Which of the following best describes the purpose of broadcast variables in Spark?

The purpose of broadcast variables in Spark is to efficiently share variables across nodes. When you have a large dataset or configuration that needs to be used by multiple tasks across different executors, broadcasting allows you to send a read-only variable to all nodes where tasks are running, minimizing the amount of data transfer required.

Utilizing broadcast variables is particularly beneficial when tasks require access to the same data. Instead of replicating this data for each task—which could lead to significant overhead and network traffic—broadcasting ensures that a single copy of the variable is sent to each node. This results in less memory consumption and improved performance for applications that involve large datasets or complex calculations.

The other options do not accurately reflect the specialized use of broadcast variables. Counting tasks executed, storing log information, and maintaining records of user sessions do not align with the definition or functionality of broadcast variables in the context of Apache Spark.