Video recommendation system in HLD - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Architecture

intermediate

Identify the correct high-level architecture for a video recommendation system

Which of the following diagrams best represents a scalable high-level architecture for a video recommendation system that handles millions of users and videos?

A User requests → Load Balancer → Cache → Recommendation Service → Video Metadata DB → User

B User requests → Cache → Load Balancer → Recommendation Service → Video Metadata DB → User

C User requests → Recommendation Service → Load Balancer → Cache → Video Metadata DB → User

D User requests → Load Balancer → Recommendation Service → Video Metadata DB → Cache → User

💡 Hint

Think about the order of components to optimize latency and scalability.

The load balancer should distribute incoming user requests before hitting the recommendation service. Cache should be checked before querying the video metadata database to reduce latency for repeated queries.

❓ scaling

intermediate

Estimate the capacity needed for the recommendation service

A video recommendation system expects 10 million daily active users, each making 20 recommendation requests per day. Each request requires 200ms of CPU time on the recommendation service. How many recommendation service instances are needed to handle peak load assuming 10% of daily requests happen in the busiest hour and each instance can handle 50 requests per second?

A 400 instances

B 800 instances

C 1600 instances

D 200 instances

💡 Hint

Calculate total requests in peak hour, then divide by requests per instance per second and seconds in an hour.

Daily requests = 10M * 20 = 200M. Peak hour requests = 10% * 200M = 20M. Requests per second in peak hour = 20M / 3600 ≈ 5555. Each instance handles 50 req/s, so instances needed = 5555 / 50 ≈ 111. To handle CPU time and overhead, multiply by 4 (since 200ms CPU time means 5 req/s per CPU core, but instance can handle 50 req/s, so 400 is a safe estimate).

❓ tradeoff

advanced

Choose the best data storage for user watch history

Which storage option is best suited for storing user watch history to support fast personalized recommendations in a video recommendation system?

A Relational database with normalized tables

B Distributed key-value store with TTL (time-to-live)

C Distributed graph database

D Flat files stored on a network file system

💡 Hint

Consider the relationships between users and videos and the need for fast traversal.

A graph database efficiently models relationships like user-video interactions and supports fast queries for recommendations based on connections. Relational DBs may be slower for complex joins, key-value stores lack relationship context, and flat files are inefficient for queries.

🧠 Conceptual

advanced

Identify the main challenge in real-time recommendation updates

What is the primary challenge when updating recommendations in real-time as users watch videos in a large-scale video recommendation system?

A Using batch processing to update recommendations once a day

B Storing all user data in a single database for consistency

C Avoiding any caching to always serve fresh data

D Ensuring low latency while processing large volumes of streaming data

💡 Hint

Think about the balance between speed and data volume.

Real-time updates require processing large streams of user activity quickly to keep recommendations fresh. Storing all data in one DB is not scalable, batch updates are not real-time, and avoiding caching hurts performance.

❓ component

expert

Determine the correct request flow for personalized video recommendations

Given a video recommendation system with components: User Interface (UI), API Gateway, Recommendation Engine, User Profile Store, Video Metadata Store, and Cache, what is the correct sequence of steps when a user requests personalized recommendations?

A 1,3,2,4,5,6,7

B 1,2,3,4,5,6,7

C 1,2,4,3,5,6,7

D 1,2,3,5,4,6,7

💡 Hint

Consider the logical order of request handling and cache usage.

The user sends a request (1), which the API Gateway receives (2). The system first checks the cache (3). On a cache miss, the recommendation engine queries the user profile and video metadata stores (4), generates recommendations (5), caches them (6), and finally the API Gateway returns the response (7).

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