Tempo + Alloy + Grafana: Distributed Tracing

Tempo for Storage, Alloy for Collection, Grafana for Visualization

What You'll Learn

  • Deploy Tempo for trace storage with automatic Prometheus metrics generation from trace data
  • Configure Alloy as a telemetry collection agent for routing traces to appropriate backends
  • Build comprehensive Grafana dashboards with trace exploration and service dependency visualization
  • Analyze distributed traces to identify performance bottlenecks and root causes in microservices
  • Master advanced trace filtering and correlation techniques for effective performance troubleshooting

Description

Deploy a complete distributed tracing stack using Tempo, Alloy, and Grafana on Kubernetes to solve the critical challenge of identifying performance bottlenecks in microservices architectures. This comprehensive tutorial demonstrates how to trace request lifecycles across multiple services, enabling you to pinpoint exactly where slowdowns occur in complex distributed systems.

Learn why distributed tracing is essential when requests flow through multiple microservices and it becomes impossible to identify bottlenecks without granular visibility. When a user request takes 5 seconds to complete across multiple services, distributed tracing reveals which specific operations are causing delays, allowing you to optimize the actual root causes rather than guessing.

Set up Tempo as your trace storage backend that not only stores trace data but automatically generates valuable Prometheus metrics including service graphs and span metrics. These derived metrics provide both high-level system health views and detailed performance insights that can trigger alerts when processing times exceed thresholds.

Deploy Alloy as your telemetry collection agent that receives traces from applications and forwards them to Tempo for storage and analysis. Configure the complete pipeline where trace generators send data to Alloy on the standard telemetry port 4317, which then processes and routes the data to appropriate backends.

Implement advanced Grafana visualizations including trace exploration for detailed request analysis, service graphs that reveal microservice interdependencies and failure rates, and the traces drill-down plugin for sophisticated performance analysis. Learn to identify which services are experiencing high error rates, which operations have the slowest 90th percentile response times, and how to correlate metrics with specific traces.

Master trace analysis techniques including filtering by duration to focus on problematic requests, drilling down into individual spans to identify the exact operations causing delays, and using the heat map visualizations to understand overall system performance patterns. Explore how parallel operations affect overall request latency and learn to distinguish between sequential and concurrent bottlenecks.

This hands-on approach to distributed tracing provides the observability foundation needed for modern microservices architectures. By combining trace storage, intelligent metric generation, and powerful visualization tools, you'll gain the insights needed to maintain high-performance distributed systems at scale.