Design a large-scale Image Processing Platform

Question

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

Codemia · Accepted Answer

### Functional Requirements
1. **Real-time Image Processing**: The system must process images from multiple sensors in real-time, providing low-latency responses for immediate analysis.
2. **Scalability**: The platform should handle millions of image requests per minute, accommodating varying loads during peak hours.
3. **Multi-region Support**: The system must operate across multiple geographical regions to serve a global user base, ensuring minimal latency.
4. **Image Storage and Retrieval**: Efficient storage solutions for processed images and their metadata are needed, enabling quick retrieval for further analysis.
5. **API for External Services**: Provide RESTful APIs for external services to submit images and retrieve analysis results.

### Non-Functional Requirements
1. **Availability**: The system should ensure 99.99% uptime, employing redundancy and failover mechanisms.
2. **Performance**: The average latency for image processing should be under 200ms.
3. **Data Consistency**: The platform should implement eventual consistency for processed data to balance performance with integrity.
4. **Monitoring and Logging**: Comprehensive logging and monitoring should be in place for system health and performance metrics.

To estimate the capacity needed for the image processing platform, let's consider the following:

1. **Image Size**: Assume each image is around 1 MB in size.
2. **Request Volume**: Target processing 10 million images per day, which translates to approximately 115 images per second.
3. **Processing Time**: Each image requires 200ms for processing, meaning:
   - **Concurrent Requests**: To handle peak loads, we need to support at least 500 concurrent requests.
4. **Storage**: If we store processed images for 30 days, we would need:
   - Total storage = 10M images/day * 30 days * 1 MB/image = 300 TB.
5. **Compute Resources**: Assuming each processing node can handle 50 images/second, we would need around 10 nodes running concurrently to manage the load efficiently.

### Component Diagram
- **Load Balancer**: Routes incoming requests to processing nodes based on current load.
- **Image Processing Nodes**: Docker containers running the image processing algorithms, horizontally scaled via Kubernetes.
- **Message Queue (Kafka)**: Buffers incoming image requests to decouple processing from request handling, ensuring smooth load management.
- **Database (Cassandra)**: Stores processed image metadata and results, providing high write availability and eventual consistency.
- **Object Storage (Google Cloud Storage)**: Used for storing raw and processed images due to its scalability and durability.
- **Monitoring Tools (Prometheus, Grafana)**: Collect metrics and visualize system health and performance.
- **Alerting System (PagerDuty)**: Notifies engineers of critical issues in real-time.

### Schema Design
- **Processed Images Table** (Cassandra):
  - `image_id (UUID)`: Unique identifier for each image.
  - `timestamp (timestamp)`: Time when the image was processed.
  - `metadata (JSON)`: Contains processing details (e.g., analysis results, confidence scores).
  - `location (string)`: Geographical metadata for where the image was captured.
- **Raw Image Storage**: Stored in Google Cloud Storage with a flat structure using `image_id` as the filename to ensure quick retrieval.
- **Access Patterns**: The primary access patterns include fetching processed images by ID and querying based on geographical location, which aligns well with Cassandra’s partitioning model.

### Architectural Decisions
1. **Consistency vs. Availability**: Choosing Cassandra allows high availability and partition tolerance, but it necessitates eventual consistency. This is acceptable given the use case, where immediate consistency is not critical.
2. **Message Queue Overhead**: Implementing Kafka introduces latency due to buffering but enhances system resilience and allows for rate-limiting incoming requests, preventing overloads.
3. **Data Storage Choices**: While a relational database could ensure strong consistency, the scale and write frequency favor a NoSQL solution like Cassandra to handle horizontal scaling effectively.
4. **Monitoring Complexity**: The choice of multiple monitoring tools (Prometheus and Grafana) increases operational complexity but allows for in-depth observability and proactive issue resolution.

### Operational Excellence
- **Deployment Strategy**: Utilize CI/CD pipelines for rolling deployments to minimize downtime.
- **Chaos Engineering**: Regularly test system resilience by simulating failures in different components.
- **SLOs/SLIs**: Define SLOs for image processing times and system availability, continuously monitoring to ensure compliance.

Design a large-scale Image Processing Platform

Waymo

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