Video recommendation system in HLD - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to identify the main component responsible for storing user watch history.

HLD

The component that stores user watch history is called the [1].

Drag options to blanks, or click blank then click option'

AWatch History Store

BUser Profile Database

CRecommendation Engine

DContent Delivery Network

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing the recommendation engine with the storage component.

Choosing content delivery network which only serves videos.

The Watch History Store keeps track of videos watched by users, which is essential for personalized recommendations.

2fill in blank

medium

Complete the code to specify the type of database best suited for storing video metadata.

HLD

For storing video metadata like title, description, and tags, a [1] database is preferred.

Drag options to blanks, or click blank then click option'

AGraph

BRelational

CTime-series

DKey-Value

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing key-value stores which are better for caching.

Choosing graph databases which are better for social connections.

Relational databases are ideal for structured data like video metadata with defined schema.

3fill in blank

hard

Fix the error in the description of the recommendation algorithm type.

HLD

The system uses a [1] filtering algorithm that recommends videos based on user similarity.

Drag options to blanks, or click blank then click option'

ACollaborative

BContent-based

CHybrid

DRule-based

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing content-based filtering which uses item features.

Choosing rule-based which is manual and static.

Collaborative filtering recommends items based on similarities between users' preferences.

4fill in blank

hard

Fill both blanks to complete the request flow for serving a video recommendation.

HLD

User request -> [1] -> [2] -> Response with recommended videos

Drag options to blanks, or click blank then click option'

ARecommendation Service

BUser Profile Store

CVideo Metadata Store

DContent Delivery Network

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up user profile store with video metadata store.

Placing content delivery network before recommendation service.

The Recommendation Service processes the request using data from the Video Metadata Store to generate recommendations.

5fill in blank

hard

Fill all three blanks to complete the capacity estimation formula for the recommendation system.

HLD

Total requests per second = [1] * [2] * [3]

Drag options to blanks, or click blank then click option'

ANumber of active users

BAverage requests per user per second

CReplication factor

DCache hit ratio

Attempts:

3 left

💡 Hint

Common Mistakes

Including cache hit ratio which reduces load, not increases it.

Confusing replication factor with cache hit ratio.

The total requests depend on how many users are active, how often they request, and how many replicas handle the load.

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