Observability Infrastructure
Comprehensive monitoring and observability platform with Prometheus/Grafana tracking application performance across PostgreSQL databases and 11-service Docker Compose architecture.
Overview
A production-grade observability stack providing comprehensive monitoring, logging, and tracing capabilities for a complex microservices architecture. The platform aggregates metrics from 11 containerized services, providing real-time insights into application performance, database operations, and system health. Built with industry-standard tools like Prometheus for metrics collection, Grafana for visualization, Loki for log aggregation, and OpenTelemetry for distributed tracing, the system enables rapid troubleshooting and proactive issue detection.
Challenge
Operating an 11-service microservices architecture without centralized observability created blind spots that made troubleshooting time-consuming and reactive. Logs were scattered across containers, metrics existed in isolated silos, and there was no way to trace requests across service boundaries. When issues occurred, the team spent hours correlating logs from different sources, often missing critical context. The lack of proactive monitoring meant problems were discovered by users rather than operations.
Solution
Architected a comprehensive observability stack using Prometheus for metrics collection from all services and PostgreSQL databases. Deployed Grafana with custom dashboards visualizing key performance indicators, request rates, error rates, and database query performance. Implemented structured logging across all Python services using Structlog, centralized with Grafana Loki and collected via Promtail agents. Integrated OpenTelemetry for distributed tracing, enabling request flow visualization across service boundaries. Created automated alerting rules for critical thresholds.
Impact
Reduced mean time to resolution (MTTR) for incidents by 75% through centralized logging and tracing. Achieved sub-second dashboard query times even with 6 months of historical data. Detected and resolved 12 potential outages proactively through automated alerting before users were impacted. Improved application performance by identifying and optimizing slow database queries revealed by metrics analysis.
Key Features
- Unified metrics collection from 11 microservices with Prometheus
- Custom Grafana dashboards for real-time performance visibility
- Distributed tracing with OpenTelemetry for request flow analysis
- Centralized logging with Loki and structured log formats
- Automated alerting for proactive issue detection