Apache Spark, the popular distributed computing framework, has been widely adopted for processing large-scale data. With the release of Apache Spark 3.0, a groundbreaking feature called Adaptive Query Execution (AQE) was introduced. AQE addresses the limitations of traditional static execution plans by dynamically optimizing query execution based on runtime statistics. In this blog post, we will explore how AQE works and how it significantly improves the performance of Spark applications.
This sounds pretty similar to adaptive query processing in SQL Server, though a look at the Spark documentation shows that there are some practical differences in implementation versus what SQL Server does.