prometheus-configuration
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: (.+)
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)
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'
Best Practices
- Use consistent naming for metrics (prefix_name_unit)
- Set appropriate scrape intervals (15-60s typical)
- Use recording rules for expensive queries
- Implement high availability (multiple Prometheus instances)
- Configure retention based on storage capacity
- Use relabeling for metric cleanup
- Monitor Prometheus itself
- Implement federation for large deployments
- Use Thanos/Cortex for long-term storage
- 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'
Related Skills
grafana-dashboards- For visualizationslo-implementation- For SLO monitoringdistributed-tracing- For request tracing
More from anton-abyzov/specweave
technical-writing
Technical writing expert for API documentation, README files, tutorials, changelog management, and developer documentation. Covers style guides, information architecture, versioning docs, OpenAPI/Swagger, and documentation-as-code. Activates for technical writing, API docs, README, changelog, tutorial writing, documentation, technical communication, style guide, OpenAPI, Swagger, developer docs.
45spec-driven-brainstorming
Spec-driven brainstorming and product discovery expert. Helps teams ideate features, break down epics, conduct story mapping sessions, prioritize using MoSCoW/RICE/Kano, and validate ideas with lean startup methods. Activates for brainstorming, product discovery, story mapping, feature ideation, prioritization, MoSCoW, RICE, Kano model, lean startup, MVP definition, product backlog, feature breakdown.
43kafka-architecture
Apache Kafka architecture expert for cluster design, capacity planning, and high availability. Use when designing Kafka clusters, choosing partition strategies, or sizing brokers for production workloads.
34docusaurus
Docusaurus 3.x documentation framework - MDX authoring, theming, versioning, i18n. Use for documentation sites or spec-weave.com.
29frontend
Expert frontend developer for React, Vue, Angular, and modern JavaScript/TypeScript. Use when creating components, implementing hooks, handling state management, or building responsive web interfaces. Covers React 18+ features, custom hooks, form handling, and accessibility best practices.
29reflect
Self-improving AI memory system that persists learnings across sessions in CLAUDE.md. Use when capturing corrections, remembering user preferences, or extracting patterns from successful implementations. Enables continual learning without starting from zero each conversation.
27