Rating and review system in LLD - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Architecture

intermediate

Design components for a scalable rating and review system

Which of the following component sets best supports a scalable rating and review system that handles millions of users submitting reviews and ratings concurrently?

A Single web server, relational database, no caching, direct database writes

B Load balancer, API servers, relational database, caching layer, message queue, search engine

C Load balancer, API servers, flat file storage, no caching, no message queue

D Single web server, NoSQL database, no caching, no message queue

💡 Hint

Think about components that help with high traffic, fast reads, and asynchronous processing.

Option B includes load balancing for traffic distribution, caching for fast reads, message queue for asynchronous processing, and a search engine for efficient review search. This combination supports scalability and performance.

❓ scaling

intermediate

Handling sudden spikes in review submissions

During a product launch, the system experiences a sudden spike in review submissions. Which approach best helps maintain system stability?

A Reject all new review submissions until the spike ends

B Directly write all reviews synchronously to the database without any queueing

C Use a message queue to buffer incoming reviews and process them asynchronously

D Store reviews only in cache without persisting to the database

💡 Hint

Think about how to handle bursts without overwhelming the database.

Using a message queue buffers incoming requests and allows the system to process reviews at a manageable rate, preventing database overload and improving stability.

❓ tradeoff

advanced

Choosing database type for storing reviews and ratings

Which database choice best balances consistency, scalability, and query flexibility for a rating and review system?

A Relational database with strong consistency and support for complex queries

B NoSQL document store with eventual consistency and flexible schema

C In-memory key-value store without persistence

D Flat file storage on a single server

💡 Hint

Consider the need for accurate ratings and complex queries like filtering and sorting reviews.

Relational databases provide strong consistency and support complex queries needed for filtering, sorting, and aggregating reviews and ratings, which is critical for accuracy and user experience.

🧠 Conceptual

advanced

Ensuring data integrity in concurrent review submissions

What mechanism best prevents race conditions when multiple users submit or update reviews for the same product simultaneously?

A Use optimistic concurrency control with version numbers or timestamps

B Lock the entire database during every review submission

C Allow all writes without any concurrency control and overwrite data

D Store reviews only in cache without synchronization

💡 Hint

Think about a method that detects conflicts without blocking all users.

Optimistic concurrency control allows multiple users to work concurrently and detects conflicts by checking version numbers or timestamps before committing changes, preventing lost updates.

❓ estimation

expert

Estimating storage needs for a rating and review system

Estimate the approximate storage needed per year if the system expects 10 million new reviews annually, each review averaging 1 KB, and ratings stored separately averaging 100 bytes each. Assume each review has one rating.

A Approximately 100 MB per year

B Approximately 1.1 TB per year

C Approximately 110 GB per year

D Approximately 11 GB per year

💡 Hint

Calculate total size by adding review and rating sizes multiplied by number of reviews.

Each review is 1 KB and each rating is 0.1 KB (100 bytes). Total per review+rating = 1.1 KB. For 10 million reviews: 10,000,000 * 1.1 KB = 11,000,000 KB = ~11 GB.

Practice

1. What is the primary purpose of a rating and review system in an online store?

easy

A. To process payment transactions

B. To collect user feedback and calculate average product ratings

C. To manage product inventory levels

D. To store user passwords securely

Solution

1. Step 1: Understand the system's goal

   A rating and review system is designed to gather user opinions and ratings about products.

2. Step 2: Identify the main function

   It calculates average ratings to help other users make decisions quickly.

3. Final Answer:

   To collect user feedback and calculate average product ratings -> Option B

4. Quick Check:

   Rating system = Collect feedback + average rating [OK]

Hint: Focus on feedback and rating calculation [OK]

Common Mistakes:

• Confusing rating system with payment or inventory systems
• Thinking it manages user credentials
• Assuming it handles shipping or delivery

2. Which data structure is best suited to store individual reviews for quick lookup by product ID?

easy

A. Hash map with product ID as key and list of reviews as value

B. Array of reviews without indexing

C. Linked list of all reviews

D. Stack of reviews

Solution

1. Step 1: Consider lookup efficiency

   Quick lookup by product ID requires a data structure with fast key-based access.

2. Step 2: Choose appropriate structure

   A hash map (dictionary) allows O(1) average time to find reviews by product ID.

3. Final Answer:

   Hash map with product ID as key and list of reviews as value -> Option A

4. Quick Check:

   Fast lookup = Hash map [OK]

Hint: Use hash maps for fast key-based lookup [OK]

Common Mistakes:

• Using arrays without indexing causes slow searches
• Linked lists have O(n) lookup time
• Stacks do not support direct lookup by key

3. Given the following pseudocode for updating average rating after a new review:

current_avg = 4.0
num_reviews = 5
new_rating = 5
new_avg = (current_avg * num_reviews + new_rating) / (num_reviews + 1)

What is the value of new_avg?

medium

A. 4.17

B. 4.16

C. 4.0

D. 4.5

Solution

1. Step 1: Calculate total rating sum before new review

   Total sum = current_avg * num_reviews = 4.0 * 5 = 20

2. Step 2: Add new rating and compute new average

   New sum = 20 + 5 = 25
   New average = 25 / (5 + 1) = 25 / 6 ≈ 4.1667

3. Final Answer:

   4.17 -> Option A

4. Quick Check:

   Average update formula ≈ 4.17 [OK]

Hint: Multiply avg by count, add new, divide by count+1 [OK]

Common Mistakes:

• Forgetting to add new rating to total sum
• Dividing by old count instead of count+1
• Rounding too early causing wrong average

4. A rating system stores average rating and count per product. After deleting a review, the average becomes incorrect. What is the likely cause?

medium

A. Recalculating average using sum of all reviews

B. Using integer division instead of float division

C. Not updating the count of reviews after deletion

D. Storing reviews in a hash map

Solution

1. Step 1: Understand average calculation

   Average = sum of ratings / count of reviews. Both must be accurate.

2. Step 2: Identify deletion impact

   If count is not decreased after deleting a review, average calculation divides by wrong count.

3. Final Answer:

   Not updating the count of reviews after deletion -> Option C

4. Quick Check:

   Count mismatch causes wrong average [OK]

Hint: Always update count when reviews change [OK]

Common Mistakes:

• Ignoring count update after deletion
• Assuming recalculation always fixes average
• Confusing data structure choice with calculation error

5. You want to design a scalable rating and review system for millions of products and users. Which approach best balances fast average rating queries and frequent review updates?

hard

A. Store all reviews and compute average on each query

B. Use a single database table without indexes

C. Cache only the latest review per product

D. Maintain precomputed average and count, update incrementally on review changes

Solution

1. Step 1: Consider query and update load

   Millions of products and users mean many queries and updates.

2. Step 2: Choose efficient strategy

   Precomputing average and count and updating them incrementally avoids scanning all reviews each time.

3. Step 3: Evaluate other options

   Computing average on each query is slow; no indexes cause slow lookups; caching only latest review misses full rating info.

4. Final Answer:

   Maintain precomputed average and count, update incrementally on review changes -> Option D

5. Quick Check:

   Precompute + incremental update = scalable [OK]

Hint: Precompute averages, update on changes for scale [OK]

Common Mistakes:

• Recomputing averages on every query
• Ignoring indexing and caching strategies
• Caching incomplete data causing stale info