Trade-offs and Design Decisions

Every system design involves critical trade-offs. Here are the key decisions made in this architecture and their implications:

Consistency vs. Availability

Decision: Eventual consistency for most operations

• Trade-off: We prioritize availability over strong consistency
• Rationale: Video streaming can tolerate slight delays in metadata updates (view counts, likes) but cannot afford service downtime
• Implementation:
• Video uploads use eventual consistency between metadata and actual video files
• View counts may be slightly delayed but videos remain watchable
• Critical user data (authentication) maintains stronger consistency

Storage Strategy

Decision: Object storage over traditional databases for video files

• Trade-off: Lose ACID properties but gain massive scalability and cost efficiency
• Rationale: Video files are immutable and don’t require complex queries
• Implementation:
• Videos stored in distributed object storage (AWS S3, Google Cloud Storage)
• Metadata kept in traditional databases for complex queries
• Hybrid approach balances performance and functionality

Caching Strategy

Decision: Multi-layer caching (CDN + Application cache)

• Trade-off: Increased complexity and potential cache invalidation issues
• Rationale: Video streaming is read-heavy with high geographic distribution
• Implementation:
• CDN caches popular videos globally
• Application cache stores frequently accessed metadata
• Cache invalidation strategy accepts brief inconsistency windows

Microservices vs. Monolith

Decision: Microservices architecture with API gateways

• Trade-off: Higher operational complexity vs. better scalability and team autonomy
• Rationale: Different services have vastly different scaling requirements
• Implementation:
• Upload service needs different scaling than recommendation engine
• Independent deployments reduce blast radius of failures
• Each team can optimize their service independently

Synchronous vs. Asynchronous Processing

Decision: Hybrid approach – sync for user-facing, async for background tasks

• Trade-off: Complexity in handling both patterns vs. optimal user experience
• Rationale: Users need immediate feedback but heavy processing can be deferred
• Implementation:
• Video uploads return immediately after initial validation
• Transcoding happens asynchronously with status updates
• Real-time features (comments, likes) use synchronous APIs

Data Partitioning Strategy

Decision: Horizontal partitioning by video ID and user ID

• Trade-off: Cross-shard queries become complex but individual operations scale linearly
• Rationale: Most queries are isolated to single videos or users
• Implementation:
• Video data partitioned by video ID hash
• User data partitioned by user ID
• Recommendation engine handles cross-partition aggregations

Search Implementation

Decision: Dedicated search service (Elasticsearch) vs. database queries

• Trade-off: Additional infrastructure complexity vs. search performance and flexibility
• Rationale: Video search requires full-text, fuzzy matching, and ranking algorithms
• Implementation:
• Elasticsearch indexes video metadata asynchronously
• Database handles exact matches and simple queries
• Search service provides advanced features like autocomplete and personalized results

These trade-offs reflect the reality that no single solution fits all requirements. The key is making conscious decisions that align with your specific use case, scale, and team capabilities.

Summary

In this architecture diagram, I’ve designed a scalable video streaming platform modeled after services like YouTube. The goal is to ensure the system is modular, horizontally scalable, and optimized for performance, availability, and maintainability.

Client & Entry Point

• The diagram begins with a Client sending a request, which is routed through a Load Balancer.
• The Load Balancer is responsible for:
  – SSL termination
  – Traffic distribution
  – Redirecting to appropriate internal services

Note:
SSL/TLS termination means decrypting the HTTPS (encrypted) traffic at the load balancer level, so that the traffic from the LB to backend services is plain text (HTTP) (or optionally re-encrypted). Decrypting TLS at each backend service is CPU-intensive. Offloading it to the LB centralizes the decryption and reduces server load.

API Gateways (Layered Routing)

• I’ve introduced multiple API Gateway instances to decouple external traffic from internal service logic.
• Each API Gateway can route traffic based on path, headers, or roles to specific backend services (e.g., Uploads, Video, Metadata).
• This helps with rate limiting, authentication, CORS handling, and centralized routing.

Backend Services

• Services like Upload, Transcoding, and Video Service are organized logically behind the gateways.
• Each service is stateless and horizontally scalable.
• Communication between services is mostly synchronous (solid arrows), with dashed lines indicating asynchronous flows (e.g., transcoding completion).

Object Storage & Databases

• Videos are stored in a dedicated Object Storage system optimized for large media files.
• Metadata DB stores video metadata (title, duration, tags, etc.)
• Video DB stores additional structured video information used by recommendation engines.

CDN and Analytics

• CDN + Cache ensures videos are served efficiently to global users with low latency.
• A dedicated Recommendations and Analytics Service processes viewership data, helping power personalized content suggestions.

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Justification

• Layered routing with API Gateways improves scalability, isolation, and operational flexibility.
• Decoupling upload/transcode/video services ensures each can scale independently.
• Object storage is chosen over traditional DBs for handling large video files efficiently.
• Modular layout makes it easier to onboard new teams, isolate failures, and apply CI/CD independently.
• Using a CDN and recommendation engine helps optimize user experience and platform engagement.

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