errorful_ecommerce_platform

This dummy project helps in the generation of few errors, which will be used for further study by our team.

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🛒 E-commerce Platform Dummy Project

Simulates realistic incidents and failures in an e-commerce system to generate logs, metrics, and traces for postmortem analysis.

This project is ideal for experimenting with LLM-powered RCA agents, remediation planners, and postmortem generators.

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🚀 Quick Setup

1️⃣ Clone & Setup Project Structure

mkdir -p ecommerce-platform/{config,services/{user-service,product-service,order-service,payment-service},infrastructure,monitoring,logs,scripts}

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Run the Simulation

python run_ecommerce_platform.py

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📂 Project Overview

🔧 Components

• run_ecommerce_platform.py → Main entry point, simulates incidents & logs
• logs/ → Application, error, and access logs (with timestamped filenames)
• services/ → Placeholder microservices (user, product, order, payment)
• monitoring/ → Metrics & health check generators

📝 Log Files

Each run generates timestamped log files in logs/:

• application_YYYYMMDD_HHMMSS.log → Info & warnings
• error_YYYYMMDD_HHMMSS.log → Errors & critical events
• access_YYYYMMDD_HHMMSS.log → Simulated HTTP access logs

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💥 Incident Types Simulated

• 🗄 Database Issues → Connection pool exhaustion, stuck queries
• 🧠 Memory Leaks → OOM errors in product service
• ⚙️ Configuration Errors → Wrong timeouts, missing API keys
• 🌐 Network Issues → Timeouts, external API failures
• 🔑 Authentication Failures → JWT validation problems
• 💻 Resource Constraints → High CPU, disk exhaustion
• 🔌 Third-Party Failures → Payment gateway (Stripe) downtime

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📊 Data Generated

• Structured application logs with timestamps
• Error logs with stack traces & service context
• Access logs with realistic HTTP request patterns
• Metrics → CPU, memory, disk usage, latency, throughput
• Service health status changes → Running → Degraded → Critical → Down

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🤖 Use Case: Postmortem Automation

Feed the generated logs into your LLM-driven incident management pipeline:

1. RCA Agent → Identify root causes (e.g., config errors, resource exhaustion)
2. Remediation Agent → Suggest fixes (config rollback, scale infra, retry policies)
3. Report Generator → Produce structured postmortem reports (timeline, impact, action items)

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🧪 Example Run

$ python run_ecommerce_platform.py
🚀 Starting E-commerce Platform Simulation
✅ user-service (port 8001): HEALTHY
📊 Generating baseline metrics...
🌐 Generating access logs...
🎯 Simulating incident scenarios...
🔥 product-service: CRITICAL - Memory leak detected
❌ payment-service: DOWN - Config rollback required
🏁 Simulation completed!
📝 Check logs/ directory for detailed incident logs
🔍 Run your postmortem automation platform on these logs

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🎯 Why This Project?

This is a small but comprehensive sandbox for practicing:

• Incident simulation & failure injection
• Observability log/metric collection
• LLM-powered postmortem analysis pipelines

It deliberately introduces bugs, misconfigurations, and outages you’d face in real-world systems.

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