In the context of Apache Spark’s SQL module, the configuration parameter “spark.sql.shuffle.partitions” determines the number of partitions used when performing shuffles during query execution. Shuffling is the process of redistributing data across partitions, usually occurring after transformations that require data to be reorganized, such as “group by” or “join” operations.

By setting the value of “spark.sql.shuffle.partitions,” you control the default number of partitions used in shuffling operations. This can affect the performance and resource utilization of your Spark application. A higher value can lead to more parallelism but might require more memory, while a lower value can reduce memory usage but may lead to lower parallelism.

It’s important to consider the resources available in your cluster and the characteristics of your data when configuring this parameter. You can adjust this parameter based on your specific use case to achieve optimal performance for your Spark SQL queries.