Search functionality design in LLD - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Architecture

intermediate

Designing a scalable search architecture for a large e-commerce platform

You need to design the search functionality for a large e-commerce platform with millions of products. Which architectural component is essential to ensure fast and relevant search results?

A A simple key-value store that stores product IDs only

B A relational database with complex SQL queries for every search request

C A distributed search index like Elasticsearch to handle full-text queries and ranking

D A batch job that updates search results once a day

💡 Hint

Think about how to handle fast text search and ranking at scale.

Distributed search indexes like Elasticsearch are designed for fast full-text search and relevance ranking, which is essential for large-scale e-commerce search.

❓ scaling

intermediate

Handling high query volume in search functionality

Your search service receives a sudden spike to 10,000 queries per second. Which approach best helps maintain low latency and high availability?

A Run all queries synchronously on a single server

B Increase the database connection timeout to handle more queries

C Disable search autocomplete to reduce load

D Implement caching of popular search queries and results at the edge

💡 Hint

Think about reducing repeated work for common queries.

Caching popular queries at the edge reduces load on the search backend and improves response times during traffic spikes.

❓ tradeoff

advanced

Choosing between real-time and batch indexing for search

Which tradeoff is true when choosing real-time indexing over batch indexing for search data?

A Real-time indexing provides fresher data but requires more resources and complexity

B Batch indexing is always faster and more resource-efficient than real-time indexing

C Real-time indexing eliminates the need for any data validation

D Batch indexing guarantees zero downtime during updates

💡 Hint

Consider freshness of data versus system complexity.

Real-time indexing updates search data immediately but needs more compute and complex pipelines, while batch indexing is simpler but less fresh.

❓ component

advanced

Key components of a search system architecture

Which component is NOT typically part of a modern search system architecture?

A Load balancer that distributes search requests across servers

B Relational database that stores raw images for search results

C Indexing pipeline that processes and stores searchable data

D Query parser that interprets user input and generates search queries

💡 Hint

Think about components directly related to search query processing and indexing.

Raw images are usually stored in object storage or CDN, not in relational databases as part of search architecture.

❓ estimation

expert

Estimating storage requirements for search index

You have 100 million documents averaging 1 KB each. Your search index expands data by 3x due to inverted indexes and metadata. Approximately how much storage do you need for the search index?

A Around 300 GB

B Around 30 GB

C Around 3 TB

D Around 1 TB

💡 Hint

Calculate base data size and multiply by expansion factor.

100 million docs * 1 KB = 100 GB base data. 3x expansion means 300 GB total index size approximately.