For writing a large JSON dataset to Parquet without shuffling data, which strategy yields the best performance?

The strategy of setting spark.sql.files.maxPartitionBytes to 512 MB to handle writing a large JSON dataset to Parquet without shuffling data can yield the best performance due to its focus on optimizing partition sizes during the write process.

By configuring spark.sql.files.maxPartitionBytes, you can control how large each partition of the dataset will be when it's read from disk. This approach helps in reducing the overhead associated with managing many small files, which can slow down data processing and increase the time it takes to write the output. A larger partition size allows for more efficient use of resources, leading to improved read and write performance by minimizing the number of files created and the metadata overhead in managing them.

This setting directly affects the read and write operations without necessitating any shuffling of data, which can be expensive in terms of performance. The optimization is significant when dealing with a large dataset where you want to maintain efficiency.

In contrast, other choices involve settings that either cause shuffling (spark.sql.shuffle.partitions) or do not directly address partition size during the writing phase (spark.sql.adaptive.advisoryPartitionSizeInBytes). Repartitioning can lead to unnecessary data movement and overhead, which may counteract the benefits gained from other