Subdatabases

Subdatabases, also known as sharded databases or database partitioning, represent a technique for horizontally scaling databases. Instead of storing all data on a single, large server, subdatabases distribute data across multiple smaller, independent database instances. Each instance, or shard, holds a subset of the overall data. This distribution allows for parallel processing of queries and write operations, significantly improving performance and handling larger datasets than a monolithic database could manage.

The process of dividing data into shards is called sharding. Common sharding strategies include range-based sharding, where data is divided based on a range of values in a specific column, and hash-based sharding, which uses a hash function to determine which shard a record belongs to. The choice of sharding key is crucial for effective distribution and performance.

Implementing subdatabases introduces complexities. Managing multiple database instances requires robust infrastructure and sophisticated tooling. Query routing becomes a critical component, as requests must be directed to the appropriate shard(s). Maintaining data consistency across shards, especially during writes or transactions that span multiple shards, can also be challenging. However, the benefits of improved scalability, availability, and performance make subdatabases a popular solution for large-scale applications and big data scenarios.