You have hands‑on experience with PySpark, right? How does a PySpark job execute, and how do you run a PySpark job?

A PySpark job starts with a driver program that runs your Python script. The driver creates a SparkContext and builds a logical plan for the transformations you define. Spark then converts this logical plan into a physical DAG (Directed Acyclic Graph) of stages. Each stage is a set of tasks that can run in parallel on executors. Executors are JVM processes that run on worker nodes; they pull tasks from the driver, execute them, and return results. The driver coordinates the stages, handles failures, and collects the final result. To run a PySpark job, you typically use the spark-submit command, specifying the Python file, any dependencies, and configuration options such as master URL, executor memory, and number of cores. For example: spark-submit --master yarn --deploy-mode cluster --executor-memory 4G --num-executors 10 my_script.py. In local mode, you can simply run python my_script.py if you have Spark installed locally. The job will then be scheduled by the cluster manager (YARN, Mesos, or Kubernetes) and executed across the cluster.