Redis Fibonacci API

A Flask application with Redis that computes Fibonacci numbers using iterative algorithm with intelligent caching.

Prerequisites

• Python 3.12+
• Redis server running (default: localhost:6379)

Running the Application

Option 1: Using Docker Compose (Recommended)

1. Copy the environment template:

cp .env.example .env

2. Edit .env and add your Polar Signals token (optional):

POLAR_SIGNALS_TOKEN=your_actual_token_here

3. Run with Docker Compose:

docker-compose up --build

This will start Redis, the Flask app, and Parca agent for continuous profiling. The application will be available at http://localhost:5000

Option 2: Local Development

1. Make sure Redis is running on your system
2. Install dependencies and activate the virtual environment:

poetry install
poetry shell

3. Run the Flask app:

python app.py

The application will start on http://localhost:5000

API Endpoints

GET /fibonacci/{n}

Computes the nth Fibonacci number using iterative algorithm with Redis caching.

Limits: n must be between 0 and 100,000 (supports very large Fibonacci numbers).

Response:

{
  "n": 10,
  "fibonacci": 55,
  "computation_time_ms": 0.15,
  "stats": {
    "cache_hits": 8,
    "cache_misses": 2,
    "cache_miss_rate_percent": 20.0
  }
}

GET /fibonacci/cache

Returns statistics about cached Fibonacci values.

Response:

{
  "total_cached_entries": 3,
  "entries": [
    {
      "n": 10,
      "fibonacci": 55,
      "ttl_seconds": 3542
    }
  ]
}

DELETE /fibonacci/cache

Clears all cached Fibonacci values.

Response:

{
  "message": "Cleared 5 cache entries"
}

GET /

Health check endpoint.

Response:

{
  "status": "ok",
  "message": "Flask + Redis Fibonacci API"
}

Environment Variables

• REDIS_HOST: Redis host (default: localhost)
• REDIS_PORT: Redis port (default: 6379)
• REDIS_DB: Redis database number (default: 0)
• POLAR_SIGNALS_TOKEN: Bearer token for Polar Signals continuous profiling (optional)

Example Usage

# Compute Fibonacci number
curl http://localhost:5000/fibonacci/10

# Compute larger Fibonacci number
curl http://localhost:5000/fibonacci/35

# Get cache statistics
curl http://localhost:5000/fibonacci/cache

# Clear cache
curl -X DELETE http://localhost:5000/fibonacci/cache

# Health check
curl http://localhost:5000/

How Caching Works

1. Iterative Algorithm: Uses iterative Fibonacci implementation (no recursion depth limits)
2. Smart Cache Lookup: Searches for the highest cached intermediate value to start computation from
3. Redis Caching: Each computed value is automatically cached in Redis with 1-hour TTL
4. Cache Benefits: Computation starts from the highest cached value, not from F(0)
5. Performance: Very large Fibonacci numbers (e.g., F(100000)) are computed efficiently
6. Automatic Optimization: Computing F(1000) caches all intermediate values (F(2), F(3), ..., F(999))
7. Cache Statistics: Each request reports:
   • cache_hits: Number of values found in cache
   • cache_misses: Number of values computed and stored
   • cache_miss_rate_percent: Percentage of cache misses (lower is better)

Continuous Profiling

The application includes Parca agent for continuous profiling with Polar Signals:

• CPU Profiling: Samples at 19Hz to capture performance characteristics
• Memory Profiling: Tracks memory allocation patterns
• Performance Insights: Identify bottlenecks in Fibonacci computation and caching
• Production Ready: Low overhead profiling suitable for production environments

To enable profiling, set the POLAR_SIGNALS_TOKEN environment variable with your Polar Signals API token.