What does sharding refer to in database scaling?

What does sharding refer to in database scaling?

• A. Partitioning data across multiple databases to scale horizontally.
• B. Replicating the entire database across servers.
• C. Increasing RAM within a single database instance.
• D. Caching query results in memory.

Answer: (A)

Sharding means dividing the data into smaller pieces, called shards, and storing each shard on a separate database server. This approach lets you scale out by adding more servers, so reads and writes can be handled in parallel across different machines and the total data footprint grows without overloading a single system. A shard key (like user ID) determines which shard holds a given piece of data, and a routing mechanism directs queries to the right shard (with some operations potentially spanning multiple shards). This is different from replicating the entire database on multiple servers, which provides redundancy and read scaling but doesn’t partition data. It’s also not about adding RAM to one instance or caching results, both of which stay confined to a single system rather than spreading data across many.

Sharding means dividing the data into smaller pieces, called shards, and storing each shard on a separate database server. This approach lets you scale out by adding more servers, so reads and writes can be handled in parallel across different machines and the total data footprint grows without overloading a single system. A shard key (like user ID) determines which shard holds a given piece of data, and a routing mechanism directs queries to the right shard (with some operations potentially spanning multiple shards). This is different from replicating the entire database on multiple servers, which provides redundancy and read scaling but doesn’t partition data. It’s also not about adding RAM to one instance or caching results, both of which stay confined to a single system rather than spreading data across many.