⛈️ Spring Boot 4 Advanced Weather API

A high-performance Weather API Proxy engineered with Spring Boot 4 and Redis, demonstrating advanced backend architecture patterns such as dual-mapper serialization for secure polymorphic caching and distributed rate limiting.

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🚀 Key Architectural Features

1. Dual-Mapper Serialization Strategy (Security)

Problem: Redis requires type metadata (e.g., @class) for correct deserialization, but exposing this metadata in API responses is a security risk.

Solution: Implemented the Local Mapper Pattern:

• Internal Dirty Mapper (Redis): Uses activateDefaultTyping + JsonTypeInfo.As.PROPERTY to store type metadata.
• External Clean Mapper (API): Standard Spring Boot mapper without class metadata.

Result:

• Cache deserializes cleanly.
• Frontend receives clean, safe JSON.

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2. Distributed Rate Limiting (Fixed Window Algorithm)

A custom jakarta.servlet.Filter protects the API from overuse.

Mechanism:

• Client IP is extracted.
• Redis INCR increments a counter key: rate_limit:{ip}.
• TTL of 60 seconds is applied on the first request.
• If count > 10, return 429 Too Many Requests.

Why Redis? Works across distributed instances, making it horizontally scalable.

Rate Limiter Flow

graph LR
    A[Client Request] --> B{Redis Counter < 10?}
    B -- Yes --> C[Proceed to Controller]
    B -- No --> D[Return 429 Too Many Requests]

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3. Spring Boot 4 & Jackson 3 Migration

• Uses Spring Boot 4 (Preview) and modern Jackson 3 (tools.jackson.databind).
• Fully migrated from com.fasterxml.jackson.

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🛠️ Tech Stack

• Framework: Spring Boot 4.0 (Snapshot/Preview)
• Language: Java 17+
• Caching/DB: Redis (Dockerized)
• Libraries: Spring Data Redis, Jackson 3, Lombok
• Tools: Maven, Docker, Postman

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⚡ API Flow

1. Rate Limiting Filter

All incoming requests pass through the custom RedisRateLimitingFilter.

2. Caching Layer (Cache-Aside Pattern)

• Cache Hit: Return cached weather data instantly.

• Cache Miss:

  1. Call external Weather API
  2. Serialize with internal mapper
  3. Store in Redis with TTL
  4. Return clean API JSON

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⚙️ Setup & Installation

Prerequisites

• Java 17+
• Maven
• Redis (localhost:6379)

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Step 1: Start Redis

docker run -d -p 6379:6379 --name my-redis redis

Step 2: Clone & Build Project

git clone https://github.com/your-username/advanced-redis-weather-api.git
cd advanced-redis-weather-api
mvn clean install

Step 3: Run Application

mvn spring-boot:run

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🧪 Testing the Rate Limiter

API Request:

GET http://localhost:8080/weather/London

Responses (1–10 requests):

{
  "city": "London",
  "country": "UK",
  "temperature": 15.5,
  "condition": "Cloudy",
  "source": "Api Call"
}

Response (11+ requests):

Status: 429 Too Many Requests

{
  "error": "Too Many Requests",
  "message": "Rate limit exceeded. Try again later."
}

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📂 Project Structure

src/main/java
├── configs
│   └── RedisConfigs.java
├── controller
│   └── WeatherController.java
├── filter
│   └── RedisRateLimitingFilter.java
├── model
│   └── WeatherResponse.java
├── services
│   └── WeatherService.java
└── RedisCachingApplication.java

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🖼️ Screenshots

(Images + direct links)

API Flow Screens

• Link: images/ApiCall1.png
• Link: images/ApiCall2.png

Cache Screens

• Link: images/Cached1.png
• Link: images/Cached2.png

Error Screens

• Link: images/Notfound.png
• Link: images/ratelimitexceeded.png

Config Screens

• Link: images/redisconfig.png
• Link: images/servicelayer.png

👨‍💻 Resume Highlights

• Implemented Cache-Aside pattern reducing API calls by ~90%.
• Built a distributed rate limiter using Redis atomic operations.
• Early adopter of Spring Boot 4 + Jackson 3 modernization.

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Built by Vishal