How to understand when to partition or sharding?

Partitioning and sharding are methods of data separation used to improve performance and scalability, but they are applied in different contexts.

• Partitioning involves dividing a single table or collection into parts (partitions) within one database, usually based on a range of values (e.g., dates). It helps speed up queries and simplifies management of large data volumes.

• Sharding involves distributing data across different servers or databases (shards), each storing a part of the data. It is used for horizontal scaling.

When to choose:

• If data is growing and the load on a single database becomes too large, scalability is needed — sharding is chosen.
• If you need to optimize queries and data management within a single database — partitioning is sufficient.

Example: if you have billions of records in one table and one database cannot handle the load, sharding is advisable. If data can be logically divided by dates or regions to speed up queries, partitioning is enough.