Spark Lazy Evaluation Explained

Spark lazy evaluation refers to Spark not immediately executing all transformation operations when performing tasks, but instead saving them in memory until an action operation is needed. This delayed execution method can optimize task performance and improve efficiency.

Specifically, in a Spark program, the transformation operations are converted into a Directed Acyclic Graph (DAG), and Spark will only start executing the transformation operations based on the dependencies of the DAG when it encounters an action operation. This helps avoid unnecessary recomputations and allows for optimization during the execution process.

The feature of delayed execution enables Spark to have better performance and flexibility, allowing the execution plan to be dynamically adjusted based on actual conditions, thereby improving the efficiency of task execution. At the same time, delayed execution can also reduce waste of memory and computing resources, making Spark programs more efficient and reliable.