, and integrating with Alertmanager and Grafana.

How This Skill Works

Prometheus periodically scrapes instrumented endpoints at /metrics, stores time series data, and evaluates configured recording and alert rules. It routes alerts to Alertmanager and provides data for Grafana dashboards and optional long-term storage with Thanos or Cortex.

When to Use It

  • Set up Prometheus monitoring
  • Configure metric scraping
  • Create recording rules
  • Design alert rules
  • Implement service discovery

Quick Start

  1. Step 1: Install Prometheus with Helm in Kubernetes or run docker-compose for local testing
  2. Step 2: Create or edit prometheus.yml with global scrape_interval and scrape_configs
  3. Step 3: Deploy Alertmanager and Grafana and verify metrics and alerts

Best Practices

  • Start with a minimal, well defined prometheus.yml and gradually add scrape_configs
  • Use relabel_configs to normalize instance labels and avoid duplicate metrics
  • Separate concerns by using Alertmanager for alerts and Grafana for dashboards
  • Enable long term storage with Thanos or Cortex when needed
  • Version control configuration files and test changes in dev first

Example Use Cases

  • Deploy kube-prometheus-stack with Helm in a Kubernetes cluster
  • Run Prometheus with Docker Compose for local testing
  • Use Kubernetes pod discovery with annotations to scrape app metrics
  • Define recording rules to pre aggregate metrics
  • Integrate Prometheus with Grafana dashboards and Alertmanager notifications

Frequently Asked Questions

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