Apache Spark GraphX and Graph Processing 3

Question 1

What does the Triangle Count algorithm in GraphX compute for each vertex?

Accepted Answer

The number of triangles that the vertex participates in

Answer

GraphX's triangleCount() computes the number of triangles passing through each vertex, which is useful for measuring local clustering and network density.

Question 2

What does `joinVertices` allow you to do in GraphX?

Accepted Answer

Update vertex attributes by joining graph vertices with values from an external RDD

Answer

joinVertices performs a left-outer join between the graph's VertexRDD and an external RDD[(VertexId, U)], applying a map function to update vertex attributes with the joined values.

Question 3

What is the Label Propagation Algorithm (LPA) used for in GraphX?

Accepted Answer

Detecting communities or clusters in a graph

Answer

LPA in GraphX detects communities by having each vertex iteratively adopt the label most common among its neighbors until the labels stabilize.

Question 4

How do you access the underlying distributed data of a GraphX graph as standard RDDs?

Accepted Answer

graph.vertices and graph.edges

Answer

A GraphX Graph exposes its data directly via `graph.vertices` (VertexRDD) and `graph.edges` (EdgeRDD), which can be used as regular Spark RDDs.

Question 5

What is the key advantage of the `EdgePartition2D` strategy in GraphX over `EdgePartition1D`?

Accepted Answer

It reduces vertex replication by hashing both source and destination IDs when assigning edges to partitions

Answer

EdgePartition2D hashes both the src and dst vertex IDs together to assign edges, distributing vertex ghost copies more evenly and reducing total replication compared to 1D.

Apache Spark Practice Test

Apache Spark Practice Test

Apache Spark GraphX and Graph Processing 3