Which of the following statements about Spark jobs is incorrect?
Which of the following statements about Spark jobs is incorrect?
In Spark, jobs are collections of tasks that are divided based on when an action is called, not based on when language variables are defined. This statement is incorrect because it misrepresents how tasks are grouped within a job. Spark creates a job whenever an action (like count() or collect()) is called on a DataFrame or RDD. These jobs are then broken down into stages and tasks, orchestrated based on the logical execution plan derived from the actions, not from the definition of language variables.
There are two incorrect answers in the original question. Option D, "There is no way to monitor the progress of a job," is incorrect. As I mentioned earlier, Spark provides various tools and interfaces for monitoring the progress of a job, including the Spark UI, which provides real-time information about the job's stages, tasks, and resource utilization. Other tools, such as the Spark History Server, can be used to view completed job information. Option E, "Jobs are collections of tasks that are divided based on when language variables are defined," is also incorrect. The division of tasks in a Spark job is not based on when language variables are defined, but rather based on when actions are called.
The incorrect statement is: D. There is no way to monitor the progress of a job. Explanation: Spark provides several ways to monitor the progress of a job. The Spark UI (Web UI) provides a graphical interface to monitor the progress of Spark jobs, stages, tasks, and other relevant metrics. It displays information such as job status, task completion, execution time, and resource usage. Additionally, Spark provides programmatic APIs, such as the JobProgressListener interface, which allows developers to implement custom job progress monitoring logic within their Spark applications.