Databricks Data Engineer Professional Question 51
Single answerYou are working on a large Spark DataFrame in Databricks and need to optimize its partitioning to balance the load across executors. The dataset is skewed, and you want to ensure that the partitions are evenly distributed. Which of the following partitioning strategies would be the most appropriate choice?
- A
Use the coalesce() method to reduce the number of partitions.
- B
Use the repartition() method to increase the number of partitions.
- C
Use the repartitionByRange() method to partition the data based on specific column ranges.
- D
Use the rebalance() method to redistribute data evenly across partitions.
Show answer and explanation
Correct answer: D
Explanation
When dealing with skewed data, the rebalance() method is the most effective option because it redistributes data evenly across all partitions. Other methods like coalesce and repartition can either reduce or increase the number of partitions but do not directly address skewness or ensure even distribution. RepartitionByRange is useful for range-based partitioning but is not ideal for balancing skewed data.
- A. Incorrect.
Coalesce is used to reduce the number of partitions, typically to optimize for smaller datasets or narrow transformations. It does not redistribute data evenly, making it unsuitable for handling skewed data.
- B. Incorrect.
Repartition increases the number of partitions and redistributes the data evenly, but it can be computationally expensive, especially when not used with specific ranges or column-based partitioning.
- C. Incorrect.
RepartitionByRange is effective for range-based partitioning of data, but it is not specifically designed to address skewed data or ensure an even distribution across partitions.
- D. Correct.
Rebalance is specifically designed to evenly redistribute data across partitions, making it the most appropriate choice for addressing skewed data and ensuring balanced load across executors.