Video Sharing Platform Backend Architecture Design

This document presents a scalable backend architecture for a video sharing platform similar to YouTube, covering core components, data models, and system design trade-offs for handling massive video content and user engagement.

1. Executive Summary

Clarifying questions

1. What is the expected read/write ratio for video content and user interactions?
2. What are the consistency requirements for real-time features like live comments or concurrent video uploads?
3. Will this system operate globally or be regionally scoped (e.g., North America only)?
4. How sensitive is the system to latency for video playback vs. user interactions?
5. What are the data ownership and compliance requirements (e.g., GDPR, HIPAA)?
6. Are there any external integrations with third-party services (e.g., ad networks, analytics)?
7. What is the expected growth trajectory in terms of DAU and video uploads per day?

Problem being solved

Design a scalable, highly available video sharing platform that supports global users uploading, consuming, and engaging with video content. The system must handle massive scale of concurrent users, real-time interactions, and large media files while maintaining low latency and high availability.

Back-of-envelope calculations

Assuming 100M DAU with 10:1 peak-to-average ratio:

• Peak QPS = 100M * 10 / 86400 ≈ 11,574 QPS
• Average video size = 200MB, average metadata = 5KB → ~200.005MB/user
• Total storage growth per year = 100M * 200.005MB ≈ 20PB/year
• Bandwidth estimate (read + write):
  • Write: 100M * 200MB = 20TB/day (video uploads)
  • Read: 100M * 50MB = 5TB/day (video streaming)
  • Total: ~25TB/day (read + write)
• Assuming 80% cache hit rate, DB QPS = 11,574 * (1 - 0.8) ≈ 2,315 QPS
• Servers needed at peak (assuming 500 QPS per server) = 2,315 / 500 ≈ 5 servers

SLA targets

• 99.9% availability (3.6s downtime/day)
• p50: 200ms, p95: 500ms, p99: 1s for video playback
• 99.9% data durability with RPO < 1 hour

Key architectural decisions summary

• Multi-region active-active deployment for global reach and high availability
• Microservices architecture with domain-driven decomposition (User, Video, Comments, Search)
• Hybrid storage strategy combining object storage for videos and relational DBs for metadata
• Event-driven architecture with Kafka for real-time processing of user interactions
• Service mesh with Istio for secure inter-service communication and observability
• Caching at multiple levels (CDN, API gateway, application layer) to minimize database load
• Comprehensive observability stack with distributed tracing, metrics, and structured logging
• Zero-trust security model with mTLS and fine-grained access control
• Feature flags and progressive rollout for safe deployments

2. System Goals & Non-Functional Requirements

Latency Targets

• p50: 200ms for video playback start, 500ms for video metadata fetch
• p95: 500ms for playback start, 1s for metadata fetch
• p99: 1s for playback start, 2s for metadata fetch

Throughput Requirements

• Average QPS: 100K (assuming 1M DAU with 100 video views/day per user)
• Peak QPS: 1M (10x average, 86400 seconds in a day)
• Read bandwidth: ~200 MB/s average, peak ~2GB/s (video streaming)
• Write bandwidth: ~50 MB/s average, peak ~200MB/s (video uploads)

Availability Target

• 99.9% (3.6s downtime/day) - Justification: Critical for user retention and monetization, matches YouTube's SLA expectations. Lower availability would cause significant revenue loss and user churn.

Durability and Consistency Requirements

• Consistency Model: Eventual consistency for video metadata, strong consistency for user account data and monetization
• Durability: 99.99%+ (RPO < 15 minutes) - Critical for content ownership, monetization, and compliance
• CAP Trade-off: Prioritize availability and partition tolerance over consistency (AP in CAP) for user-generated content, but ensure strong consistency for financial transactions and user profiles

Data Retention and Compliance Requirements

• Video retention: 10 years (default) with configurable policies
• User data: 5 years after account deactivation, with GDPR compliance for data deletion requests
• Compliance: GDPR, CCPA, HIPAA (if applicable), SOC2 Type II certification required

Security and Regulatory Requirements

• Authentication: OAuth2/OIDC with 2FA support for accounts
• Authorization: RBAC model with granular permissions (video owner, editor, viewer)
• Data encryption: AES-256 at rest and TLS 1.3 in transit
• Audit logging: All content modification, access, and monetization actions must be logged for compliance
• Content moderation: Automated and manual review systems to comply with local laws and platform policies

Additional Non-Functional Requirements

• Scalability: Support 10M+ concurrent users and 100M+ videos
• Global distribution: Multi-region support for low-latency content delivery
• Cost efficiency: Optimize for cost while maintaining performance and availability
• Operational simplicity: Reduce operational overhead through automation and observability
• Extensibility: Modular design to support new features like live streaming, comments, and recommendations

3. High-Level Architecture Diagram

C4Context
    title Video Sharing Platform - System Context

    Person(user, "End User", "Regular user who uploads, watches, and interacts with videos")
    Person(contentCreator, "Content Creator", "User who uploads videos to the platform")
    
    System_Boundary(b1, "Video Sharing Platform") {
        System(backend, "Backend Services", "Core backend services handling video processing, user management, etc.")
        System(apiGateway, "API Gateway", "Entry point for all external requests")
        System(edge, "Edge Layer", "CDN and edge services for global content delivery")
        System(auth, "Auth Service", "Authentication and authorization service")
        System(search, "Search Service", "Real-time video search capabilities")
        System(notification, "Notification Service", "Push and email notifications")
    }
    
    System_Ext(storage, "Object Storage (S3/GCS)", "Storage for video files and metadata")
    System_Ext(db, "Database Cluster", "Relational and NoSQL databases for user data and metadata")
    System_Ext(messageBroker, "Message Broker (Kafka)", "Event streaming for processing and notifications")
    System_Ext(cache, "Cache Layer (Redis)", "Distributed caching for performance optimization")
    
    Rel(user, apiGateway, "Uses")
    Rel(contentCreator, apiGateway, "Uploads")
    Rel(apiGateway, backend, "Routes requests")
    Rel(backend, storage, "Stores/retrieves videos")
    Rel(backend, db, "Reads/writes user/data")
    Rel(backend, cache, "Caches frequently accessed data")
    Rel(backend, messageBroker, "Publishes events")
    Rel(backend, auth, "Authenticates users")
    Rel(backend, search, "Searches videos")
    Rel(backend, notification, "Sends notifications")
    Rel(apiGateway, edge, "Routes to edge services")

C4Container
    title Video Sharing Platform - Container Diagram

    Container_Boundary(c1, "User Management Service") {
        Container(userSvc, "User Service", "java", "Handles user registration, profile management, and preferences")
        Container(authSvc, "Auth Service", "go", "Handles authentication and authorization")
        Container(notificationSvc, "Notification Service", "nodejs", "Manages push/email notifications")
    }

    Container_Boundary(c2, "Content Management Service") {
        Container(videoSvc, "Video Service", "go", "Handles video upload, processing, and metadata management")
        Container(commentSvc, "Comment Service", "java", "Handles comments on videos")
        Container(likeSvc, "Like/Dislike Service", "go", "Handles like/dislike interactions")
    }

    Container_Boundary(c3, "Search & Discovery Service") {
        Container(searchSvc, "Search Service", "go", "Provides real-time video search")
        Container(recommendationSvc, "Recommendation Service", "python", "Provides content recommendations")
    }

    Container_Boundary(c4, "Infrastructure & Edge") {
        Container(apiGateway, "API Gateway", "nginx", "Routes external requests to appropriate services")
        Container(edgeLayer, "Edge Layer", "cloudflare", "CDN and edge caching")
        Container(cache, "Cache Layer", "redis", "Distributed cache for performance")
        Container(messageBroker, "Event Broker", "kafka", "Event streaming and processing")
    }

    Container_Boundary(c5, "Data Stores") {
        Container(userDb, "User DB", "PostgreSQL", "Relational storage for user data")
        Container(videoDb, "Video Metadata DB", "MongoDB", "Document store for video metadata")
        Container(viewDb, "View Count DB", "Redis", "Key-value store for view counts")
        Container(storage, "Object Storage", "S3", "Storage for video files and thumbnails")
    }

    Rel(apiGateway, userSvc, "REST")
    Rel(apiGateway, videoSvc, "REST")
    Rel(apiGateway, searchSvc, "REST")
    Rel(userSvc, authSvc, "JWT")
    Rel(videoSvc, storage, "Upload/Download")
    Rel(videoSvc, messageBroker, "Publish Events")
    Rel(commentSvc, userDb, "Read User Data")
    Rel(searchSvc, videoDb, "Search Index")
    Rel(recommendationSvc, videoDb, "Recommendation Data")
    Rel(cache, userDb, "Cache User Data")
    Rel(cache, viewDb, "Cache View Counts")
    Rel(edgeLayer, storage, "Serve Video Content")

Major Components and Services

Core Backend Services:

• User Service: Handles user registration, profile management, and preferences
• Video Service: Manages video upload, processing, metadata handling, and lifecycle management
• Comment Service: Processes user comments and replies on videos
• Like/Dislike Service: Manages user engagement metrics for videos

Infrastructure Services:

• API Gateway: Entry point for all external traffic, handles request routing and rate limiting
• Edge Layer: CDN and edge caching for global content delivery
• Auth Service: Authentication and authorization with JWT tokens
• Search Service: Real-time video search capabilities using Elasticsearch or similar
• Notification Service: Push and email notifications for user interactions

Data Stores:

• Relational Database (PostgreSQL): For user profiles, preferences, and relational data
• Document Store (MongoDB): For video metadata, comments, and content structure
• Key-Value Store (Redis): For caching, view counts, session data, and real-time metrics
• Object Storage (S3/GCS): For storing video files, thumbnails, and media assets

Traffic Flow and Data Flow

1. User Authentication: End users authenticate through Auth Service, which issues JWT tokens
2. Content Upload: Content creators upload videos through API Gateway to Video Service, which stores files in Object Storage
3. Processing Pipeline: Video Service publishes events to Kafka for processing (transcoding, thumbnail generation)
4. Search & Discovery: Search Service indexes video metadata for real-time search capabilities
5. Content Delivery: Edge Layer serves videos from Object Storage with CDN caching
6. User Interactions: Comments, likes, and notifications are handled through dedicated services with data stored in appropriate databases

External Integrations

• Object Storage: S3 or GCS for video file storage
• Message Broker: Kafka for event streaming and processing pipeline
• Search Engine: Elasticsearch or OpenSearch for content search
• CDN: Cloudflare or AWS CloudFront for global content delivery
• Notification Service: Push notification providers (Firebase, APNs)
• Monitoring: Datadog or AWS X-Ray for observability and tracing

4. Service Architecture

Microservices vs. Monolith vs. Modular Monolith Decision

For a video sharing platform, we'll adopt a modular monolith approach for the initial phase, transitioning to microservices as the system scales. This decision balances:

• Development velocity - faster iteration and deployment cycles
• Operational simplicity - reduced complexity in service coordination and communication
• Team autonomy - clear module boundaries while maintaining shared infrastructure
• Cost efficiency - lower operational overhead during early growth phases

The modular monolith will be structured around core domains: User Management, Video Processing, Content Delivery, Search & Recommendations, and Analytics.

Service Decomposition Strategy

Based on domain-driven design principles:

1. User Management Service - handles authentication, profiles, subscriptions
2. Video Processing Service - video transcoding, thumbnail generation, metadata extraction
3. Content Delivery Service - CDN integration, video streaming, adaptive bitrate
4. Search & Recommendations Service - content search, personalization engine
5. Analytics & Metrics Service - user behavior tracking, engagement metrics

Each service will own its data and have clear boundaries to prevent cross-service dependencies.

Service Boundaries and Contracts

User Management Service:

• REST API for user registration, login, profile management
• GraphQL endpoint for content-specific user data

Video Processing Service:

• gRPC API for video upload and transcoding requests
• Event-driven notifications for processing completion

Content Delivery Service:

• REST API for video streaming endpoints (HLS/DASH)
• Webhook integration with CDN providers

Search & Recommendations Service:

• REST API for search queries and recommendation endpoints
• Event-driven content indexing

Analytics & Metrics Service:

• REST API for analytics data retrieval
• Event-driven real-time metrics processing

Inter-service Communication Pattern

Synchronous (REST/gRPC):

• User Management ↔ Content Delivery (for authentication)
• Video Processing ↔ Analytics (for processing status)

Asynchronous (Event-driven):

• Video Processing ↔ Search & Recommendations (content indexing)
• Content Delivery ↔ Analytics (viewing events)

Service Mesh Consideration

We'll implement Istio for service mesh capabilities to:

• Handle cross-service authentication and authorization
• Enable traffic management (rate limiting, retries)
• Provide observability features (distributed tracing, metrics)
• Support gradual service upgrades and canary deployments

Service Registry and Discovery Strategy

Service Registry: Consul for service discovery with health checks Load Balancing: Client-side load balancing with circuit breaker patterns Service Mesh Integration: Istio for advanced traffic management and security policies

Service Design Principles

1. Stateless Services: All services designed to be stateless for horizontal scaling
2. Idempotent Operations: Critical operations made idempotent to handle retries gracefully
3. Async Processing: Heavy operations like video transcoding offloaded to background workers
4. Event-Driven Architecture: Services communicate through events for loose coupling and scalability
5. Fault Isolation: Each service isolated to prevent cascading failures

Scalability Considerations

• Services designed with horizontal scaling in mind
• Database-per-service pattern to avoid cross-service database contention
• CQRS applied where appropriate for read-heavy operations (e.g., search service)
• Service-level auto-scaling with HPA based on CPU and memory utilization

Future Migration Path

Phase 1: Modular monolith with clear service boundaries Phase 2: Gradual migration to microservices using the same domain-driven approach Phase 3: Full microservice architecture with service mesh and advanced observability

This approach provides the flexibility to scale while maintaining operational simplicity during early growth phases, with clear migration paths for future architectural evolution.

5. Data Architecture

Database Selection per Service

User Service

• Database: PostgreSQL (primary) + Redis (caching)
• Justification: Relational model for user profiles, preferences, and relationships. PostgreSQL provides ACID compliance for user data consistency. Redis used for session caching and quick lookups.

Video Service

• Database: MongoDB (primary) + DynamoDB (for metadata)
• Justification: Document model suits video metadata structure (title, description, tags). MongoDB's flexible schema allows for evolving video properties. DynamoDB used for high-throughput metadata operations.

Comments Service

• Database: Cassandra (primary) + Redis (caching)
• Justification: Time-series nature of comments with high write throughput. Cassandra's wide-column model handles timestamped comment data efficiently. Redis for caching recent comments.

Analytics Service

• Database: TimescaleDB (primary) + Elasticsearch (search)
• Justification: Time-series data for user engagement and view metrics. TimescaleDB provides efficient time-series queries with PostgreSQL compatibility. Elasticsearch used for complex analytics queries and search.

Recommendations Service

• Database: Redis (primary) + Neo4j (graph)
• Justification: In-memory cache for real-time recommendations. Redis provides fast key-value operations. Neo4j handles complex user-to-video relationships and graph-based recommendations.

Data Partitioning Strategy

Horizontal Sharding

• Strategy: Hash-based sharding on user ID for User and Video services
• Justification: Even distribution of data across shards. Hash-based sharding provides consistent partitioning and predictable query routing.

Range Partitioning

• Strategy: Time-based range partitioning for Analytics service
• Justification: Efficient time-series query patterns. Partitioning by date ranges allows for optimized historical data queries and easier archival.

Data Replication Strategy

Primary-Replica Model

• Implementation: Master-slave replication with automatic failover
• Justification: Ensures high availability and read scalability. Primary database handles writes, replicas handle reads.

Multi-Primary

• Implementation: Multi-region read-write replication for User service
• Justification: Global availability with eventual consistency model. Supports cross-region user data updates.

Cross-Service Data Ownership

No Shared Databases Rule

• Enforcement: Service-specific databases with strict API boundaries
• Justification: Prevents tight coupling and ensures data ownership. Each service owns its data and exposes it through well-defined APIs.

CQRS Pattern Application

Video Service

• Usage: Separate read/write models for video metadata
• Justification: Write operations (upload) and read operations (search) have different performance characteristics. Separate models optimize each path.

Comments Service

• Usage: Read model optimized for comment threads
• Justification: Comments are frequently read in thread format, while writes are less frequent.

Event Sourcing Consideration

Video Upload Service

• Consideration: Not implemented due to complexity and performance overhead
• Justification: Event sourcing would add significant complexity for a simple operation like video upload. Simpler state-based approach is more maintainable.

Object Storage Design

Storage Layer

• Provider: Amazon S3 (primary) + CloudFront (CDN)
• Design:
  • Video files stored in S3 with versioning enabled
  • Metadata stored separately in database
  • CloudFront for global content delivery with caching at edge locations

Access Patterns

• Read: Direct S3 access for thumbnails, CDN for videos
• Write: Upload to S3 with metadata stored in database

Storage Tiering Strategy

Hot Tier

• Storage: SSD-backed storage for frequently accessed videos and thumbnails
• Justification: Fast access for popular content

Warm Tier

• Storage: HDD-backed storage for recently uploaded videos
• Justification: Cost-effective for content with moderate access patterns

Cold Tier

• Storage: Glacier or S3 Standard-IA for archived videos
• Justification: Minimal cost for rarely accessed content

Backup and Restore Strategy

Backup Frequency

• Daily: Full database backups with point-in-time recovery capability
• Hourly: Incremental backups for critical services

RTO/RPO Targets

• RTO: 15 minutes for all services
• RPO: 5 minutes for critical data, 1 hour for non-critical

Restore Process

• Automated: Disaster recovery scripts with pre-configured restore procedures
• Manual: For complex scenarios requiring human intervention

Cross-Region Replication

• Implementation: Multi-region backups with automatic failover capability
• Justification: Ensures data availability during regional outages

6. Caching Architecture

Cache Layers Overview

The video sharing platform will implement a multi-tier caching strategy to optimize read performance and reduce database load:

graph LR
    A[Client Apps] --> B[CDN Edge]
    B --> C[API Gateway]
    C --> D[Application Layer]
    D --> E[In-Memory Cache]
    E --> F[Redis Cluster]
    F --> G[Database]

Tiered Caching Strategy

L1 - Client-side Caching (CDN Level)

• CDN edge caches video thumbnails, metadata, and short-form content
• Uses HTTP caching headers (ETag, Cache-Control) with 24-hour TTL for static assets
• Reduces origin server load by 70-80% for static content

L2 - API Gateway Level

• Implements request-level caching for common queries (user profiles, video lists)
• Caches responses for 5-10 minutes based on content freshness requirements
• Reduces API gateway to database round-trips by 60%

L3 - Application Layer (In-Memory)

• Service-specific in-memory caches using Caffeine or Guava
• Stores frequently accessed user sessions, video metadata, and user preferences
• Provides sub-50ms response times for hot data paths

L4 - Distributed Cache (Redis Cluster)

• Primary cache for session state, user preferences, and content recommendations
• Redis cluster with 3 master nodes and 3 replica nodes for HA
• Implements cache warming and invalidation strategies

Cache Sizing Strategy

• Per-service cache size: 2GB per application instance (1GB for session state, 1GB for content)
• Redis cluster: 20GB total (10GB per shard with 2 replicas)
• Cache eviction policy: LRU with time-based expiration (TTL = 30 minutes for content, 24 hours for static assets)
• Cache warming: Pre-warm hot content during off-peak hours (10% of total video catalog)

Cache Consistency Strategy

Cache Invalidation Patterns:

• Write-through for user preferences and session data
• Write-behind for content metadata updates (batched every 5 minutes)
• Cache warming for new video uploads and trending content
• Event-driven invalidation using Kafka streams to notify cache services

Cross-service Cache Coordination:

• Shared cache namespace with service-specific prefixes (e.g., user:{userId}, video:{videoId})
• Cache invalidation events published to message broker for cross-service coordination
• Distributed lock implementation using Redis for cache consistency during updates

Cache Warming Strategy

Cold Start Mitigation:

• Pre-warm top 1000 trending videos and user favorites at service startup
• Warm cache with popular content every 2 hours during peak hours
• Implement cache warming API endpoint for manual warm-up during content spikes

Content Classification:

• Hot content (most viewed in last 24 hours) - 100% cache hit rate target
• Warm content (moderately viewed) - 80% cache hit rate target
• Cold content (rarely viewed) - 60% cache hit rate target

Performance Targets

• P50 Response Time: < 20ms for cached content
• P95 Response Time: < 50ms for cached content
• Cache Hit Rate: > 90% for read-heavy operations
• Cache Miss Penalty: < 200ms (including database round-trip)

Cache Failure Handling

• Fallback to database reads with circuit breaker pattern (50% failure threshold)
• Graceful degradation for non-critical content (showing cached metadata while fetching video streams)
• Cache warming during service recovery to minimize impact on user experience

This caching architecture balances performance optimization with cache consistency, ensuring that the system maintains high availability while reducing database load by up to 85% for read-heavy operations.

7. Messaging & Event Streaming Architecture

Message Broker Selection

For a video sharing platform, we'll select Apache Kafka as the primary message broker due to:

• High throughput requirements: Video platform needs to handle millions of events per second (video uploads, views, comments, likes)
• Durability and persistence: Critical for video metadata, user actions, and analytics events that must not be lost
• Scalability: Kafka's partitioned model supports horizontal scaling across multiple brokers
• Stream processing capabilities: Integration with KStreams for real-time analytics and recommendations
• Ecosystem maturity: Rich ecosystem of connectors, monitoring tools, and enterprise support

Event Schema Registry and Versioning

We'll implement Confluent Schema Registry to manage event schemas with:

• Backward compatibility: New schema versions must be compatible with older consumers
• Version control: 3 major versions supported with automatic migration paths
• Schema evolution policies:
  • BACKWARD compatibility for most events (user actions, video metadata)
  • FORWARD compatibility for analytics events where consumers can handle new fields

Topic Partitioning and Consumer Group Design

Partitioning Strategy:

• Video events: Hash partitioned by video_id (ensures all events for a video stay together)
• User events: Hash partitioned by user_id (ensures consistent processing per user)
• Analytics events: Round-robin partitioning for load distribution

Consumer Group Design:

• Event Processing Service: Consumer groups per event type (video processing, user notifications)
• Analytics Service: Separate consumer groups for real-time and batch analytics
• Notification Service: Dedicated consumer groups for push notifications, emails, and in-app alerts

Event Ordering Guarantees

Per-partition ordering:

• Critical for maintaining video processing sequence (encoding, thumbnail generation, metadata extraction)
• Non-critical for user notifications where eventual consistency is acceptable

Global ordering:

• Not required for most use cases but implemented for critical workflows like video approval/rejection sequences

Exactly-once vs. At-least-once Processing Trade-offs

Decision: At-least-once processing with idempotent consumers

Justification:

• Video processing workflows: Critical to ensure all encoding steps complete (at-least-once preferred)
• User interaction events: Acceptable to process duplicates (e.g., multiple view events for same user)
• Analytics events: Idempotent processing prevents skewed metrics
• Cost vs. complexity trade-off: Kafka's built-in exactly-once semantics add significant complexity without proportional benefit for most use cases

Event Replay and Reprocessing Strategy

Replay capability:

• Event retention: 30 days of event history for replay capability
• Replay API: REST endpoints to trigger reprocessing of specific time ranges or video IDs
• Reprocessing queue: Dedicated Kafka topic for replay requests with priority processing

Reprocessing triggers:

• System upgrades requiring data migration
• Bug fixes in event processors
• Data quality issues requiring manual intervention

Dead Letter Queue and Poison Pill Handling

Dead Letter Queue Strategy:

• Automatic DLQ: Failed messages moved to separate Kafka topic after max retries (3 attempts)
• Monitoring: DLQ events trigger alerts and manual investigation
• Reprocessing capability: DLQ messages can be replayed after root cause fixes

Poison Pill Handling:

• Rate limiting: Failed consumers temporarily throttled to prevent system overload
• Alerting: Immediate PagerDuty alert for DLQ queue growth above threshold (500 messages)
• Manual intervention: Dedicated dashboard for DLQ message inspection and reprocessing

Event Processing Pipeline

Event Flow:

1. Ingestion: Events published to Kafka topics via service APIs
2. Processing: Consumers process events in parallel across multiple partitions
3. Storage: Processed data written to databases and object stores
4. Notification: Events published to notification topics for real-time updates
5. Analytics: Events consumed by analytics services for real-time dashboards

Cross-Service Event Communication

Event-driven architecture pattern:

• Synchronous: Direct API calls for real-time operations (video search, user authentication)
• Asynchronous: Kafka topics for decoupled processing (video encoding, analytics, notifications)
• Event-driven microservices: Services communicate through events rather than direct service calls

Service contracts:

• Video Service: Publishes video.uploaded, video.processed, video.deleted events
• User Service: Publishes user.created, user.updated, user.deleted events
• Analytics Service: Consumes and produces various analytics-related events

Performance Considerations

Throughput targets:

• Peak: 100,000 events/sec for video uploads and views
• Average: 25,000 events/sec for user interactions and notifications
• Latency: < 100ms end-to-end for real-time event processing

Scalability:

• Horizontal scaling: Kafka clusters can scale to handle millions of events/sec
• Partition management: Dynamic partition rebalancing to handle traffic spikes
• Consumer scaling: Auto-scaling of consumer groups based on event volume

Monitoring and Alerting

Kafka monitoring:

• Lag monitoring: Consumer lag alerts for processing delays
• Throughput metrics: Event rate and partition utilization tracking
• Broker health: Disk space, memory usage, and CPU monitoring

Alerting thresholds:

• High lag: > 10 minutes for analytics consumers
• Partition imbalance: > 20% variance in partition load
• DLQ growth: > 500 messages in dead letter queue

Integration with External Systems

External event flows:

• Content delivery: Events published to CDN for real-time content distribution
• Analytics platforms: Events forwarded to third-party analytics services (Google Analytics, Mixpanel)
• Third-party integrations: Events published to partner APIs for content distribution

API gateway integration:

• Event ingestion: API gateway routes events to Kafka topics with rate limiting
• Event publishing: Services publish events through API gateway for external consumption

This messaging architecture provides the foundation for a scalable, resilient video sharing platform that can handle millions of concurrent users and events while maintaining performance and reliability requirements.

8. Reliability Engineering

Failure Mode Analysis

Single Points of Failure:

• API Gateway/Load Balancer: Single point for all incoming traffic
• Centralized Authentication Service: Could block entire platform if down
• Primary Database Cluster: Single database cluster for all writes
• Content Delivery Network (CDN): Could cause global video delivery issues
• Object Storage Service: Could impact all video availability
• Message Queue System: Could halt event processing and user actions

Service-Level Failures:

• Video Processing Pipeline: Could cause videos to never become available
• Search Indexing Service: Could make videos unsearchable
• User Profile Service: Could block user interactions and content creation

Fault Tolerance Patterns

Circuit Breaker Pattern:

• Applied to external service calls (e.g., payment processing, analytics)
• Implemented using Resilience4j or Hystrix
• Triggers after 5 consecutive failures within 10 seconds
• Opens for 30 seconds before half-open state testing

Bulkhead Pattern:

• Isolated thread pools per service to prevent cascading failures
• Database connection pools limited to 20 connections per service
• Queue size limits for async processing (max 1000 messages)

Timeout & Retry Strategy:

• Service-to-service timeouts: 250ms for fast services, 1s for database queries
• Retry logic with exponential backoff (100ms → 500ms → 1s) for transient failures
• Max 3 retries with jitter to prevent thundering herd

Multi-Region / Multi-AZ Strategy

Active-Active Architecture:

• Primary region (us-east-1) handles 80% of traffic
• Secondary region (us-west-2) handles 20% and acts as failover
• Cross-region replication for user data and content metadata
• Geo-distributed CDN edge locations with automatic failover

Data Replication:

• Multi-region database clusters with read replicas in each region
• Async cross-region replication for user and content metadata
• Synchronous replication for critical transactional data (payments, subscriptions)
• Automatic failover using AWS Route53 health checks

Chaos Engineering Strategy

GameDays:

• Monthly 4-hour GameDay sessions simulating:
  • Database outages (PostgreSQL, MongoDB)
  • CDN failures and regional outages
  • External service unavailability (payment provider)
  • Network partitioning between services

Steady-State Hypotheses:

• Video playback continues with 50% cache hit rate
• User can still create content even when database is slow
• Search results remain available with 20% latency increase

Chaos Monkey Integration:

• Random service instance termination in staging
• Simulated network latency (500ms-2s) between services
• Random database connection pool exhaustion

Disaster Recovery Plan

RTO/RPO Targets:

• User Data: RTO < 5 minutes, RPO < 1 hour
• Video Content: RTO < 30 minutes, RPO < 15 minutes
• Search Indexes: RTO < 2 hours, RPO < 1 hour
• Analytics Data: RTO < 1 hour, RPO < 30 minutes

Recovery Procedures:

1. Database Failover: Automatic switch to read replica in secondary region
2. Content Recovery: Restore from S3 backup with CDN cache invalidation
3. Search Index Rebuild: Re-index from database snapshot within 2 hours
4. Service Restart: Rollback to last stable version with traffic shift

Runbook Design for Top-5 Failure Scenarios

1. CDN Outage

• Detection: Monitor 404s and latency spikes in CloudWatch
• Action: Route traffic to secondary CDN provider or fallback to origin servers
• Recovery: Wait for CDN provider to restore service or switch to backup

2. Database Connection Pool Exhaustion

• Detection: Monitor database connection count and timeout errors
• Action: Trigger circuit breaker for all database-dependent services
• Recovery: Restart database connections and scale up connection pool

3. Video Processing Pipeline Failure

• Detection: Monitor queue depth and processing time increases
• Action: Pause new video uploads, retry failed jobs in batch
• Recovery: Restart processing workers and reprocess failed queue items

4. Authentication Service Down

• Detection: Monitor authentication failures and 503 errors
• Action: Implement temporary session-based fallback for existing users
• Recovery: Route to backup auth service or use cached tokens with short TTL

5. Search Service Degradation

• Detection: Monitor search query latency and error rates
• Action: Return cached search results with 5-minute staleness
• Recovery: Rebuild search index from database snapshot or use read replicas

9. Observability Architecture

Logging Stack

• Centralized logging system: ELK (Elasticsearch, Logstash, Kibana) stack with Loki as alternative for cost-effective log aggregation
• Log format: Structured JSON logs with standardized fields (timestamp, service, level, trace_id, span_id, message, error_details)
• Log retention: 30 days for operational logs, 90 days for audit logs
• Log filtering: Pre-filtering at application level to reduce noise and improve query performance
• Log aggregation: All services send logs to centralized logging system via fluentd or vector

Distributed Tracing

• Tracing system: Jaeger with OpenTelemetry instrumentation for distributed trace collection and visualization
• Trace sampling: 1% sample rate for production traces with adaptive sampling for high-volume services
• Trace context propagation: HTTP headers (traceparent) and gRPC metadata for cross-service trace correlation
• Trace granularity: Per-request traces with service entry points, database calls, external API calls, and internal service calls
• Trace analysis: Integration with Grafana for trace visualization and performance anomaly detection

Metrics Stack

• Monitoring system: Prometheus + Grafana for time-series metrics and dashboard visualization
• Metrics collection: Push-based model with Prometheus client libraries in each service
• Metric types: Counter, Gauge, Histogram, Summary for latency, throughput, error rates, and resource utilization
• Service-level metrics: QPS, latency (p50/p95/p99), error rate, cache hit ratio, database query times
• Infrastructure metrics: CPU, memory, disk I/O, network bandwidth, and container resource usage

Alerting Strategy

• Severity levels:
  • P0: Critical (system down, data loss)
  • P1: High (high latency, service degradation)
  • P2: Medium (warning thresholds)
  • P3: Low (informational)
• Alerting channels: PagerDuty for P0/P1, Slack for P2/P3
• Alert grouping: Group alerts by service and error type to reduce noise
• Escalation policy: 5-minute escalation for P0, 15-minute escalation for P1
• Silence rules: Temporary silencing of alerts during maintenance windows

SLI/SLO Definition and Error Budget Tracking

• SLIs:
  • Video upload success rate (99.9%)
  • Video playback availability (99.9%)
  • API response latency (p95 < 200ms)
  • System availability (99.9% uptime)
• SLOs:
  • 99.9% availability (2.6m downtime/month)
  • 95th percentile response time < 200ms (1.4s monthly)
• Error budget:
  • 0.1% error rate for availability SLO (2.6m downtime/month)
  • Error budget burn rate monitoring with dashboard alerts
• Budget exhaustion:
  • When error budget is exhausted, stop new feature development and focus on reliability improvements

Capacity Planning and Load Forecasting

• Load forecasting:
  • Historical data analysis for peak hour prediction (e.g., 8-10 PM)
  • Seasonal trends and promotional campaign impact modeling
  • Forecasting using ML models for traffic prediction (e.g., Prophet or LSTM)
• Capacity planning:
  • Resource utilization trends for CPU, memory, and disk
  • Auto-scaling triggers based on metrics (e.g., >80% CPU utilization)
  • Predictive scaling with forecast-driven horizontal scaling
• Performance baseline:
  • Establish performance baselines for all services under normal load conditions
  • Baseline drift detection to identify performance regressions
• Capacity alerting:
  • Alert when predicted resource usage exceeds 85% capacity
  • Forecasted capacity exhaustion alerts 24-48 hours in advance

Observability Tooling Integration

• Dashboard strategy:
  • Service health dashboards (API, storage, compute)
  • User experience dashboards (video playback, upload success)
  • Infrastructure monitoring dashboards (K8s cluster, network)
• Real-time alerting:
  • Real-time metrics and trace analysis for immediate incident response
  • Automated alert routing based on service ownership and severity
• Incident response:
  • Runbook integration with alerting system for automated incident triage
  • Playbook templates for common failure scenarios (e.g., cache miss, DB timeout)
• Audit logging:
  • Comprehensive audit trail for all critical operations (user actions, admin changes)
  • Integration with compliance monitoring systems for regulatory reporting

10. API Gateway & Edge Layer

API Gateway Selection

For a video sharing platform, I recommend AWS API Gateway as the primary API gateway with Nginx as a complementary edge layer for specific use cases.

Justification:

• AWS API Gateway provides managed, scalable, and highly available API management with built-in features like rate limiting, authentication, and request/response transformation
• Nginx serves as a lightweight reverse proxy for content delivery and static asset serving, offering fine-grained control over HTTP headers and caching
• The combination provides optimal performance for both API traffic and media delivery

Request Routing Strategy

The edge layer will implement a multi-tier routing strategy:

1. Static Content (videos, thumbnails, metadata) → Nginx + CDN
2. API Requests → AWS API Gateway with Lambda integration
3. Real-time Streaming → WebSocket connections through API Gateway

Authentication and Authorization at Gateway Layer

• User Authentication: JWT tokens via AWS Cognito (OIDC integration)
• Service-to-Service: mTLS with SPIFFE/SPIRE for internal service communication
• API Key Management: Per-user API keys for third-party integrations
• Rate Limiting: Tiered rate limiting (1000 req/min for free, 10k req/min for premium)

Rate Limiting and Throttling Design

Global Limits:

• 10,000 requests/minute for authenticated users
• 500 requests/minute for unauthenticated users

Per-Client Limits:

• User-specific rate limiting based on account tier
• IP-based fallback for anonymous users

Request Transformation and Protocol Translation

The gateway layer will handle:

• Protocol Translation: HTTP/HTTPS to gRPC for internal services
• Content Negotiation: Format conversion (HLS, DASH) based on client capabilities
• Header Manipulation: Adding security headers, CORS configuration, and content-type negotiation

BFF (Backend for Frontend) Design

For the web frontend, we'll implement a BFF pattern:

1. Web BFF:

   • Aggregates multiple service responses (video metadata, comments, recommendations)
   • Handles complex client-side logic (user preferences, content filtering)
   • Implements caching strategies for frequently accessed data

2. Mobile BFF:

   • Optimized for mobile network conditions
   • Implements adaptive bitrate streaming selection
   • Handles push notification integration and device-specific optimizations

The BFF layer will:

• Reduce client-side complexity
• Implement intelligent caching strategies
• Handle cross-service data aggregation
• Provide protocol-specific optimizations

CDN Integration Strategy

The edge layer will integrate with Amazon CloudFront for global content delivery:

• Video Streaming: HLS/DASH streams served through CloudFront
• Static Assets: Thumbnails, metadata, and client-side assets cached at edge locations
• Dynamic Content: API responses cached with appropriate TTLs based on content freshness requirements

Security Considerations at Edge Layer

1. DDoS Protection: AWS Shield Standard integrated with API Gateway
2. WAF Rules: Custom rules for rate limiting, SQL injection prevention, and XSS protection
3. TLS Termination: End-to-end encryption with certificate management through AWS ACM
4. Request Validation: Input sanitization and content-type validation at edge layer

Performance Optimization

The edge layer will implement:

• Compression: Gzip/Brotli compression for text-based responses
• Caching: Multi-level caching (CDN → API Gateway → Application cache)
• Connection Pooling: Efficient handling of concurrent requests
• Load Distribution: Round-robin and weighted distribution across backend services

Monitoring and Metrics

The edge layer will expose:

• Request/Response Metrics: Throughput, latency, error rates
• Rate Limiting Data: Per-user and global rate limiting statistics
• Content Delivery Metrics: CDN hit rates, origin pull statistics
• Security Events: WAF violations, authentication failures

This design provides a robust, scalable edge layer that handles the high-volume, low-latency requirements of a video sharing platform while maintaining security and performance standards appropriate for a FAANG-scale system.

11. Authentication & Authorization Architecture

Identity Provider Integration

For a video sharing platform, we'll integrate with AWS Cognito as the primary identity provider due to:

• Native support for social login providers (Google, Facebook, Apple)
• Built-in user pools with federation capabilities
• Seamless integration with AWS services and third-party identity providers
• Cost-effective for moderate scale video platforms (under 10M users)
• Strong compliance with GDPR and SOC2 standards

OAuth2/OIDC Flow Design

sequenceDiagram
    participant U as User
    participant A as Auth0/Amazon Cognito
    participant B as Backend Services
    participant R as Resource Server

    U->>A: Request login (OIDC flow)
    A->>U: Redirect to auth provider
    U->>A: Authenticate and return ID token
    A->>B: Validate token and issue JWT
    B->>U: Return session token
    U->>B: Request protected resource
    B->>R: Validate JWT and authorize access
    R->>B: Return resource
    B->>U: Return response

The system implements the Authorization Code Flow with PKCE for web applications and Implicit Flow for mobile apps, supporting:

• Standard OpenID Connect claims (sub, iss, aud, exp, iat, auth_time, acr)
• JWT token format with HS256 signing (for internal services)
• Session management with refresh tokens for long-lived access

Service-to-Service Authentication

For inter-service communication, we implement mTLS + JWT hybrid approach:

• mTLS for transport-level security between services within the same cluster
• JWT tokens for cross-cluster or external service communication
• SPIFFE/SPIRE integration for service identity management at scale

RBAC vs ABAC Model

We adopt a hybrid RBAC/ABAC model:

• RBAC for basic role-based access (User, Creator, Moderator, Admin)
• ABAC for content-specific permissions (video ownership, privacy settings, geographic restrictions)

Key RBAC roles:

• Anonymous: Read-only access to public videos
• User: Basic account features, comment, like
• Creator: Upload, manage own content, analytics access
• Moderator: Content moderation, user management
• Admin: Full system access, configuration

Token Revocation Strategy

We implement a hybrid approach:

• Short-lived JWT tokens (15-30 minutes) with refresh token rotation
• Blacklist service for immediate revocation of compromised tokens
• Token cache invalidation across services on logout/revoke
• Refresh token rotation with automatic session termination after 30 days

Token Security Considerations

• JWT signing: HS256 for internal services, RS256 for external integrations
• Token storage: Secure HTTP-only cookies for web, secure storage for mobile apps
• Token validation: Full validation including audience, issuer, expiration
• Session management: In-memory session store with TTL for refresh tokens

Multi-Factor Authentication (MFA)

For enhanced security, we implement:

• Optional MFA for creators and admin users
• TOTP-based authentication using Google Authenticator compatible codes
• Backup codes for MFA recovery scenarios
• Risk-based MFA that triggers based on login location, device fingerprint, and time patterns

Compliance and Audit

• Token audit logging for all authentication/authorization events
• Compliance with OAuth2.0 RFC and OpenID Connect standards
• Audit trail for role changes, token revocations, and access attempts
• Regular security assessments of authentication components

Scalability Considerations

• Token validation cache (Redis) to reduce database lookups
• Distributed session store for multi-region deployments
• Token signing key rotation with 30-day lifecycle
• Rate limiting on authentication endpoints to prevent abuse

Integration Points

• Social login providers: Google, Facebook, Apple, GitHub
• Enterprise SSO: SAML 2.0 integration for enterprise customers
• Third-party API access: OAuth2 client credentials flow for partner integrations
• Mobile app authentication: Device fingerprinting with token binding

This architecture balances security, scalability, and developer experience while maintaining compliance with industry standards and regulatory requirements.

12. Search Architecture

Search Indexing Pipeline Design

The search system follows a multi-stage indexing pipeline:

1. Source: Video metadata (title, description, tags, category) and user content (comments, captions)
2. Transform: Content processing pipeline that extracts text, normalizes language, removes stop words, and generates embeddings for semantic search
3. Index: Real-time indexing into Elasticsearch cluster with field-level optimizations

Index Schema and Query Pattern Design

• Primary Fields: title (full-text), description (full-text), tags (keyword), category (facet), duration (numeric range), upload_date (date range)
• Secondary Fields: user_id (facet), view_count (numeric), thumbnail_url (stored field)
• Query Patterns:
  • Full-text search with autocomplete (p50: 20ms, p95: 100ms)
  • Faceted filtering (category, date range, view count buckets)
  • Semantic similarity search for related content

Real-time vs. Near-real-time Indexing Trade-offs

• Real-time: Immediate indexing with 100ms latency (requires more compute resources)
• Near-real-time: 1-second indexing window with reduced resource overhead
• Decision: Near-real-time (1s) for optimal balance between freshness and cost, with a 500ms warm-up period for content that requires immediate visibility

Relevance Tuning Strategy

• Ranking Algorithm: BM25 with custom boosting factors for title match (3x), view count (2x), recency (1.5x)
• Query Expansion: Synonym expansion for common video terms and auto-suggest integration
• Personalization: User behavior signals (watch history, engagement) to adjust relevance scores

Search Scaling Design

• Sharding Strategy: Hash-based sharding by content ID across 32 shards for horizontal scaling
• Replication: 2x replica per shard for high availability and read scalability
• Query Distribution: Load balancing across shards with query routing based on content type (video vs. channel)
• Performance Targets: p50: 25ms, p95: 150ms for search queries with 99.9% availability

Search Service Integration

• Primary Service: Dedicated search microservice (search-service) that handles all query routing and result aggregation
• Caching Layer: Redis-based query cache for popular search terms (50% cache hit rate target)
• Fallback Strategy: When Elasticsearch is unavailable, return cached results with degraded quality indicators
• Monitoring: Search latency SLI with 95% of queries completing under 100ms; alert thresholds at p95 > 200ms

13. Scalability Deep Dive

Bottleneck Identification Per Component

User Upload Pipeline:

• Peak QPS: 100K (assuming 1M DAU with 10:1 peak-to-average ratio)
• Storage per video: 2GB average (high-quality videos)
• Bandwidth requirement: 10TB/day upload traffic (250MB/s peak write)
• Database QPS: 5K (user metadata + video metadata)
• CDN cache hit rate: 80% → 20% of requests hit origin DB

Video Playback Pipeline:

• Peak QPS: 500K (1M DAU × 250 videos/day ÷ 86400s)
• Cache hit rate: 95% → 5% of requests hit origin DB
• Database QPS: 25K (video metadata + user preferences)
• Media processing: 1000 concurrent transcoding jobs

Search Pipeline:

• Peak QPS: 50K (user queries)
• Index size: 100TB (100M videos × 1GB average)
• Query latency: <200ms p95

Horizontal vs. Vertical Scaling Decisions

Stateless Services (API Gateway, Video Processing, Search):

• Horizontal scaling preferred due to stateless nature
• Auto-scaling groups with HPA based on CPU/memory utilization
• Service mesh (Istio) for traffic management and resilience

Stateful Services (User Metadata, Video Metadata):

• Vertical scaling initially for simplicity
• Horizontal sharding for metadata services (PostgreSQL with read replicas)
• Database connection pooling to handle concurrent connections

Media Processing Services:

• Horizontal scaling with dedicated transcoding clusters
• Auto-scaling based on queue depth and CPU utilization
• Stateless transcoding workers with shared storage (S3)

Database Scaling Path

Phase 1 - Single Instance:

• PostgreSQL for user and video metadata
• 100GB storage capacity (initial 50M users × 2GB per user)
• Single master with read replicas for read-heavy operations

Phase 2 - Read Scaling:

• 5 read replicas for analytical queries and search indexing
• Connection pooling (pgbouncer) to reduce connection overhead
• Query optimization with materialized views for common reports

Phase 3 - Write Scaling:

• Horizontal sharding by user ID (hash-based partitioning)
• 100 shards for initial scaling (5M users per shard)
• Master-slave replication within each shard

Phase 4 - Distributed Database:

• Migration to CockroachDB or Spanner for cross-shard transactions
• Multi-region replication for global availability

Stateless vs. Stateful Service Design Implications

Stateless Services:

• Easy to scale horizontally with no session affinity required
• Load balancing can be done at any instance level
• Failures don't impact ongoing user sessions

Stateful Services:

• Session affinity required for some services (e.g., transcoding jobs)
• Data replication and consistency challenges
• Need for proper state management (e.g., Redis session store)

Session Affinity and Its Implications

Video Playback Service:

• Session affinity not required for read operations
• Load balancing can be done at CDN or API gateway level
• Cache warming strategies to minimize cold starts

Transcoding Service:

• Session affinity required for job tracking and status updates
• Use Redis-based session store with TTL to manage state
• Implement circuit breaker for failed transcoding jobs

Performance Characteristics and Scaling Factors

Storage Growth Projection:

• 100M videos × 2GB average = 200TB storage (5-year projection)
• 10% annual growth rate → 220TB by year 5
• Object storage (S3/GCS) preferred for media files with lifecycle policies

Network Bandwidth Requirements:

• 500K QPS × 1MB average response size = 500MB/s peak read bandwidth
• 100K QPS × 250MB write bandwidth = 25GB/s peak write bandwidth
• CDN distribution reduces origin bandwidth requirements by 80%

Compute Resource Allocation:

• API services: 2 vCPU per instance (1000 instances for peak)
• Media processing: 4 vCPU + GPU per instance (500 instances for transcoding)
• Database services: 4 vCPU + 16GB RAM per instance (50 instances for metadata)

Auto-Scaling Triggers and Thresholds

API Services:

• CPU utilization > 80% for 5 minutes → scale up
• CPU utilization < 20% for 15 minutes → scale down
• Response time > 500ms p95 → trigger immediate scaling

Media Processing:

• Queue depth > 1000 items → scale up transcoding workers
• CPU utilization > 90% for 10 minutes → add worker instances
• Job completion time > 30 seconds → trigger reprocessing

Database Services:

• Connection pool utilization > 90% → scale up database instances
• Query response time > 100ms p95 → trigger query optimization
• Disk I/O wait time > 50ms → consider read replica addition

Cross-Service Scaling Dependencies

Content Delivery Network:

• CDN scaling directly impacts database QPS through cache hit ratio
• Cache warming strategy affects initial read performance for new videos

Search Service:

• Search indexing pipeline must scale to keep up with content ingestion
• Index size growth affects query performance and storage costs

User Authentication:

• Auth service must scale to handle concurrent login requests
• Token validation performance impacts all downstream services

Capacity Planning Considerations

Peak Hour Analysis:

• 10% of daily traffic occurs in peak hour (2.5GB/s write, 12.5GB/s read)
• 90th percentile QPS: 150K (write), 250K (read)
• Required infrastructure: 1500 API instances, 500 transcoding workers, 200 database instances

Seasonal Growth Patterns:

• Video upload increases by 30% during major events (e.g., Olympics, elections)
• Playback traffic peaks during evening hours (6PM-10PM)
• Content moderation requires additional processing capacity

Resource Utilization Targets:

• CPU utilization target: 60% average (15% headroom for spikes)
• Memory usage target: 70% average (20% headroom for growth)
• Disk I/O utilization target: 80% average (20% headroom for bursts)

Scalability Metrics and Monitoring

Service-Level Indicators:

• QPS, latency, error rate, throughput for each service
• Cache hit ratio, database connection pool utilization, queue depths
• Resource utilization (CPU, memory, disk I/O) per service

Infrastructure-Level Indicators:

• Auto-scaling group metrics (instance count, scaling events)
• Network bandwidth utilization and latency
• Storage capacity and I/O performance

Proactive Scaling Signals:

• Predictive analytics for traffic patterns (ML-based forecasting)
• Anomaly detection for unexpected usage spikes
• Capacity planning dashboards with trend analysis and alerts

Trade-offs in Scaling Approach

Consistency vs. Availability:

• Eventual consistency for metadata updates (user preferences, comments)
• Strong consistency required for video upload status and playback permissions

Cost vs. Performance:

• Using more expensive instances (e.g., GPU for transcoding) for better performance
• Using cheaper storage tiers for less frequently accessed content

Complexity vs. Simplicity:

• Horizontal sharding adds complexity but enables linear scaling
• Monolithic approach simplifies development but limits scalability

Latency vs. Throughput:

• Caching reduces latency but increases complexity
• Batch processing improves throughput but adds latency to real-time operations

14. Infrastructure & Deployment Architecture

Cloud Provider and Region Selection

Primary Cloud: AWS (with multi-region support) - Justification: AWS provides mature global infrastructure with extensive service ecosystem, robust CDN (CloudFront), and established compliance certifications (SOC2, HIPAA). The platform will be deployed across US-East-1, EU-West-1, and AP-Southeast-1 for optimal global reach.

Multi-Region Strategy: Active-active deployment with automatic failover capability. US-East-1 as primary region, EU-West-1 and AP-Southeast-1 as secondary regions with read-only replicas for disaster recovery.

Kubernetes Cluster Design

Cluster Architecture:

• Control Plane: Managed EKS clusters with 3 control plane nodes across multiple AZs for high availability
• Node Pools:
  • Compute-intensive pool (for video transcoding) with GPU-enabled instances
  • General-purpose pool (for API services and stateless workloads)
  • Memory-optimized pool (for caching and search services)
• Namespace Strategy:
  • prod namespace for production workloads
  • staging namespace for pre-production testing
  • dev namespace for development teams with resource quotas

Resource Management:

• CPU and memory requests/limits set per service to enable efficient resource allocation
• Resource quotas per namespace to prevent cross-team resource contention
• Pod disruption budgets (PDBs) for critical services to maintain availability during maintenance

Container Image Strategy

Base Images:

• Alpine Linux or Ubuntu LTS as base for minimal attack surface
• Official language-specific images (Node.js, Python) with security scanning

Build Process:

• Multi-stage Docker builds to minimize final image size
• Build-time security scanning using Trivy or Snyk
• Image signing and verification using Cosign for supply chain security

Registry:

• ECR (Elastic Container Registry) with image lifecycle policies
• Regional registries for faster pull times and reduced latency

Deployment Strategy

Release Pattern:

• Blue-Green deployment for critical services (API gateway, user management)
• Canary deployments for new features with 5% traffic split
• Rollback capability with automatic rollback triggers based on error rates and latency

Traffic Management:

• Kubernetes Service mesh (Istio) for traffic routing, load balancing, and observability
• Traffic splitting using Istio VirtualServices to enable gradual feature rollouts
• Circuit breaker patterns implemented at service mesh level

Auto-scaling Design

Horizontal Pod Autoscaler (HPA):

• CPU and memory-based scaling for stateless services
• Custom metrics from Prometheus for video processing queue depth
• Target CPU utilization: 70% (balanced between performance and cost)

Vertical Pod Autoscaler (VPA):

• Memory and CPU recommendations for optimal resource allocation
• Auto-adjusts pod resource requests based on historical usage patterns

KEDA (Kubernetes Event-Driven Autoscaling):

• Auto-scaling based on message queue depth (SQS for video processing)
• Auto-scaling based on search index size and query volume
• Trigger thresholds tuned for cost optimization and performance

Infrastructure as Code Approach

IaC Tools:

• Terraform for infrastructure provisioning and management
• Terragrunt for module organization and environment-specific configuration
• GitOps approach using ArgoCD for declarative deployment management

Module Organization:

• VPC modules with security groups and route tables
• EKS cluster modules with managed node groups
• Service-specific modules for databases, caches, and storage

Environment Management:

• Separate Terraform workspaces for dev, staging, and prod environments
• Shared modules for common infrastructure (VPC, IAM roles, security groups)
• Environment-specific variables and secrets management

Security Integration:

• Pre-commit hooks for IaC validation and security checks
• Automated compliance scanning using InSpec or Open Policy Agent (OPA)
• GitOps with automated drift detection and remediation

Multi-Tenant Considerations

Resource Isolation:

• Resource quotas and limits per team/namespace
• Network policies to restrict inter-team communication
• Dedicated compute pools for high-priority services

Monitoring and Alerting:

• Per-team dashboard views in Grafana
• Team-specific alerting channels (Slack, PagerDuty)
• Resource usage reporting for cost allocation

Infrastructure Monitoring and Health Checks

Liveness and Readiness Probes:

• Custom health check endpoints for each service
• Graceful shutdown handling to prevent data loss during scaling events
• Readiness probes ensure services are fully initialized before accepting traffic

Cluster Health Monitoring:

• Prometheus and Grafana for cluster-level metrics
• Kubernetes dashboard with node and pod status monitoring
• Automated cluster diagnostics and log collection for troubleshooting

Disaster Recovery and Backup Strategy

Infrastructure-Level Backup:

• Automated snapshotting of EKS cluster configurations
• Backup of critical stateful services (databases, caches) to S3 with versioning
• Cross-region replication of critical infrastructure components

Application-Level Backup:

• Scheduled database backups with point-in-time recovery capabilities
• Video content backup to S3 Glacier for long-term retention
• Configuration and state backups with automated restore testing

Recovery Time Objectives (RTO):

• 15 minutes for critical services (API, user management)
• 2 hours for content delivery and search services
• 4 hours for non-critical services (analytics, reporting)

Recovery Point Objectives (RPO):

• 5 minutes for critical data (user sessions, content metadata)
• 1 hour for video content and media assets
• 24 hours for analytics and reporting data

Performance Optimization

Network Optimization:

• VPC peering between regions for cross-region communication
• CloudFront CDN with automatic cache invalidation
• Optimized network ACLs and security group rules to minimize latency

Storage Optimization:

• EBS volumes with provisioned IOPS for database workloads
• S3 lifecycle policies to move infrequently accessed content to cheaper storage tiers
• Database connection pooling to reduce overhead and improve throughput

Compute Optimization:

• Spot instances for non-critical batch processing workloads (video transcoding)
• Reserved instances for predictable compute requirements
• GPU instances for video processing workloads with automatic scaling based on queue depth

15. Security Architecture

VPC Design and Network Segmentation

The platform will utilize a multi-VPC architecture with strict network segmentation:

• Public VPC: Contains API gateway, load balancers, and edge services (AWS VPC with public subnets across 3 AZs)
• Private VPC: Contains all backend services, databases, and internal components (private subnets with NAT gateways for outbound access)
• DMZ VPC: Dedicated zone for external integrations and third-party API connections
• Network ACLs (NACLs) will enforce strict ingress/egress rules at subnet level
• Security groups will provide fine-grained access control at instance level

Zero-Trust Network Model

Implementation of zero-trust principles across all services:

• All internal service communication requires mTLS (mutual Transport Layer Security)
• Service-to-service authentication using SPIFFE/SPIRE for identity management
• Network micro-segmentation with service mesh (Istio) enforcing policy-based access control
• All external traffic must pass through API gateway with strict authentication and authorization
• No implicit trust between services - every request requires validation

TLS Termination Strategy

Edge Layer:

• TLS termination at API Gateway (AWS API Gateway or Kong) with certificate management via AWS ACM
• Support for multiple TLS versions (TLS 1.3, 1.2) with strong cipher suites only

Internal Services:

• mTLS termination at service mesh (Istio) for inter-service communication
• Certificate rotation handled through SPIRE or HashiCorp Vault integration
• No plaintext communication between services

DDoS Protection Strategy

Implementation of AWS Shield Advanced with:

• Automatic DDoS mitigation at network and transport layers
• Rate limiting at API Gateway level (10,000 RPS per client by default)
• Anycast routing for global DDoS protection
• Integration with Cloudflare or Akamai for additional edge protection
• Automated scaling of WAF rules based on traffic patterns

Secrets Management Architecture

Centralized Secret Store:

• AWS Secrets Manager or HashiCorp Vault for credential management
• Secrets rotated every 90 days with automated rotation triggers
• Service accounts with least privilege access to secrets

Secrets Distribution:

• Secrets mounted as environment variables or injected via sidecars in Kubernetes
• No hardcoded credentials in application code or configuration files
• Secret lifecycle management with version control and audit trails

Vulnerability Scanning in CI/CD

Pre-deployment Scanning:

• Trivy integration for container image vulnerability scanning
• Snyk for dependency security checks in build pipeline
• OWASP ZAP integration for API security testing

Runtime Monitoring:

• Runtime vulnerability detection using Aqua Security or Twistlock
• Automated security patching for base images and OS components

WAF Rules for Critical Endpoints

API Gateway WAF Configuration:

• Rate-based rules (500 requests/minute per IP for video upload endpoints)
• IP address blocking for suspicious traffic patterns
• SQL injection and XSS protection rules for all user input endpoints
• Size constraints on request bodies (50MB max for video uploads)
• Block known malicious user agents and bot traffic

Additional Security Controls

Data Protection:

• Encryption at rest using AWS KMS with customer-managed keys
• Encryption in transit using TLS 1.3 for all external communications
• Video content encryption with AES-256 for sensitive media

Compliance and Auditing:

• Audit logging for all admin operations and sensitive data access
• Integration with AWS CloudTrail for comprehensive audit trail
• Regular compliance assessments (SOC2, HIPAA, GDPR) with automated checks

Access Control:

• Role-based access control (RBAC) for administrative functions
• Multi-factor authentication (MFA) required for admin access
• Session management with automatic timeout after 30 minutes of inactivity

16. Deployment & Release Strategy

Deployment Strategy

The video sharing platform will utilize a blue-green deployment strategy for production releases to ensure zero-downtime deployments and seamless rollback capabilities. This approach involves maintaining two identical production environments where only one is active at any given time. When deploying a new version, the system routes traffic to the inactive environment while the previous version continues serving existing requests. Once validation is complete, traffic is switched to the new environment and the old version is prepared for reuse.

For database migrations, a rolling update approach will be implemented with careful coordination to avoid service disruption. Critical database schema changes will be executed during maintenance windows using a phased migration pattern that updates tables in batches while maintaining read/write availability.

Artifact Promotion Model

The deployment pipeline follows a multi-stage promotion model:

• Dev Stage: Continuous integration with automated unit and integration tests
• Staging Stage: Full end-to-end testing including performance and security validation
• Production Gate: Manual approval process with automated smoke tests and health checks

Each artifact promotion requires passing through all previous stages' quality gates, with automated rollback triggers for any failures beyond acceptable thresholds.

Zero-Downtime Database Migration

Database migrations will follow a schema migration pattern using database versioning tools like Liquibase or Flyway. Schema changes are applied incrementally with:

1. Read/write compatibility maintained throughout migration process
2. New columns added as nullable initially, populated during off-peak hours
3. Data migration jobs run asynchronously to avoid blocking primary operations
4. Feature flags controlling new functionality until migration completes

Feature Flag Integration and Progressive Rollout

Feature flags will be implemented using LaunchDarkly or Flagship for granular control over feature rollouts. The system supports:

• Gradual rollouts (0% → 100%) with configurable percentages
• Targeted rollouts to specific user segments or regions
• A/B testing capabilities for new features
• Real-time flag updates without service restarts

Canary Analysis Automation and Auto-Rollback Triggers

Canary deployments will be implemented with:

• Traffic split: 5% of traffic routed to new version initially
• Automated monitoring: Real-time performance and error rate tracking
• Rollback thresholds: If error rate exceeds 1% or latency increases by 50%, automatic rollback to previous version
• Health check integration: Custom liveness and readiness probes for each service

Release Cadence and Branching Strategy

The platform follows a trunk-based development model with:

• Release cadence: Weekly minor releases, monthly major releases
• Branching strategy: Main branch is always stable and production-ready
• Feature branches: Short-lived, merged via pull requests with automated testing
• Release branches: Created only for hotfixes and security patches

Deployment Automation Framework

The deployment pipeline includes:

• CI/CD orchestration: Jenkins or GitHub Actions for automated build and test execution
• Infrastructure as Code: Terraform modules for consistent environment provisioning
• Container orchestration: Kubernetes with Helm charts for service deployment and configuration management
• Monitoring integration: Automated alerts and rollback procedures triggered by system health metrics

Rollback Strategy

In case of deployment failures:

1. Immediate rollback: Switch traffic back to previous stable version within 5 minutes
2. Database state recovery: Restore from recent backups if schema changes caused issues
3. Service health validation: Comprehensive health checks before traffic redirection
4. Post-rollback monitoring: Enhanced monitoring for 30 minutes after rollback to detect any residual issues

Release Validation Process

Each release undergoes:

• Automated testing: Unit, integration, and end-to-end test suites
• Performance validation: Load testing with 10x peak traffic capacity
• Security scanning: Vulnerability assessment and penetration testing
• User acceptance testing: Limited user group validation for new features
• Compliance verification: Regulatory and data protection checks

Production Release Process

Production releases follow a structured process:

1. Pre-release validation: All automated checks pass successfully
2. Manual approval: Release coordinator reviews deployment artifacts and health status
3. Deployment execution: Blue-green switch with traffic routing
4. Post-deployment monitoring: 15-minute validation window for critical metrics
5. Stable state confirmation: All systems operating within normal parameters before finalizing release

17. Cost Architecture

Major Cost Drivers Analysis

Compute Costs: The primary driver is video transcoding and processing services, which require significant CPU/GPU resources. With 10M DAU and 200GB daily video uploads, peak compute usage can reach 500+ vCPUs during high-traffic hours.

Storage Costs: Video storage is the largest expense, projected at $50M annually for 10PB of video content. Object storage (S3/GCS) costs $0.02-0.05/GB/month for hot tier, with additional costs for lifecycle transitions.

Network Costs: Egress traffic dominates cost structure. With 500TB/month of video delivery, and $0.05-0.10/GB egress pricing, network costs can reach $25M annually.

Database & Caching: Relational DBs and Redis clusters cost ~$2M/year, with $0.01-0.02/GB for caching.

Cost Optimization Strategy

Storage Tiering: Implement multi-tier storage strategy:

• Hot: 10% of most accessed videos (last 30 days) stored in SSD
• Warm: 70% of popular content in standard storage with 1-2 day latency
• Cold: 20% of older content in infrequent access storage

Reserved vs. Spot Instances:

• 60% of compute workloads on reserved instances for predictable workloads
• 40% on spot instances for batch transcoding jobs with tolerance for interruptions

Content Delivery Optimization:

• CDN edge locations reduce origin server costs by 70%
• Pre-warmed cache reduces compute load by 30%
• Video compression strategies reduce storage and bandwidth costs

Data Egress Cost Minimization

Edge Caching:

• 80% of video requests served from CDN edge nodes
• Regional caching clusters reduce origin traffic by 60%

Compression and Adaptive Streaming:

• Implement HLS/DASH adaptive streaming to reduce bandwidth usage by 40%
• Dynamic bitrate selection based on client network conditions

FinOps Dashboards and Budget Alerts

Cost Monitoring Stack:

• Real-time cost dashboards using Grafana with Prometheus integration
• Daily budget alerts for teams exceeding 80% of allocated spend
• Weekly capacity planning reports with cost trend analysis

Budget Allocation Model:

• Compute: 40% of budget (peak usage optimization)
• Storage: 35% of budget (tiered storage strategy)
• Network: 20% of budget (CDN and caching efficiency)
• Other: 5% of budget (services, monitoring, security)

Automated Cost Control:

• Auto-scaling policies to prevent over-provisioning
• Scheduled maintenance windows for batch jobs to reduce compute costs
• Cost allocation tags per service and team for accountability

Proactive Optimization:

• Monthly cost review cycles with root cause analysis of anomalies
• Quarterly storage optimization audits to identify and remove redundant content
• Automated cleanup policies for temporary files and failed job artifacts

18. Open Questions, Risks & Trade-offs

Decisions requiring product / engineering alignment

• Content moderation strategy: Should we implement real-time content filtering or rely on user reports and post-upload moderation? This affects latency requirements and system complexity.
• Monetization integration: When to introduce ads, subscriptions, or creator monetization features and how they integrate with core video sharing functionality.
• Cross-platform support: Whether to build a unified mobile/web experience or separate native applications with shared backend services.
• User-generated content policies: How to handle copyright violations, user-generated content with sensitive material, and community guidelines enforcement.

Key architectural risks

Risk	Impact	Likelihood	Mitigation
Video transcoding pipeline failure	High - affects content availability	Medium - complex pipeline with multiple steps	Implement circuit breaker pattern, fallback to lower resolution streams, and redundant transcoding clusters
CDN edge cache invalidation storms	High - causes excessive origin traffic	Medium - bursty invalidations during content updates	Implement cache warming strategies, batch invalidations, and rate limiting on cache invalidation requests
Database sharding complexity	High - affects scalability and consistency	High - complex data distribution logic	Start with single region sharding strategy, implement robust tooling for shard management and migration
Search relevance degradation	Medium - impacts user engagement	Medium - requires continuous tuning	Implement A/B testing framework for search ranking, establish search quality metrics and SLIs
Multi-region data consistency	High - affects global user experience	Medium - complex cross-region sync	Use eventual consistency model with conflict resolution strategies, implement proper event sourcing

Trade-offs made with justification

• Consistency vs. availability: Chose eventual consistency for video metadata to enable global reads with strong availability, trading immediate consistency for better scalability and fault tolerance.
• Storage vs. compute: Prioritized storage efficiency over compute cost by using object storage for videos and leveraging CDN for distribution, reducing compute overhead.
• Monolithic vs. microservices: Started with a modular monolith to reduce initial complexity and enable faster iteration, with plans to decompose services as traffic scales.
• Real-time vs. near-real-time: Accepting near-real-time processing for video analytics and recommendations to reduce latency and infrastructure costs while maintaining acceptable user experience.

Alternatives considered and rejected

• Traditional CDN approach: Rejected due to higher latency for content updates and less flexibility in customizing edge behavior.
• Full distributed database: Rejected due to complexity in maintaining cross-region consistency and higher operational overhead compared to managed services.
• In-memory caching only: Rejected as it would not provide persistence for cache warming and would increase database load during cold starts.
• Centralized video processing: Rejected due to single point of failure and scalability limitations for handling peak traffic.

Technical debt acknowledged

• Service coupling in initial implementation: Some services share common data models and dependencies that will require refactoring as the system scales.
• Limited search capabilities in Phase 1: Basic search implementation will need enhancement to support advanced features like semantic search and content-based recommendations.
• Simplified user session management: Initial design uses in-memory session storage which will need to be replaced with distributed session stores as the system scales.
• Basic monitoring and alerting: Initial observability stack is minimal and will require expansion to support comprehensive error budget tracking and proactive alerting.
• Hardcoded content delivery rules: Content delivery logic is currently hardcoded but will need to be made configurable for better flexibility in content distribution strategies.

19. Phased Implementation Roadmap

Phase 1: MVP (Minimum Viable Product)

Timeline: 3-4 months Objective: Build core video sharing functionality with basic user and content management

Core Components:

• User registration, authentication, and profile management
• Video upload, storage, and playback functionality (with basic transcoding)
• Simple video search and discovery
• Basic comment and like systems
• Content moderation tools (flagging, blocking)
• Admin dashboard for content moderation

Services to Build:

1. User Service (authentication, profiles)
2. Video Service (upload, storage, playback)
3. Content Service (search, discovery)
4. Comment Service
5. Moderation Service

Infrastructure:

• Single-region deployment (us-west-1 or eu-west-1)
• Monolithic architecture with microservices as separate modules
• PostgreSQL for user and content data
• S3-compatible object storage for video files
• Redis for session caching
• Basic CDN for video delivery

Phase 2: Scale & Hardening (6-8 weeks)

Timeline: 4-5 months Objective: Add horizontal scaling, reliability features, and enhanced user experience

Core Components:

• Multi-region support (active-active)
• Advanced video transcoding pipeline
• Real-time comment and notification systems
• Enhanced search with relevance ranking
• User-generated content moderation (AI-based)
• Advanced analytics and reporting
• Multi-language support

Services to Build:

1. Video Processing Service (transcoding, thumbnail generation)
2. Notification Service (real-time updates)
3. Analytics Service (user behavior, content performance)
4. Search Service (Elasticsearch integration)
5. Recommendation Service (content suggestions)

Infrastructure:

• Multi-region deployment across 2-3 regions
• Horizontal sharding of user data
• Kafka for event streaming between services
• Redis cluster for distributed caching
• CDN with global edge locations

Phase 3: Excellence & Optimization (8-10 weeks)

Timeline: 6-7 months Objective: Achieve enterprise-grade reliability, observability, and cost optimization

Core Components:

• Advanced content recommendation engine (ML-based)
• Multi-tenant architecture for enterprise customers
• Comprehensive analytics and A/B testing capabilities
• Advanced security features (DDoS protection, WAF)
• Automated compliance and audit logging
• Cost optimization and FinOps dashboard

Services to Build:

1. ML Recommendation Service (content suggestions)
2. Enterprise Management Service (multi-tenant support)
3. Advanced Analytics Service (real-time dashboards)
4. Compliance Service (audit logging, data retention)
5. Cost Optimization Service (budget alerts, resource optimization)

Infrastructure:

• Multi-region active-active with automatic failover
• Kubernetes-based deployment with HPA and VPA
• Advanced observability stack (Prometheus, Jaeger, Grafana)
• Service mesh implementation (Istio)
• Advanced caching strategies with cache warming
• Automated security scanning and compliance checks

Critical Path Dependencies:

1. Phase 1 must complete before Phase 2 begins
2. Video Processing Service in Phase 2 depends on successful completion of Phase 1's storage and upload systems
3. Search Service in Phase 2 requires completion of content indexing pipeline from Phase 1
4. Phase 3 can begin once Phase 2's multi-region infrastructure is stable and tested

Milestones:

• Month 1: MVP user registration and basic video playback
• Month 2: Video upload and storage capabilities completed
• Month 3: Multi-region deployment and basic search functionality
• Month 4: Advanced content moderation and real-time notifications
• Month 5: Recommendation engine and analytics capabilities
• Month 6: Enterprise features and cost optimization

Risk Mitigation:

• Maintain backward compatibility between phases
• Implement automated testing and rollback capabilities
• Plan for gradual user migration between phases
• Establish monitoring and alerting for each phase's critical services