Which of the following best describes the function of accumulators in Spark?

Accumulators in Spark are primarily used to track metrics and summary information across different stages of a computation. They can be utilized to aggregate information such as counters or sums during job execution, allowing developers to gather insights into specific aspects of their jobs.

Accumulators are particularly useful when you want to monitor the performance of your Spark jobs without affecting the overall performance. They can be added to, but not read from, tasks, ensuring that they behave correctly across distributed computations. This functionality supports various use cases, such as counting events or summarizing statistics while the application runs.

In contrast, the other options represent different features of Spark's functionality. Storing large datasets for quick access relates more to Spark’s core ability to handle distributed datasets rather than accumulators specifically. Partitioning data is a fundamental aspect of Spark’s execution model that enables parallel processing and efficient resource usage, which is also distinct from the purpose of accumulators. Real-time data streaming involves Spark Streaming, which is focused on processing live streams of data, separate from the accumulator's metric-tracking feature.

Thus, the best description of the function of accumulators aligns with the role they play in tracking metrics and summarizing information during executed tasks.