Changes

== Architecture ==

One of the distinguishing features of Spark is that it processes data in RAM using a concept known as Resilient Distributed Datasets (RDDs) - an immutable distributed collection of objects which can contain any type of Python, Java, or Scala objects, including user-defined classes. Each dataset is divided into logical partitions which may be computed on different nodes of the cluster. Spark's RDDs function as a working set for distributed programs that offer a restricted form of distributed shared memory.

At a fundamental level, an Apache Spark application consists of two main components: a driver, which converts the user's code into multiple tasks that can be distributed across worker nodes, and executors, which run on those nodes and execute the tasks assigned to them. The processes are coordinated by the SparkContext object in the driver program. The SparkContext can connect to several types of cluster managers which allocate resources across applications. Once connected, Spark acquires executors on nodes in the cluster, which are processes that run computations and store data for the application. Next, it sends the application code to the executors and finally sends tasks to the executors to run.