Apache Spark GraphX and Graph Processing 1 — Questions and Answers
Question 1: What is Apache Spark GraphX primarily used for?
- Graph-parallel computation and graph analytics (Correct answer)
- Real-time stream processing of graph data
- Machine learning on tabular datasets
- SQL queries on graph databases
Correct answer: Graph-parallel computation and graph analytics
GraphX is Spark's API for graph-parallel computation, enabling graph creation, transformation, and execution of graph algorithms at scale.
Question 2: In GraphX, what are the two fundamental components of a Property Graph?
- Edges and Paths
- Vertices and Edges (Correct answer)
- Nodes and Relationships
- Rows and Columns
Correct answer: Vertices and Edges
A Property Graph in GraphX consists of vertices (nodes) and edges (connections), each carrying associated user-defined properties.
Question 3: Which data type is used as a vertex identifier (VertexId) in GraphX?
- String
- UUID
- Long (64-bit integer) (Correct answer)
- Integer (32-bit)
Correct answer: Long (64-bit integer)
GraphX defines VertexId as a type alias for Long, providing a large namespace of unique IDs suitable for billions of vertices.
Question 4: What distributed collection type does GraphX use internally to store vertex attributes?
- DataFrame
- VertexRDD (Correct answer)
- HashSet
- DenseVector
Correct answer: VertexRDD
GraphX uses VertexRDD[VD], which extends RDD[(VertexId, VD)], to store and index vertex attributes as key-value pairs.
Question 5: What method in GraphX creates a graph from an RDD of edges, assigning a default attribute to all vertices?
- Graph.fromVertices()
- Graph.fromEdges() (Correct answer)
- Graph.apply()
- Graph.edgeListFile()
Correct answer: Graph.fromEdges()
Graph.fromEdges() constructs a graph from an RDD[Edge[ED]] and automatically creates vertex entries with a supplied default attribute.
Question 6: What does the `triplets` property of a GraphX Graph return?
- Three separate RDDs for source, edge, and destination
- An RDD of EdgeTriplet objects containing source vertex, edge, and destination vertex attributes (Correct answer)
- A tuple of (VertexRDD, EdgeRDD, PartitionRDD)
- An array of three graphs representing different structural views
Correct answer: An RDD of EdgeTriplet objects containing source vertex, edge, and destination vertex attributes
The `triplets` property returns an RDD[EdgeTriplet[VD, ED]], where each triplet combines the source vertex, the connecting edge, and the destination vertex with all their attributes.
Question 7: Which GraphX edge partitioning strategy assigns edges to partitions based solely on the source vertex ID?
- RandomVertexCut
- CanonicalRandomVertexCut
- EdgePartition1D (Correct answer)
- EdgePartition2D
Correct answer: EdgePartition1D
EdgePartition1D assigns edges to partitions by hashing only the source vertex ID, colocating all outgoing edges from the same vertex in one partition.
Apache Spark GraphX and Graph Processing 1
What is Apache Spark GraphX primarily used for?