Design a search autocomplete in HLD - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Architecture

intermediate

Identify the main components in a search autocomplete system

Which of the following lists correctly identifies the essential components needed to build a scalable search autocomplete system?

A User Interface, Query Processor, Autocomplete Engine, Data Storage, Cache

B User Interface, Query Processor, Image Renderer, Cache

C User Interface, Payment Gateway, Autocomplete Engine, Data Storage

D User Interface, Autocomplete Engine, Video Processor, Cache

💡 Hint

Think about components that handle user input, process queries, store data, and speed up responses.

A search autocomplete system needs a user interface to capture input, a query processor to interpret input, an autocomplete engine to generate suggestions, data storage to hold searchable data, and cache to speed up frequent queries.

❓ scaling

intermediate

Scaling autocomplete for millions of users

When millions of users use the autocomplete feature simultaneously, which approach best helps to handle the load efficiently?

A Use a single powerful server with a large database to handle all requests

B Store all autocomplete data in a local file on each server without replication

C Distribute autocomplete requests across multiple servers with caching layers

D Process all autocomplete queries on the client side without server interaction

💡 Hint

Think about how to avoid bottlenecks and reduce latency for many users.

Distributing requests across multiple servers and using caching reduces load on any single server and speeds up response times, which is essential for scaling.

❓ tradeoff

advanced

Tradeoff between latency and freshness in autocomplete data

Which design choice best balances low latency and fresh autocomplete suggestions?

A Use a cache with a short expiration time to refresh autocomplete data frequently

B Update autocomplete data in real-time on every user input, causing high latency

C Update autocomplete data in batch every 24 hours, ensuring low latency but stale data

D Never update autocomplete data after initial load to keep latency minimal

💡 Hint

Consider how caching can help balance speed and data freshness.

Using a cache with a short expiration time allows the system to serve suggestions quickly while keeping data reasonably fresh.

🧠 Conceptual

advanced

Choosing the right data structure for autocomplete

Which data structure is most suitable for efficiently storing and retrieving autocomplete suggestions based on prefix matching?

A Hash Map

B Trie (Prefix Tree)

C Stack

D Queue

💡 Hint

Think about a structure that organizes words by their prefixes.

A Trie stores strings in a tree structure where each node represents a character, making prefix searches very efficient for autocomplete.

❓ estimation

expert

Estimate storage requirements for autocomplete data

Assume you have 10 million unique searchable terms averaging 10 characters each. Each character uses 1 byte. If you store these terms in a Trie with an average branching factor of 26 and each node requires 40 bytes of metadata, approximately how much memory (in GB) will the Trie consume?

A About 40 GB

B About 10 GB

C About 20 GB

D About 4 GB

💡 Hint

Calculate total nodes roughly as number of characters times terms, then multiply by node size, convert bytes to GB.

Each term has about 10 characters, so total nodes ~ 10 million * 10 = 100 million nodes. Each node is 40 bytes, so total bytes = 4 billion bytes = ~4 GB.