Apache Spark 3 for Data Engineering and Analytics with Python - Managing Performance Errors

The video tutorial addresses the challenges of using Apache Spark on a single node, highlighting potential errors such as disk block manager issues. It provides solutions for stopping and restarting Spark sessions to resolve these errors. The tutorial also covers managing large files by converting code cells to raw text to prevent errors. Finally, it suggests restarting the entire notebook or system to clean up rogue processes and ensure sufficient disk space.

What is a common issue when using Spark on a single node computer?

• It automatically scales to multiple nodes.

• It may experience performance issues and errors.

• It requires no temporary space.

• It has a built-in cluster manager.

What is the first step to take when encountering a disk block manager error in Spark?

• Reinstall Spark.

• Increase the memory allocation.

• Restart the entire machine.

• Stop the Spark session.

How can you ensure that large files do not cause errors in Spark?

• Disable the Spark executor.

• Use a different programming language.

• Increase the number of nodes.

• Convert the code cells to raw text.

What should you do if problems persist after restarting the Spark session?

• Restart the entire machine.

• Ignore the errors.

• Contact technical support immediately.

• Switch to a different software.

What is a recommended minimum amount of free space on your hard drive when using Spark?

• 2 GB

• 3 GB

• 1 GB

• 4 GB