RDDs are Spark's primary distributed Dataset abstraction. It is a collection of data that is immutable, distributed, lazily evaluated, type inferred, and cacheable. Prior to execution, the developer code (using higher-level constructs such as SQL, DataFrames, and Dataset APIs) is converted to a DAG of RDDs (ready for execution).
You can create RDDs by parallelizing an existing collection of data or accessing a Dataset residing in an external storage system, such as the file system or various Hadoop-based data sources. The parallelized collections form a distributed Dataset that enable parallel operations on them.
You can create a RDD from the input file with number of partitions specified, ...
