Design a search autocomplete in HLD - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to identify the main component responsible for storing user queries.

HLD

The component that stores user queries for autocomplete suggestions is called the [1].

Drag options to blanks, or click blank then click option'

ADatabase

BCache

CLoad Balancer

DAPI Gateway

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing cache with persistent storage.

The database stores user queries and autocomplete data for retrieval.

2fill in blank

medium

Complete the code to specify the component that handles user input and returns suggestions.

HLD

The [1] receives user input and returns autocomplete suggestions.

Drag options to blanks, or click blank then click option'

ALoad Balancer

BDatabase

CCache

DAutocomplete Service

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing database or cache instead of service.

The Autocomplete Service processes input and fetches suggestions.

3fill in blank

hard

Fix the error in the description of the data structure used for fast prefix search.

HLD

A common data structure for fast prefix search in autocomplete is a [1].

Drag options to blanks, or click blank then click option'

ALinked List

BHash Map

CTrie

DStack

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing hash map or linked list which are less efficient for prefix search.

Trie is used for efficient prefix-based search in autocomplete systems.

4fill in blank

hard

Fill both blanks to complete the caching strategy for autocomplete suggestions.

HLD

To improve performance, autocomplete suggestions are stored in a [1] which is updated every [2] minutes.

Drag options to blanks, or click blank then click option'

Acache

Bdatabase

C5

D60

Attempts:

3 left

💡 Hint

Common Mistakes

Using database instead of cache for fast access.

Updating cache too rarely or too often.

Caching suggestions reduces latency. Updating every 5 minutes balances freshness and load.

5fill in blank

hard

Fill all three blanks to complete the request flow for autocomplete.

HLD

User input is sent to the [1], which queries the [2]. If data is missing, it fetches from the [3].

Drag options to blanks, or click blank then click option'

AAutocomplete Service

BCache

CDatabase

DLoad Balancer

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing roles of cache and database.

Confusing load balancer with service.

The Autocomplete Service handles input, checks cache first, then database if needed.

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