When is it better to use partitioning, and when sharding?

Partitioning and sharding are methods of data separation, but they are used in different contexts:

• Partitioning — dividing a table or index within a single database into logical parts (partitions) based on a certain criterion (for example, by date or value range). This improves query performance and simplifies data management (for example, archiving old partitions).

• Sharding — horizontal distribution of data across multiple independent servers or databases (shards). Each shard contains a part of the data, and together they form a complete database. This scales the system by load and data volume.

When to use:

• If you need to optimize work with large tables within a single database and have the ability to use built-in partitioning — choose partitioning.

• If the data is too large for one server or high availability and scalability are needed — apply sharding.

Example:

// Partitioning — configuration in DBMS, usually working with partitions through SQL in Go
// Sharding — logic for distributing requests across different databases

func getShard(userID int) int {
    return userID % numberOfShards // simple hash function for shard selection
}

// Then requests go to the selected shard