Repartition() vs Coalesce() in PySpark
Repartition() vs Coalesce() in PySpark In PySpark, optimizing the number of partitions in a DataFrame is essential for performance. repartition() and coalesce() are two methods commonly used for this purpose. 1. Purpose: - repartition() can both increase and decrease the number of partitions, whereas coalesce() is specifically used to decrease the number of partitions. These functions aim to enhance performance and reduce execution time. 2. Differences: - Scenario: Suppose there are 3 partitions, and you want only 2 partitions. - repartition() shuffles all partitions, distributing the data into 2 partitions to ensure balanced shuffling. - coalesce() takes the data from the 3rd partition and places it into the 1st and 2nd partitions without balancing. It may allocate, for example, 70% of the 3rd partition to the 1st and 30% to the 2nd partition. 3. Example Code: ```PySpark ...