prometheus
Prometheus Monitoring and Alerting
Overview
Prometheus is a powerful open-source monitoring and alerting system designed for reliability and scalability in cloud-native environments. Built for multi-dimensional time-series data with flexible querying via PromQL.
Architecture Components
- Prometheus Server: Core component that scrapes and stores time-series data with local TSDB
- Alertmanager: Handles alerts, deduplication, grouping, routing, and notifications to receivers
- Pushgateway: Allows ephemeral jobs to push metrics (use sparingly - prefer pull model)
- Exporters: Convert metrics from third-party systems to Prometheus format (node, blackbox, etc.)
- Client Libraries: Instrument application code (Go, Java, Python, Rust, etc.)
- Prometheus Operator: Kubernetes-native deployment and management via CRDs
- Remote Storage: Long-term storage via Thanos, Cortex, Mimir for multi-cluster federation
Data Model
- Metrics: Time-series data identified by metric name and key-value labels
More from cosmix/loom
ci-cd
Designs and implements CI/CD pipelines for automated testing, building, deployment, and security scanning across multiple platforms. Covers pipeline optimization, test integration, artifact management, and release automation.
61grafana
|
58logging-observability
Comprehensive logging and observability patterns for production systems including structured logging, distributed tracing, metrics collection, log aggregation, and alerting. Triggers for this skill - log, logging, logs, trace, tracing, traces, metrics, observability, OpenTelemetry, OTEL, Jaeger, Zipkin, structured logging, log level, debug, info, warn, error, fatal, correlation ID, span, spans, ELK, Elasticsearch, Loki, Datadog, Prometheus, Grafana, distributed tracing, log aggregation, alerting, monitoring, JSON logs, telemetry.
56data-validation
Data validation patterns including schema validation, input sanitization, output encoding, and type coercion. Use when implementing validate, validation, schema, form validation, API validation, JSON Schema, Zod, Pydantic, Joi, Yup, sanitize, sanitization, XSS prevention, injection prevention, escape, encode, whitelist, constraint checking, invariant validation, data pipeline validation, ML feature validation, or custom validators.
50event-driven
Event-driven architecture patterns including message queues, pub/sub, event sourcing, CQRS, and sagas. Use when implementing async messaging, distributed transactions, event stores, command query separation, domain events, integration events, data streaming, choreography, orchestration, or integrating with RabbitMQ, Kafka, Apache Pulsar, AWS SQS, AWS SNS, NATS, event buses, or message brokers.
46refactoring
|
40