Video recommendation system in HLD - Architecture Diagram

System Overview - Video recommendation system

This system suggests videos to users based on their interests and past behavior. It must handle many users and videos, provide quick recommendations, and update suggestions as users watch more videos.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  v
+----------------+       +----------------+
| Recommendation  |<----->| User Behavior   |
| Service        |       | Service        |
+----------------+       +----------------+
        |                        |
        v                        v
+----------------+       +----------------+
| Video Metadata |       | Cache          |
| Database       |       +----------------+
+----------------+                |
        |                         v
        +----------------->+----------------+
                           | NoSQL Database |
                           | (User Profiles) |
                           +----------------+

Components

User

user

Person who requests video recommendations

Load Balancer

load_balancer

Distributes incoming user requests evenly to API Gateway instances

API Gateway

api_gateway

Receives user requests and routes them to appropriate services

Recommendation Service

service

Generates video recommendations based on user data and video metadata

User Behavior Service

service

Tracks and processes user watch history and interactions

Video Metadata Database

database

Stores information about videos such as titles, categories, and tags

Cache

cache

Stores frequently accessed recommendations to reduce latency

NoSQL Database

database

Stores user profiles and behavior data for fast access

Request Flow - 14 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Cache

Cache → API Gateway

API Gateway → Recommendation Service

Recommendation Service → User Behavior Service

User Behavior Service → NoSQL Database

User Behavior Service → Recommendation Service

Recommendation Service → Video Metadata Database

Video Metadata Database → Recommendation Service

Recommendation Service → Cache

Recommendation Service → API Gateway

API Gateway → Load Balancer

Load Balancer → User

Failure Scenario

Component Fails:Cache

Impact:Recommendations requests take longer because cache misses cause direct database queries

Mitigation:System falls back to querying databases and services directly; cache is rebuilt asynchronously

Architecture Quiz - 3 Questions

Which component first receives the user's recommendation request?

A Load Balancer

B API Gateway

C Recommendation Service

D Cache

Design Principle

This design uses caching to reduce latency and load on databases. It separates concerns by having dedicated services for user behavior and recommendations. Load balancing and API Gateway ensure scalability and proper routing. The system gracefully handles cache misses by falling back to database queries.

Practice

1. What is the primary goal of a video recommendation system?

easy

A. To store all videos in a single database

B. To delete unpopular videos automatically

C. To compress videos for faster streaming

D. To personalize video content to increase user engagement

Solution

1. Step 1: Understand the purpose of recommendation systems

   Recommendation systems aim to suggest content that matches user preferences to keep them engaged.

2. Step 2: Identify the main goal in video platforms

   Personalizing video content helps users find videos they like, increasing engagement and satisfaction.

3. Final Answer:

   To personalize video content to increase user engagement -> Option D

4. Quick Check:

   Personalization = Engagement [OK]

Hint: Focus on user benefit and engagement goals [OK]

Common Mistakes:

• Confusing storage with recommendation
• Thinking compression is the main goal
• Assuming deletion is automatic

2. Which component is essential for real-time video recommendations?

easy

A. Real-time data streaming and processing

B. Offline video transcoding

C. Batch processing system only

D. Static video metadata storage

Solution

1. Step 1: Identify real-time needs in recommendations

   Real-time recommendations require processing fresh user interactions quickly.

2. Step 2: Match components to real-time processing

   Data streaming and processing systems handle live data to update recommendations instantly.

3. Final Answer:

   Real-time data streaming and processing -> Option A

4. Quick Check:

   Real-time = Streaming & Processing [OK]

Hint: Real-time means instant data handling, not batch [OK]

Common Mistakes:

• Choosing batch processing for real-time needs
• Confusing transcoding with recommendation
• Ignoring dynamic data updates

3. Consider a system where user watch history is updated every hour, but recommendations are generated in real-time. What is the likely output when a user watches a new video?

medium

A. The new video immediately influences recommendations

B. Recommendations update only after the next hourly batch

C. Recommendations never change after initial setup

D. The system crashes due to conflicting updates

Solution

1. Step 1: Understand real-time recommendation generation

   Real-time generation means recommendations can change instantly based on new data.

2. Step 2: Analyze watch history update frequency

   Even if watch history updates hourly, real-time components can use streaming data to update recommendations immediately.

3. Final Answer:

   The new video immediately influences recommendations -> Option A

4. Quick Check:

   Real-time generation = Immediate update [OK]

Hint: Real-time generation overrides batch delay [OK]

Common Mistakes:

• Assuming batch update controls recommendation timing
• Thinking system crashes on data conflict
• Believing recommendations are static

4. A video recommendation system is showing outdated videos despite recent user activity. What is the most likely cause?

medium

A. User profiles are deleted frequently

B. Real-time data pipeline is broken or delayed

C. Video metadata is missing thumbnails

D. Batch processing runs too frequently

Solution

1. Step 1: Identify the symptom - outdated recommendations

   Outdated videos suggest the system is not processing recent user actions timely.

2. Step 2: Check real-time data pipeline status

   If the real-time pipeline is broken or delayed, fresh user data won't update recommendations promptly.

3. Final Answer:

   Real-time data pipeline is broken or delayed -> Option B

4. Quick Check:

   Outdated = Pipeline delay [OK]

Hint: Outdated results often mean broken real-time updates [OK]

Common Mistakes:

• Blaming user profile deletion
• Confusing metadata issues with recommendation freshness
• Assuming batch runs cause outdated data

5. To scale a video recommendation system for millions of users, which design choice best balances freshness and computational cost?

hard

A. Generate recommendations on-demand without caching

B. Only use batch processing with daily updates

C. Use a hybrid approach combining batch model training with real-time feature updates

D. Store all user data in a single monolithic database

Solution

1. Step 1: Understand scalability challenges

   Millions of users require efficient computation and timely recommendations without overload.

2. Step 2: Evaluate design options for freshness and cost

   Hybrid systems use batch training for accuracy and real-time updates for freshness, balancing cost and performance.

3. Final Answer:

   Use a hybrid approach combining batch model training with real-time feature updates -> Option C

4. Quick Check:

   Hybrid approach = Freshness + Cost balance [OK]

Hint: Hybrid systems combine batch and real-time strengths [OK]

Common Mistakes:

• Relying only on batch causes stale data
• On-demand without caching is costly
• Monolithic DB limits scalability