Explanation:
* The results will form a hierarchy of folders for each partition key. - Yes
* The resulting file partitions can be read in parallel across multiple nodes. - Yes
* The resulting file partitions will use file compression. - No
Partitioning data by columns such as year, month, and day, as shown in the DataFrame write operation, organizes the output into a directory hierarchy that reflects the partitioning structure. This organization can improve the performance of read operations, as queries that filter by the partitioned columns can scan only the relevant directories. Moreover, partitioning facilitates parallelism because each partition can be processed independently across different nodes in a distributed system like Spark. However, the code snippet provided does not explicitly specify that file compression should be used, so we cannot assume that the output will be compressed without additional context.
References =
* DataFrame write partitionBy
* Apache Spark optimization with partitioning