Observability Stack — See Inside Your Systems with Metrics, Logs, and Traces
Monitoring tells you something is wrong. Observability tells you why. A proper observability stack combines metrics (what is happening system-wide), logs (what happened in a specific component), and traces (how a request flowed across services). We set up the full stack — Prometheus, Grafana, Loki, and Jaeger, or a managed alternative like Datadog — with dashboards, alerts, and the instrumentation needed to debug production issues in minutes, not hours.
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The Three Pillars of Observability
Metrics tell you the health and performance of your systems at a glance. Request rate, error rate, latency percentiles (the RED method), CPU, memory, disk — these are time-series data points that you aggregate, visualize, and alert on. Metrics are cheap to store, fast to query, and excellent for dashboards. Prometheus is the standard for self-hosted metrics; Datadog and New Relic for managed.
Logs tell you what happened in detail. A specific error, a specific request, a specific decision point in your code. Logs are high-volume, expensive to store, and slow to query — which is why you need a proper aggregation system, not ssh and grep. Structured JSON logs with consistent fields (timestamp, service, request_id, level, message) make logs queryable. Loki, Elasticsearch, and CloudWatch Logs are common backends.
Traces tell you how a request flowed across services. Service A called Service B which called Service C which queried the database — all connected by a trace ID. Traces reveal exactly where latency is introduced, which service is the bottleneck, and how cascading failures propagate. Jaeger, Tempo, and Datadog APM are common backends. OpenTelemetry is the standard instrumentation library that feeds traces (and metrics and logs) to any backend.
Each pillar alone has blind spots. Metrics tell you errors are up but not which requests are failing. Logs tell you which requests failed but not the cross-service context. Traces show the full request flow but not the system-wide trends. Together, they give you complete visibility.
Our Observability Implementation
Metrics (Prometheus + Grafana): We deploy Prometheus with service discovery (automatically scraping new services as they appear), recording rules for pre-computed aggregations, and retention configured for your data volume. Grafana connects to Prometheus with pre-built dashboards for the RED method (per-service), USE method (per-resource), and infrastructure metrics. For Kubernetes, we deploy the kube-prometheus-stack which includes node exporters, kube-state-metrics, and a full set of cluster dashboards.
Logs (Loki or ELK): We deploy Grafana Loki for log aggregation — it is significantly cheaper and simpler than Elasticsearch for most workloads. Application logs ship via Promtail (on VMs) or the Loki Docker/Kubernetes logging driver. We configure log labels for efficient querying and set retention policies to manage storage costs. For teams that need full-text search on logs, we deploy Elasticsearch with Kibana and Filebeat.
Traces (Jaeger/Tempo + OpenTelemetry): We instrument your applications with OpenTelemetry SDKs that generate traces automatically for HTTP requests, database queries, and external API calls. Traces export to Jaeger or Grafana Tempo. We configure trace sampling (capture 100% of errors, 10% of successful requests) to manage storage costs while ensuring you always have traces for failures.
Alerting: We configure Alertmanager (with Prometheus) or Grafana Alerting with tiered severity. Critical alerts (service down, error rate > 5%) go to PagerDuty or phone. Warning alerts (high latency, disk filling up) go to Slack. Info alerts (deployment completed, scaling event) go to a dedicated channel. We define alert rules based on SLOs — not just thresholds. Example: alert when the error budget for the month is burning too fast, not when the error rate exceeds a static number.
What You Get
A complete observability stack:
- Metrics — Prometheus or Datadog with per-service RED dashboards and infrastructure monitoring
- Logs — Loki or ELK with structured logging, retention policies, and query patterns
- Traces — Jaeger/Tempo with OpenTelemetry instrumentation across all services
- Dashboards — Grafana dashboards for services, infrastructure, and business metrics
- Alerting — tiered alerts with SLO-based rules and appropriate routing
- Correlation — linked metrics, logs, and traces via common labels and trace IDs
- On-call integration — PagerDuty/Opsgenie setup with escalation policies
- Runbook — debugging procedures for common alert scenarios
Why Anubiz Engineering
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