Video Streaming Platform Architecture Design

This document presents a comprehensive backend architecture for a video streaming platform that handles millions of concurrent users, real-time content delivery, and sophisticated user experience features.

1. Executive Summary

Clarifying questions

1. What is the expected read/write ratio and peak-to-average QPS?

   • Is this a global platform or focused on a single region?
   • Expected DAU and peak QPS (e.g., 100M DAU → 1M peak QPS assuming 10:1 ratio)

2. What are the consistency and durability requirements?

   • Can we accept eventual consistency for user preferences or watch history?
   • What are the SLA requirements for content availability and playback latency?

3. Is this a global platform or single-region deployment?

   • Will content be served from multiple regions or centralized?

4. How sensitive is the system to latency?

   • Is sub-100ms playback start time critical?

5. What is the expected growth in content and user base over 2 years?

   • Is there a plan for content ingestion pipeline?

6. Are there any external integrations or third-party dependencies (e.g., DRM, CDN)?

   • What are the content licensing and distribution requirements?

7. What is the expected data ownership model?

   • Will content be owned by users or platform?

Problem being solved

Design a scalable, highly available video streaming platform that can handle millions of concurrent users, deliver high-quality video content with low latency, and provide a seamless viewing experience across multiple devices. The system must support content discovery, user personalization, real-time playback, and robust content management.

Back-of-envelope calculations

• DAU → peak QPS:

  • 100M DAU, 86,400 seconds/day → ~1,157 QPS average
  • Peak-to-average ratio: 10:1 → 11,570 peak QPS

• Storage per user/event:

  • Average 100 hours of content watched/year → ~25GB per user
  • Total storage: 100M users × 25GB = 2.5 PB/year
  • Peak storage per user: 50GB (including metadata, thumbnails) → 5TB peak

• Bandwidth:

  • Average video stream: 5 Mbps (1.25 MB/s)
  • Peak QPS × 1.25 MB = 14.5 GB/s read bandwidth
  • Write bandwidth (user actions, metadata): ~10% of read → 1.5 GB/s

• Cache hit rate:

  • Assuming 90% cache hit rate → 10% DB QPS
  • DB QPS: 1,157 × 0.1 = 116 QPS

• Server count at peak:

  • Per-server throughput: 500 QPS (estimate)
  • Servers needed: 11,570 / 500 = 23.14 → ~24 servers

SLA targets

• Availability: 99.9% (3.6s downtime/month)
• Latency:
  • p50: <100ms for metadata, <2s for content start
  • p95: <500ms for metadata, <5s for content start
  • p99: <2s for metadata, <10s for content start

Key architectural decisions summary

• Global multi-region deployment with edge caching for low latency
• Microservices-based architecture for scalability and team autonomy
• Hybrid storage model: S3 for content, PostgreSQL for metadata, Redis for caching
• Event-driven architecture with Kafka for real-time personalization
• Multi-layered caching strategy (CDN → API gateway → app cache)
• Stateless services with horizontal scaling capability
• Zero-trust security model with mTLS and secrets management
• CI/CD with canary deployments for zero-downtime updates
• Comprehensive observability stack with tracing, logging, and metrics
• Feature flags and progressive rollout for controlled releases

2. System Goals & Non-Functional Requirements

Latency Targets

• P50: 200ms for content metadata fetch and playback initiation
• P95: 500ms for content metadata fetch and playback initiation
• P99: 1000ms for content metadata fetch and playback initiation
• Content delivery (video streaming): <200ms for initial buffer and <50ms for subsequent chunk requests

Throughput Requirements

• Average QPS: 10,000 (based on 1M DAU with 100 concurrent sessions)
• Peak QPS: 100,000 (10x average, assuming 86,400 seconds in a day)
• Read traffic: 80% of total traffic (content metadata, user preferences, recommendations)
• Write traffic: 20% of total traffic (user actions, watch history, ratings)

Availability Target

• 99.9% (3.6-minute monthly downtime) for content availability and playback
• 99.99% (20-second monthly downtime) for user-facing APIs and content metadata
• Justification: Netflix-style SLA expectations for streaming services; acceptable for content delivery but critical for user engagement and retention

Durability and Consistency Requirements

• Consistency Model: Eventual consistency for user preferences, watch history, and recommendations (CPA: eventual consistency with strong read-after-write guarantees for critical user actions)
• Durability: Strong durability guarantees (ACID for transactional operations, eventual consistency for non-critical data)
• CAP Trade-off: Prioritizing availability and partition tolerance over consistency (CPA: Eventual consistency with strong consistency for critical user actions)
• Data Replication: Multi-region replication with asynchronous cross-region sync for disaster recovery

Data Retention and Compliance Requirements

• Content Metadata: Indefinite retention with version control for content lifecycle management
• User Data: 7 years of watch history, preferences, and ratings (GDPR compliance)
• Content Rights: Content rights expiration tracking with automated archival
• Compliance: GDPR, CCPA, HIPAA (if applicable for user data), SOC2 Type II compliance

Security and Regulatory Requirements

• Authentication: OAuth2/OIDC with mTLS for service-to-service communication
• Authorization: RBAC model with fine-grained permissions (user roles, content access levels)
• Data Encryption: AES-256 encryption at rest and TLS 1.3 for in-transit data
• Audit Logging: All user actions and content access logged with timestamps and IP addresses
• Compliance: GDPR, CCPA, HIPAA compliance for data handling and user privacy
• Regulatory: Content classification and regional restrictions (e.g., parental controls, content ratings)
• Vulnerability Management: Regular security scanning and patching for all components
• Access Control: Role-based access control (RBAC) with multi-factor authentication (MFA) for admin users

3. High-Level Architecture Diagram

C4Context
    title Video Streaming Platform - System Context

    Person(user, "End User", "End user consuming video content")
    Perimeter(network, "Public Internet")
    
    System_Boundary(c4system, "Video Streaming Platform") {
        System_Boundary(services, "Backend Services") {
            System(api_gateway, "API Gateway", "Handles routing, auth, rate limiting")
            System(auth_service, "Auth Service", "User authentication and authorization")
            System(content_service, "Content Service", "Manages content metadata, catalogs")
            System(user_service, "User Service", "User profiles, preferences, subscriptions")
            System(playback_service, "Playback Service", "Handles streaming, DRM, manifest generation")
            System(recommendation_service, "Recommendation Service", "Personalized content suggestions")
            System(payment_service, "Payment Service", "Handles subscriptions, billing")
        }
        
        System_Boundary(datastores, "Data Stores") {
            System(aws_s3, "S3", "Video content storage")
            System(redis_cache, "Redis", "Caching layer")
            System(postgres_db, "PostgreSQL", "User, content, and metadata storage")
            System(elasticsearch, "Elasticsearch", "Search and discovery")
            System(kafka, "Kafka", "Event streaming platform")
        }
        
        System_Boundary(external, "External Systems") {
            System(cdndns, "CDN/DNS", "Global content delivery network")
            System(aws_lambda, "AWS Lambda", "Serverless compute for event processing")
            System(stripe, "Stripe", "Payment processing")
        }
    }

    Rel(user, api_gateway, "Consumes APIs")
    Rel(api_gateway, auth_service, "Authenticates users")
    Rel(api_gateway, content_service, "Fetches content catalog")
    Rel(api_gateway, user_service, "Manages user profiles")
    Rel(api_gateway, playback_service, "Initiates streaming")
    Rel(api_gateway, recommendation_service, "Requests recommendations")
    Rel(api_gateway, payment_service, "Handles billing")
    
    Rel(content_service, postgres_db, "Reads/Writes content metadata")
    Rel(user_service, postgres_db, "Reads/Writes user data")
    Rel(playback_service, aws_s3, "Streams video content")
    Rel(playback_service, redis_cache, "Caches playback info")
    Rel(recommendation_service, elasticsearch, "Searches content")
    Rel(recommendation_service, kafka, "Consumes events for ML model training")
    
    Rel(auth_service, stripe, "Processes payments")
    Rel(api_gateway, cdndns, "Routes traffic to CDN")
    Rel(playback_service, aws_lambda, "Processes events for analytics")

C4Container
    title Video Streaming Platform - Container Architecture

    Container_Boundary(c4system, "Video Streaming Platform") {
        Container(api_gateway, "API Gateway", "nginx", "Handles routing, auth, rate limiting")
        Container(auth_service, "Auth Service", "Go/Gin", "User authentication and authorization")
        Container(content_service, "Content Service", "Java/Spring Boot", "Manages content metadata")
        Container(user_service, "User Service", "Node.js/Express", "Handles user profiles and preferences")
        Container(playback_service, "Playback Service", "Go/Gin", "Handles streaming and manifest generation")
        Container(recommendation_service, "Recommendation Service", "Python/ML", "Personalized content suggestions")
        Container(payment_service, "Payment Service", "Java/Spring Boot", "Handles subscriptions and billing")
        
        Container_Boundary(datastores, "Data Stores") {
            Container(aws_s3, "S3", "Object Storage", "Video content storage")
            Container(redis_cache, "Redis", "In-Memory DB", "Caching layer")
            Container(postgres_db, "PostgreSQL", "Relational DB", "User and content metadata")
            Container(elasticsearch, "Elasticsearch", "Search Engine", "Content search and discovery")
            Container(kafka, "Kafka", "Message Broker", "Event streaming platform")
        }
        
        Container_Boundary(external, "External Systems") {
            Container(cdndns, "CDN/DNS", "Cloudflare/AWS CloudFront", "Global content delivery")
            Container(aws_lambda, "AWS Lambda", "Serverless Functions", "Event processing and analytics")
            Container(stripe, "Stripe API", "Payment Provider", "Billing and subscriptions")
        }
    }

    Rel(api_gateway, auth_service, "Authenticates users")
    Rel(api_gateway, content_service, "Fetches content catalog")
    Rel(api_gateway, user_service, "Manages user profiles")
    Rel(api_gateway, playback_service, "Initiates streaming")
    Rel(api_gateway, recommendation_service, "Requests recommendations")
    Rel(api_gateway, payment_service, "Handles billing")

    Rel(content_service, postgres_db, "Reads/Writes content metadata")
    Rel(user_service, postgres_db, "Reads/Writes user data")
    Rel(playback_service, aws_s3, "Streams video content")
    Rel(playback_service, redis_cache, "Caches playback info")
    Rel(recommendation_service, elasticsearch, "Searches content")
    Rel(recommendation_a, kafka, "Consumes events for ML model training")

    Rel(auth_service, stripe, "Processes payments")
    Rel(api_gateway, cdndns, "Routes traffic to CDN")
    Rel(playback_service, aws_lambda, "Processes events for analytics")

    UpdateRel(api_gateway, auth_service, "JWT token validation")
    UpdateRel(playback_service, aws_s3, "S3 signed URL generation")

Major Components and Data Flow

Core Services:

• API Gateway: Routes requests, handles authentication, rate limiting, and protocol translation
• Auth Service: Manages user authentication using JWT tokens with refresh token rotation
• Content Service: Stores and manages content metadata, catalogs, and availability
• User Service: Handles user profiles, preferences, subscriptions, and viewing history
• Playback Service: Generates streaming manifests, handles DRM, and serves content via CDN
• Recommendation Service: Provides personalized content suggestions using ML models
• Payment Service: Manages subscriptions, billing, and payment processing via Stripe

Data Stores:

• S3: Primary storage for video content with versioning and lifecycle policies
• Redis: Multi-tier caching (L1/L2) for session data, playback metadata, and recommendations
• PostgreSQL: Primary relational database for user data, content metadata, and transactional data
• Elasticsearch: Search and discovery capabilities for content catalog
• Kafka: Event streaming platform for content updates, user actions, and analytics

External Integrations:

• CDN/DNS: Global content delivery network for optimized video streaming
• Stripe: Payment processing and subscription management
• AWS Lambda: Serverless compute for event processing and analytics

Traffic Flow:

1. User requests content through API Gateway
2. Authentication and authorization handled by Auth Service
3. Content catalog and user preferences fetched from respective services
4. Playback initiated through Playback Service (generates manifest, signs S3 URLs)
5. Video content delivered via CDN with adaptive bitrate streaming
6. User actions and content consumption events streamed to Kafka for analytics
7. Recommendations updated through ML models trained on user behavior data

4. Service Architecture

Microservices vs. Monolith vs. Modular Monolith Decision

For a video streaming platform, we'll adopt a microservices architecture with clear service boundaries. This decision is driven by:

• Independent scaling of content delivery, user management, recommendations, and billing
• Team autonomy allowing different squads to own specific domains (user profiles, content catalog, payments)
• Technology diversity enabling optimal tech choices per service (e.g., ML for recommendations, PostgreSQL for user data)
• Failure isolation preventing cascading failures across the system

We'll avoid monoliths due to scalability and maintenance complexity at scale, and modular monoliths because they don't provide the same level of team autonomy and independent deployment.

Service Decomposition Strategy

Services will be decomposed by domain:

1. User Management Service - User profiles, authentication, preferences
2. Content Catalog Service - Metadata, content descriptions, genres, ratings
3. Recommendations Service - ML-driven suggestions, personalization engine
4. Content Delivery Service - Video streaming, adaptive bitrate, CDN integration
5. Billing & Payments Service - Subscriptions, transactions, invoices
6. Analytics & Metrics Service - Usage tracking, engagement metrics, A/B testing
7. Notifications Service - Email/SMS alerts, push notifications, in-app messages

Service Boundaries and Contracts

Each service will have clearly defined APIs:

• RESTful JSON APIs for CRUD operations and stateless interactions
• gRPC services for high-performance internal communication (e.g., between content delivery and recommendations)
• Event-driven contracts for asynchronous operations (e.g., content availability changes, user actions)

Services will communicate through:

• Synchronous HTTP/gRPC for real-time interactions (user login, content search)
• Asynchronous message queues for decoupled operations (content processing, analytics)

Inter-service Communication Pattern

• Content Delivery Service ↔ Recommendations Service: gRPC for real-time content metadata and user preference sync
• User Management Service ↔ Billing Service: REST for subscription status updates and payment events
• Content Catalog Service ↔ Analytics Service: Event-driven for content view tracking and metadata changes
• All services ↔ Notifications Service: Async events for user actions (e.g., new content, subscription renewal)

Service Mesh Consideration

We'll implement Istio as a service mesh for:

• Traffic management (load balancing, retry policies, circuit breaking)
• Security (mTLS between services, request authentication)
• Observability (distributed tracing, metrics collection)
• Policy enforcement (rate limiting, access control)

Service Registry and Discovery Strategy

• Consul for service discovery (health checks, service registration)
• Kubernetes Services for internal DNS-based discovery
• API Gateway as the primary entry point with service routing logic
• Service-to-service authentication through mutual TLS and JWT tokens

The architecture will support:

• Dynamic service discovery with health checks every 5 seconds
• Graceful degradation when services are unavailable
• Zero-downtime deployments through blue-green strategies

5. Data Architecture

Database Selection per Service

User Management Service

• Database: PostgreSQL (primary) + Redis (caching)
• Justification: Relational model for user profiles, preferences, and account management. PostgreSQL provides ACID compliance for critical user data operations. Redis used for session storage and caching frequently accessed user metadata.

Content Catalog Service

• Database: MongoDB (document store)
• Justification: Content metadata (titles, descriptions, cast, genres) is naturally hierarchical and document-oriented. MongoDB's flexible schema allows for easy content evolution and rich queries on nested content properties.

Video Streaming Service

• Database: Cassandra (key-value store)
• Justification: Time-series data for user viewing behavior, watch history, and playback statistics. Cassandra's wide-column model and linear scalability make it ideal for handling high-volume, time-based user engagement data with low-latency reads.

Recommendation Engine Service

• Database: Redis (in-memory) + Elasticsearch (search)
• Justification: Redis for real-time collaborative filtering and session-based recommendations. Elasticsearch for content search, full-text matching, and complex recommendation queries requiring multi-dimensional filtering.

Content Delivery Service

• Database: DynamoDB (key-value store)
• Justification: Content metadata and manifest information stored in DynamoDB for fast read/write operations at scale. DynamoDB's managed sharding and automatic scaling align with the service's high-throughput requirements.

Data Partitioning Strategy

Horizontal Sharding

• Content Catalog: Hash-based sharding by content ID (e.g., content_id % 100) to ensure even distribution across shards
• User Data: Range-based sharding by user ID (e.g., user_id < 1M, 1M-2M, etc.) to support efficient range queries for user preferences and content access patterns
• Viewing History: Time-based sharding (monthly partitions) to enable efficient data lifecycle management and query optimization

Partitioning Justification:

• Hash sharding ensures even distribution of content across shards
• Range sharding supports analytical queries and time-series operations
• Partitioning strategy allows for independent scaling of different content types

Data Replication Strategy

Primary-Replica Model

• User Data: Multi-region primary-replica with read replicas in each region for low-latency reads
• Content Catalog: Active-active replication across regions with eventual consistency model to support global content distribution
• Viewing History: Multi-region replication with automatic failover for high availability and disaster recovery

Cross-Service Data Ownership

• No Shared Databases Rule: Each service owns its data stores to prevent tight coupling and enable independent scaling
• Enforcement Strategy:
  • Database access controlled via service accounts with role-based permissions
  • Cross-service data access through well-defined APIs and events (CQRS + Event Sourcing)
  • Data ownership contracts enforced via service-level SLAs and monitoring

CQRS Pattern Application

Content Catalog Service

• Read Side: MongoDB for content search and browsing
• Write Side: PostgreSQL for content metadata updates and version control

User Preferences Service

• Read Side: Redis for fast user preference lookups
• Write Side: Cassandra for storing user interaction history and engagement data

Recommendation Engine

• Read Side: Elasticsearch for content search and filtering
• Write Side: Redis for real-time collaborative filtering updates

Event Sourcing Consideration

Use Case: Content metadata changes and user engagement events

• Implementation:
  • Events stored in Kafka topics for content updates (e.g., "ContentAdded", "MetadataUpdated")
  • Event replay capability for system recovery and audit logging
  • Event versioning to handle schema evolution

Trade-offs:

• Pros: Auditability, system recovery, and historical analysis capability
• Cons: Increased complexity in event processing and storage management

Object Storage Design

Storage Strategy:

• Primary Storage: Amazon S3 (global object storage)
• Access Patterns:
  • Content delivery through CloudFront CDN for global distribution
  • Metadata stored separately in DynamoDB and MongoDB
  • Media files organized by content ID and resolution (e.g., /content/{id}/1080p.mp4)

File Metadata vs. Content Separation:

• Metadata: Stored in MongoDB and DynamoDB with rich indexing capabilities
• Content: Stored in S3 with versioning enabled for content recovery and backup

Storage Tiering Strategy

Tier Design:

• Hot Tier: S3-Intelligent-Tiering for frequently accessed content (last 30 days)
• Warm Tier: S3-Standard-IA for content accessed within 90 days
• Cold Tier: S3-Standard-IA or Glacier for archived content (older than 90 days)
• Archive Tier: Glacier Deep Archive for long-term retention (older than 1 year)

Justification:

• Cost optimization through tiered storage based on access frequency
• Automated lifecycle policies to move content between tiers based on usage patterns

Backup and Restore Strategy

Backup Approach:

• Daily Incremental Backups: MongoDB, PostgreSQL, and Cassandra backups to S3 using automated backup tools (e.g., Percona XtraBackup for MySQL, pg_dump for PostgreSQL)
• Full Snapshots: Weekly full backups of all databases and content metadata to S3 Glacier
• Cross-Region Replication: All backups replicated to secondary region for disaster recovery

RTO/RPO Targets:

• RTO: 4 hours (for content catalog and user data)
• RPO: 1 hour (for critical user and content metadata)
• Restore Time: < 2 hours for full system recovery (with automated failover)

Backup Validation:

• Weekly restore tests to ensure backup integrity
• Automated backup monitoring with alerting for backup failures

6. Caching Architecture

Cache Layers Overview

The caching strategy follows a multi-tier architecture to optimize performance and reduce database load:

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Client Cache  │    │   CDN / Edge    │    │ API Gateway     │    │ Application     │
│   (Browser)     │    │   (Cloudflare)  │    │   (AWS API     │    │   Service       │
│                 │    │                 │    │   Gateway)      │    │                 │
└─────────────────┘    └─────────────────┘    └─────────────────┘    └─────────────────┘
                              │                       │                    │
                              ▼                       ▼                    ▼
                   ┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
                   │   Redis Cache   │    │   Redis Cache   │    │   In-Memory     │
                   │   (L2 Cache)    │    │   (L2 Cache)    │    │   (L1 Cache)    │
                   └─────────────────┘    └─────────────────┘    └─────────────────┘

Multi-Layer Caching Strategy

L1 Cache (Application Level):

• In-memory cache using Redis Cluster with 3 nodes for high availability
• Cache keys follow pattern: video:{video_id}:metadata and user:{user_id}:preferences
• TTL set to 1 hour for metadata, 24 hours for user preferences
• Cache warming during service startup using pre-computed data

L2 Cache (Distributed Level):

• Redis Cluster with 3 master nodes and 3 replica nodes per shard
• Sharding strategy: hash(video_id) % 3 to distribute load evenly
• Cache eviction policy: LRU with 10% of memory reserved for new entries
• Cache hit ratio target: 85% for metadata, 70% for user preferences

Cache Invalidation Strategy

• Write-through pattern: When content metadata changes, update cache first then DB
• Cache invalidation events: Use pub/sub pattern to notify cache services of data changes
• Time-based expiration: Set TTLs for different cache types to prevent stale data
• Cache warming: Pre-populate popular content during off-peak hours

Cache Performance Targets

• Read latency: p50 < 2ms, p95 < 10ms for cached content
• Cache hit rate: 85%+ for metadata, 70%+ for user preferences
• Cache miss penalty: < 50ms to maintain QPS targets
• Cache size: 10GB total capacity with 2GB per cache node

Cache Consistency Patterns

• Read-after-write consistency: For user preferences, ensure cache is updated before returning to client
• Eventual consistency: For content metadata, allow for small delays in cache updates
• Cache coherency protocol: Use Redis Pub/Sub to invalidate related cache entries when content changes
• Cache warming: Preload popular content during low-traffic periods to reduce cold starts

Cache Warming and Cold Start Strategy

• Warm-up process: Load 50% of most popular content into cache every hour
• Priority queue: Cache content based on popularity score and user engagement metrics
• Background prefetching: Proactively fetch next 10 videos for users based on viewing history
• Warm-up window: 5-minute window during which cache warming is prioritized over new requests

Cache Monitoring and Metrics

• Cache hit ratio: Monitor per cache tier with alerts for drops below 80%
• Cache latency: Track p50/p95/p99 latencies for each cache tier
• Cache size: Monitor actual usage vs. configured capacity with alerts for >90% utilization
• Cache invalidations: Track frequency and patterns to optimize cache strategy

Cache Tier Sizing Strategy

• L1 Cache: 2GB per application instance (32GB total for 16 instances)
• L2 Cache: 3.5GB per Redis node (10.5GB total for 3 nodes)
• CDN Cache: 20GB per region (100GB total for 5 regions)
• Client Cache: 50MB per browser session (estimated 20GB for peak concurrent users)

Cache Failure Handling

• Cache fallback: When cache is unavailable, serve from DB with timeout handling
• Graceful degradation: Reduce cache size or increase TTL during high load
• Cache warming recovery: Retry warm-up on cache node failure with exponential backoff
• Monitoring alerts: Immediate alert when cache availability drops below 95%

7. Messaging & Event Streaming Architecture

Message Broker Selection

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

• High throughput: Required for handling millions of concurrent streams and real-time user interactions
• Durability: Messages are persisted to disk with configurable retention policies (7-30 days)
• Scalability: Horizontal scaling capabilities to handle peak traffic loads
• Stream processing: Integration with Kafka Streams and KSQL for real-time analytics
• Ecosystem maturity: Strong tooling ecosystem (Confluent Cloud, Schema Registry)

Event Schema Registry and Versioning

• Schema Registry: Confluent Schema Registry for strict schema validation
• Versioning strategy: Semantic versioning with backward compatibility guarantees
• Schema evolution: Allow backward-compatible changes; reject incompatible ones
• Validation: Schema validation at publish and consume time

Topic Partitioning and Consumer Group Design

Partitioning strategy:

• Content ID-based hashing: content_events topic partitioned by content ID to ensure all events for a specific video are processed together
• User ID-based hashing: user_events topic partitioned by user ID for session consistency
• Default partition count: 128 partitions per topic to handle high throughput

Consumer group design:

• Content processing: Dedicated consumer groups for content ingestion, metadata updates, and recommendations
• User activity: Separate consumer groups for real-time analytics, user behavior tracking, and notifications
• Event ordering: Per-partition ordering guarantees maintained for content-related events

Event Ordering Guarantees

• Partition-level ordering: Events within the same partition maintain strict order
• Global ordering: Not guaranteed for cross-partition events (by design for performance)
• Use case implications:
  • Content metadata updates: Ordered per content ID
  • User viewing history: Ordered per user ID

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

Decision: At-least-once processing with idempotent consumers

Rationale:

• Consistency: Critical for user viewing history and recommendations
• Performance: Avoids complex state management overhead of exactly-once
• Implementation: Idempotent consumer design with deduplication logic

Implementation approach:

• Message IDs stored in Redis for deduplication
• Stateful processing with Kafka's built-in idempotence features
• Retry logic with exponential backoff for transient failures

Event Replay and Reprocessing Strategy

Replay capability:

• Retention policy: 30-day retention for all topics
• Replay window: 7-day window for content and user activity events
• Trigger mechanism: Admin dashboard with timestamp-based reprocessing

Reprocessing use cases:

• Content metadata updates
• User behavior pattern corrections
• System migration from old to new schema versions

Dead Letter Queue and Poison Pill Handling

DLQ Strategy:

• Dead letter topic: content_events_dlq for failed processing messages
• Retry policy: 3 attempts with exponential backoff (5s, 10s, 20s)
• Error categorization:
  • Transient errors (retryable) → DLQ after max retries
  • Permanent errors (schema issues, invalid data) → DLQ immediately

Poison pill handling:

• Monitoring: Alert on DLQ message volume exceeding threshold
• Manual intervention: Admin console for DLQ message inspection and reprocessing
• Root cause analysis: Correlation of DLQ messages with system health metrics

Event Streaming Pipeline Architecture

graph TD
    A[Content Upload] --> B[Kafka Producer]
    B --> C[content_events Topic]
    C --> D[Content Processing Service]
    D --> E[Recommendation Engine]
    D --> F[Metadata Service]
    
    G[User Activity] --> H[Kafka Producer]
    H --> I[user_events Topic]
    I --> J[Analytics Service]
    J --> K[User Behavior Analysis]
    
    L[Content Metadata] --> M[Kafka Producer]
    M --> N[content_metadata Topic]
    N --> O[Content Service]

Cross-Service Event Flow

Primary event flows:

1. Content ingestion pipeline: Upload → Metadata extraction → Content availability
2. User interaction pipeline: View start → Progress tracking → Completion
3. Recommendation pipeline: User behavior → Real-time analytics → Content suggestions

Event types:

• content.created - New content available
• user.viewed - User starts watching content
• user.progress - User progress update (every 30s)
• user.completed - Content completion event
• content.updated - Metadata changes for existing content

Integration with External Systems

External event sources:

• Content delivery: Integration with CDN providers (Cloudflare, Akamai) for real-time content availability updates
• Analytics: Integration with third-party analytics platforms (Google Analytics, Mixpanel)
• Notifications: Integration with SMS/email services for user alerts and recommendations

Performance Considerations

Throughput targets:

• Peak QPS: 100,000+ events/sec during peak hours
• Latency targets: < 100ms for event processing in most cases
• Partition distribution: Even distribution across Kafka brokers to prevent hotspots

Monitoring and metrics:

• Lag monitoring: Consumer lag per topic and partition
• Throughput metrics: Messages/sec, bytes/sec for each topic
• Error rates: Failure rate per event type and consumer group

Security and Access Control

Authentication:

• SASL/SCRAM: For Kafka cluster authentication
• TLS encryption: End-to-end encryption for all event streams

Authorization:

• ACLs: Fine-grained access control per topic and consumer group
• Service-to-service authentication: mTLS for internal service communication

Disaster Recovery and Data Protection

Cross-region replication:

• Multi-region Kafka clusters: Active-passive setup for disaster recovery
• Cross-cluster replication: MirrorMaker2 for content and user event synchronization
• RTO/RPO targets: RTO < 5 minutes, RPO < 1 hour for critical event streams

Future Extensibility

Scalability features:

• Kafka Connect: For integrating with external data sources and sinks
• Kafka Streams: For real-time processing pipelines
• Schema evolution: Support for gradual migration between schema versions

Monitoring capabilities:

• Real-time dashboards: Kafka cluster health and performance metrics
• Alerting system: Automated alerts for consumer lag, topic size thresholds, and processing failures

8. Reliability Engineering

Failure Mode Analysis

Single Points of Failure:

• Content Delivery Network (CDN) - Global edge caching layer failure could impact all users
• Primary Database Cluster - Single region database cluster failure would halt content delivery and user operations
• API Gateway Layer - Centralized request routing point failure would block all traffic
• Authentication Service - Failure would prevent user login and content access
• Media Processing Pipeline - Failure to transcode or encode content would prevent streaming

Service-Level Failures:

• User Profile Service - Could cause login failures and content recommendation issues
• Content Metadata Service - Would prevent content discovery and browsing
• Payment Processing Service - Could block subscription functionality
• Recommendation Engine - Degraded user experience but not complete system failure

Fault Tolerance Patterns

Circuit Breaker Pattern:

• Implemented at service boundaries (e.g., content metadata service calls to database)
• Trip after 5 consecutive failures within 10 seconds
• Half-open state for health check before full reset
• Prevents cascading failures during downstream service outages

Bulkhead Pattern:

• Isolated thread pools for different content types (HD vs SD)
• Separate connection pools for transcoding and metadata services
• Rate limiting per content category to prevent resource exhaustion

Timeout & Retry Strategy:

• 200ms timeout for internal service calls
• Exponential backoff retry with jitter (100ms → 500ms → 1s → 2s)
• Max 3 retries for non-idempotent operations
• Immediate retry for idempotent operations (e.g., content metadata fetch)

Multi-Region / Multi-AZ Strategy

Active-Active Architecture:

• Primary region (e.g., us-east-1) handles 70% of traffic
• Secondary region (e.g., us-west-2) handles 30% with automatic failover
• Cross-region replication for user sessions and preferences
• Geo-replicated content delivery using CDN edge locations

Service Redundancy:

• Database clusters in 3 availability zones per region
• Load balancer across multiple AZs with automatic failover
• Auto-scaling groups across zones to handle regional outages

Chaos Engineering Strategy

GameDays:

• Weekly 2-hour sessions testing failure injection in content delivery pipeline
• Simulate CDN edge node failures, database outages, and network partitions
• Validate service recovery and automatic failover capabilities

Steady-State Hypotheses:

• User can continue watching content during database failover
• System maintains 95% availability during CDN edge node failures
• Content recommendations remain functional with metadata service degradation

Disaster Recovery Plan

RTO/RPO Targets:

• Content Delivery: RTO 5 minutes, RPO 1 minute (CDN edge caching)
• User Data: RTO 30 minutes, RPO 5 minutes (replicated databases)
• Payment Data: RTO 10 minutes, RPO 1 minute (payment service replication)
• Media Assets: RTO 2 hours, RPO 1 hour (S3 cross-region replication)

Recovery Procedures:

• Automated failover to secondary region within 5 minutes of primary failure
• Manual intervention required for database recovery (30 minute window)
• Content re-encoding and CDN cache invalidation procedures

Runbook Design for Top-5 Failure Scenarios

1. CDN Edge Node Outage

• Immediate traffic redirect to secondary CDN edge locations
• Cache warming from origin servers within 5 minutes
• Alert escalation to on-call team with incident response

2. Database Cluster Failure

• Automatic failover to read replicas in secondary availability zone
• Failover monitoring and alerting within 30 seconds
• Manual database recovery steps if primary zone is unreachable

3. Authentication Service Outage

• Cached JWT tokens with 15-minute TTL for continued access
• Fallback to read-only mode for content browsing
• Emergency service restart and health check validation

4. Media Processing Pipeline Failure

• Queued content transcoding jobs with retry logic
• Manual content reprocessing queue management
• Content delivery from backup encoded versions

5. Regional Network Partition

• Traffic routing to nearest available region
• Session state synchronization across regions
• User session timeout and re-authentication handling

9. Observability Architecture

Logging Stack

The system will implement a structured logging approach using JSON format to enable efficient parsing and querying. Logs will be collected via Fluentd or Vector (open-source log collectors) and shipped to a centralized logging system like Loki (Grafana's log aggregation) or CloudWatch for AWS environments. Each service will maintain structured log entries with fields like timestamp, service name, request ID, user ID, operation type, and error details. Log retention will be set to 90 days with automatic archiving for compliance purposes.

Distributed Tracing

Distributed tracing will be implemented using OpenTelemetry (OTel) with Jaeger or AWS X-Ray as the backend tracing system. All services will instrument their code to generate trace spans for incoming requests, database calls, external API calls, and cache operations. The tracing system will capture the complete request flow across microservices to identify bottlenecks and latency sources. Span context propagation will be handled automatically through OTel SDKs, ensuring end-to-end trace visibility.

Metrics Stack

Metrics collection will be handled by Prometheus as the primary time-series database, with Grafana for visualization and alerting. Each service will expose Prometheus metrics via standard endpoints, including:

• Request count and latency (p50/p95/p99)
• Error rates by status code
• Cache hit/miss ratios
• Database query performance
• Memory and CPU usage
• Network I/O metrics

Additional monitoring will include Datadog or New Relic for enhanced APM capabilities and infrastructure monitoring.

Alerting Strategy

The alerting system will follow a severity-based approach:

• Critical (P0): Service unavailability, database connection failures, cache outages, >95% error rate
• High (P1): Latency above thresholds, cache miss rates >50%, storage capacity >80%
• Medium (P2): Slow response times, moderate error rates, cache warming issues

Alerts will be routed through PagerDuty with escalation policies. Alert thresholds will be dynamically adjusted based on historical patterns and traffic baselines.

SLI/SLO Definition and Error Budget Tracking

SLIs will be defined for core user journeys:

• Video playback success rate (99.5% SLO)
• Content search response time (p95 < 200ms, 99.5% SLO)
• Video streaming start time (p95 < 3s, 99% SLO)

Error budget will be calculated as (1 - SLO) × 100% and monitored in real-time. When error budget is exhausted, new feature releases will be paused until service stability improves.

Capacity Planning and Load Forecasting

Capacity planning will utilize historical traffic patterns, seasonal trends, and user behavior analytics to predict resource needs. Load forecasting models will be built using time-series analysis tools like Prophet or ARIMA to predict peak demand periods. The system will automatically scale based on forecasted load, with capacity alerts triggered when predicted usage exceeds current provisioned resources by more than 20%.

The observability stack will be designed with multi-region support, ensuring that monitoring and alerting capabilities remain available even during regional outages. All telemetry data will be replicated across regions with appropriate retention policies to support disaster recovery scenarios.

10. API Gateway & Edge Layer

API Gateway Selection

For a video streaming platform, we'll select AWS API Gateway as the primary API gateway with custom edge services for specific capabilities. This decision balances managed service benefits with the flexibility needed for media streaming optimizations.

Justification:

• Managed service benefits: Handles authentication, rate limiting, and request/response transformation out-of-the-box
• Media streaming requirements: Custom edge services can handle HLS/DASH manifest generation, adaptive bitrate switching, and content delivery optimizations
• Global distribution: Leverages AWS CloudFront for edge caching and low-latency content delivery
• Cost efficiency: Pay-per-request model with built-in throttling prevents abuse and reduces infrastructure costs

Request Routing Strategy

The API gateway implements a multi-tier routing strategy:

1. Content Delivery Tier: Routes media requests to CloudFront edge locations
2. Service Discovery Tier: Routes API requests to appropriate microservices via service registry
3. Authentication Tier: Handles JWT validation and token refresh for session management

graph LR
    A[User Client] --> B(API Gateway)
    B --> C[CloudFront Edge]
    B --> D[Service Registry]
    D --> E[User Service]
    D --> F[Content Service]
    D --> G[Payment Service]

Authentication and Authorization at Gateway Layer

Authentication Flow:

• JWT-based authentication for user sessions
• OAuth2/OIDC integration with AWS Cognito for social login providers
• Token refresh capability at gateway level to reduce backend load

Authorization Strategy:

• Role-based access control (RBAC) for content access
• Content-based permissions (subscription tiers, geographic restrictions)
• Token validation and claims extraction at gateway level before forwarding to services

Rate Limiting and Throttling Design

Global Rate Limits:

• 10,000 requests/minute per user for content API
• 500 requests/minute for metadata API
• 1,000 requests/minute for authentication endpoints

Per-Client Limits:

• Device-specific rate limiting (100 requests/minute per device)
• Geographic rate limiting for content delivery (500 requests/hour per region)

Throttling Implementation:

• Token bucket algorithm for burst handling
• Dynamic throttling based on regional capacity and content popularity

Request Transformation and Protocol Translation

Protocol Translation:

• HTTP/HTTPS to gRPC for internal service communication
• HLS/DASH manifest translation for client compatibility
• Adaptive bitrate switching based on client capabilities

Request Transformation:

• Content-type negotiation for different client devices
• Parameter normalization (e.g., converting timestamp formats)
• Header enrichment with user context and session metadata

BFF (Backend for Frontend) Design

BFF Implementation:

• Mobile App BFF: Optimized for mobile network conditions, includes offline support and adaptive streaming
• Web App BFF: Handles complex UI rendering, multi-language content, and progressive enhancement
• TV/Console BFF: Optimized for low-latency, high-bandwidth scenarios with 4K content delivery

BFF Responsibilities:

• Content aggregation from multiple services (metadata, recommendations, user preferences)
• Client-specific content filtering and formatting
• Caching strategy optimization for different device types
• Session management and user context persistence

BFF Architecture:

graph LR
    A[Mobile Client] --> B[BFF - Mobile]
    B --> C[Content Service]
    B --> D[User Service]
    B --> E[Recommendation Service]
    
    F[Web Client] --> G[BFF - Web]
    G --> H[Content Service]
    G --> I[User Service]
    G --> J[Recommendation Service]

Key BFF Features:

• Content Aggregation: Single endpoint for complex content bundles (metadata + recommendations)
• Protocol Adaptation: Converts internal gRPC responses to optimized HTTP responses
• Caching Strategy: Device-specific caching with intelligent invalidation
• Offline Support: Mobile BFF includes local content caching and sync capabilities

Edge Layer Integration:

• CloudFront Integration: Media content delivery through CloudFront edge locations
• Regional Caching: BFFs maintain regional caches for frequently accessed content
• Content Compression: Automatic compression of responses based on client capabilities
• Security Headers: Implementation of security headers and CORS policies at edge layer

Performance Considerations

Latency Optimization:

• Pre-warmed connection pools to backend services
• Asynchronous request handling for non-critical operations
• Content compression and pre-fetching strategies
• Multi-region deployment to minimize latency for global users

Scalability:

• Stateless gateway design supporting horizontal scaling
• Integration with Kubernetes HPA for automatic scaling
• Load distribution across multiple gateway instances
• Regional deployment strategy to support global user base

Monitoring and Metrics:

• Gateway-level request/response metrics (latency, throughput)
• Authentication and authorization success/failure rates
• Content delivery performance indicators
• Rate limiting and throttling effectiveness tracking

This API gateway and edge layer design provides the foundation for handling millions of concurrent users while maintaining low latency, robust security, and optimal content delivery across global infrastructure.

11. Authentication & Authorization Architecture

Identity Provider Integration

For a global video streaming platform, we'll integrate with Amazon Cognito as the primary Identity Provider. This choice provides:

• Native integration with AWS services and global availability
• Support for social identity providers (Google, Facebook, Apple)
• Built-in user pools and federated identities
• Multi-factor authentication (MFA) support
• Seamless integration with AWS WAF and Shield for DDoS protection

Alternative considerations included Auth0 (more mature SaaS) and custom IdP (higher control but more operational overhead). Cognito was chosen for its AWS-native ecosystem and global reach.

OAuth2 / OIDC Flow Design

sequenceDiagram
    participant Client
    participant API_Gateway
    participant Auth_Service
    participant Cognito
    participant User

    Client->>API_Gateway: Request protected resource
    API_Gateway->>Auth_Service: Validate JWT token
    Auth_Service->>Cognito: Verify token signature and claims
    Cognito-->>Auth_Service: Token validation result
    Auth_Service-->>API_Gateway: Access granted/denied
    API_Gateway-->>Client: Resource access or 401

The system implements the Authorization Code Flow with PKCE for web applications and Implicit Flow for mobile apps. This ensures secure token exchange without exposing secrets in client-side code.

Service-to-Service Authentication

For inter-service communication, we implement mTLS with service accounts using SPIFFE/SPIRE for identity management:

• Each microservice has a unique SPIFFE ID
• mTLS certificates are provisioned via SPIRE server
• Certificate rotation handled automatically by SPIRE agent
• Services authenticate using certificate-based mutual TLS
• No JWT tokens required for internal service communication

RBAC / ABAC Model Design

We implement a hybrid RBAC/ABAC model:

Role-Based Access Control (RBAC)

• Admin: Full access to all content, user management, billing
• Content Manager: Content upload, metadata editing, scheduling
• User: Access to subscribed content, personal settings, watch history
• Guest: Limited access to public content

Attribute-Based Access Control (ABAC)

• Content access based on user's subscription tier, geolocation, and device type
• Premium users can access 4K content, while Basic users are limited to HD
• Regional restrictions based on content licensing agreements
• Device-based restrictions (e.g., mobile-only access for family plans)

Token Revocation Strategy

We implement a short-lived JWT strategy with refresh token rotation:

1. Access tokens: 15-minute TTL with HMAC-SHA256 signature
2. Refresh tokens: 30-day TTL with rotation on use
3. Token blacklisting: JWTs are stored in Redis with expiration matching access token TTL
4. Immediate revocation: When a user logs out or changes password, tokens are invalidated in Redis
5. Token rotation: Refresh tokens are rotated after each use to prevent replay attacks

Session Management

• Stateless sessions: JWT tokens contain all necessary claims for authorization decisions
• Session timeout: 24 hours of inactivity before requiring re-authentication
• Multi-device support: Users can be logged into multiple devices simultaneously
• Session binding: Device fingerprinting to detect session hijacking attempts

Authentication Flow for Different User Types

1. Regular Users: OAuth2 flow through Cognito with social login support
2. Admin Users: Direct Cognito admin console access for content management
3. Third-party Integrations: OAuth2 client credentials flow for API integrations
4. Anonymous Users: Temporary guest sessions with limited content access

Security Considerations

• All tokens are transmitted over HTTPS only
• Token storage in secure HTTP-only cookies for web clients
• Rate limiting on authentication endpoints to prevent brute force attacks
• Session management with automatic logout on device change detection
• Regular security audits of authentication infrastructure

Compliance and Regulatory Requirements

• GDPR compliance: User consent management, data portability, right to deletion
• HIPAA compliance: If handling health-related content, secure storage and transmission of user data
• SOC2 Type II: Regular third-party security assessments for authentication infrastructure
• Data residency: Cognito user pools configured to comply with regional data storage requirements

Token Lifecycle Management

• Token generation: JWTs signed with RSA256 keys, rotated quarterly
• Token validation: All services validate tokens against Cognito JWKS endpoint
• Token refresh: Refresh tokens are rotated and stored in secure Redis cache with TTL matching JWT expiration
• Token invalidation: Immediate revocation on logout, password change, or security incident detection

This authentication and authorization architecture provides a secure, scalable foundation for a global video streaming platform while maintaining compliance with industry standards and regulatory requirements.

12. Search Architecture

Search Indexing Pipeline Design

The search system follows a three-stage indexing pipeline:

1. Source: Content metadata from content management system (CMS) and ingestion services
2. Transform: Data enrichment including genre classification, actor/crew mapping, content ratings, and language tagging
3. Index: Real-time indexing into search engine with field-level configuration for full-text, faceted, and geospatial queries

Index Schema and Query Pattern Design

Primary Index Fields:

• title (full-text searchable with stemming and synonym expansion)
• description (partial match with relevance scoring)
• genres (multi-value field for faceted filtering)
• release_year (range query support)
• rating (numeric range filtering)
• actors/crew (multi-value field for content-based recommendations)
• duration (numeric range queries)
• content_type (enum: movie, series, episode)

Query Patterns:

• Full-text search: "action movies from 2023"
• Faceted filtering: "Movies with action genre and rating > 8.0"
• Autocomplete: "Search as you type with suggestions"
• Recommendations: "Similar to [current content]"

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

Real-time indexing (1-2 second latency):

• Critical for content availability and metadata updates
• Implemented via Kafka streams processing content events
• Trade-off: Higher CPU/memory usage, potential for indexing delays during peak ingestion

Near-real-time indexing (5-10 second latency):

• Acceptable for content updates that don't require immediate availability
• Reduces indexing overhead and improves system stability
• Used for batch content updates and metadata enrichment

Relevance Tuning Strategy

Scoring Algorithm Components:

1. Title match weight: 0.4 (boosts exact and partial title matches)
2. Genre relevance: 0.2 (matches user preferences from watch history)
3. Release year proximity: 0.15 (favors recent content)
4. Popularity score: 0.2 (based on view count and engagement metrics)
5. Content rating: 0.05 (higher-rated content gets preference)

Tuning Mechanism:

• A/B testing of different scoring weights using search result click-through rates
• Dynamic weight adjustment based on user feedback and engagement metrics
• Weekly retraining of relevance models using historical search behavior

Search Scaling Design

Sharding Strategy:

• Horizontal sharding by content type (movies vs. series) to reduce cross-shard queries
• Each shard contains 10M-50M documents with consistent distribution across nodes
• Shard size optimized for query performance (avoiding oversized shards that cause latency spikes)

Replication Strategy:

• 3x replication for high availability (primary + 2 replicas)
• Read replicas distributed across availability zones for fault tolerance
• Leader election protocol ensures consistent search results during shard failures

Query Performance Optimization:

• Pre-computed popular query result caches for frequently searched terms
• Query result caching with TTL of 5 minutes for stable search results
• Index field compression to reduce memory footprint and improve query response times

Search Engine Selection Justification: Elasticsearch chosen over alternatives due to:

• Mature ecosystem with extensive search capabilities and plugins
• Strong support for real-time indexing and complex query patterns
• Built-in faceted search and aggregation capabilities
• Proven scalability in large-scale content platforms
• Rich ecosystem for analytics and monitoring integration

13. Scalability Deep Dive

Bottleneck Identification Per Component

User Concurrency & Request Processing

• Peak QPS: 100K (10x average of 10K)
• DAU: 50M (assumed for initial scale)
• Peak QPS calculation: 50M * 1000 / 86400 = ~579 QPS average, peak ~5.8K (10x)
• For 50M users with 10 concurrent sessions = 500M concurrent user sessions
• Each session requires ~200ms for content delivery → 500M * 0.2s = 100M seconds/sec processing requirement
• Per-server throughput: 5K QPS → ~20 servers needed at peak (100K / 5K = 20)

Content Delivery & CDN

• Bandwidth requirement: 100GB/s peak (assumed)
• CDN edge nodes: 5000 nodes globally
• Cache hit rate: 90% → 10% cache misses = 10GB/s write to origin
• Origin server bandwidth: 10GB/s (per region)
• Storage per user: 50GB/month → 600GB/year/user

Database & Storage

• Read QPS: 100K (cache hit rate 90% → 10K DB QPS)
• Write QPS: 5K (user actions, ratings, watch history)
• Storage growth: 600GB/year/user * 50M users = 30PB/year
• Storage per user: 50GB/month * 12 months = 600GB/year

Horizontal vs. Vertical Scaling Decisions

Stateless Services (API Gateway, User Service)

• Horizontal scaling preferred: Stateless services can scale independently
• Auto-scaling based on QPS (HPA) with 3x buffer for burst
• Target: 500 concurrent requests per pod (2500 QPS/pod)

Stateful Services (Content Service, User Preferences)

• Vertical scaling for read-heavy services (PostgreSQL → 32-core instances)
• Horizontal sharding for write-heavy services (User Preferences, Content Metadata)
• Multi-region replication for availability

Database Scaling Path

1. Read Replicas: 50 read replicas for content metadata, user preferences
2. Connection Pooling: 1000 concurrent connections per DB instance
3. Sharding Strategy: Hash-based sharding on user_id for content metadata, range-based on content_id for content delivery
4. Distributed DB: Spanner or CockroachDB for global consistency

Database Scaling Path

User Service

• Vertical scaling: 32-core PostgreSQL instances for read-heavy operations
• Horizontal sharding: Range-based on user_id (1M users per shard)
• Read replicas: 50 for content metadata, 20 for user preferences

Content Service

• Horizontal sharding: Hash-based on content_id (100M content items)
• Multi-region replication: 2 regions for availability
• Cache layer: Redis clusters per region (50GB capacity)

Content Delivery Service

• CDN edge nodes: 5000 nodes globally
• Storage tiering: Hot (10% of content), Warm (80%), Cold (10%)
• Auto-scaling: 2000 edge nodes per region

Stateless vs. Stateful Service Design Implications

Stateless Services

• Can scale horizontally with no session affinity
• Easier to maintain and update
• Requires external state management (Redis, DynamoDB)
• Load balancing via round-robin or consistent hashing

Stateful Services

• Session affinity required for user preferences
• Requires replication and failover mechanisms
• Database sharding for horizontal scaling
• State synchronization across regions

Session Affinity and Its Implications

User Session Affinity

• Required for content preferences, watch history, and user settings
• Implemented via consistent hashing on user_id
• Session storage: Redis clusters with 2x replication for HA
• TTL: 30 days for session persistence

Content Delivery Affinity

• CDN edge nodes handle session affinity automatically
• Content caching: 24-hour TTL for popular content
• Regional caching with automatic invalidation

Performance Bottlenecks & Mitigation Strategy

Content Delivery Bottleneck

• Solution: CDN edge distribution with 5000 nodes
• Cache warming strategy for popular content
• Regional content storage with automatic failover

Database Read Bottleneck

• Solution: Read replicas (50) for content metadata
• Connection pooling (1000 connections per instance)
• Query optimization and indexing strategy

API Gateway Bottleneck

• Solution: Horizontal scaling with 200 pods
• Rate limiting and request buffering
• Circuit breaker for downstream services

Scaling Strategy Summary

Immediate Scale (0-6 months)

• 200 API Gateway pods
• 50 database read replicas
• 5000 CDN edge nodes (5 regions)
• 20 Redis clusters per region

Medium Scale (6-12 months)

• 500 API Gateway pods
• 200 database read replicas
• 10,000 CDN edge nodes (10 regions)
• 100 Redis clusters per region

Large Scale (12+ months)

• 1000 API Gateway pods
• 500 database read replicas
• 25,000 CDN edge nodes (25 regions)
• 250 Redis clusters per region

Resource Allocation

• Compute: 1000 CPU cores for API services, 5000 for content delivery
• Memory: 10TB RAM for caching, 20TB for database
• Storage: 50PB for content, 10PB for metadata
• Network: 100GB/s bandwidth for CDN, 50GB/s for database

Failure Mode Mitigation

• Auto-scaling triggers: 80% CPU utilization for API services
• Health checks every 30 seconds
• Regional failover with 15-minute RTO targets
• Cache warming for new content to reduce cold start impact

14. Infrastructure & Deployment Architecture

Cloud Provider and Region Selection

Primary Provider: AWS (with multi-region support)

• Justification: Mature ecosystem, global reach, comprehensive service portfolio for video streaming (S3, CloudFront, Lambda, ECS, RDS, etc.)
• Regions: us-east-1 (primary), eu-west-1 (secondary), ap-southeast-1 (Tier 2)
• Multi-region strategy: Active-active with cross-region replication for disaster recovery and low-latency content delivery

Kubernetes Cluster Design

Cluster Architecture:

• Multi-tenant clusters: Separate namespaces per service team with resource quotas
• Control plane: Managed EKS (Amazon Elastic Kubernetes Service) for HA and managed upgrades
• Node pools:
  • General purpose (m5.large/m5.xlarge) for stateless services
  • Compute-optimized (c5.xlarge/c5.2xlarge) for transcoding services
  • Memory-optimized (r5.xlarge/r5.2xlarge) for cache services
• Auto-scaling: HPA (Horizontal Pod Autoscaler) with custom metrics for CPU/memory and service-specific triggers

Container Image Strategy

Build Pipeline:

• Multi-stage Docker builds: Build stage → Runtime stage (minimize image size)
• Base images: Alpine Linux for smaller footprint, Ubuntu for compatibility
• Image registry: ECR (Elastic Container Registry) with image scanning integration
• Versioning: Git commit hash + semantic version tags for traceability

Deployment Strategy

Release Management:

• Primary strategy: Blue-green deployment with traffic shifting via ALB (Application Load Balancer)
• Canary rollouts: Gradual traffic shift (5% → 25% → 100%) with automated rollback on error thresholds
• Traffic management:
  • Kubernetes Ingress for routing rules
  • Istio service mesh for advanced traffic management (rate limiting, retries)
• Rollback capability: Automated rollback to previous stable version on failure detection

Auto-scaling Design

Horizontal Scaling:

• HPA (Kubernetes HPA): CPU and memory utilization thresholds (70% target)
• VPA (Vertical Pod Autoscaler): Memory and CPU recommendations for optimal resource allocation
• KEDA (Kubernetes Event-Driven Autoscaling):
  • S3 event triggers for content ingestion
  • SQS queue depth for transcoding job processing
• Scaling triggers: Service-specific (e.g., CPU usage, queue depth, cache miss rate)

Infrastructure as Code Approach

IaC Strategy:

• Primary tool: Terraform (OpenTofu compatible) for infrastructure provisioning
• Module organization:
  • VPC modules (public/private subnets, NAT gateways)
  • EKS cluster modules with managed node groups
  • Service-specific modules (RDS, S3 buckets, Lambda functions)
• State management: Terraform backend with S3 + DynamoDB locking for multi-team collaboration
• Version control: GitOps approach using ArgoCD or FluxCD for declarative deployment of Kubernetes manifests
• Security scanning: Integration with Snyk or Trivy in CI/CD pipeline for image and infrastructure vulnerabilities

Deployment Pipeline Components

CI/CD Integration:

• Build system: Jenkins or GitHub Actions with parallel builds
• Testing pipeline: Unit, integration, and end-to-end tests (including load testing)
• Deployment orchestration: ArgoCD for GitOps-based Kubernetes deployments
• Security scanning: Pre-deployment vulnerability checks and compliance validation
• Feature flags: Integration with LaunchDarkly or custom feature flag service for progressive rollouts

Resource Management and Cost Optimization

Resource Allocation:

• Resource quotas: Per-namespace resource limits (CPU, memory) to prevent resource contention
• Pod disruption budgets: For critical services to ensure availability during maintenance
• Spot instances: For non-critical batch processing and transcoding jobs where fault tolerance is acceptable
• Reserved instances: For predictable, high-throughput services (e.g., media processing)

Network and Service Mesh Integration

Network Design:

• Service mesh: Istio for traffic management, security, and observability
• Network policies: Kubernetes NetworkPolicies to enforce service-to-service communication security
• DNS integration: ExternalDNS for automatic DNS record updates from Kubernetes services

Monitoring and Alerting Integration

Monitoring Stack:

• Metrics collection: Prometheus + Grafana for service and infrastructure monitoring
• Log aggregation: Loki + Promtail for structured log collection from containers
• Tracing: Jaeger for distributed tracing of requests across microservices
• Alerting: Alertmanager with PagerDuty integration for critical service failures

Disaster Recovery and Backup Strategy

DR Implementation:

• Cross-region replication: Media content and metadata replicated across regions
• Database backups: Automated RDS snapshots with cross-region replication
• Stateless design: Services designed to be stateless for easy recovery and failover
• Automated failover: CloudWatch alarms triggering automatic traffic shifting to secondary region

Multi-Region Considerations

Cross-Region Design:

• Content distribution: CloudFront edge locations for global content delivery
• Database replication: Multi-region RDS with read replicas in secondary regions
• Service redundancy: Critical services deployed in both primary and secondary regions with automatic failover
• Data consistency: Event-driven architecture to maintain consistency across regions

This infrastructure design emphasizes operational excellence, scalability, and reliability while maintaining cost efficiency through proper resource allocation, IaC practices, and multi-region deployment strategies. The architecture supports both horizontal scaling for traffic spikes and vertical scaling for compute-intensive operations, with robust monitoring and automated recovery capabilities.

15. Security Architecture

VPC Design and Network Segmentation

The platform operates within a multi-region VPC architecture with public and private subnets across at least 3 availability zones per region. Public subnets host load balancers, API gateways, and edge services, while private subnets contain application services, databases, and internal microservices. Network segmentation is enforced through security groups that strictly control traffic flows between components, with each service operating in its own isolated VPC segment to minimize blast radius.

Zero-Trust Network Model

All network traffic is treated as untrusted, regardless of origin. Services communicate through mTLS (mutual Transport Layer Security) with certificate-based authentication, ensuring that only authorized services can communicate. Network policies enforce strict access control lists (ACLs) at the VPC level, preventing lateral movement and implementing principle of least privilege.

TLS Termination Strategy

TLS termination occurs at the edge layer (API Gateway and Load Balancers) using industry-standard certificates from AWS ACM or equivalent. Application services maintain end-to-end encryption, with internal service-to-service communication using mTLS for additional security. This approach reduces computational overhead on application services while maintaining strong encryption.

DDoS Protection Strategy

DDoS protection is implemented through AWS Shield Advanced (or equivalent cloud provider service) with automatic mitigation of Layer 3-7 attacks. Rate limiting and traffic filtering rules are configured at the VPC level to detect and block malicious traffic patterns. The system includes automatic scaling of WAF rules and IP address blacklisting for sustained attack vectors.

Secrets Management Architecture

Sensitive credentials, API keys, and configuration values are managed through AWS Secrets Manager (or equivalent) with automatic rotation policies for database credentials and service tokens. Secrets are accessed via IAM roles with least-privilege permissions, and all secrets are encrypted at rest using AWS KMS (Key Management Service). The system implements a centralized secrets rotation pipeline to prevent credential exposure.

Vulnerability Scanning in CI/CD

Vulnerability scanning is integrated into the CI/CD pipeline using tools like Trivy or Snyk to scan container images for known vulnerabilities before deployment. Security scanning is performed at multiple stages:

• Container image scanning during build process
• Runtime security monitoring for containerized services
• Dependency analysis to detect vulnerable third-party libraries

WAF Rules for Critical Endpoints

Web Application Firewall (WAF) rules are implemented to protect critical endpoints from common attack vectors:

• SQL injection and XSS protection
• Rate limiting for API endpoints to prevent abuse
• IP address filtering for known malicious sources
• Protection against brute force login attempts and session hijacking

Additional Security Controls

• Encryption at Rest: All data is encrypted using AES-256 encryption keys managed by AWS KMS.
• Data Loss Prevention: Implementation of data classification and monitoring for sensitive content.
• Audit Logging: Comprehensive audit trails for all administrative actions and critical data access events.
• Compliance Frameworks: Implementation of SOC2, HIPAA, and GDPR compliance controls with automated validation checks.
• Security Monitoring: Integration with SIEM solutions for real-time threat detection and incident response.

The security architecture follows defense-in-depth principles, combining network-level controls, application-layer security, and continuous monitoring to provide robust protection against both internal and external threats.

16. Deployment & Release Strategy

Deployment Strategy and Traffic Management

The video streaming platform will implement a blue-green deployment strategy for production releases, with canary rollouts for minor feature releases. This approach ensures zero-downtime deployments and provides quick rollback capability in case of issues.

Traffic Management:

• Traffic will be split using weighted routing across blue and green environments
• Service mesh (Istio) will handle traffic splitting with 5% canary weight for new releases
• Gradual traffic increase to 100% over 30 minutes for stable releases
• Automated rollback triggers when error rates exceed 1% threshold (5-minute window)
• Feature flag integration enables progressive rollouts to specific user segments

Artifact Promotion and Release Process

The deployment pipeline follows a trunk-based development model with the following stages:

1. Dev Environment: Continuous integration with automated unit and integration tests (5 min)
2. Staging Environment: Full end-to-end testing including load and performance tests (30 min)
3. Production Gates:
   • Manual approval for major releases
   • Automated canary analysis with latency and error rate monitoring
   • Rollback capability if thresholds exceeded

Zero-Downtime Database Migration Strategy

Schema migrations will be performed using the "Blue-Green" approach:

• New database schema deployed in parallel with existing system
• Data migration performed using CDC (Change Data Capture) tools
• Service configuration updated to point to new schema
• Old database maintained for 30 days for rollback capability
• Migration completed in batches to minimize impact on streaming performance

Feature Flag Integration and Progressive Rollout

Feature flags will be implemented using:

• LaunchDarkly for centralized feature management
• Gradual rollouts: 0% → 25% → 50% → 75% → 100% over 24 hours
• User segmentation: Target specific user cohorts (VIP, beta testers, new users)
• A/B testing capability for content recommendation algorithms
• Real-time flag updates without service restarts

Canary Analysis Automation and Auto-Rollback Triggers

Automated canary analysis includes:

• Latency monitoring: p99 latency must remain within 20% of baseline
• Error rate thresholds: Error rate must not exceed 1% for 5-minute window
• Throughput validation: 95% of expected QPS maintained
• Cache hit rate monitoring: Must not drop below 85% of baseline
• Auto-rollback triggers: If any metric breaches thresholds, automatic rollback to previous stable version

Release Cadence and Branching Strategy

Release cadence:

• Major releases: Every 2 weeks (feature freeze 1 week before release)
• Minor releases: Daily for bug fixes and small features
• Hotfixes: Immediate deployment when critical issues are identified

Branching strategy:

• Trunk-based development with feature branches for major features
• Release branches created for stable versions (v1.0, v1.1, etc.)
• Hotfix branches for urgent production fixes
• Pull request review process: Minimum 2 approvals required for production changes

Release Automation and Rollback Capability

Release automation includes:

• CI/CD pipeline: Jenkins or GitHub Actions for automated builds and deployments
• Infrastructure as Code: Terraform modules for environment provisioning
• Automated testing: Unit, integration, and end-to-end test suites (15 min execution time)
• Rollback automation: One-click rollback to previous stable release with full state restoration
• Release notes generation: Automated documentation of changes for stakeholders

Rollback capability:

• Full state restoration: Database snapshots and configuration backups
• Service version rollback: Kubernetes deployment rollback to previous image tag
• Feature flag reset: All feature flags reverted to default state during rollback
• Monitoring alerting: Immediate notification when rollback occurs for incident investigation

Deployment Validation and Monitoring

Pre-deployment validation:

• Health checks: All services must pass readiness and liveness probes
• Load testing: 2x peak traffic capacity verified before production deployment
• Security scanning: Vulnerability assessment and compliance checks (15 min)
• Performance benchmarks: CPU, memory, and network utilization within thresholds

Post-deployment monitoring:

• Real-time alerting: Critical metrics monitored with 5-minute alert windows
• Error budget tracking: Error budget consumption monitored across services
• User experience monitoring: Client-side performance and error reporting integrated
• Capacity planning: Resource utilization trends analyzed for future scaling needs

17. Cost Architecture

Major Cost Drivers Analysis

Compute Costs (30-40% of total):

• Video transcoding jobs (EC2 instances, Fargate)
• Media processing and metadata extraction
• API gateway and edge services (ECS/Fargate)
• CDN edge nodes (CloudFront/S3 Transfer Acceleration)

Storage Costs (25-35% of total):

• Video content storage (S3 with lifecycle policies)
• Metadata and user data (RDS, DynamoDB)
• Cache layers (ElastiCache, Redis)
• Object storage for thumbnails and manifests

Network & Data Transfer Costs (20-30% of total):

• Egress from CDN to end users (highest cost component)
• Cross-region data replication
• Inter-service communication over VPC
• S3 to EC2 data transfer (if applicable)

Database & Caching Costs (10-15% of total):

• RDS read replicas and provisioned IOPS
• DynamoDB capacity units (autoscaling)
• ElastiCache cluster nodes and memory usage

Cost Optimization Strategy Per Layer

Compute Layer:

• Use spot instances for transcoding jobs (50% cost reduction)
• Implement auto-scaling with warm-up periods to reduce over-provisioning
• Use Fargate for stateless services to eliminate EC2 management overhead

Storage Layer:

• Tier content using S3 lifecycle policies (Standard → Standard-IA → Glacier)
• Implement multi-region replication with cross-region read-only access to reduce latency
• Use DynamoDB global tables for multi-region access without replication costs

Network Layer:

• Leverage CloudFront edge locations to minimize egress costs
• Use S3 Transfer Acceleration for faster uploads with reduced data transfer costs
• Implement content compression (gzip, brotli) to reduce bandwidth usage

Caching Layer:

• Use Redis with memory-efficient data structures (RedisJSON, Streams)
• Implement cache warming to reduce cold start costs
• Use Redis Cluster for horizontal scaling with reduced per-node cost

Reserved vs. Spot Instance Strategy

Spot Instances:

• 70% of transcoding jobs can run on spot instances (15-20% cost savings)
• Use spot fleet with fallback to on-demand for critical jobs
• Implement pre-warming of spot instances during low-traffic periods

Reserved Instances:

• 30% of compute capacity reserved for critical services (API gateway, DB)
• Use 3-year reserved instances for predictable workloads
• Implement hybrid model with mixed reserved and on-demand capacity

Data Egress Cost Minimization

CDN Strategy:

• Use CloudFront with multiple edge locations to minimize egress costs
• Implement regional content distribution to reduce long-distance data transfer
• Use S3 Transfer Acceleration for faster uploads with reduced bandwidth costs

Cross-Region Replication:

• Use DynamoDB global tables to avoid cross-region data transfer costs
• Implement read replicas in same region as consumers to reduce latency and cost

FinOps Dashboards and Budget Alerts

Cost Monitoring:

• Set up AWS Cost Explorer with custom dashboards for monthly cost trends
• Implement real-time alerts for cost anomalies (5% increase in spend)
• Create per-service cost allocation tags for better visibility

Budget Management:

• Set up monthly budget alerts with 80% and 100% thresholds
• Implement cost allocation by team/project for better accountability
• Use AWS Budgets to set up automatic cost optimization recommendations

Optimization Automation:

• Implement cost optimization scripts for idle resources (EC2, RDS)
• Use AWS Trusted Advisor for automated cost-saving recommendations
• Set up scheduled cleanup of temporary files and unused assets

Resource Right-sizing:

• Monitor EC2 instance utilization to right-size instances (50% of instances underutilized)
• Use RDS performance insights to optimize database instance sizes
• Implement auto-scaling policies with cost-aware thresholds

This cost architecture provides a balanced approach to cost optimization while maintaining system performance and reliability. The strategy focuses on leveraging spot instances, proper storage tiering, and efficient resource utilization to achieve significant cost savings without compromising user experience.

18. Open Questions, Risks & Trade-offs

Decisions requiring product / engineering alignment

• Content licensing model: Whether to implement a traditional subscription model or hybrid ad-supported model, which affects monetization strategy and user experience design
• Regional content availability: How to handle regional content restrictions and localization requirements that impact global distribution strategy
• Content ingestion pipeline: Whether to support live streaming, on-demand content, or both, affecting real-time processing requirements and infrastructure design

Key architectural risks

Risk	Impact	Likelihood	Mitigation
Content delivery latency	High	Medium	Implement multi-region CDN with edge caching and pre-warming strategies
Database sharding complexity	High	Medium	Use consistent hashing with proper partitioning keys and implement read-replica strategy
User session consistency	Medium	High	Implement distributed session store with eventual consistency model
Real-time content recommendation accuracy	Medium	High	Design feedback loop with A/B testing and ML model retraining pipeline
Multi-region content synchronization	High	Medium	Implement conflict-free replicated data structures with version control

Trade-offs made with justification

• Consistency vs. availability: Chose eventual consistency for user preferences and watch history to maintain high availability during network partitions, with strong consistency for critical operations like billing and content access
• Global vs. regional caching: Implemented global CDN with regional edge caches to balance latency and cost, accepting slightly higher latency for better geographic distribution
• Monolithic vs. microservices: Started with modular monolith for faster initial development and easier cross-service operations, with clear service boundaries planned for future decomposition
• Real-time vs. batch processing: Prioritized real-time content recommendation with batch-based content metadata updates to balance user experience and system complexity

Alternatives considered and rejected

• Traditional CDN vs. custom edge infrastructure: Rejected custom edge infrastructure due to high operational complexity and cost, choosing proven CDN solutions with global distribution capabilities
• PostgreSQL vs. MongoDB for user profiles: Rejected MongoDB due to lack of strong consistency guarantees and complex cross-document transactions, favoring PostgreSQL for user data consistency
• Kafka vs. Amazon SQS: Rejected SQS for high-throughput content processing due to message size limitations and lack of advanced consumer group features, choosing Kafka for better scalability and message retention
• Redis vs. Memcached: Rejected Memcached due to lack of persistence and complex clustering, choosing Redis for better data durability and rich data structures

Technical debt acknowledged

• Service coupling in user profile updates: Current design has tight coupling between user preferences and content recommendation services, creating potential for cascading failures during content updates
• Content metadata normalization: Inconsistent content metadata formats across ingestion sources create challenges for search and recommendation systems, requiring ongoing data normalization efforts
• Caching invalidation strategy: Current cache invalidation relies on TTL-based expiration with manual refresh triggers, creating potential for stale content in edge caches during content updates
• Feature flag implementation: Early feature flag system lacks proper rollback and analytics capabilities, requiring future enhancement for better experimentation support
• Cross-service transaction handling: Lack of distributed transaction support creates eventual consistency challenges in user content operations, requiring compensating actions for error recovery

19. Phased Implementation Roadmap

Phase 1: MVP (Minimum Viable Product)

Timeline: 3-4 months Core Focus: Basic video streaming capabilities with minimal infrastructure

Milestones:

• User authentication and profile management
• Content catalog with basic metadata (title, description, duration)
• Video playback with adaptive bitrate streaming
• Basic content recommendation engine (collaborative filtering)
• Simple content delivery network (CDN) integration
• Mobile and web client MVPs

Dependencies:

• Content ingestion pipeline (manual upload)
• Basic database schema for users and content
• Single-region deployment infrastructure

Critical Path:

1. Authentication service + user profiles (Week 1-2)
2. Content catalog and metadata service (Week 2-3)
3. Video delivery pipeline with CDN (Week 3-4)
4. Basic client applications (Week 4-6)

Phase 2: Scale & Hardening

Timeline: 4-6 months Core Focus: Multi-region support, content personalization, and performance optimization

Milestones:

• Multi-region deployment with active-active architecture
• Advanced recommendation engine (content-based + collaborative filtering)
• Content search and discovery features
• Multi-language and multi-subtitle support
• Enhanced caching strategy with edge computing
• Content analytics and engagement tracking

Dependencies:

• Multi-region Kubernetes clusters
• Advanced content processing pipeline (transcoding, thumbnails)
• Analytics and telemetry infrastructure
• Multi-language content management system

Critical Path:

1. Multi-region infrastructure (Week 1-2)
2. Content processing pipeline (Week 2-4)
3. Advanced recommendation engine (Week 4-6)
4. Enhanced client features and analytics (Week 6-8)

Phase 3: Excellence & Optimization

Timeline: 6-8 months Core Focus: Reliability, observability, and cost optimization

Milestones:

• Comprehensive observability stack (logging, tracing, metrics)
• Advanced reliability patterns (chaos engineering, circuit breakers)
• Automated capacity planning and auto-scaling
• Cost optimization and FinOps implementation
• Advanced security features (DDoS protection, WAF)
• Content governance and compliance systems
• Advanced content delivery with real-time streaming capabilities

Dependencies:

• Production-grade monitoring and alerting systems
• Advanced security infrastructure (zero-trust, DDoS protection)
• Comprehensive testing and validation frameworks
• Advanced analytics and machine learning platforms

Critical Path:

1. Observability stack implementation (Week 1-2)
2. Reliability engineering and chaos testing (Week 2-4)
3. Cost optimization and FinOps (Week 4-6)
4. Advanced security and compliance (Week 6-8)

Risk Mitigation & Contingency Planning

High-Risk Dependencies:

• Content licensing and distribution agreements (2-3 months lead time)
• CDN provider selection and integration (1-2 months)
• Regulatory compliance and content governance (3-4 months)

Alternative Approaches:

• Start with single-region MVP and migrate to multi-region later
• Use managed services for initial content processing (e.g., AWS MediaConvert)
• Implement progressive rollout with feature flags to reduce risk

Success Metrics & Validation

Phase 1:

• 95% video playback success rate
• <2s average content load time
• 99.9% availability for core streaming features

Phase 2:

• Multi-region failover within <30 seconds
• 99.99% content availability across regions
• 50% improvement in recommendation accuracy

Phase 3:

• <100ms p99 latency for content delivery
• 99.999% system availability with automated recovery
• 30% reduction in infrastructure costs through optimization