Monitoring and Evidence-First Incidents

Scenario: The login page is healthy while builds wait an hour behind hung deployments, prompting an evidence-destroying restart.

Pipeline example

pipeline { agent any; options { timeout(time: 30, unit: 'MINUTES'); timestamps() }; stages { stage('Bounded work') { steps { sh './delivery.sh' } } } }

Observe scheduling and execution

Monitor JVM heap and collection, CPU, disk bytes and inodes, HTTP latency, remoting, online agents, executor use, queue length, and oldest item age. Segment queue by label so a missing Windows node is not hidden by idle Linux capacity. Track build duration percentiles, provisioning, checkout, flaky tests, retries, and external timeouts. Alert on user impact and exhaustion trends.

Bound Pipelines with timeouts and concurrency controls so hung delivery does not become platform failure. Retention prevents disk exhaustion, but deleting logs during an incident destroys evidence. Correlate queue growth with executor utilization and dependency health; adding agents during a repository outage amplifies pressure.

Preserve evidence before restarting

Capture timeline, affected labels and jobs, queue reasons, controller and agent logs, support data, disk state, recent core/plugin/configuration changes, and external status. Find the first causal error rather than the last cascade. Restart only with a hypothesis about cleared state and lost running work.

Communicate scope, build links, workaround, owner, and next update. Convert findings into a guardrail: timeout, label capacity alert, plugin canary, restore test, ownership route, or runbook. Fast repair depends on distinguishing controller health, scheduling health, agent health, and external delivery-system health.

Goal: Practice the concrete behavior in jenkins-artifact-promotion. Learn sections have no Jenkins terminal; the Jenkins lab opens the real shared service in a new full-screen tab inside your private folder.