Design a Image Processing for Bloomberg

Question

Design a fault-tolerant image processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.

Codemia · Accepted Answer

### Functional Requirements:
1. **Image Upload**: Users can upload images in various formats (JPEG, PNG, GIF).
2. **Image Processing**: Support operations like resizing, filtering, and format conversion.
3. **Image Retrieval**: Users can retrieve processed images with a unique identifier.
4. **Batch Processing**: Ability to handle batch uploads for processing multiple images at once.
5. **Rate Limiting**: Implement API rate limiting to manage traffic and prevent abuse.

### Non-Functional Requirements:
1. **Scalability**: Must handle millions of requests per day with minimal latency.
2. **Fault Tolerance**: System should be resilient to failures, ensuring high availability.
3. **Consistency**: Ensure that the latest image versions are consistently served across different regions.
4. **Performance**: Image processing tasks should ideally complete within a few seconds.
5. **Monitoring and Logging**: Comprehensive metrics for monitoring system health and performance.

### Back-of-Envelope Calculations:
1. **Daily Requests**: Assume Bloomberg processes approximately 10 million image requests per day.
2. **Peak Load**: Expect peak load at 1000 requests per second (RPS) during business hours.
3. **Image Size**: Average image size is about 500 KB. 
4. **Storage Requirements**: 
   - Daily storage: 10 million requests * 500 KB = 5 TB/day.
   - Monthly storage: 5 TB/day * 30 days = 150 TB.
5. **Processing Throughput**: Target image processing time of 2 seconds per image means handling 1800 images per hour per processing instance.
   - To handle peak load: 1000 RPS = 3600 images/hour, requiring at least 2 processing instances with a buffer for additional load.
6. **Database Writes**: Each processed image will be written to the database, estimating around 5000 writes per second during peak.

### Component Diagram:
1. **API Gateway**: Acts as an entry point, handles authentication and rate limiting (AWS API Gateway).
2. **Load Balancer**: Distributes incoming requests across multiple application servers (Nginx or AWS ELB).
3. **Image Processing Service**: Microservices architecture using Docker containers orchestrated by Kubernetes for scaling.
4. **Distributed Cache**: Redis or Memcached for caching frequently accessed images and reducing database load.
5. **Storage**: AWS S3 for storing original and processed images with versioning enabled.
6. **Database**: NoSQL Database (DynamoDB or MongoDB) for storing metadata and image processing results.
7. **Monitoring Tools**: Prometheus and Grafana for monitoring system performance and alerting.

### Schema Design:
1. **Image Metadata Table:**
   - `image_id` (Primary Key): Unique identifier for each image.
   - `user_id`: ID of the user who uploaded the image.
   - `upload_timestamp`: Timestamp of the upload.
   - `status`: Current processing status (e.g., processing, completed, failed).
   - `processed_url`: URL of the processed image in S3.
   - `original_url`: URL of the original image in S3.
   - `processing_time`: Time taken for processing.
2. **User Table:**
   - `user_id` (Primary Key): Unique identifier for the user.
   - `email`: User's email.
   - `rate_limit`: Current rate limit status for the user.

### Architectural Decisions:
1. **Consistency vs. Availability (CAP Theorem)**: Prioritize availability and partition tolerance over strong consistency due to the global scale of Bloomberg operations. Use eventual consistency for image metadata but ensure strong consistency for retrieval of the latest processed images.
2. **Caching Strategies**: Use a distributed cache (Redis) for performance, but this introduces potential stale data issues. Implement cache invalidation strategies upon image updates.
3. **Image Processing**: Opt for microservices for processing to allow independent scaling, but this increases complexity in deployment and management.
4. **Data Storage**: Using S3 for storage provides durability and availability but can introduce latency for image retrieval. Implement a CDN (like CloudFront) for faster access to frequently requested images.
5. **Operational Excellence**: Rolling updates with blue-green deployment strategies will minimize downtime but require additional resources during the transition.

Design a Image Processing for Bloomberg

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