Rating and review system in LLD - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main purpose of a rating and review system?

To collect user feedback in the form of ratings and written reviews to help others make informed decisions.

beginner

Name two common data elements stored in a review entry.

User ID (who wrote the review) and rating value (usually a number like 1 to 5).

intermediate

Why is it important to handle duplicate reviews in a rating system?

To prevent users from submitting multiple reviews for the same item, which can skew the overall rating and reduce trust.

advanced

How can a rating and review system scale to handle millions of users?

By using caching for popular items, sharding databases, and asynchronous processing for review submissions.

intermediate

What is a common approach to calculate the average rating efficiently?

Store and update the total sum of ratings and count of reviews to compute the average without scanning all reviews each time.

Which data structure is best to quickly retrieve all reviews for a product?

A Stack of reviews

B Linked list of all reviews

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

D Queue of reviews

A hash map allows fast lookup by product ID to get all related reviews.

What is a common rating scale used in review systems?

A 1 to 5 stars

B 1 to 100 points

C True or False

D Red, Yellow, Green

1 to 5 stars is a widely accepted and easy-to-understand rating scale.

Why should reviews be moderated or filtered?

A To increase the number of reviews

B To remove spam and inappropriate content

C To make the system slower

D To hide negative feedback

Moderation ensures quality and trustworthiness by filtering spam or harmful content.

Which technique helps reduce database load when showing average ratings?

A Precomputing and storing average ratings

B Recalculating average on every request

C Deleting old reviews

D Using a single database for all data

Precomputing averages avoids expensive calculations on every request.

What is a key challenge in designing a rating and review system?

A Allowing unlimited reviews per user per item

B Making reviews invisible

C Ignoring user feedback

D Handling fake or biased reviews

Detecting and preventing fake or biased reviews is crucial for system reliability.

Explain how you would design a scalable rating and review system for an e-commerce platform.

Think about how to store data, keep it accurate, and serve many users quickly.

Describe the flow of a user submitting a review and how the system processes it.

Consider each step from user action to data update.

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