Best of Observability — August 2025

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    Article
    Avatar of microservicesioMicroservices.io·36w

    Microservices rules #9: Develop observable services

    Part of a comprehensive microservices rules series, this installment focuses on developing observable services as a critical architectural principle. Observability enables fast flow in microservices environments by providing visibility into system behavior, performance, and issues. The rule emphasizes the importance of designing services with built-in observability capabilities from the ground up, rather than adding monitoring as an afterthought.

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    Article
    Avatar of last9Last9·33w

    What is Database Monitoring

    Database monitoring involves tracking query execution times, resource utilization, and connection patterns to proactively identify performance issues before they impact users. The guide covers native database tools versus unified observability platforms, monitoring strategies for different database architectures (relational vs NoSQL), and implementation approaches including metrics collection, alerting, and correlation with application performance. It emphasizes the importance of real-time observability in distributed systems and provides practical examples for PostgreSQL, MySQL, SQL Server, and Oracle monitoring setups.

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    Article
    Avatar of svelteSvelte Blog·34w

    Introducing integrated observability in SvelteKit

    SvelteKit now includes built-in OpenTelemetry tracing support and a dedicated instrumentation.server.ts file for setting up observability monitoring. The framework can emit traces for handle hooks, load functions, form actions, and remote functions, with spans including request attributes and context information. The instrumentation file ensures monitoring tools are loaded before application code, with support across all official SvelteKit adapters that have server components.

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    Article
    Avatar of spaceliftSpacelift·36w

    Kubernetes Observability: Pillars, Tools & Best Practices

    Kubernetes observability involves collecting metrics, logs, and traces to understand cluster internal state and performance. The three pillars include metrics for quantitative data, logs for timestamped events, and traces for request paths through microservices. Key tools include Metrics-Server for basic monitoring, Kube-Prometheus-Stack for comprehensive metrics and visualization, ELK stack for log management, and OpenTelemetry for distributed tracing. Implementation challenges include managing multiple data types, monitoring dynamic resources, handling large data volumes, and preventing data silos. Best practices emphasize setting up alerts, consistent resource labeling, application instrumentation, selective data collection, and compliance alignment.

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    Article
    Avatar of freecodecampfreeCodeCamp·33w

    How to Debug Kubernetes Pods with Traceloop: A Complete Beginner's Guide

    Traceloop provides system call tracing for Kubernetes pods using eBPF technology, acting as a flight recorder that continuously captures application interactions with the operating system. The guide covers installation through Inspektor Gadget, demonstrates debugging scenarios including container crashes and startup failures, and shows how to analyze system calls to diagnose issues that traditional kubectl commands and logs cannot reveal.

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