Observed Signal · Apr 10, 2026 · Technical Release · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
Instrumenting Kotlin Spring Boot with OpenTelemetry
This tutorial demonstrates how to move beyond basic logging by instrumenting a Kotlin + Spring Boot scheduled job using the OpenTelemetry Java Agent. The author shows why plain logs fail in concurrent and multi-instance environments, explains OpenTelemetry's three signal types (traces, metrics, logs) and architecture (instrumentation, Collector, exporters), and provides step-by-step instructions: clone the sample repo, enable async execution, download and attach the opentelemetry-javaagent.jar to the JVM, configure bootRun JVM args (service name and exporters), and update log patterns to include MDC-based trace_id and span_id. Sample log output illustrates how trace IDs let you filter interleaved logs to isolate a single execution. The article links the full example code on GitHub and suggests next steps such as adding structured fields and exporting telemetry to backends like Jaeger or Grafana.
Practical, hands‑on guide for implementing OpenTelemetry-based tracing and log correlation; useful to engineers building observability but not industry-shifting.
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Key Takeaways & Evidence Grounding
- Author instruments a Kotlin + Spring Boot backend job with the OpenTelemetry Java Agent
- OpenTelemetry supplies three signal types: traces, metrics, and logs, and separates instrumentation, Collector, and exporters
- The tutorial configures the agent by adding -javaagent:<path>/opentelemetry-javaagent.jar and JVM properties such as -Dotel.service.name and -Dotel.traces.exporter=logging
- The OpenTelemetry agent injects trace_id and span_id into the logging context (MDC); the article shows log pattern updates using %mdc{trace_id} and %mdc{span_id}
- Sample application repository: https://github.com/kimanikevin254/jetbrains-otel-order-summary.git
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OpenTelemetry in .NET Microservices Guide
This technical guide explains how to implement OpenTelemetry (OTel) for production-grade observability in .NET microservices. It describes OTel's role as a CNCF vendor-neutral instrumentation framework for traces, metrics and logs, and shows how .NET's native APIs (System.Diagnostics.Activity and System.Diagnostics.Metrics) integrate with the OpenTelemetry .NET SDK. The article provides concrete C# examples for automatic and manual instrumentation, recommended NuGet packages and exporter options (OTLP, Azure Monitor, Console, Jaeger/Zipkin), and deployment patterns including running the OpenTelemetry Collector in Kubernetes with tail-based sampling and Kubernetes metadata enrichment. Best practices covered include naming conventions, sampling strategies, PII scrubbing, limiting db.statement capture, and an implementation checklist for safely rolling out observability in an AKS/Kubernetes environment.
Java Observability Pipeline: Metrics, Logs, Traces Guide
A technical guide that breaks Java observability into a four-phase pipeline: instrumentation, agents/collectors, storage backends, and visualization. The article maps common tools to each phase (e.g., Micrometer/OpenTelemetry and SLF4J/Logback for instrumentation; OpenTelemetry Collector and Grafana Alloy as universal routers; Prometheus/Mimir/Datadog for metrics; Tempo/Zipkin/Jaeger for traces; Loki/OpenSearch/Elasticsearch for logs; Grafana for unified visualization). It discusses push vs pull models (Prometheus scrapes/pull; Mimir/Datadog use push), practical workflows for metric/trace/log journeys, and architectural trade-offs when choosing the LGTM integrated stack versus custom best-of-breed stacks (Prometheus, Zipkin, OpenSearch, Fluent Bit). The guide emphasizes decoupling business logic from backend storage so backends can be swapped without changing application code.
Monitoring AI Agents in Production with OpenTelemetry
This technical guide explains how to monitor autonomous AI agents in production using distributed tracing and OpenTelemetry GenAI conventions. It argues that logs alone are insufficient because one user request can spawn many LLM calls, tool invocations, retries and handoffs. The article describes span types (gen_ai.chat, gen_ai.tool, agent.step), recommends auto-instrumentation libraries (OpenLLMetry, OpenInference, OpenLIT) for minimal integration, and shows how to export OTLP traces to OpenObserve for SQL-queryable trace data, token/cost dashboards, alerting, and an MCP server for LLM-driven queries. A production checklist covers PII redaction, tail-based sampling, and four alert rules for latency, cost, tool failures and trace-volume anomalies.
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