Which cluster configuration would yield maximum performance for a job with a wide transformation?

To determine which cluster configuration would yield maximum performance for a job with a wide transformation, it is important to consider the trade-off between the number of virtual machines (VMs) and the memory allocated per executor. A wide transformation often requires significant memory to handle large shuffles of data, and therefore, having a single VM with a large amount of memory can be beneficial in this case.

Option A, which features one VM with 400 GB per executor, provides an extensive amount of memory concentrated in one place. This is particularly advantageous for wide transformations that require large amounts of data to be processed because it minimizes the overhead typically associated with distributed computing, such as network communication and data shuffling between multiple executors. With more memory available per executor, this configuration can reduce the chances of out-of-memory errors and improve the efficiency of data processing.

Conversely, the other options distribute the memory across multiple VMs with smaller executor sizes. While these configurations might help parallelize tasks, they may lead to increased overhead from managing multiple executors and require data shuffling across the network, which can slow down the overall performance for wide transformations. The more VMs you have with less memory each, the higher the likelihood that you will run into performance bottlenecks