Why Rating and review system in LLD? - Purpose & Use Cases

The Big Idea

What if your customers' voices could instantly shape your business reputation without extra work?

The Scenario

Imagine running a small online store where customers send their feedback by email or phone. You try to keep track of their ratings and reviews using a simple spreadsheet.

As orders grow, you struggle to organize and display this feedback clearly on your website.

The Problem

Manually collecting and updating reviews is slow and error-prone. You miss some feedback, mix up customer details, and can't easily show average ratings or filter reviews.

This leads to unhappy customers and lost sales because your site looks untrustworthy and outdated.

The Solution

A rating and review system automates collecting, storing, and displaying customer feedback. It calculates average ratings, shows recent reviews, and lets users submit new ones easily.

This system keeps data organized, consistent, and instantly available to all visitors, building trust and improving sales.

Before vs After

✗ Before

Add review to spreadsheet
Calculate average manually
Update website HTML

✓ After

Submit review via form
Store review in database
Display average rating dynamically

What It Enables

It enables real-time, reliable customer feedback that boosts trust and helps businesses grow.

Real Life Example

Think of Amazon's product pages showing star ratings and customer reviews that update instantly as new feedback arrives.

Key Takeaways

Manual tracking of reviews is slow and error-prone.

Automated rating and review systems organize and display feedback clearly.

This builds customer trust and supports business growth.

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