Which of the following operations is most likely to result in a shuffle?
Which of the following operations is most likely to result in a shuffle?
A shuffle operation involves redistributing and reorganizing data across partitions, typically necessary when data needs to be arranged or merged based on a specific key or condition. DataFrame.join() combines two DataFrames based on a common key column, often requiring data to be shuffled so that matching records are located on the same executor or partition. This process results in significant data movement and network communication overhead, making join operations most likely to result in a shuffle among the given options.
The operation that is most likely to result in a shuffle is DataFrame.join(). Join operation requires data to be combined from two different sources based on a common key, and this typically involves a reorganization of the data such that the data with the same keys are co-located in the same executor. This process is known as a shuffle operation, which can be a performance-intensive operation, especially for large datasets. The other DataFrame operations such as filter(), union(), where() or drop() do not require data to be shuffled across the nodes.
The most likely operation to result in a shuffle is: A. DataFrame.join() Explanation: A shuffle operation in Spark involves redistributing and reorganizing data across partitions. It typically occurs when data needs to be rearranged or merged based on a specific key or condition. DataFrame joins involve combining two DataFrames based on a common key column, and this operation often requires data to be shuffled to ensure that matching records are located on the same executor or partition. The shuffle process involves exchanging data between nodes or executors in the cluster, which can incur significant data movement and network communication overhead.