How does MongoDB handle large data sets?

MongoDB effectively manages large data sets primarily through sharding, indexing, and storing data in BSON format. Sharding allows MongoDB to distribute data across multiple servers, enabling horizontal scalability. This means that as the amount of data grows, additional servers can be added to facilitate the storage and processing needs without compromising performance.

Indexing is another crucial feature that enhances query performance by allowing the database to quickly locate and access data without needing to scan the entire dataset. This is particularly vital in large datasets where efficient data retrieval is essential for maintaining application responsiveness.

The BSON format, which is a binary representation of JSON-like documents, is optimized for performance and storage efficiency. It allows MongoDB to store complex data types and perform rapid data reading and writing operations, contributing to the database's overall capability to handle extensive and complex data constructs effectively.

In contrast, other options, such as replicating the entire dataset across servers or relying heavily on traditional relational database management techniques, do not address the dynamic scalability and efficiency needed for large data sets in the same manner as sharding and indexing do. Additionally, solely using in-memory storage is not feasible for all data workloads as it can be limited by memory constraints.