High database utilization — what to suggest: partitioning, sharding?

High database utilization can be caused by various reasons: large data volume, high query load, or hardware limitations.

Partitioning is the division of tables within a single database into logical parts (partitions) based on a specific criterion (for example, by date). This helps speed up queries that work with a limited data range and simplifies maintenance (such as archiving old data).

Sharding is the horizontal division of data across different servers or database instances. Each shard contains a part of the data, and the load is distributed among them. This scales the system horizontally and reduces the load on a single server.

What to choose:

• If the problem is with the performance of individual queries and data volume, but the infrastructure allows scaling a single database, start with partitioning.
• If the load is very high and one database cannot cope, or scaling across multiple servers is required — sharding.

Often, both approaches are used together: partitioning within each shard.

Example: in PostgreSQL, you can set up table partitioning by date range, and implement sharding through external tools or middleware.