System requirements

Functional Requirements

1. Image Storage and Retrieval: The system must store high-resolution geospatial images, including satellite and aerial imagery, and serve them to users based on location and zoom level.
2. Efficient Image Processing: Images must be pre-processed (e.g., tiling, compression) to ensure rapid retrieval and rendering. Image tiling should support multiple zoom levels.
3. Spatial Querying: Users should be able to request images using spatial queries, such as latitude/longitude bounds or tile coordinates.
4. Scalable API: Provide a set of APIs for users to retrieve image tiles efficiently. This should include endpoints for retrieving metadata (e.g., image resolution, bounds) and image tiles.
5. User Authentication & Authorization: Authenticate users for access control and usage monitoring. Implement authorization to restrict access to premium content or features.
6. Global Availability: Serve images with low latency worldwide, which may involve using a content delivery network (CDN) or deploying servers in multiple regions.
7. Error Handling and Fallbacks: Implement robust error handling to provide appropriate responses in case of missing data, server errors, or out-of-bounds requests.

Non-Functional Requirements

1. Performance: The server should handle thousands of concurrent requests with a target latency of under 200ms for most image retrievals.
2. Scalability: The architecture must scale horizontally to handle increased load and storage requirements as more data is added and more users access the service.
3. Reliability: Ensure high availability (99.9% uptime) using redundant systems and data replication.
4. Data Consistency: Ensure consistency of image data across all replicas, especially when new images are ingested or old images are updated.
5. Security: Implement measures to secure data both at rest and in transit, including encryption and secure access protocols (e.g., HTTPS, OAuth).
6. Storage Efficiency: Use storage optimization techniques, such as image compression and deduplication, to minimize the storage footprint without compromising image quality.
7. Cost Efficiency: Optimize infrastructure and data storage solutions to balance performance and cost.

Capacity estimation

• Number of Users: 1 million monthly active users (MAU).
• Concurrent Users: On average, 5% of MAU are active concurrently, leading to 50,000 concurrent users.
• Requests Per User: Each user makes an average of 5 requests per minute when active.
• Image Size: Each image tile is 256x256 pixels, resulting in an average size of 50 KB per tile.
• Image Tiling: At each zoom level, the area is divided into 256x256 pixel tiles, with higher zoom levels having exponentially more tiles.
• Zoom Levels: 18 zoom levels, where level 1 covers the entire world with a single tile, and level 18 covers the world in 68,719,476,736 tiles.
• Cache Hit Ratio: 80% of the requests are expected to hit the cache, reducing the need for real-time processing and storage access.

Recommendations:

• Use a CDN: To handle global traffic efficiently and minimize latency, use a content delivery network to cache frequently accessed tiles at edge locations.
• Distributed Caching Layer: Implement a distributed caching system to reduce backend load, potentially using a tool like Redis or Memcached.
• Shard Data Storage: Use sharding strategies to partition storage for high zoom levels across multiple servers.

API design

1. Image Metadata API
2. User Authentication & Authorization API
3. Image Upload API
4. Image Tile Retrieval API

Endpoint: /api/v1/tiles/{z}/{x}/{y}

Method: GET

Description: Retrieves a specific tile based on zoom level (z), x-coordinate (x), and y-coordinate (y).

API Considerations

• Caching: The Image Tile Retrieval API should leverage caching to minimize server load. Utilize headers such as Cache-Control and ETag to manage cache effectively.
• Rate Limiting: Implement rate limiting on the APIs to prevent abuse, especially for unauthenticated endpoints.
• Authentication: Secure endpoints using JWT tokens. All user-specific operations should require a valid JWT token.

Database design

Key Considerations

1. Spatial Indexing: Use spatial indexing techniques, such as QuadTree or R-Tree, to quickly locate images based on geographical coordinates.
2. Image Storage: Use a combination of relational and non-relational databases. Store image metadata in a relational database, and store image files in a distributed object storage system (e.g., S3, Google Cloud Storage).
3. Normalization: Normalize metadata tables to avoid redundancy but denormalize query-heavy tables for better performance.

Database Schema

The database will consist of several tables, each serving a specific purpose:

Images Table

• • Stores metadata about each geospatial image, such as the zoom level and geographical bounds.
  • This table will be indexed using a spatial index on the latitude and longitude bounds to support fast querying.

Users Table

• Stores user information for authentication and access control.

Requests Table

• Logs API requests for monitoring and debugging purposes. This table can be used for rate limiting or identifying popular tiles.

Region Metadata Table

• Stores metadata for different regions, such as names, descriptions, and geographical bounds.

Storage Considerations

• Image File Storage: Use a distributed storage service, such as Amazon S3, for storing image files. Store file paths in the Images table to map metadata to the actual file location.
• Data Indexing: Implement spatial indexing on the Images table for efficient geospatial queries. Consider using PostGIS (if using PostgreSQL) for advanced geospatial operations.

High-level design

Core Components

1. API Gateway
   • Serves as the entry point for all client requests.
   • Routes requests to appropriate services, such as the Tile Retrieval Service, Metadata Service, or Authentication Service.
   • Performs rate limiting, logging, and basic authentication.
2. Tile Retrieval Service
   • Handles requests for retrieving image tiles based on zoom level and tile coordinates.
   • Performs caching using a distributed in-memory cache (e.g., Redis).
   • Interacts with the Image Storage Service to retrieve image files if not found in the cache.
3. Image Processing Service
   • Processes raw images to generate tiles at different zoom levels.
   • Uses geospatial libraries (e.g., GDAL) to slice, compress, and optimize image files for efficient storage and retrieval.
   • Stores processed tiles in the Image Storage Service.
4. Image Storage Service
   • Manages the storage of image tiles using a distributed storage system (e.g., Amazon S3).
   • Handles storage of image metadata in a relational database (e.g., PostgreSQL with PostGIS).
   • Provides efficient read and write access to image files for the Tile Retrieval Service.
5. Authentication Service
   • Manages user authentication and issues JWT tokens.
   • Interacts with the Users table in the database to validate credentials and permissions.
6. Metadata Service
   • Provides metadata about stored images, such as region bounds, available zoom levels, and resolution.
   • Interacts with the Regions and Images tables in the database.
7. Distributed Cache
   • Caches frequently requested tiles to minimize database and storage system load.
   • Supports cache invalidation mechanisms to keep data consistent.
8. Content Delivery Network (CDN)
   • Caches image tiles at edge locations to minimize latency for global users.
   • Works in conjunction with the Tile Retrieval Service to deliver images quickly.

Design Flow

1. User Request Handling: All requests from clients are first routed through the API Gateway. The gateway handles authentication and then forwards the request to the appropriate service.
2. Tile Retrieval:
   • The Tile Retrieval Service first checks the Distributed Cache to see if the requested tile is available.
   • If the tile is not in the cache, the service fetches it from the Image Storage Service.
   • If the tile does not exist in storage, the request is forwarded to the Image Processing Service to generate it.
   • The Content Delivery Network (CDN) caches the tile at edge locations after it is retrieved, reducing latency for subsequent requests.
3. Metadata Retrieval: The Metadata Service retrieves information from the relational database (e.g., Regions and Images tables) to provide metadata for querying or filtering tiles.
4. Image Processing: New images are uploaded to the Object Storage by administrators. The Image Processing Service slices the images into tiles at multiple zoom levels and stores the processed tiles in the Image Storage Service.
5. User Authentication: User credentials are verified by the Authentication Service, which issues JWT tokens that the API Gateway uses for validating subsequent requests.

Request flows

Tile Retrieval Request

This flow describes how a user retrieves an image tile based on specific zoom level and tile coordinates.

Image Metadata Query

This flow covers a user querying metadata about images available for a specific region and zoom level.

User Authentication

This flow shows how a user logs in and obtains a JWT token for subsequent authenticated requests.

Detailed component design

Tile Retrieval Service

The Tile Retrieval Service is responsible for handling requests for image tiles based on zoom level and tile coordinates. It plays a crucial role in ensuring low-latency responses for user requests.

Key Responsibilities

1. Cache Lookup: Checks the distributed cache (e.g., Redis) to see if the requested tile is already cached.
2. Storage Retrieval: Retrieves the tile from the storage service (e.g., S3) if it is not found in the cache.
3. Tile Caching: Stores tiles in the distributed cache and pushes them to the CDN for faster subsequent retrievals.
4. CDN Interaction: Pushes tiles to the Content Delivery Network for global availability.

Image Processing Service

The Image Processing Service is responsible for slicing large geospatial images into tiles at different zoom levels and storing these tiles in the storage service.

Key Responsibilities

1. Image Ingestion: Accepts high-resolution geospatial images for processing.
2. Image Slicing: Breaks down large images into smaller 256x256 pixel tiles at multiple zoom levels.
3. Compression and Optimization: Applies image compression (e.g., JPEG, PNG) to reduce file size while maintaining quality.
4. Tile Storage: Stores processed tiles in the storage service with appropriate metadata.

Distributed Caching Layer

The Distributed Caching Layer helps reduce the load on the Tile Retrieval Service by caching frequently requested tiles and metadata.

Key Responsibilities

1. Tile Caching: Stores frequently requested tiles in-memory for fast retrieval.
2. Cache Invalidation: Ensures cache consistency through cache invalidation mechanisms when new images are processed or existing images are updated.
3. Spatial Query Caching: Caches results of spatial queries (e.g., bounding box queries) to reduce redundant computations.

Explanation:

1. Tile Retrieval Service: The user’s request flows through the cache layer, storage service, and is returned to the user.
2. Image Processing Service: New image uploads are queued in an ingestion queue, processed by the image processor, and stored in the storage service.
3. Caching and Storage Integration: The cache layer interacts directly with the storage service to ensure that frequently accessed tiles are quickly available.

Trade offs/Tech choices

Latency vs. Storage Redundancy

• Decision: Utilize a CDN and distributed cache to reduce latency for global users, but this requires replicating tiles across multiple locations.
• Trade-off: While this approach reduces latency, it increases storage costs due to data duplication. However, the benefit of low-latency access justifies the higher cost, especially for a global service.

On-Demand vs. Preprocessed Tiling

• Decision: Preprocess tiles for high-traffic areas and popular zoom levels.
• Trade-off: Preprocessing tiles requires more upfront computation and storage. However, it allows the system to serve tiles faster during high demand and avoids runtime processing overhead.

Object Storage vs. File System

• Decision: Store image tiles in a distributed object storage (e.g., Amazon S3) instead of a traditional file system.
• Trade-off: Object storage has higher latency than local file systems but provides better scalability and durability.

Summary of Tech Choices and Justifications

• Caching and CDN: Critical for reducing latency and enhancing user experience. Chose Redis and a CDN provider (CloudFront/Akamai) to offload server load.
• Database and Storage: PostGIS for spatial queries and object storage (S3) for large-scale image storage.
• Programming Languages: Go or Node.js for performance-critical services; Python for processing-heavy components like the Image Processing Service.

Failure scenarios/bottlenecks

1. If a tile is not found in the CDN cache or if the CDN service itself becomes unavailable, all requests will fall back to the origin server.
2. If the API Gateway fails, all incoming traffic will be blocked, making the system inaccessible to users.
3. If the distributed cache service becomes unavailable, the Tile Retrieval Service will experience increased load as all requests will go directly to the Image Storage Service.
4. The message queue (e.g., RabbitMQ, Kafka) used for managing image processing tasks experiences a backlog due to a surge in image uploads or a slowdown in processing speed.
5. Network latency or a partition causes delays or prevents communication between services, especially in a multi-region setup.

Future improvements

1. Implement Multi-Level Caching: Introduce a multi-level caching system that includes in-memory cache (Redis/Memcached), disk cache, and CDN cache. This can further reduce latency and improve cache hit ratios.
2. Utilize GPUs for Image Processing: Move the image processing pipeline to a GPU-based setup using libraries like CUDA to speed up operations like image slicing, compression, and tiling.
3. Implement Hierarchical Storage Management: Use a hierarchical storage management (HSM) system where frequently accessed tiles are stored in faster storage (SSD) and less frequently accessed tiles are moved to cheaper, slower storage (HDD).
4. Integration with Machine Learning Models: Use machine learning models to predict areas of interest based on user behavior, enhancing the relevance and performance of spatial queries.