Rating and review system in LLD - Architecture Diagram

System Overview - Rating and review system

This system allows users to submit ratings and reviews for products or services. It must handle high volumes of user requests, store reviews reliably, and provide fast access to aggregated ratings and recent reviews.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  v
Review Service <--> Cache
  |
  v
Database
  |
  v
Message Queue
  |
  v
Analytics Service

Components

User

client

End user submitting or viewing ratings and reviews

Load Balancer

load_balancer

Distributes incoming user requests evenly across API Gateway instances

API Gateway

api_gateway

Handles authentication, routing, and request validation

Review Service

service

Processes rating and review submissions and queries

Cache

cache

Stores frequently accessed reviews and aggregated ratings for fast retrieval

Database

database

Stores all ratings and reviews persistently

Message Queue

message_queue

Queues review data for asynchronous processing like analytics

Analytics Service

service

Processes queued data to generate insights and aggregate statistics

Request Flow - 13 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Review Service

Review Service → Cache

Cache → Review Service

Review Service → Database

Review Service → Cache

Review Service → Message Queue

Message Queue → Analytics Service

Analytics Service → Database

Review Service → API Gateway

API Gateway → Load Balancer

Load Balancer → User

Failure Scenario

Component Fails:Cache

Impact:Cache misses increase, causing more direct database queries and higher latency for users

Mitigation:System continues to function by reading from the database; cache is rebuilt asynchronously

Architecture Quiz - 3 Questions

Which component handles user authentication and routes requests to the correct service?

A Load Balancer

B Review Service

C API Gateway

D Message Queue

Design Principle

This design uses caching to speed up read requests and a message queue to handle analytics asynchronously, ensuring the system remains responsive and scalable under high load.

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