My Solution for Design a Global Content Distribution Network with Score: 8/10

by iridescent_luminous693

System requirements


Functional Requirements

  1. Content Delivery:
    • Serve static and dynamic content from edge servers close to users.
    • Support various content types (HTML, CSS, JS, images, videos, APIs).
  2. Caching:
    • Cache frequently accessed content on edge servers.
    • Implement cache invalidation and content freshness policies.
  3. Load Balancing:
    • Distribute traffic among edge servers based on location, server load, and latency.
    • Failover mechanisms to redirect traffic to healthy servers.
  4. Geographic Routing:
    • Direct users to the nearest edge server based on geolocation.
  5. Security:
    • Protect against Distributed Denial of Service (DDoS) attacks.
    • Use HTTPS with TLS for secure communication.
    • Implement Web Application Firewall (WAF) for application-layer protection.
  6. Analytics and Monitoring:
    • Provide real-time metrics for content usage, latency, and traffic patterns.
    • Detect and mitigate cyber threats.
  7. API Integration:
    • Provide APIs for content management (e.g., purge cache, add rules).
    • Support integration with origin servers for updates and synchronization.

Non-Functional Requirements

  1. Performance:
    • Ensure low latency (<50ms) for static content delivery.
    • Achieve 99.99% availability globally.
  2. Scalability:
    • Handle peak traffic spikes efficiently (e.g., during global events).
    • Support millions of concurrent users and requests per second.
  3. Reliability:
    • Provide failover mechanisms and redundancy for uninterrupted service.
    • Monitor health checks for edge servers.
  4. Security:
    • Ensure data privacy and integrity during transit.
    • Protect against common web vulnerabilities (e.g., XSS, SQL injection).
  5. Cost-Effectiveness:
    • Reduce bandwidth usage by caching at edge locations.
    • Optimize resource usage through dynamic scaling.





Capacity estimation

Estimate the scale of the system you are going to design...


User Base: 500 million active users monthly.

Traffic Volume:

  • Peak: 1 million requests per second.
  • Average: 100 million requests per day.

Cache Storage:

  • Assume 50,000 files cached per edge server (~10 GB/server).
  • Total cache storage across 1,000 edge servers: ~10 TB.

Edge Servers:

  • 1,000 servers globally, distributed across 50 locations.



API design

Define what APIs are expected from the system...



Content Management APIs:

  • POST /cache/purge: Invalidate cached content.
  • POST /cache/upload: Push content to edge servers.
  • GET /cache/status: Get cache usage and status.

Traffic Routing APIs:

  • GET /routing/nearest: Determine the nearest edge server for a user.
  • GET /routing/health: Check health status of servers.

Security APIs:

  • POST /security/rules: Add or update WAF rules.
  • GET /security/threats: Retrieve detected threats.

Monitoring APIs:

  • GET /metrics/latency: Fetch latency metrics for regions.
  • GET /metrics/traffic: Retrieve traffic usage statistics.



Database design

Defining the system data model early on will clarify how data will flow among different components of the system. Also you could draw an ER diagram using the diagramming tool to enhance your design...


1. Edge Server Metadata

  • Schema Details:
sql Copy code Table: EdgeServers Columns: - server_id (UUID, PK) - location (VARCHAR) - status (ENUM: healthy, degraded, offline) - ip_address (VARCHAR) - capacity (INTEGER)
  • Purpose: Stores metadata about edge servers, including location and health status.
  • Tech Used: PostgreSQL.
  • Trade-Off: Relational database ensures data consistency but might face performance issues during large-scale queries.

2. Content Cache Index

  • Schema Details:
sql Copy code Table: CacheIndex Columns: - content_id (UUID, PK) - edge_server_id (FK) - url (TEXT) - last_updated (TIMESTAMP) - ttl (INTEGER)
  • Purpose: Tracks cached content across edge servers.
  • Tech Used: Redis (in-memory for high-speed lookups).
  • Trade-Off: Limited persistence and requires external storage for data durability.

3. Traffic Metrics

  • Schema Details:
sql Copy code Table: TrafficMetrics Columns: - metric_id (UUID, PK) - timestamp (TIMESTAMP) - edge_server_id (FK) - requests_count (INTEGER) - average_latency (FLOAT)
  • Purpose: Logs traffic and performance metrics for monitoring.
  • Tech Used: Elasticsearch.
  • Trade-Off: Optimized for search and analytics but requires more storage.

4. Security Logs

  • Schema Details:
sql Copy code Table: SecurityLogs Columns: - log_id (UUID, PK) - timestamp (TIMESTAMP) - edge_server_id (FK) - ip_address (VARCHAR) - threat_type (VARCHAR) - action_taken (VARCHAR)
  • Purpose: Logs security-related events and actions.
  • Tech Used: MongoDB.
  • Trade-Off: Flexible schema for varied threat data but less efficient for complex relational queries.




High-level design

You should identify enough components that are needed to solve the actual problem from end to end. Also remember to draw a block diagram using the diagramming tool to augment your design. If you are unfamiliar with the tool, you can simply describe your design to the chat bot and ask it to generate a starter diagram for you to modify...


Edge Servers:

  • Overview: Distributed servers located globally that deliver cached content closest to users. Handle requests for static content (HTML, CSS, JS, images) and some dynamic content.
  • Role: Improve latency by serving content locally and reduce load on origin servers.

Origin Servers:

  • Overview: Central servers where the original content is stored. They handle requests that cannot be served from the edge cache.
  • Role: Provide authoritative responses for uncached or dynamic content.

Load Balancers:

  • Overview: Distribute incoming traffic among edge servers or origin servers based on location, server health, and load.
  • Role: Ensure high availability and balanced utilization of resources.

Caching Layer:

  • Overview: Implements tiered caching at edge servers and regional hubs to store frequently accessed content.
  • Role: Reduces origin server traffic and speeds up content delivery.

DNS-Based Geolocation Routing:

  • Overview: Routes users to the nearest edge server based on their geographic location.
  • Role: Minimizes latency and improves response times.

Content Management API:

  • Overview: Provides tools for managing cached content (e.g., invalidating cache, pushing updates).
  • Role: Offers control over content distribution.

Monitoring and Analytics:

  • Overview: Tracks metrics like traffic patterns, latency, cache hit ratios, and security events.
  • Role: Enables performance optimization and threat detection.

Security Layer:

  • Overview: Protects against cyber threats like DDoS attacks, SQL injection, and XSS.
  • Role: Ensures secure delivery of content and user data protection.




Request flows

Explain how the request flows from end to end in your high level design. Also you could draw a sequence diagram using the diagramming tool to enhance your explanation...


1. User Makes a Request

  • Description: A user requests a resource (e.g., image, webpage) by accessing a URL.
  • Action: The request reaches the DNS server associated with the CDN.

2. DNS-Based Geolocation Routing

  • Description: The DNS system determines the user's location and directs the request to the nearest edge server.
  • Action: Uses IP geolocation to find the optimal edge server with the lowest latency.

3. Request Reaches the Edge Server

  • Description: The edge server checks its cache to see if the requested content is available.
    • Cache Hit: The content is found in the cache and served directly to the user.
    • Cache Miss: The request is forwarded to the origin server or regional hub.

4. Cache Miss Handling

  • Description:
    • If the content is not found in the edge cache, the edge server checks the next caching tier (regional hub).
    • If still not found, the request is forwarded to the origin server.
  • Action: Content fetched from the origin server is stored in the cache for future requests.

5. Content Compression

  • Description: Before serving the content, the edge server compresses static files (e.g., gzip or Brotli).
  • Action: Reduces bandwidth usage and speeds up delivery.

6. Content Delivery

  • Description: The edge server delivers the content to the user over HTTPS, ensuring secure communication.
  • Action: Logs the request and updates traffic metrics.

Example Request Flow

Scenario: A user requests an image file (https://cdn.example.com/image.jpg).

  1. DNS Lookup:
    • The user’s browser queries the DNS.
    • The DNS server routes the request to the nearest edge server based on geolocation.
  2. Edge Server Processing:
    • The edge server checks its cache for image.jpg.
    • If found, the image is compressed (if necessary) and served.
    • If not found, the edge server forwards the request.
  3. Regional Hub Check:
    • The request is passed to a regional caching hub.
    • If the image is cached, it is returned to the edge server for delivery.
  4. Origin Server Fetch:
    • If the image is not in the regional cache, the request is sent to the origin server.
    • The origin server retrieves the image, and the edge server caches it for future requests.
  5. Delivery:
    • The image is delivered to the user with optimized latency.




Detailed component design

Dig deeper into 2-3 components and explain in detail how they work. For example, how well does each component scale? Any relevant algorithm or data structure you like to use for a component? Also you could draw a diagram using the diagramming tool to enhance your design...


1. Edge Servers

End-to-End Working

Edge servers are the backbone of the CDN. When a user requests a resource:

  1. Request Handling:
    • The DNS routes the user’s request to the nearest edge server based on geolocation.
    • The edge server receives the request and checks its cache for the resource.
  2. Cache Handling:
    • If the resource is in the cache (cache hit), it is served directly.
    • If the resource is not found (cache miss), the edge server forwards the request to either a regional hub or the origin server.
  3. Content Delivery:
    • The edge server compresses static assets (e.g., gzip, Brotli) and serves the response over HTTPS.

Communication

  • Frontend (User) to Edge Server:
    • Uses HTTPS for secure communication.
    • Leverages HTTP/2 or HTTP/3 (QUIC) for improved latency and parallelization.
  • Edge Server to Other Layers:
    • Uses gRPC or HTTP/2 to communicate with regional hubs or origin servers for cache misses.

Data Structures & Algorithms

  • Caching Algorithm:
    • Implements LRU (Least Recently Used) for managing cache eviction. This ensures that frequently accessed content remains in memory while older, unused content is removed.
    • In edge servers, a cache trie is used to store hierarchical paths of cached resources, enabling quick lookups.
  • Compression Algorithms:
    • Uses Brotli for text compression, offering better compression ratios than gzip.
    • Binary data is optimized with WebP for images and H.265 for videos.

Scaling for Peak Traffic

  • Horizontal Scaling:
    • New edge server instances are added dynamically using orchestration tools like Kubernetes.
    • Auto-scaling policies trigger based on CPU, memory, and network bandwidth usage.
  • Global Distribution:
    • Servers are pre-deployed in strategic locations worldwide to ensure redundancy and availability.
  • Content Partitioning:
    • Popular content is replicated across multiple edge servers to distribute the load.

Edge Cases

  • Cold Cache:
    • When a new edge server comes online, its cache is empty. The system ensures that cache priming happens for frequently accessed resources to avoid user delays.
  • Overloaded Edge Server:
    • Requests are rerouted to nearby edge servers via load balancers.
  • Cache Invalidation:
    • Ensures updated content is delivered by invalidating stale cache entries through API triggers or time-based TTL.

2. Origin Servers

End-to-End Working

The origin server is the source of truth for all content:

  1. Request Handling:
    • Receives requests from edge servers for uncached content.
    • Validates the request, fetches the content, and sends it back to the requesting edge server.
  2. Content Management:
    • Hosts static assets, dynamic content, and APIs.
    • Syncs content updates with edge servers and regional hubs.

Communication

  • Edge Servers to Origin Servers:
    • Use HTTPS or HTTP/2 for secure and efficient data transfer.
  • API Integration:
    • REST APIs or GraphQL are used for dynamic content delivery.

Data Structures & Algorithms

  • Content Storage:
    • Files are stored hierarchically on disk or in object storage like AWS S3, with metadata indexed in databases for quick retrieval.
  • Compression:
    • Static files are pre-compressed and stored in multiple formats to reduce server-side processing.

Scaling for Peak Traffic

  • Auto-Scaling:
    • Virtual machines or containers hosting origin servers are scaled based on load.
  • Caching Proxy:
    • Proxies like Varnish Cache are used at the origin to reduce the load on the application.
  • Regional Caches:
    • Origin requests are first routed through regional hubs to minimize direct load.

Edge Cases

  • Content Updates:
    • Stale data in caches can cause inconsistencies. Cache invalidation mechanisms (via APIs) ensure freshness.
  • Server Downtime:
    • Redundant origin servers with active-active failover configurations mitigate single points of failure.

3. Load Balancers

End-to-End Working

Load balancers manage traffic distribution:

  1. Traffic Routing:
    • Incoming requests are routed to the nearest, least-loaded edge server.
    • Health checks ensure requests are sent only to active servers.
  2. Failover:
    • Detects server failures and redirects traffic to healthy nodes.
  3. Geographic Distribution:
    • Uses geolocation data to route users to the closest edge server.

Communication

  • DNS Layer:
    • Integrates with DNS servers for initial request routing.
  • Internal:
    • Uses gRPC or HTTP/2 to communicate with servers for health checks and traffic updates.

Data Structures & Algorithms

  • Round-Robin Load Balancing:
    • Distributes traffic evenly across servers.
  • Weighted Load Balancing:
    • Allocates more traffic to servers with higher capacity.
  • Consistent Hashing:
    • Ensures requests for the same content are routed to the same server to maximize cache hits.

Scaling for Peak Traffic

  • Horizontal Scaling:
    • Additional load balancers are provisioned dynamically.
  • Cloud-Based Solutions:
    • Uses managed services like AWS Elastic Load Balancing or Google Cloud Load Balancer for auto-scaling.

Edge Cases

  • Server Health Degradation:
    • Periodic health checks and automated failover minimize disruptions.
  • Traffic Spikes:
    • Burst traffic is rerouted to under-utilized servers in real time.

4. Security Layer

End-to-End Working

The security layer protects the CDN from threats:

  1. DDoS Protection:
    • Filters malicious traffic at the network edge using IP reputation and rate-limiting.
  2. Web Application Firewall (WAF):
    • Inspects incoming requests for malicious patterns (e.g., SQL injection, XSS).
  3. TLS/SSL Encryption:
    • Encrypts all communications to prevent eavesdropping and man-in-the-middle attacks.

Communication

  • Network Layer:
    • Implements filtering with firewalls and intrusion prevention systems (IPS).
  • Application Layer:
    • Integrates with CDN services to analyze traffic patterns.

Data Structures & Algorithms

  • Bloom Filters:
    • Efficiently track blacklisted IPs with minimal memory usage.
  • Rate Limiting:
    • Token bucket algorithms control request bursts while allowing consistent traffic.

Scaling for Peak Traffic

  • Edge-Level Protection:
    • Security measures are deployed at edge servers to handle attacks locally.
  • Cloud-Based DDoS Protection:
    • Services like AWS Shield or Cloudflare provide elastic protection against large-scale attacks.

Edge Cases

  • False Positives:
    • Legitimate traffic can be flagged as malicious. Adaptive algorithms reduce false positives.
  • Evolving Threats:
    • Regular updates to threat detection rules ensure continued protection.




Trade offs/Tech choices

Explain any trade offs you have made and why you made certain tech choices...



Caching Strategy:

  • Choice: LRU caching for simplicity and effectiveness in handling frequent requests.
  • Trade-Off: Might evict important but infrequently accessed data. Alternative algorithms (e.g., LFU) are more complex.

DNS-Based Routing:

  • Choice: Simple and effective for global traffic routing.
  • Trade-Off: Slower than Anycast for dynamic traffic adjustments.

Edge Server Compression:

  • Choice: Pre-compressed files for static assets reduce load times.
  • Trade-Off: Slightly higher storage requirements to maintain multiple compression formats.



Failure scenarios/bottlenecks

Try to discuss as many failure scenarios/bottlenecks as possible.


Cold Cache:

  • Scenario: New edge servers with empty caches result in high latency for initial requests.
  • Mitigation: Implement proactive cache priming by preloading frequently accessed content into edge servers.

Traffic Spikes:

  • Scenario: Sudden surges during global events overload edge servers or load balancers.
  • Mitigation: Use auto-scaling mechanisms and pre-allocate additional resources during anticipated high-traffic periods.

Server Downtime:

  • Scenario: Regional hub or edge server failures lead to increased latency and potential service disruptions.
  • Mitigation: Deploy redundant servers with active-active failover configurations and health checks for quick failover.

DNS Propagation Delays:

  • Scenario: Updates in DNS routing take time to propagate globally, affecting user redirection.
  • Mitigation: Transition to dynamic Anycast-based routing for near-instant traffic adjustments.

False Positives in Security:

  • Scenario: Legitimate traffic flagged as malicious, disrupting user access.
  • Mitigation: Implement adaptive algorithms for traffic analysis and provide bypass mechanisms for flagged users.

DDoS Attacks:

  • Scenario: High-volume attacks overwhelm edge servers and degrade performance.
  • Mitigation: Use cloud-based DDoS protection (e.g., AWS Shield, Cloudflare), rate limiting, and traffic scrubbing.

Origin Server Overload:

  • Scenario: Cache misses result in a surge of requests to the origin server.
  • Mitigation: Use regional caches and implement rate limiting to reduce direct origin traffic.

Load Balancer Bottlenecks:

  • Scenario: Single points of failure in load balancers can disrupt traffic distribution.
  • Mitigation: Deploy multiple load balancers with redundancy and horizontal scaling.

Edge Server Overload:

  • Scenario: Localized spikes overwhelm specific edge servers.
  • Mitigation: Use dynamic traffic rerouting to distribute load across nearby edge servers.

Latency Due to Network Congestion:

  • Scenario: Network congestion increases latency for edge-to-origin communication.
  • Mitigation: Optimize routing algorithms and leverage CDN peering agreements to improve data flow.




Future improvements

What are some future improvements you would make? How would you mitigate the failure scenario(s) you described above?


Proactive Cache Priming:

  • Preload frequently accessed content into new edge servers to avoid cold cache latency.

Dynamic Anycast Routing:

  • Replace DNS-based routing with Anycast for faster, real-time traffic adjustments.

Auto-Scaling Refinement:

  • Enhance Kubernetes auto-scaling with predictive algorithms based on historical traffic data.

Advanced Security Measures:

  • Implement AI-driven anomaly detection for dynamic threat mitigation.

Edge Compute Integration:

  • Introduce edge compute for dynamic content processing, reducing reliance on origin servers.

Enhanced Monitoring:

  • Real-time anomaly detection in traffic patterns to preemptively address bottlenecks.