GRAPHX is a part of Spark framework, which use for graph and graph based PARALLEL processing, GraphX extends the Spark RDD by introducing a NEW Graph abstraction: a directed multigraph with properties attached to each vertex and edge. To support graph computation.

• Extension on Spark RDD to perform computation on graph DB.
• Follow directed multigraph data structure.
• Support operator like (joinGraph, joinVertices and mapReduceTriplet)
• Support both Supervised and unsupervised algorithms. 
• GraphX optimizes the representation of vertex and edge types when they are primitive data types (e.g., int, double, etc…) reducing the in-memory footprint by storing them in specialized arrays.
• GraphX exposes a set of fundamental operators (e.g., subgraph, joinVertices, and aggregate Messages)
• Package need to import is “import org.apache.spark.graphx._”
• val vertexRDD: RDD[(Long, (STRING, Int))] = sc.parallelize(vertexArray)
• val edgeRDD: RDD[Edge[Int]] = sc.parallelize(edgeArray)
• val graph: Graph[(String, Int), Int] = Graph(vertexRDD, edgeRDD)

GraphX in spark are immutable, distributed, and fault-tolerant. Changes to the values or structure of the graph are accomplished by producing a new graph with the desired changes.