Product catalog design in HLD - Architecture Diagram

System Overview - Product catalog design

This system manages a product catalog for an online store. It allows users to browse products, view details, and search efficiently. The design must support fast reads, handle many users, and keep product data consistent.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  v
Product Service <--> Cache
  |
  v
Database

Components

User

client

End user accessing the product catalog through a web or mobile app

Load Balancer

load_balancer

Distributes incoming user requests evenly to API Gateway instances

API Gateway

api_gateway

Handles routing, authentication, and request validation before forwarding to services

Product Service

service

Processes product catalog requests, fetches data from cache or database

Cache

cache

Stores frequently accessed product data to reduce database load and improve response time

Database

database

Stores all product catalog data including product details, categories, and inventory

Request Flow - 11 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Product Service

Product Service → Cache

Cache → Product Service

Product Service → Database

Database → Product Service

Product Service → Cache

Product Service → API Gateway

API Gateway → Load Balancer

Load Balancer → User

Failure Scenario

Component Fails:Cache

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

Mitigation:System continues to serve requests by querying the database directly; cache is rebuilt over time

Architecture Quiz - 3 Questions

Which component first handles user requests after they reach the system?

A Load Balancer

B API Gateway

C Product Service

D Cache

Design Principle

This design uses caching to reduce database load and improve response times. The API Gateway centralizes request routing and security, while the load balancer ensures even distribution of user requests. This layered approach supports scalability and reliability.

Practice

1.

What is the primary purpose of a product catalog in an e-commerce system?

easy

A. To organize products into categories for easy browsing

B. To process payments securely

C. To manage user login sessions

D. To handle shipping and delivery logistics

Solution

1. Step 1: Understand the role of a product catalog

   A product catalog groups products logically so users can browse easily.

2. Step 2: Differentiate from other system components

   Payment, user sessions, and shipping are separate concerns from product organization.

3. Final Answer:

   To organize products into categories for easy browsing -> Option A

4. Quick Check:

   Product catalog = Organize products [OK]

Hint: Catalog = product organization, not payments or shipping [OK]

Common Mistakes:

• Confusing catalog with payment processing
• Mixing catalog with user authentication
• Thinking catalog manages shipping

2.

Which data structure is most suitable to represent categories and subcategories in a product catalog?

A. Array
B. Linked List
C. Tree
D. Hash Map

easy

A. Array

B. Linked List

C. Hash Map

D. Tree

Solution

1. Step 1: Analyze category relationships

   Categories have parent-child relationships, forming a hierarchy.

2. Step 2: Choose data structure for hierarchy

   A tree structure naturally represents hierarchical data with branches and leaves.

3. Final Answer:

   Tree -> Option D

4. Quick Check:

   Hierarchy = Tree [OK]

Hint: Hierarchies = Trees, not flat lists or arrays [OK]

Common Mistakes:

• Using arrays which are flat and unordered
• Choosing linked lists which are linear
• Hash maps don't represent hierarchy well

3.

Consider a product catalog system that uses an inverted index for search. What is the main benefit of using an inverted index?

medium

A. It manages user reviews and ratings

B. It stores product images efficiently

C. It speeds up product search by mapping keywords to product IDs

D. It handles payment transactions securely

Solution

1. Step 1: Understand inverted index purpose

   An inverted index maps keywords to the list of documents or products containing them.

2. Step 2: Apply to product search

   This mapping allows fast lookup of products matching search terms, improving speed.

3. Final Answer:

   It speeds up product search by mapping keywords to product IDs -> Option C

4. Quick Check:

   Inverted index = fast keyword search [OK]

Hint: Inverted index = keyword to product map for fast search [OK]

Common Mistakes:

• Thinking it stores images
• Confusing with user review storage
• Mixing with payment processing

4.

A product catalog system caches product details but users report seeing outdated information. What is the likely cause?

medium

A. Cache invalidation is not handled properly after product updates

B. The database schema is incorrect

C. User authentication is failing

D. The product images are missing

Solution

1. Step 1: Identify caching issue

   Outdated info usually means cache still holds old data after updates.

2. Step 2: Understand cache invalidation

   Proper cache invalidation removes or refreshes cached data when products change.

3. Final Answer:

   Cache invalidation is not handled properly after product updates -> Option A

4. Quick Check:

   Outdated cache = invalidation problem [OK]

Hint: Outdated data? Check cache invalidation first [OK]

Common Mistakes:

• Blaming database schema without evidence
• Confusing with authentication issues
• Assuming missing images cause outdated text

5.

You are designing a product catalog for a global e-commerce platform with millions of products and frequent updates. Which design choice best supports scalability and fast search?

A. Use a distributed NoSQL database with indexing and cache layers
B. Store all products in a single relational database without caching
C. Use flat files to store product data and search sequentially
D. Keep product data only in application memory without persistence

hard

A. Store all products in a single relational database without caching

B. Use a distributed NoSQL database with indexing and cache layers

C. Use flat files to store product data and search sequentially

D. Keep product data only in application memory without persistence

Solution

1. Step 1: Consider scalability needs

   Millions of products and frequent updates require a scalable, distributed system.

2. Step 2: Evaluate design options

   A distributed NoSQL database supports horizontal scaling; indexing enables fast search; caching improves response time.

3. Step 3: Reject unsuitable options

   Single DB limits scale; flat files are slow; in-memory only risks data loss.

4. Final Answer:

   Use a distributed NoSQL database with indexing and cache layers -> Option B

5. Quick Check:

   Scalable + fast search = distributed NoSQL + index + cache [OK]

Hint: Scale needs distributed DB + index + cache, not flat files [OK]

Common Mistakes:

• Choosing single DB without caching for large scale
• Using flat files causing slow search
• Relying on memory only risking data loss