End-to-End Observability – A Full-Stack Case Study 🔍 Overview – Observability in Action In this case study, we explore how Prometheus, ...
End-to-End Observability – A Full-Stack Case Study
🔍 Overview – Observability in Action
In this case study, we explore how Prometheus, Loki, Tempo, and Grafana can be integrated to achieve full-stack observability in a cloud-native environment. By leveraging logs, metrics, and traces, the case study demonstrates how a distributed system can proactively detect anomalies, automate rollbacks, and improve overall system resilience.
🚀 Scenario: Observability for an E-Commerce Platform
Environment:
Multi-cloud setup across AWS (EKS), Azure (AKS), and GCP (GKE).
Microservices architecture for an online retail platform with services for:
Product Catalog
Checkout & Payments
User Authentication
Order Fulfillment
Challenges:
Services experience performance degradation during peak hours.
Difficult to trace root causes across services due to distributed nature.
Manual rollbacks lead to prolonged downtime.
🛠️ Observability Stack
| Tool | Role | Description |
|---|---|---|
| Prometheus | Metrics Collection | Monitors microservices, capturing performance metrics. |
| Loki | Log Aggregation | Centralized logging from distributed microservices. |
| Tempo | Distributed Tracing | Tracks request flows across services. |
| Grafana | Visualization and Dashboards | Provides a single pane of glass for logs, metrics, and traces. |
📐 Architecture Overview
┌───────────────┐
│ Grafana │
│ Dashboards │
└─────┬─────────┘
│
┌───────────────┼──────────────────┐
│ │ │
┌──────┐ ┌──────┐ ┌──────┐
│ Loki │ │ Tempo│ │ Prom.│
│(Logs)│ │(Trace│ │(Metrics)│
└──────┘ └──────┘ └──────┘ 🔧 Deployment and Configuration
Prometheus Setup
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
name: ecommerce-monitor
spec:
selector:
matchLabels:
app: ecommerce-platform
endpoints:
- port: http
interval: 15s Loki Log Ingestion
pipeline_stages:
- match:
selector: '{job="checkout"}'
stages:
- json:
expressions:
status: .status
message: .msg
- drop:
expression: "status=200" Tempo Tracing Configuration
tracing_config:
active: true
endpoint: tempo:4317
sampler:
ratio: 0.5 📊 Monitoring Key Metrics and Logs
Critical Metrics Monitored:
Checkout Service: Request duration, error rates, and throughput.
Payment Gateway: Payment failures and latency.
Authentication Service: Login success rates and 500 errors.
Log Aggregation:
Error logs from microservices are streamed to Loki.
Log patterns such as "failed payment" trigger alerts.
🚨 Incident Detection and Automated Rollbacks
Problem Detected:
During a deployment, error rates for the payment gateway spike by 15% over 10 minutes.
Action Taken:
Prometheus alert triggers rollback through ArgoCD.
Grafana dashboards highlight performance degradation in real-time.
alerting_rules:
groups:
- name: rollback_alerts
rules:
- alert: HighErrorRate
expr: rate(payment_errors_total[5m]) > 0.1
for: 5m
labels:
severity: critical
annotations:
summary: "Error rate exceeding threshold, initiating rollback." 🔄 Root Cause Analysis with Tempo Tracing
Tempo traces reveal that the payment service's downstream database is experiencing slow queries.
Tracing data shows increased latency in the checkout service due to database lock contention.
A fix is implemented, and new deployment proceeds smoothly.
📈 Results and Improvements
| Metric | Before Observability | After Observability |
| Mean Time to Detect | 30 minutes | 5 minutes |
| Rollback Time | 25 minutes | 3 minutes |
| Error Rate | 5% | <1% |
🔮 Conclusion
By integrating Prometheus, Loki, Tempo, and Grafana, the e-commerce platform achieved end-to-end observability, resulting in faster incident detection, automated rollbacks, and significant improvements in system resilience.
🔔 Next Steps: Continue refining your observability stack by incorporating AI/ML anomaly detection to further enhance proactive monitoring.
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