Databricks Data Engineer Professional Question 236
Select 3A data engineering team is running a Spark job on Databricks that processes a large dataset. Users report that the job is taking significantly longer than expected. As a data engineer, you investigate the issue using the Spark UI and notice the following:
- The 'Tasks' tab shows a high percentage of task failures.
- The 'Stages' tab indicates that one stage has an extremely high duration compared to others.
- The Ganglia UI reveals a sharp increase in disk I/O activity during the job.
Based on these observations, what could be the most likely causes of the performance problem?
- A
The Spark job is suffering from excessive data shuffling between stages.
- B
The cluster is running out of memory, causing frequent garbage collection pauses.
- C
The cluster nodes are experiencing disk I/O bottlenecks, possibly due to insufficient disk space or high spill activity.
- D
The Spark job has an inefficient join operation, resulting in skewed partitions.
- E
The number of tasks in each stage is very low, leading to underutilization of cluster resources.
Show answer and explanation
Correct answers: A, C, D
Explanation
The observations indicate performance issues that are likely caused by inefficient data movement and resource usage. Excessive shuffling (Option 1) and skewed partitions (Option 4) are common causes of high stage durations and task failures in Spark jobs, while increased disk I/O activity (Option 3) points to resource bottlenecks related to disk operations. These factors together explain the prolonged job execution time.
- A. Correct.
Excessive data shuffling between stages can lead to high task failure rates and long stage durations due to the overhead of transferring large amounts of data between nodes.
- B. Incorrect.
While memory issues can cause performance problems, the provided observations do not mention high garbage collection times or 'Out of Memory' errors, making this unlikely based on the given scenario.
- C. Correct.
High disk I/O activity, as indicated by the Ganglia UI, typically suggests that the cluster is spilling data to disk due to insufficient memory or inefficient operations, causing delays.
- D. Correct.
Skewed partitions can lead to one stage taking significantly longer than others, as some tasks might process disproportionately large amounts of data compared to others.
- E. Incorrect.
A low number of tasks might underutilize the cluster, but the given observations do not indicate task parallelism issues, making this option irrelevant in this context.