Pipeline Observability#

You cannot improve what you do not measure. Most teams have detailed monitoring for their production applications but treat their CI/CD pipelines as black boxes. When builds are slow, flaky, or failing, the response is anecdotal – “builds feel slow lately” – rather than data-driven. Pipeline observability turns CI/CD from a cost center you tolerate into infrastructure you actively manage.

Core CI/CD Metrics#

Build Duration#

Total time from pipeline trigger to completion. Track this as a histogram, not an average, because averages hide bimodal distributions. A pipeline that takes 5 minutes for code-only changes and 25 minutes for dependency updates averages 15 minutes, which describes neither case accurately.

Trace, Span, and Context#

A trace represents a single request flowing through a distributed system. It is identified by a 128-bit trace ID. A span represents one unit of work within that trace – an HTTP handler, a database query, a message publish. Each span has a name, start time, duration, status, attributes (key-value pairs), and events (timestamped annotations). Spans form a tree: every span except the root has a parent span ID.

What OpenTelemetry Is#

OpenTelemetry (OTel) is a vendor-neutral framework for generating, collecting, and exporting telemetry data: traces, metrics, and logs. It provides APIs, SDKs, and the Collector – a standalone binary that receives, processes, and exports telemetry. OTel replaces the fragmented landscape of Jaeger client libraries, Zipkin instrumentation, Prometheus client libraries, and proprietary agents with a single standard.

The three signal types:

• Traces: Record the path of a request through distributed services as a tree of spans. Each span has a name, duration, attributes, and parent reference.
• Metrics: Numeric measurements (counters, gauges, histograms) emitted by applications and infrastructure. OTel metrics can be exported to Prometheus.
• Logs: Structured log records correlated with trace context. OTel log support bridges existing logging libraries with trace correlation.

The OTel Collector Pipeline#

The Collector is the central hub. It has three pipeline stages:

Setting Up Full Observability from Scratch#

This operational sequence deploys a complete observability stack on Kubernetes: metrics (Prometheus + Grafana), logs (Loki + Promtail), traces (Tempo + OpenTelemetry), and alerting (Alertmanager). Each phase is self-contained with verification steps. Complete them in order – later phases depend on earlier infrastructure.

Prerequisite: a running Kubernetes cluster with Helm installed and a monitoring namespace created.

kubectl create namespace monitoring --dry-run=client -o yaml | kubectl apply -f -
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo add grafana https://grafana.github.io/helm-charts
helm repo add open-telemetry https://open-telemetry.github.io/opentelemetry-helm-charts
helm repo update

Phase 1 – Metrics (Prometheus + Grafana)#

Metrics are the foundation. Logging and tracing integrations all route through Grafana, so this phase must be solid before continuing.