Horizontal vs vertical partitioning in HLD - Architecture Patterns Compared

System Overview - Horizontal vs vertical partitioning

This system demonstrates two common database partitioning methods: horizontal and vertical partitioning. Horizontal partitioning splits data rows across multiple tables or databases, while vertical partitioning splits data columns. The goal is to improve performance, scalability, and manageability of large datasets.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  +-----------------------+
  |                       |
  v                       v
Horizontal Partitioning   Vertical Partitioning
  |                       |
  v                       v
DB Shard 1               DB Table A (Columns 1-3)
DB Shard 2               DB Table B (Columns 4-6)
DB Shard 3               DB Table C (Columns 7-9)
  |                       |
  +----------+------------+
             v
           Cache
             |
             v
          Response

Components

User

user

Initiates requests to the system

Load Balancer

load_balancer

Distributes incoming requests evenly to API Gateway instances

API Gateway

api_gateway

Routes requests to appropriate partitioning strategy services

Horizontal Partitioning

service

Handles data split by rows across multiple database shards

Vertical Partitioning

service

Handles data split by columns across multiple database tables

DB Shard 1

database

Stores a subset of rows for horizontal partitioning

DB Shard 2

database

Stores another subset of rows for horizontal partitioning

DB Shard 3

database

Stores another subset of rows for horizontal partitioning

DB Table A (Columns 1-3)

database

Stores first group of columns for vertical partitioning

DB Table B (Columns 4-6)

database

Stores second group of columns for vertical partitioning

DB Table C (Columns 7-9)

database

Stores third group of columns for vertical partitioning

Cache

cache

Stores frequently accessed data to reduce database load

Response

response

Sends processed data back to the user

Request Flow - 18 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Horizontal Partitioning

Horizontal Partitioning → Cache

Cache → Horizontal Partitioning

Horizontal Partitioning → DB Shard 1/2/3

DB Shard 1/2/3 → Horizontal Partitioning

Horizontal Partitioning → Cache

Horizontal Partitioning → Response

API Gateway → User

API Gateway → Vertical Partitioning

Vertical Partitioning → Cache

Cache → Vertical Partitioning

Vertical Partitioning → DB Table A/B/C

DB Table A/B/C → Vertical Partitioning

Vertical Partitioning → Cache

Vertical Partitioning → Response

API Gateway → User

Failure Scenario

Component Fails:Cache

Impact:Cache misses increase, causing more direct database queries and higher latency

Mitigation:System continues working by querying databases directly; cache rebuilds over time

Architecture Quiz - 3 Questions

Which component directs requests to either horizontal or vertical partitioning?

A Cache

B Load Balancer

C API Gateway

D User

Design Principle

This architecture shows how horizontal and vertical partitioning improve database scalability by splitting data either by rows or columns. Using a cache reduces database load and latency. The API Gateway cleanly routes requests to the appropriate partitioning strategy, enabling flexible data access patterns.

Practice

1. Which of the following best describes horizontal partitioning in system design?

easy

A. Combining multiple tables into one large table

B. Splitting a database table into multiple tables by columns

C. Splitting a database table into multiple tables by rows

D. Duplicating the entire database for backup

Solution

1. Step 1: Understand horizontal partitioning

   Horizontal partitioning means dividing data by rows, so each partition holds a subset of rows.

2. Step 2: Compare with other options

   Vertical partitioning splits by columns, combining tables merges data, and duplication is for backup, not partitioning.

3. Final Answer:

   Splitting a database table into multiple tables by rows -> Option C

4. Quick Check:

   Horizontal partitioning = splitting by rows [OK]

Hint: Rows split = horizontal, columns split = vertical [OK]

Common Mistakes:

• Confusing horizontal with vertical partitioning
• Thinking partitioning means duplicating data
• Mixing partitioning with database backup

2. Which syntax correctly describes vertical partitioning?

easy

A. Splitting data by rows into multiple tables

B. Splitting data by columns into multiple tables

C. Merging rows from different tables

D. Backing up the entire database

Solution

1. Step 1: Define vertical partitioning

   Vertical partitioning splits a table by columns, separating attributes into different tables.

2. Step 2: Eliminate incorrect options

   Options about rows, merging, or backup do not describe vertical partitioning.

3. Final Answer:

   Splitting data by columns into multiple tables -> Option B

4. Quick Check:

   Vertical partitioning = splitting by columns [OK]

Hint: Columns split = vertical partitioning [OK]

Common Mistakes:

• Confusing vertical with horizontal partitioning
• Assuming partitioning means merging tables
• Mixing partitioning with backup processes

3. Consider a user database table with columns: id, name, email, password, address. If we apply vertical partitioning by splitting sensitive data into a separate table, which of the following is a valid partition?

medium

A. Table 1: id, password; Table 2: name, email, address

B. Table 1: id, name; Table 2: email, password, address

C. Table 1: id, name, password; Table 2: email, address

D. Table 1: id, name, email; Table 2: id, password, address

Solution

1. Step 1: Identify sensitive data columns

   Password and address are sensitive, so they should be separated from general info.

2. Step 2: Check partitioning by columns

   Table 1: id, name, email; Table 2: id, password, address separates password and address into one table, and id, name, email into another, matching vertical partitioning.

3. Final Answer:

   Table 1: id, name, email; Table 2: id, password, address -> Option D

4. Quick Check:

   Vertical partitioning splits columns, sensitive data separated [OK]

Hint: Sensitive columns go to separate table in vertical partitioning [OK]

Common Mistakes:

• Mixing rows and columns in partitioning
• Not separating sensitive data properly
• Splitting columns unevenly without reason

4. A system uses horizontal partitioning by user region but experiences slow queries on user emails. What is the likely cause and fix?

medium

A. Cause: Partitioning by rows only; Fix: Add vertical partitioning to separate email column

B. Cause: Partitioning by columns only; Fix: Add horizontal partitioning by region

C. Cause: No partitioning; Fix: Add horizontal partitioning by email

D. Cause: Too many partitions; Fix: Merge all partitions into one

Solution

1. Step 1: Analyze the problem

   Horizontal partitioning splits by rows (region), but email queries are slow because email column is not separated.

2. Step 2: Suggest fix

   Adding vertical partitioning to separate email column improves query speed on emails.

3. Final Answer:

   Cause: Partitioning by rows only; Fix: Add vertical partitioning to separate email column -> Option A

4. Quick Check:

   Slow email queries fixed by vertical partitioning [OK]

Hint: Slow column queries? Add vertical partitioning [OK]

Common Mistakes:

• Confusing cause with partition type
• Suggesting wrong partitioning fix
• Ignoring query patterns in design

5. You design a large e-commerce database with millions of orders. You want to improve performance by splitting data. Which approach is best and why?

hard

A. Use horizontal partitioning by customer ID and vertical partitioning by payment info for balanced load

B. Use vertical partitioning by splitting order details and customer info into separate tables to reduce row size

C. Use no partitioning and rely on caching for performance

D. Use horizontal partitioning by order date to distribute rows across servers, improving query speed on recent orders

Solution

1. Step 1: Understand data and query patterns

   Orders have many rows and columns; splitting by customer ID (horizontal) balances load; splitting payment info (vertical) reduces row size.

2. Step 2: Evaluate options

   Use horizontal partitioning by customer ID and vertical partitioning by payment info for balanced load combines horizontal and vertical partitioning for scalability and performance, better than single approach or no partitioning.

3. Final Answer:

   Use horizontal partitioning by customer ID and vertical partitioning by payment info for balanced load -> Option A

4. Quick Check:

   Combine horizontal and vertical partitioning for large data [OK]

Hint: Combine horizontal and vertical for big, complex data [OK]

Common Mistakes:

• Using only one partitioning type for complex data
• Ignoring query patterns and data distribution
• Relying solely on caching without partitioning