While working on a project, you notice that your Apache Spark Job is underperforming.
Which of the following can be a possible reason for a slower performance on such join or shuffle jobs?
Click on the arrows to vote for the correct answer
A. B. C. D. E.Correct Answer: C
The data skew can be the most common reason for the slower performance of your join or shuffle jobs because of existing asymmetry in your job data.
Being a distributed system in Spark, Data is divided into several pieces, known as partitions, moved into the diverse cluster nodes, and processed in parallel.
If a partition gets much larger than the other, the node processing it is likely to face resource issues and slow down the whole execution.
This type of data imbalance is known as data skew.
Option A is incorrect.
Bucketing does not result in the slow performance of join or shuffle jobs.
Option B is incorrect.
Using the Cache option is likely to increase, not decrease the performance.
Option C is correct.
The data skew is the most common reason for the slower performance of your join or shuffle jobs.
Option D is incorrect.
Enabling Auto scaling can't be the possible cause of slow performance on Join or Shuffle jobs.
Option E is incorrect.
Option C Data Skew is the correct choice.
To know more about data-skew and how to resolve data skew problems, please visit the below-given link:
The possible reason for slower performance on join or shuffle jobs in Apache Spark can be data skew.
Data skew occurs when the data in a specific partition is significantly larger than the others. This can cause delays in processing as the nodes performing the work on these partitions will be overloaded. The skewed partitions may take longer to complete their work, resulting in slower performance of the entire job.
Bucketing is a technique to optimize the join operation in Spark, where the data is pre-partitioned based on specific columns. This can help to avoid shuffling of data during the join operation and can improve performance. However, bucketing alone cannot solve the problem of data skew.
Using the Cache option can help to improve performance for subsequent queries, as it stores the data in memory for faster access. However, it may not solve the problem of data skew.
Enabling autoscaling can help to dynamically adjust the number of nodes in the cluster based on workload. However, it may not necessarily solve the problem of data skew.
Therefore, the correct answer is option C, data skew, as it can significantly impact the performance of join or shuffle jobs in Apache Spark. To mitigate data skew, one can use techniques such as data repartitioning, using skewed join or skewed partitioner, or using dynamic allocation to allocate more resources to the skewed partitions.