The pyspark.SparkContext is a Python library that acts as the main entry point for interacting with Apache Spark. It allows Python programs to efficiently distribute and process large amounts of data across a cluster of compute nodes. SparkContext provides methods for creating RDDs (Resilient Distributed Datasets) which are the fundamental data structure used in Spark to perform distributed computations. It also coordinates the execution of tasks and manages the resources needed for the Spark application. Overall, pyspark.SparkContext enables developers to leverage the power of Apache Spark from their Python programs.
Python SparkContext - 60 examples found. These are the top rated real world Python examples of pyspark.SparkContext extracted from open source projects. You can rate examples to help us improve the quality of examples.