Databricks Data Engineer Professional Question 49
Single answerYou are working with a large dataset in Databricks and need to optimize its performance for a downstream join operation. The dataset is highly skewed, and you want to ensure that the data is evenly distributed across all partitions to avoid performance bottlenecks. Which of the following partitioning strategies would be most appropriate in this scenario?
- A
Use
coalesce()to reduce the number of partitions. - B
Use
repartition()to increase the number of partitions. - C
Use
repartitionByRange()to partition the dataset based on specified column ranges. - D
Use
rebalance()to evenly distribute the data across all partitions.
Show answer and explanation
Correct answer: D
Explanation
In scenarios where data skew causes performance issues, rebalance() is the best option as it redistributes the data evenly across all partitions. Other strategies, like coalesce(), repartition(), and repartitionByRange(), have specific use cases but do not directly solve the problem of uneven data distribution caused by skew.
- A. Incorrect.
coalesce()reduces the number of partitions but does not perform a full shuffle, making it unsuitable for redistributing skewed data evenly. - B. Incorrect.
repartition()increases the number of partitions and performs a full shuffle, but it does not specifically address data skew. - C. Incorrect.
repartitionByRange()organizes data by column ranges, which is useful for range-based queries but does not ensure even distribution of skewed data. - D. Correct.
rebalance()is specifically designed to evenly distribute data across all partitions, making it the most appropriate choice for addressing data skew.