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(Icon) Infrastructure: (Subtitle)

📝 Overview

(Brief description of the infrastructure setup. e.g., "A distributed rate limiter using Redis and Python.")

Core Concepts

  • (Concept 1): (e.g., Token Bucket Algorithm)
  • (Concept 2): (e.g., Redis Lua Scripting for Atomicity) ...

🏭 The Scenario & Requirements

😡 The Bottleneck (The Villain)

(Describe the failure state. e.g., "Our API is getting DDOSed by bad actors, causing the database to crash.")

🦸 The Architecture (The Hero)

(How our infrastructure solves this. e.g., "Deploying an in-memory Redis cache to track IP requests in real-time and block overages instantly.")

📜 Requirements & Constraints

  1. (Functional): (e.g., Must limit users to 100 requests per minute.)
  2. (Technical): (e.g., Distributed state—must work across 5 different API server instances.) ...

🏗️ Architecture Blueprint

Network / Topology Diagram

(Visualizing the nodes, ports, and data flow) 

graph TD
    Client -->|HTTP Request| API_Gateway
    API_Gateway -->|Check Limit| Redis[(Redis Cluster)]
    API_Gateway -->|Allow| Backend_Server

🧠 Thinking Process & Approach

(Explain why you chose this specific tech stack. Why Redis over Memcached? Why Docker Compose?)

💻 Infrastructure Implementation

(Use MkDocs tabs to separate configuration files from application logic)

🚀 Deployment & Execution

How to run this locally

# 1. Start the infrastructure
docker-compose up -d

# 2. Run the test script
python test_rate_limiter.py

🔬 Why This Works

(Explain the magic. e.g., "By using a Redis Lua script, we guarantee that reading the current count and incrementing it happens atomically, preventing race conditions across distributed nodes.")

🎤 Interview Toolkit

  • Scaling Probe: (What happens if Redis runs out of memory?)
  • Fault Tolerance: (What if the Redis node dies? Do we fail open or fail closed?)
  • Observability: (How do we monitor how many users are getting rate-limited in production?)