Best of OpenTelemetryDecember 2025

  1. 1
    Article
    Avatar of hnHacker News·22w

    Your Logs Are Lying To You

    Traditional logging practices fail in modern distributed systems because they produce fragmented, context-poor log lines that are difficult to search and correlate. The solution is "wide events" (also called canonical log lines): emitting one comprehensive, structured event per request per service that contains all relevant context—user data, business metrics, infrastructure details, and error information. This approach transforms debugging from text searching into structured querying, enabling complex questions to be answered with simple SQL-like queries. Key implementation strategies include building events throughout the request lifecycle, using tail-based sampling to keep all errors while sampling successful requests, and deliberately instrumenting code with business context rather than relying on auto-instrumentation alone.

  2. 2
    Article
    Avatar of cncfCNCF·24w

    Building microservices the easy way with Dapr

    Dapr is a CNCF graduated project that simplifies microservices development by providing a sidecar runtime that handles distributed system concerns like messaging, pub-sub, service communication, storage, and secrets management. Built with observability in mind, Dapr automatically propagates traces and metrics across asynchronous and synchronous systems without requiring manual instrumentation. Recent additions include workflow orchestration, AI/LLM integration through a Conversation API, and Dapr Agents for durable autonomous workflows. The project was open source from inception, joined CNCF as an incubating project in 2021, and graduated in October 2024 with thousands of contributors from hundreds of organizations.

  3. 3
    Article
    Avatar of MLflowmlflow·21w

    AI Observability for Every TypeScript LLM Stack

    MLflow 3.6 introduces automatic tracing integrations for TypeScript and JavaScript LLM frameworks including Vercel AI SDK, LangChain.js, LangGraph.js, Mastra, Anthropic, and Gemini. These integrations use OpenTelemetry to send traces to MLflow's tracking server, capturing prompt/response payloads, token usage, tool results, and errors. Setup requires minimal configuration—typically just pointing an OTLP endpoint to your MLflow server and wrapping SDK clients. MLflow can be deployed via Docker Compose or managed cloud services, eliminating the need for a Python environment alongside JavaScript stacks.

  4. 4
    Article
    Avatar of allthingsopenAll Things Open·21w

    6 must-read Linux and open source tutorials of the year

    A curated roundup of six practical tutorials covering diverse open source topics: DDEV for unified local development across 26 frameworks, limitations of AI coding assistants, tldr-pages as a modern alternative to man pages, implementing OpenTelemetry observability in Django applications, FreeDOS 1.4's 30th anniversary release with retrocomputing features, and ten essential open source tools for everyday use. The collection spans modern development workflows, AI tooling critique, documentation improvements, application monitoring, and computing heritage preservation.

  5. 5
    Article
    Avatar of foojayioFoojay.io·24w

    OpenTelemetry Guide

    Spring Boot 4 introduces native OpenTelemetry support through a single starter dependency, simplifying observability implementation. The guide covers configuring metrics, traces, and logs using the OTLP protocol, including step-by-step setup with Micrometer integration, Logback appender configuration, and Docker Compose testing with Grafana. This eliminates the need for multiple dependencies and Java agents required in Spring Boot 3, while providing seamless integration with GraalVM and AOT compilation.