What is the difference between sharding and partitioning? When should each be used?

Sharding and partitioning are methods of data separation, but they are used in different contexts and for different purposes.

Partitioning is the logical division of data within a single database or table into parts (partitions) based on a specific criterion (for example, by date or value range). This helps improve query performance and simplifies management of large volumes of data, but the entire database remains within a single server or cluster.

Sharding is the horizontal distribution of data across different servers or nodes. Each shard contains a part of the data, and together they form a complete database. This is a scalable solution that allows handling very large volumes of data and load.

When to apply:

• Partitioning is suitable when data is large but can be effectively managed within a single server or cluster.
• Sharding is necessary when scaling across multiple servers to improve performance and fault tolerance.

Example: if you have a log table spanning several years, you can partition by year. If the load and data volume are too large for a single server, you should shard data by users or regions.