How does MongoDB achieve horizontal scaling?

MongoDB achieves horizontal scaling primarily through sharding, which involves distributing data across multiple servers. In this approach, a large dataset is partitioned into smaller, more manageable pieces called shards. Each shard can be stored on a different server or node, allowing for an increase in capacity without the need to upgrade a single server. This method enhances performance and ensures that as the database grows, additional servers can be added seamlessly to accommodate increasing workloads.

By distributing data in this manner, MongoDB can handle high volumes of read and write operations efficiently, as queries can be routed to the appropriate shard instead of overwhelming a single server. This not only improves overall system performance but also provides better availability and fault tolerance, as the failure of one server does not affect the entire database.

In contrast, duplicating data across multiple servers is related to data redundancy and high availability rather than horizontal scaling. Utilizing more powerful hardware focuses on vertical scaling, which involves enhancing a single server's capabilities rather than distributing the load. Compressing data for storage efficiency addresses storage optimization but does not directly contribute to scaling the database horizontally.