What Spark feature is equivalent to the MapReduce distributed cache?

The correct answer is the broadcast variables feature in Spark, which serves a similar purpose to the distributed cache used in MapReduce. Broadcast variables allow you to efficiently share large read-only data across all the worker nodes in an Apache Spark cluster. This mechanism is particularly effective when you need to use a large dataset repeatedly across multiple tasks within a job. By broadcasting the data, you avoid sending the same data over the network multiple times, which can significantly reduce communication costs and improve performance.

In contrast, dataframes are a higher-level abstraction for working with structured data but do not directly relate to the concept of caching data for distributed tasks. Streaming variables are utilized specifically for streaming applications, enabling access to mutable state across batch and streaming micro-batches. Accumulator variables are primarily used for aggregating information such as counters or sums during a Spark job but do not provide the caching capabilities that broadcast variables do.

Thus, broadcast variables play a pivotal role in optimizing data sharing and overall efficiency when dealing with distributed computing tasks in Spark, paralleling the intent behind using distributed caches in MapReduce architectures.