Databricks Data Engineer Professional Question 237
Select 3You are analyzing a job in Databricks that is taking significantly longer than expected to complete. Upon reviewing the Spark UI, you notice a high task deserialization time in the 'Stages' tab. In the Ganglia UI, you observe high CPU utilization, and in the Cluster UI, there are multiple executor failures. Which of the following could be the root cause of the performance issue?
- A
Large serialized task sizes due to excessive use of closures in your Spark code
- B
Insufficient executor memory leading to frequent garbage collection
- C
Skewed data distribution causing certain tasks to process significantly more data than others
- D
Inadequate number of shuffle partitions, leading to a bottleneck in data processing
- E
Network issues between the driver and executors causing delays in task distribution
Show answer and explanation
Correct answers: A, B, C
Explanation
The high task deserialization time points to issues with task size or serialization overhead, which is often caused by large closures. High CPU utilization and executor failures suggest memory pressure, likely due to insufficient executor memory. Additionally, skewed data distribution can cause uneven task execution, further degrading performance. Addressing these issues would help resolve the performance bottleneck.
- A. Correct.
Large serialized task sizes can lead to high task deserialization time as Spark needs to deserialize the tasks before executing them. This issue is often caused by poor code practices such as capturing large objects in closures.
- B. Correct.
Insufficient executor memory can result in frequent garbage collection, which impacts performance. This is also evident from high CPU utilization in the Ganglia UI and can lead to executor failures.
- C. Correct.
Data skew can cause certain tasks to process much larger amounts of data than others, leading to performance bottlenecks. This is a common cause of uneven task execution times visible in the Spark UI.
- D. Incorrect.
While an inadequate number of shuffle partitions can lead to bottlenecks, it does not directly cause high task deserialization time or executor failures.
- E. Incorrect.
Network issues could cause delays in task distribution, but they do not explain high task deserialization time or executor failures visible in the logs.