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Even with a solid understanding of the rules, learners often make common mistakes when conjugating verbs in the past tense. Being aware of these pitfalls can help you avoid them:
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Alright, let's zoom in on how Spark actually *executes* your code. Understanding the execution model is key to optimizing your Spark applications. Spark uses a **directed acyclic graph (DAG)** to represent the logical execution plan of a job. The DAG is created by the driver program based on the transformations and actions defined in your code. *Think of the DAG as a roadmap*, showing how data flows through the different operations. The DAG is then divided into stages, where each stage consists of a set of tasks that can be executed in parallel. Spark groups operations together into stages based on data dependencies. Stages are separated by shuffle boundaries, where data needs to be redistributed across the cluster. Within each stage, Spark creates tasks, which are the smallest unit of execution. Tasks are assigned to executors on the worker nodes. When a task is executed, it reads the data, performs the computation, and writes the results. Spark uses a **lazy evaluation** model. This means that transformations are not executed immediately. Instead, Spark builds up a logical plan of the transformations, and the actual execution happens only when an action is called. *This lazy evaluation allows Spark to optimize the execution plan*. When an action is called, Spark analyzes the DAG, identifies the stages, and creates the tasks. Spark also employs various optimization techniques to improve performance. For example, it uses **pipelining**, where multiple operations are executed on the same data partition in a single pass. It also uses **caching**, which allows you to store intermediate results in memory or on disk for faster access in subsequent operations.
