Introduction to PySpark

So far we've mainly focused on datasets that can fit on a single machine. For larger datasets, we may need to access them through distributed file systems such as Amazon S3 or HDFS. For this purpose, we can utilize the open-source distributed computing framework PySpark (http://spark.apache.org/docs/latest/api/python/). PySpark is a distributed computing framework that uses the abstraction of Resilient Distributed Datasets (RDDs) for parallel collections of objects, which allows us to programmatically access a dataset as if it fits on a single machine. In later chapters we will demonstrate how to build predictive models in PySpark, but for this introduction we focus on data manipulation functions in PySpark.

Creating the SparkContext

The first step in any spark application is the generation of the SparkContext. The SparkContext contains any job-specific configurations (such as memory settings or the number of worker tasks), and allows us to connect to a Spark cluster...