Product catalog design in HLD - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Architecture

intermediate

Designing a scalable product catalog architecture

You need to design a product catalog system that can handle millions of products and thousands of concurrent users. Which architectural approach best supports scalability and fast product search?

A Use a monolithic database with vertical scaling and cache all product data in memory.

B Use a relational database with heavy normalization and rely solely on SQL queries for search.

C Store all product data in flat files on a single server and use periodic batch processing for search indexing.

D Use a distributed NoSQL database with sharding and integrate a dedicated search engine like Elasticsearch.

💡 Hint

Think about how to handle large data volumes and fast search queries efficiently.

Option D uses a distributed NoSQL database that can scale horizontally and a search engine optimized for fast queries, making it the best choice for scalability and performance.

❓ scaling

intermediate

Estimating capacity for product catalog traffic

Your product catalog expects 10,000 requests per second at peak. Each request reads product details averaging 5 KB. What is the approximate network bandwidth needed to handle this load?

A 500 Mbps

B 400 Mbps

C 800 Mbps

D 50 Mbps

💡 Hint

Calculate bandwidth as requests per second multiplied by data size per request, then convert to Mbps.

10,000 requests/sec * 5 KB = 50,000 KB/sec = 50 MB/sec. 50 MB/sec * 8 = 400 Mbps. Considering overhead, 500 Mbps is a safe estimate.

❓ tradeoff

advanced

Choosing between SQL and NoSQL for product catalog

Which tradeoff is most accurate when deciding between SQL and NoSQL databases for a product catalog with complex relationships and frequent updates?

A NoSQL always provides better performance and consistency than SQL for all workloads.

B SQL offers strong consistency and complex queries but may scale less easily; NoSQL scales well but may sacrifice consistency and complex joins.

C SQL databases cannot handle product catalogs with millions of products efficiently.

D NoSQL databases require no schema design and are always easier to maintain.

💡 Hint

Consider consistency, query complexity, and scalability tradeoffs.

SQL databases provide ACID transactions and complex joins but can be harder to scale horizontally. NoSQL databases scale easily but often relax consistency and lack complex join support.

🧠 Conceptual

advanced

Handling product attribute variability in catalog design

Products have widely varying attributes (e.g., electronics vs. clothing). Which design approach best supports flexible attribute storage without schema changes?

A Use a fixed relational schema with columns for all possible attributes, leaving many nulls.

B Create separate tables for each product category with fixed attributes.

C Use a document-oriented NoSQL database storing attributes as flexible key-value pairs.

D Store all attributes as a single JSON string in a relational text column without indexing.

💡 Hint

Think about flexibility and ease of adding new attributes.

Document-oriented NoSQL databases allow storing flexible attributes as key-value pairs, enabling easy schema evolution without downtime.

❓ component

expert

Designing a product catalog update workflow

Which component sequence best ensures consistent product updates with minimal downtime and supports rollback in case of errors?

A 1, 2, 3, 4

B 2, 1, 3, 4

C 1, 3, 2, 4

D 3, 1, 2, 4

💡 Hint

Consider the logical order for safe updates and testing before production deployment.

First validate data, then write to staging, test on staging, and finally promote to production to ensure consistency and rollback capability.