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.

Practice

1. What is the primary purpose of a search autocomplete system in a web application?

easy

A. To display full search results immediately

B. To store user passwords securely

C. To suggest possible search terms as the user types

D. To block unwanted users from searching

Solution

1. Step 1: Understand autocomplete function

   Autocomplete helps users by suggesting search terms while they type, improving speed and experience.

2. Step 2: Eliminate unrelated options

   Options about password storage, blocking users, or showing full results do not match autocomplete's purpose.

3. Final Answer:

   To suggest possible search terms as the user types -> Option C

4. Quick Check:

   Autocomplete = Suggest terms [OK]

Hint: Autocomplete suggests terms as you type [OK]

Common Mistakes:

• Confusing autocomplete with full search results
• Thinking autocomplete handles security
• Assuming autocomplete blocks users

2. Which data structure is most suitable for efficiently storing and searching prefixes in an autocomplete system?

easy

A. Trie (Prefix Tree)

B. Hash Map

C. Stack

D. Queue

Solution

1. Step 1: Identify prefix search needs

   Autocomplete requires fast prefix matching, which means quickly finding all words starting with a given prefix.

2. Step 2: Match data structure to prefix search

   Trie (prefix tree) stores characters in a tree structure, enabling efficient prefix lookups compared to hash maps or linear structures.

3. Final Answer:

   Trie (Prefix Tree) -> Option A

4. Quick Check:

   Prefix search = Trie [OK]

Hint: Prefix search? Use Trie for fast lookup [OK]

Common Mistakes:

• Choosing hash map which is not prefix-optimized
• Using stack or queue which are not for prefix search
• Ignoring prefix search efficiency

3. Consider a search autocomplete system using a Trie. If the user types the prefix "app", which of the following outputs is correct assuming the Trie contains words: ["apple", "app", "application", "apt"]?

medium

A. ["apple", "apt"]

B. ["application", "apt"]

C. ["app", "apt"]

D. ["apple", "app", "application"]

Solution

1. Step 1: Identify words starting with prefix "app"

   From the list, words starting with "app" are "apple", "app", and "application".

2. Step 2: Exclude words not matching prefix

   "apt" starts with "ap" but not "app", so it is excluded.

3. Final Answer:

   ["apple", "app", "application"] -> Option D

4. Quick Check:

   Prefix "app" matches apple, app, application [OK]

Hint: Match prefix exactly, exclude partial matches [OK]

Common Mistakes:

• Including words that don't fully match prefix
• Confusing prefix length
• Ignoring exact prefix matching

4. A search autocomplete system returns no suggestions when the user types "xyz". What is the most likely cause?

medium

A. The prefix "xyz" does not exist in the data store

B. The system cache is full

C. The user has no internet connection

D. The autocomplete service is overloaded

Solution

1. Step 1: Analyze no suggestions for prefix

   No suggestions means no matching entries for the typed prefix in the autocomplete data.

2. Step 2: Evaluate other options

   Cache full or service overload might cause delays but not necessarily zero suggestions; no internet affects connectivity but question focuses on autocomplete output.

3. Final Answer:

   The prefix "xyz" does not exist in the data store -> Option A

4. Quick Check:

   No suggestions = No matching prefix [OK]

Hint: No suggestions? Check if prefix exists in data [OK]

Common Mistakes:

• Assuming cache full causes no suggestions
• Blaming internet without checking data
• Confusing overload with empty results

5. You are designing a scalable search autocomplete system for millions of users. Which combination of components best supports fast prefix search, low latency, and scalability?

hard

A. Monolithic server + No caching

B. Client-side cache + Trie-based service + Distributed cache layer

C. Flat file storage + Server-side rendering

D. Single database with full table scan + Client polling

Solution

1. Step 1: Identify scalable components for autocomplete

   Trie-based service enables fast prefix search; distributed cache reduces latency and load; client-side cache improves responsiveness.

2. Step 2: Eliminate inefficient options

   Full table scans and flat files cause slow searches; monolithic servers without caching do not scale well.

3. Final Answer:

   Client-side cache + Trie-based service + Distributed cache layer -> Option B

4. Quick Check:

   Scalable autocomplete = Trie + caching layers [OK]

Hint: Use Trie + caching layers for scalable autocomplete [OK]

Common Mistakes:

• Ignoring caching for latency
• Using full scans causing slow response
• Relying on monolithic servers only