Lesson 1Backend Node.js instrumentation: automatic vs manual tracin, HTTP middleware, capturin spans fi middleware, business logic an downstream callsDis section detail Node.js backend instrumentation, comparin automatic an manual tracin, an showin how fi capture spans fi HTTP middleware, business logic, an downstream calls fi databases, caches, an external services fi full request visibility.
Choosing automatic versus manual instrumentationTracing Express and Koa middleware chainsCapturing spans for core business operationsInstrumenting outbound HTTP and gRPC clientsHandling async context and promise boundariesLesson 2Database instrumentation: PostgreSQL query timins, slow query loggin, connection pool metrics, statement-level tracinDis section focus pon PostgreSQL instrumentation, includin query timin, slow query loggin, connection pool metrics, an statement-level tracin, so yuh can identify bottlenecks, tune queries, an understand database impact pon end-user latency.
Enabling query timing and latency histogramsConfiguring slow query thresholds and loggingMonitoring connection pool size and saturationTracing prepared statements and ORM queriesTagging queries with tenant and feature dataLesson 3Frontend instrumentation: metrics fi capture (page load, Core Web Vitals, TTFB, FID, LCP, CLS), measurin single-page application routin an synthetic transactionsDis section cover browser performance instrumentation, includin Core Web Vitals, SPA routin, an synthetic transactions, enablin yuh fi capture consistent frontend metrics, detect regressions, an link client behavior fi backend performance.
Capturing Core Web Vitals in productionMeasuring TTFB, FID, LCP, CLS, and long tasksInstrumenting SPA route changes and virtual viewsModeling synthetic user flows in the frontendTagging frontend metrics with release versionsLesson 4Loggin an metrics correlation: structured logs, log enrichment wid trace IDs, centralized log ingestion pointsDis section explain how fi correlate logs an metrics usin structured loggin, trace an span identifiers, an centralized ingestion. Yuh will learn fi build queries an dashboards dat connect events, performance, an user impact.
Designing structured log schemas and fieldsEnriching logs with trace and span identifiersCentralizing log ingestion and parsing rulesLinking metrics, logs, and traces in dashboardsDefining retention and access control policiesLesson 5Backend Java (Spring Boot) instrumentation: agent-based tracin, configurin spans fi controllers, filters, database calls an external HTTP/gRPCDis section describe Java Spring Boot instrumentation usin agents an configuration, coverin spans fi controllers, filters, database calls, an external HTTP or gRPC requests, fi achieve consistent, low-friction tracin across Java services.
Deploying Java agents in different environmentsConfiguring controller and filter span boundariesTracing JDBC, JPA, and reactive database callsInstrumenting outbound HTTP and gRPC clientsCustom spans for business and domain eventsLesson 6Distributed tracin design: trace context propagation, samplin strategies, span namin conventions an metadata/tagginDis section explain distributed tracin design, includin trace context propagation, samplin strategies, span namin, an taggin. Yuh will learn how fi create consistent, low-overhead traces dat support debuggin, SLOs, an dependency analysis.
Propagating W3C trace context across servicesDesigning head and tail sampling strategiesDefining span naming rules and hierarchiesStandardizing tags for teams and environmentsManaging trace volume and retention policiesLesson 7Frontend instrumentation: error collection (JS exceptions, source maps, unhandled promise rejections) an session/trace correlationDis section address frontend error instrumentation, includin JavaScript exceptions, source maps, unhandled promise rejections, an session correlation, so yuh can quickly diagnose client-side failures an link dem fi backend traces.
Capturing runtime JS errors and stack tracesUploading and managing source maps securelyHandling unhandled rejections and console errorsGrouping and prioritizing frontend error eventsLinking sessions to backend traces and logsLesson 8Synthetic monitorin an RUM: configurin synthetic checkout journeys an browser Real User MonitorinDis section explain how fi design an configure synthetic journeys an Real User Monitorin, so yuh can measure availability, performance, an user experience across key flows like checkout, login, an account management in production an stagin.
Designing critical synthetic user journeysConfiguring browser and API synthetic checksSetting SLAs and alert thresholds for syntheticsImplementing browser RUM beacons and samplingCorrelating RUM sessions with backend tracesLesson 9Infrastructure instrumentation: Kubernetes metrics (node, pod, container), kubelet/cadvisor, kube-state metrics an cloud provider metricsDis section cover infrastructure instrumentation fi Kubernetes an cloud platforms, includin node, pod, an container metrics, kubelet an kube-state metrics, an cloud provider telemetry, enablin capacity plannin an incident triage.
Collecting node, pod, and container metricsScraping kubelet and cAdvisor endpointsUsing kube‑state metrics for cluster healthIntegrating cloud provider metrics and quotasBuilding SLOs for infrastructure resources
