How do Spark runtimes with AQE and skew affect the computational process?

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Spark runtime with AQE (Adaptive Query Execution) and skew can significantly improve the computational process by optimizing the resources and handling data skew.

AQE allows Spark to dynamically optimize the execution plan of a query based on the data and the current state of the cluster. This optimization improves the efficiency of query execution by reducing the unnecessary shuffling of data and optimizing task scheduling. AQE can help in providing better performance in dynamic workloads and complex queries.

Data skew, which occurs when a small subset of data causes an imbalance in the processing of data, can significantly degrade the Spark job's performance. AQE can detect the data skew and adjust the query execution plan to handle it efficiently. AQE can also handle skew by partitioning the data, using appropriate join algorithms, and redistributing data to reduce the data skew, thus eliminating the bottleneck in the processing pipeline.

Overall, Spark runtime with AQE and skew handling provides better resource management, reduced query execution time, and improved performance by optimizing the execution plan, handling data skew, and leveraging the available resources efficiently.