What does the "query execution plan" do in Spark SQL?

The "query execution plan" in Spark SQL serves the purpose of outlining how Spark will execute a SQL query. When a SQL query is submitted, Spark generates an execution plan that details the various steps and operations needed to retrieve the result set. This includes information about how data will be read, transformed, and written, as well as the specific execution strategies, such as whether it will use shuffling, filters, aggregations, or joins.

Understanding the query execution plan is crucial for optimizing performance, as it reveals how Spark intends to manage resources and handle data flows. By analyzing the execution plan, users can identify potential bottlenecks or inefficiencies in their queries and make adjustments to improve performance.

On the other hand, the other options do not accurately describe the function of the query execution plan. Storing data permanently does not pertain to execution plans, as execution plans are more about the process of data handling rather than data storage. Visualizing the data results relates more to tools that display the outcome of queries rather than detailing how those queries are executed. Creating machine learning algorithms is an entirely different domain and not a function performed by the query execution plan within the context of SQL query execution.