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Docker & OpenTelemetry: Comprehensive Microservices Monitoring Without Touching Code

By admin June 22, 2026

Integrate OpenTelemetry into Docker for comprehensive Microservices monitoring. Detailed guide on using Auto-instrumentation to collect Traces/Metrics without modifying code.

Monitoring

Grafana Tempo: Setting Up Distributed Tracing and Integrating Loki + Prometheus in a Single Grafana

By admin June 13, 2026

I once spent 3 hours debugging a slow request without finding the bottleneck — that's what pushed me to set up Grafana Tempo. This article walks through installing Tempo with Docker Compose, connecting it to Loki and Prometheus, and configuring OpenTelemetry from Python so that from a single trace ID you can jump straight to logs and metrics in just a few clicks.

Monitoring

Getting Started with Grafana Alloy: Replace Prometheus Agent, Promtail, and OpenTelemetry Collector with a Single Telemetry Collection Tool

By admin June 2, 2026

Grafana Alloy merges Prometheus Agent, Promtail, and OpenTelemetry Collector into a single binary. This guide covers installation, configuring a pipeline to collect metrics, logs, and traces, along with practical tips from 6 months of running it in production.

Monitoring

Self-Hosting SigNoz with Docker: The Open-Source APM Alternative to Datadog

By admin May 25, 2026

Prometheus + Grafana are great at tracking metrics, but they won't help you correlate traces and logs when debugging. Self-hosted SigNoz solves this by consolidating Metrics, Traces, and Logs into a single interface, built on OpenTelemetry and ClickHouse — completely free, lighter than ELK, and straightforward to deploy with Docker Compose.

Monitoring

Monitoring Distributed Applications with OpenTelemetry and Jaeger: Finding Performance Bottlenecks Through Distributed Traces

By admin April 25, 2026

Prometheus can tell you your system is slow, but finding which service in a microservices chain is the actual bottleneck requires distributed tracing. This guide walks through setting up OpenTelemetry and Jaeger, instrumenting a Python application, and reading traces to diagnose performance issues — based on real-world experience.