Search and recommendation in HLD - Architecture Diagram

System Overview - Search and recommendation

This system allows users to search for items and receive personalized recommendations. It must handle large volumes of queries quickly and provide relevant results based on user preferences and item popularity.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  +--------------------+
  |                    |
  v                    v
Search Service      Recommendation Service
  |                    |
  v                    v
Search Index         User Profile DB
  |                    |
  v                    v
Cache (Search)      Cache (Recommendations)
  |                    |
  +---------+----------+
            |
            v
         Main Database

Components

User

client

Sends search queries and receives recommendations

Load Balancer

load_balancer

Distributes incoming user requests evenly to API Gateway instances

API Gateway

api_gateway

Routes requests to appropriate backend services

Search Service

service

Processes search queries using the search index

Recommendation Service

service

Generates personalized recommendations based on user profiles

Search Index

search_index

Stores indexed data for fast search retrieval

User Profile DB

database

Stores user preferences and behavior data

Cache (Search)

cache

Caches frequent search results to reduce latency

Cache (Recommendations)

cache

Caches popular recommendations for quick access

Main Database

database

Stores all item data and metadata

Request Flow - 20 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Search Service

Search Service → Cache (Search)

Cache (Search) → Search Service

Search Service → Search Index

Search Index → Search Service

Search Service → Cache (Search)

API Gateway → Recommendation Service

Recommendation Service → Cache (Recommendations)

Cache (Recommendations) → Recommendation Service

Recommendation Service → User Profile DB

User Profile DB → Recommendation Service

Recommendation Service → Main Database

Main Database → Recommendation Service

Recommendation Service → Cache (Recommendations)

Search Service → API Gateway

Recommendation Service → API Gateway

API Gateway → Load Balancer

Load Balancer → User

Failure Scenario

Component Fails:Main Database

Impact:New item data cannot be fetched, recommendation service cannot update item metadata, recommendations may be stale. Search index updates may also be delayed.

Mitigation:Use database replication for failover. Cache layers serve stale data to maintain availability. Alert system triggers for quick recovery.

Architecture Quiz - 3 Questions

Which component handles distributing incoming user requests to backend services?

A Cache (Search)

B Search Index

C Load Balancer

D User Profile DB

Design Principle

This architecture uses caching to reduce latency and load on databases, separates search and recommendation services for scalability, and employs load balancing and API gateway for efficient request routing and fault tolerance.

Practice

1. What is the primary purpose of a search system in a large-scale application?

easy

A. To help users quickly find relevant content from a large dataset

B. To store user passwords securely

C. To manage user account settings

D. To display advertisements randomly

Solution

1. Step 1: Understand the role of search systems

   Search systems are designed to help users find information efficiently from large amounts of data.

2. Step 2: Match the purpose with options

   Only To help users quickly find relevant content from a large dataset describes helping users find relevant content quickly, which is the core function of search.

3. Final Answer:

   To help users quickly find relevant content from a large dataset -> Option A

4. Quick Check:

   Search system purpose = find relevant content [OK]

Hint: Search systems focus on finding relevant data fast [OK]

Common Mistakes:

• Confusing search with unrelated features like password storage
• Thinking search manages user settings
• Assuming search is for random content display

2. Which component is essential in a recommendation system to personalize suggestions?

easy

A. Database backup scripts

B. User behavior tracking

C. Static HTML pages

D. Load balancer configuration

Solution

1. Step 1: Identify personalization needs

   Recommendation systems personalize suggestions based on user data and behavior.

2. Step 2: Match components to personalization

   User behavior tracking collects data needed to tailor recommendations, unlike static pages or infrastructure tasks.

3. Final Answer:

   User behavior tracking -> Option B

4. Quick Check:

   Personalization needs user data = User behavior tracking [OK]

Hint: Personalization needs user data collection [OK]

Common Mistakes:

• Confusing infrastructure tasks with personalization
• Thinking static pages can personalize content
• Ignoring the role of user data

3. Consider a search system that indexes 1 million documents. If the system uses an inverted index, what is the main advantage?

medium

A. Automatically deleting old documents

B. Storing documents in a single large file

C. Encrypting all documents for security

D. Faster search queries by mapping words to document lists

Solution

1. Step 1: Understand inverted index concept

   An inverted index maps each word to the list of documents containing it, enabling quick lookups.

2. Step 2: Identify the advantage for search speed

   This mapping allows the system to find relevant documents quickly without scanning all documents.

3. Final Answer:

   Faster search queries by mapping words to document lists -> Option D

4. Quick Check:

   Inverted index = fast word-to-doc lookup [OK]

Hint: Inverted index speeds up word-based search [OK]

Common Mistakes:

• Confusing indexing with storage format
• Thinking encryption is the main index benefit
• Assuming index deletes documents automatically

4. A recommendation system is returning irrelevant suggestions. Which issue is most likely causing this?

medium

A. Using HTTPS instead of HTTP

B. Too many servers in the cluster

C. Incorrect or missing user behavior data

D. Database backup frequency is too high

Solution

1. Step 1: Analyze cause of irrelevant recommendations

   Recommendations depend on accurate user data; missing or wrong data leads to poor suggestions.

2. Step 2: Evaluate other options

   Server count, protocol choice, or backup frequency do not directly affect recommendation relevance.

3. Final Answer:

   Incorrect or missing user behavior data -> Option C

4. Quick Check:

   Bad recommendations = bad user data [OK]

Hint: Check user data quality for recommendation issues [OK]

Common Mistakes:

• Blaming infrastructure instead of data quality
• Confusing network protocols with recommendation logic
• Ignoring data collection importance

5. You are designing a scalable recommendation system for millions of users. Which approach best balances personalization and system performance?

hard

A. Use a hybrid model combining collaborative filtering and content-based filtering with offline batch processing and online updates

B. Only recommend the most popular items to all users without personalization

C. Run real-time deep learning models for every user request without caching

D. Store all user data in a single database server for simplicity

Solution

1. Step 1: Understand scalability and personalization needs

   Millions of users require efficient processing; personalization improves user experience.

2. Step 2: Evaluate approaches

   Hybrid models combine strengths of different methods. Offline batch processing reduces load, while online updates keep recommendations fresh.

3. Step 3: Reject less scalable or less personalized options

   Popular-only recommendations lack personalization. Real-time deep learning per request is costly. Single DB server is a bottleneck.

4. Final Answer:

   Use a hybrid model combining collaborative filtering and content-based filtering with offline batch processing and online updates -> Option A

5. Quick Check:

   Hybrid + batch + online = scalable personalized system [OK]

Hint: Combine offline and online methods for scalable personalization [OK]

Common Mistakes:

• Ignoring scalability by doing all processing online
• Sacrificing personalization for simplicity
• Using single server for massive data