Which configuration parameter affects the size of a Spark partition during data ingestion?

The parameter that directly affects the size of a Spark partition during data ingestion is indeed the configuration related to spark.sql.files.maxPartitionBytes. This setting defines the maximum size of a single partition when reading data from files. When Spark reads data, it attempts to ensure that no partition exceeds this specified size; thus, it directly influences how the input data is partitioned.

When the data is being ingested, if the size of a file exceeds the configured maximum partition size, Spark will split the data into multiple partitions. This helps with managing data parallelism and processing efficiency. Smaller partitions can lead to better resource utilization, as they allow Spark to distribute the workload evenly across the available executors, whereas larger partitions might lead to inefficiencies if they exceed memory limits or take longer to process.

In contrast, other parameters like spark.sql.autoBroadcastJoinThreshold focus on optimizing join operations by determining the threshold for broadcasting smaller DataFrames, and spark.sql.files.openCostInBytes relates to the overhead of opening files during read operations, rather than how data is partitioned. The parameter spark.sql.adaptive.coalescePartitions.minPartitionNum helps in coalescing partitions but does not directly dictate their size during initial ingestion. Thus, understanding