Databricks Data Engineer Professional Question 235
Select 3A data engineering team is running a Spark job on a Databricks cluster that processes a large volume of data but frequently fails with an 'Out of Memory' error. Upon investigating the Spark UI, you notice that the task execution time is unusually high and the garbage collection (GC) time is consuming a significant portion of the task duration. What steps should the team take to resolve this issue?
- A
Increase the executor memory in the cluster configuration.
- B
Reduce the number of partitions in the DataFrame being processed.
- C
Increase the shuffle partitions to optimize the workload distribution.
- D
Investigate and optimize the Spark job's code to reduce memory usage.
- E
Scale up the cluster by increasing the number of worker nodes.
Show answer and explanation
Correct answers: A, C, D
Explanation
The 'Out of Memory' error and high GC time indicate that the job is experiencing memory pressure. Increasing the executor memory (Option 1) provides more memory per task, reducing the likelihood of memory-related failures. Optimizing the shuffle partitions (Option 3) ensures a more balanced workload, which can alleviate memory bottlenecks. Additionally, investigating and optimizing the Spark job's code (Option 4) can reduce memory usage by minimizing expensive operations or reducing intermediate data. While scaling up the cluster may seem like a solution, it is not the most efficient approach for resolving memory issues caused by improper job configuration or inefficiencies in the code.
- A. Correct.
Increasing the executor memory can help alleviate 'Out of Memory' errors by providing more memory for tasks to execute, especially if GC time is high.
- B. Incorrect.
Reducing the number of partitions may lead to larger partition sizes, which could exacerbate memory issues and increase task execution time.
- C. Correct.
Increasing the shuffle partitions can help evenly distribute the workload, reducing memory pressure on individual tasks and improving performance.
- D. Correct.
Optimizing the Spark job's code, such as by avoiding wide transformations or reducing the size of intermediate data, can reduce memory usage and improve performance.
- E. Incorrect.
Scaling up the cluster by adding more worker nodes may not directly address the root cause of the issue if the problem lies within the job configuration or code.