Design Microservices Architecture with Patterns and Principles - Data Partitioning: Horizontal, Vertical, and Functional

The video tutorial covers three main data partitioning strategies: horizontal, vertical, and functional. Horizontal partitioning, also known as sharding, involves dividing data into separate stores with the same schema, improving performance by distributing load across servers. Vertical partitioning splits data based on columns, allowing frequently accessed data to be stored separately for efficiency. Functional partitioning organizes data according to bounded contexts or subdomains, similar to microservices decomposition. Each method has its advantages and potential drawbacks, with horizontal partitioning being particularly useful for scaling distributed databases.

What is a key advantage of horizontal partitioning?

• It reduces the number of columns in a table.

• It allows data to be stored in a single server.

• It distributes data across multiple servers to enhance performance.

• It organizes data based on user roles.

What is a potential drawback of horizontal partitioning?

• It limits the number of users accessing the data.

• It requires more storage space.

• Choosing an incorrect partition key can lead to uneven data distribution.

• Data is stored in a single server.

How does vertical partitioning optimize database performance?

• By dividing data based on user access patterns.

• By reducing the number of database servers.

• By storing all data in a single table.

• By distributing data across different geographical locations.

In vertical partitioning, how are columns typically divided?

• Based on their size.

• Based on their data type.

• By their alphabetical order.

• According to their frequency of access.

What is the main focus of functional partitioning?

• Dividing data based on column size.

• Organizing data according to bounded contexts or subdomains.

• Storing all data in a single server.

• Reducing the number of database tables.