Which One Should Be Used When?

The decision to use sharding or partitioning depends on several factors, including the scale of your application, expected growth, query patterns, and data distribution requirements:

Use Sharding When:

• Dealing with extremely large datasets that can’t be managed efficiently by a single server.
• Needing to distribute data across multiple geographic locations for reduced latency.
• Scaling out read and write operations for high traffic applications.
• Accepting the complexity of managing distributed systems.

Use Partitioning When:

• Operating within the limits of a single database instance but still requiring performance optimization.
• Organizing data for easy management and efficient maintenance.
• Dealing with data that can be logically categorized based on certain attributes.
• Optimizing specific query patterns by limiting data scan ranges.

Traditional monolithic databases struggle to maintain optimal performance due to their single-point architecture, where a single server handles all data transactions. Sharding and partitioning emerged as strategies to alleviate this bottleneck and distribute data workload more efficiently.