Why is broadcasting used in Spark?

Broadcasting in Spark is a technique used to efficiently share large read-only data across all nodes in a cluster without incurring the overhead of data shuffling. When a large dataset is needed by multiple worker nodes, broadcasting creates a copy of this dataset on each node, so that every node has quick access to the data it requires.

This approach significantly reduces the amount of shuffling that needs to occur during operations like joins, as each node can directly access the broadcasted data instead of pulling it from another node or moving it around the network. This not only enhances performance by minimizing network communication costs but also speeds up computations since nodes can process data locally rather than waiting for remote data access. Consequently, broadcasting is particularly useful in scenarios where a small dataset needs to be used alongside a much larger dataset.

In contrast, the other options would either misrepresent the purpose of broadcasting or describe processes that don't align with its intended use. For example, broadcasting does not directly optimize data loading time or allow for real-time updates during computations. Also, while broadcasting does duplicate large datasets, its primary focus is not on efficiency through redundancy, but rather on reducing network overhead and improving speed during parallel computations.