Design a search autocomplete in HLD - Architecture Diagram

System Overview - Design a search autocomplete

This system provides search autocomplete suggestions as users type queries. It must return relevant suggestions quickly and handle many users simultaneously. The system should update suggestions based on popular searches and support low latency responses.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  v
Autocomplete Service
  |        |
  v        v
Cache <--> Search Index
  |
  v
Database

Components

User

client

Sends search query input for autocomplete suggestions

Load Balancer

load_balancer

Distributes incoming user requests evenly to API Gateway instances

API Gateway

api_gateway

Receives requests, handles authentication, routing to Autocomplete Service

Autocomplete Service

service

Processes queries, fetches suggestions from cache or search index

Cache

cache

Stores frequently requested autocomplete suggestions for fast retrieval

Search Index

search_index

Maintains indexed data of searchable terms and their popularity

Database

database

Stores raw search data and updates popularity metrics for indexing

Request Flow - 11 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Autocomplete Service

Autocomplete Service → Cache

Cache → Autocomplete Service

Autocomplete Service → Search Index

Search Index → Autocomplete Service

Autocomplete Service → Cache

Autocomplete Service → API Gateway

API Gateway → Load Balancer

Load Balancer → User

Failure Scenario

Component Fails:Cache

Impact:Cache misses increase, causing higher latency as Autocomplete Service queries Search Index more often

Mitigation:System continues working by querying Search Index directly; cache rebuilds as new queries come in

Architecture Quiz - 3 Questions

Which component handles distributing user requests to multiple API Gateway instances?

A Autocomplete Service

B Load Balancer

C Cache

D Search Index

Design Principle

This design uses caching to reduce latency and load on the search index. The load balancer and API gateway ensure scalability and security. The search index enables fast lookup of autocomplete suggestions, while the database supports data updates and popularity tracking.

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