🧭 Google Maps Microservice API Optimization .NET Core

A highly efficient, scalable microservice built in .NET Core, designed to wrap and optimize Google Maps APIs (Places, Directions, Autocomplete) with intelligent caching, dynamic routing, and data-layer persistence.

✅ This service drastically reduced client-side Google Maps API expenses from $3,000+ per month to under $100, while maintaining full feature parity and improving speed & performance across millions of records.

---

🙋‍♂️ About Me

I specialize in backend engineering, API architecture, and cost-optimized solutions for enterprise clients. This project reflects my ability to translate business problems into fast, scalable, and cost-efficient technical systems.

---

💡 Problem Solved

The client’s systems made direct calls to Google Maps APIs for each user query. With scale, this led to:

• Excessive API billing (esp. from Places & Autocomplete)
• Redundant repeated requests
• No internal visibility or control
• Performance issues at scale

🧠 Additional features

• 🌍 Optional fallback to OpenStreetMap for pricing resilience
• 🔑 Admin dashboard for managing search hot zones
• 🧠 Add ML-based relevance scoring to results
• 📤 Offline export to GeoJSON / CSV

---

🛠️ What I Built

🔹 A .NET Core Microservice That:

• Proxies and intelligently consumes Google Maps APIs
• Caches high-traffic queries (like repeated location searches)
• Persists enriched location data in a custom structured store
• Handles autocomplete, directions, and place details
• Uses rate limiting, response coalescing, and query hashing to minimize requests to Google

---

⚙️ Key Technical Features

✅ .NET 8 Core Microservice Architecture

• Clean separation of concerns with Clean Architecture principles
• Fully async pipeline with scoped DI, MediatR, and AutoMapper
• OpenAPI/Swagger for testability

🧠 Intelligent Caching & Storage

• Memory cache & Redis layered architecture
• Query fingerprints (hashes) for exact-match lookup
• Results persisted in SQL Server / PostgreSQL with indexing on:
  • PlaceId
  • Lat/Lng
  • Search Input
• Self-learning cache — hot queries automatically persisted

📍 Google Maps API Usage (Indirect, Controlled)

• Wrapped:
  • Autocomplete API
  • Directions API
  • Places Details API
• All usage throttled, batched, and recorded
• Custom headers/params to support multiple tenants and users

📈 Scalability

• Handles millions of location records
• Horizontal scaling supported via Docker/K8s
• API-level sharding support via tenant context

---

📉 Business Outcome

Metric	Before Optimization	After Optimization
Monthly Google Maps Cost	$3,000+	<$100
API Response Time (avg)	~500ms	~80ms (cached)
Duplicate Requests	1000s/day	<1% (fully cached)
Developer Control	None	Full control/logging
Brand Customization	❌	✅

---

🌐 API Endpoints

E.G GET /api/maps/autocomplete?input=Statue of Liberty GET /api/maps/place-details?placeId=ChIJ1bRlhQi3GTkRYi55HQfdkvw GET /api/maps/directions?origin=A&destination=B

Each endpoint returns Google-like accuracy but is cost-optimized, cached, and enriched.

---

🏗️ Architecture Diagram (Simplified)

        +------------+
        |  Client UI |
        +------------+
              |
              v
      +----------------+
      |  .NET Core API |
      +----------------+
              |
  +------------------------+
  |    Google Maps APIs    |
  +------------------------+
              |
      +----------------+
      |   Caching DB   | <-- Redis (hot queries)
      +----------------+
              |
      +----------------+
      |      SQL        | <-- Long-term storage
      +----------------+

---

🔐 Security & Multi-Tenant Support

• API key–based authentication
• Custom tenant scoping (x-tenant-id headers)
• Rate limiting and abuse protection middleware

---

📍 Logging & Monitoring

• Integrated Serilog for:
  • Request tracing
  • Response time monitoring
  • Cache hit/miss logs
• Exposed to centralized log sinks (e.g., Seq, ELK)

---