Databricks Data Engineer Professional Question 239
Select 2You are analyzing a Spark application that is taking significantly longer to complete than expected. Upon reviewing the Spark UI, you notice the following: 1) High task deserialization time in the 'Tasks' tab, 2) Executors are frequently running out of memory in the 'Executors' tab, and 3) Shuffle read and write sizes are unusually large in the 'Stages' tab. What actions should you take to address these performance issues?
- A
Optimize the Spark application's code to reduce the complexity of transformations and actions.
- B
Increase the executor memory and adjust the number of executor cores to reduce memory contention.
- C
Enable dynamic allocation to allow Spark to adjust the number of executors based on workload.
- D
Review the partitioning strategy and optimize the number of partitions to reduce shuffle data size.
- E
Increase the driver memory to handle the large shuffle read and write sizes.
Show answer and explanation
Correct answers: B, D
Explanation
The observed issues in the Spark UI indicate problems with executor memory and shuffle inefficiencies. Increasing executor memory (option 2) addresses memory-related issues, and optimizing the partitioning strategy (option 4) reduces shuffle data size, which can improve overall performance. Other options either do not directly address the identified problems or are unrelated to the specific issues.
- A. Incorrect.
While optimizing the application code can generally improve performance, the observed issues (high task deserialization time, frequent executor memory issues, and large shuffle sizes) are more likely caused by configuration and partitioning problems rather than code complexity.
- B. Correct.
Increasing executor memory and adjusting the number of executor cores can help mitigate frequent executor memory issues by reducing memory contention and providing more resources for processing.
- C. Incorrect.
Enabling dynamic allocation is unrelated to the issues described. Dynamic allocation is used for scaling the number of executors based on workload, but the problem here is related to memory and shuffle inefficiencies.
- D. Correct.
Optimizing the partitioning strategy to reduce the number of partitions or adjust their size can significantly decrease shuffle read and write sizes, as shuffle data is related to how data is distributed across partitions.
- E. Incorrect.
Increasing driver memory does not directly address the observed issues. Shuffle read and write sizes are handled by executors, not the driver.