Search and recommendation in HLD - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Architecture

intermediate

Design a scalable search system for millions of products

You need to design a search system that can handle millions of products with fast response times. Which architecture component is most critical to ensure scalability and low latency?

A A distributed index with sharding and replication

B A batch processing system that updates search results once a day

C A single powerful database server with all data loaded in memory

D A client-side search using local browser storage

💡 Hint

Think about how to handle large data and many users at the same time.

Distributed indexing with sharding and replication allows the system to split data across servers and handle many queries in parallel, ensuring scalability and low latency.

❓ scaling

intermediate

Handling high query volume in recommendation system

Your recommendation system receives a sudden spike in user requests. Which approach best helps maintain performance under high load?

A Store recommendations only in a slow disk-based database

B Recompute recommendations from scratch for every request

C Cache popular recommendations in memory close to users

D Send all requests to a single recommendation server

💡 Hint

Think about reducing repeated work and fast access.

Caching popular recommendations reduces computation and database hits, allowing the system to serve many users quickly during spikes.

❓ tradeoff

advanced

Choosing between real-time and batch recommendation updates

Which tradeoff is true when choosing real-time recommendation updates over batch updates?

A Real-time updates provide fresher recommendations but require more computing resources

B Real-time updates reduce resource usage but increase recommendation delay

C Batch updates provide fresher recommendations than real-time updates

D Batch updates require more frequent user interaction to trigger updates

💡 Hint

Consider freshness versus resource cost.

Real-time updates keep recommendations fresh but need continuous computation, increasing resource use compared to batch processing.

🧠 Conceptual

advanced

Understanding cold start problem in recommendation systems

What is the cold start problem in recommendation systems?

A System crash due to too many recommendations

B Difficulty recommending items to new users with no history

C Slow search queries due to large index size

D Users receiving too many irrelevant recommendations

💡 Hint

Think about what happens when the system has no data about a user.

The cold start problem occurs when the system cannot generate personalized recommendations because it lacks user interaction data.

❓ estimation

expert

Estimating storage needs for a search index

You have 100 million documents averaging 1 KB each. The search index typically requires 30% of the original data size. How much storage is needed for the index?

A Approximately 10 GB

B Approximately 300 GB

C Approximately 3 TB

D Approximately 30 GB

💡 Hint

Calculate 30% of total data size.

100 million documents * 1 KB = 100 million KB = 100 GB. 30% of 100 GB is 30 GB.

Practice

1. What is the primary purpose of a search system in a large-scale application?

easy

A. To help users quickly find relevant content from a large dataset

B. To store user passwords securely

C. To manage user account settings

D. To display advertisements randomly

Solution

1. Step 1: Understand the role of search systems

   Search systems are designed to help users find information efficiently from large amounts of data.

2. Step 2: Match the purpose with options

   Only To help users quickly find relevant content from a large dataset describes helping users find relevant content quickly, which is the core function of search.

3. Final Answer:

   To help users quickly find relevant content from a large dataset -> Option A

4. Quick Check:

   Search system purpose = find relevant content [OK]

Hint: Search systems focus on finding relevant data fast [OK]

Common Mistakes:

• Confusing search with unrelated features like password storage
• Thinking search manages user settings
• Assuming search is for random content display

2. Which component is essential in a recommendation system to personalize suggestions?

easy

A. Database backup scripts

B. User behavior tracking

C. Static HTML pages

D. Load balancer configuration

Solution

1. Step 1: Identify personalization needs

   Recommendation systems personalize suggestions based on user data and behavior.

2. Step 2: Match components to personalization

   User behavior tracking collects data needed to tailor recommendations, unlike static pages or infrastructure tasks.

3. Final Answer:

   User behavior tracking -> Option B

4. Quick Check:

   Personalization needs user data = User behavior tracking [OK]

Hint: Personalization needs user data collection [OK]

Common Mistakes:

• Confusing infrastructure tasks with personalization
• Thinking static pages can personalize content
• Ignoring the role of user data

3. Consider a search system that indexes 1 million documents. If the system uses an inverted index, what is the main advantage?

medium

A. Automatically deleting old documents

B. Storing documents in a single large file

C. Encrypting all documents for security

D. Faster search queries by mapping words to document lists

Solution

1. Step 1: Understand inverted index concept

   An inverted index maps each word to the list of documents containing it, enabling quick lookups.

2. Step 2: Identify the advantage for search speed

   This mapping allows the system to find relevant documents quickly without scanning all documents.

3. Final Answer:

   Faster search queries by mapping words to document lists -> Option D

4. Quick Check:

   Inverted index = fast word-to-doc lookup [OK]

Hint: Inverted index speeds up word-based search [OK]

Common Mistakes:

• Confusing indexing with storage format
• Thinking encryption is the main index benefit
• Assuming index deletes documents automatically

4. A recommendation system is returning irrelevant suggestions. Which issue is most likely causing this?

medium

A. Using HTTPS instead of HTTP

B. Too many servers in the cluster

C. Incorrect or missing user behavior data

D. Database backup frequency is too high

Solution

1. Step 1: Analyze cause of irrelevant recommendations

   Recommendations depend on accurate user data; missing or wrong data leads to poor suggestions.

2. Step 2: Evaluate other options

   Server count, protocol choice, or backup frequency do not directly affect recommendation relevance.

3. Final Answer:

   Incorrect or missing user behavior data -> Option C

4. Quick Check:

   Bad recommendations = bad user data [OK]

Hint: Check user data quality for recommendation issues [OK]

Common Mistakes:

• Blaming infrastructure instead of data quality
• Confusing network protocols with recommendation logic
• Ignoring data collection importance

5. You are designing a scalable recommendation system for millions of users. Which approach best balances personalization and system performance?

hard

A. Use a hybrid model combining collaborative filtering and content-based filtering with offline batch processing and online updates

B. Only recommend the most popular items to all users without personalization

C. Run real-time deep learning models for every user request without caching

D. Store all user data in a single database server for simplicity

Solution

1. Step 1: Understand scalability and personalization needs

   Millions of users require efficient processing; personalization improves user experience.

2. Step 2: Evaluate approaches

   Hybrid models combine strengths of different methods. Offline batch processing reduces load, while online updates keep recommendations fresh.

3. Step 3: Reject less scalable or less personalized options

   Popular-only recommendations lack personalization. Real-time deep learning per request is costly. Single DB server is a bottleneck.

4. Final Answer:

   Use a hybrid model combining collaborative filtering and content-based filtering with offline batch processing and online updates -> Option A

5. Quick Check:

   Hybrid + batch + online = scalable personalized system [OK]

Hint: Combine offline and online methods for scalable personalization [OK]

Common Mistakes:

• Ignoring scalability by doing all processing online
• Sacrificing personalization for simplicity
• Using single server for massive data