Sharding and partitioning are both techniques used to manage large datasets and databases by dividing them into more manageable pieces. While they are similar and sometimes used interchangeably, there are distinct differences:

Partitioning

• Basic Concept: Partitioning refers to dividing a database into smaller, more manageable pieces, but these pieces remain part of the same database instance.
• Types:
  • Vertical Partitioning: Dividing a table into smaller tables with fewer columns.
  • Horizontal Partitioning: Dividing a table into sub-tables, each with the same columns but only a subset of the rows.
• Purpose: Helps manage large tables and improve performance by reducing the volume of data accessed or transferred during query operations.
• Location: Partitioned data usually resides on the same server.

Example of Partitioning

• Suppose you have a table Orders with a large number of rows. You can horizontally partition it by date, such as having different tables for each year (Orders_2020, Orders_2021, etc.), but all these tables are still in the same database.

Sharding

• Basic Concept: Sharding, also known as horizontal partitioning, involves dividing a large database into smaller, more manageable databases, or 'shards'. Each shard is a distinct database instance.
• Key Aspect: The data in each shard is unique and independent of the data in other shards.
• Purpose: Sharding is used for scalability, as it spreads the load across multiple servers or instances, and each shard can be managed independently.
• Location: Each shard is typically located on a different server or in a different physical location.

Example of Sharding

• Consider a user database for a global application. You can shard the database by region, such as having one shard for North America, one for Europe, etc. Each shard is a separate database and can be hosted in a server located in the respective region.

Key Differences

1. Scope:

   • Partitioning is about dividing a database within the same database instance.
   • Sharding usually involves dividing a database across multiple database instances.

2. Data Distribution:

   • In partitioning, even though the data is divided, it is still managed and queried as part of the same database.
   • In sharding, each shard can be queried independently, and they operate as separate databases.

3. Use Case:

   • Partitioning is often used for performance improvement and managing data volume.
   • Sharding is primarily used for scalability and distributing loads across multiple servers.

4. Complexity:

   • Sharding tends to be more complex to implement and manage than partitioning, as it involves data distribution across multiple systems.

In practice, the choice between sharding and partitioning depends on the specific requirements of scalability, performance, and database management.

Ref: Grokking System Design Fundamentals