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Answer: Leverage graph.aggregateMessages to minimize data shuffling and apply custom partitioning strategies to the vertices RDD.
The correct answer is **C**. Leveraging `graph.aggregateMessages` minimizes data shuffling by enabling direct message passing between vertices, thus reducing memory consumption. This method also maximizes parallelism by utilizing Spark's inherent parallel processing capabilities. Custom partitioning strategies further optimize data distribution across the cluster, ensuring balanced workloads and improved performance. While other options like using DataFrames or broadcast variables have their merits, they may not offer the same efficiency and scalability for large-scale graph processing as the recommended approach.
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When optimizing large-scale graph processing in Spark using GraphX, which strategy is most effective for reducing memory usage and enhancing parallelism?
A
Implement a custom graph processing framework on top of Spark RDDs, bypassing GraphX.
B
Convert the graph to a DataFrame and use standard Spark SQL operations for graph processing.
C
Leverage graph.aggregateMessages to minimize data shuffling and apply custom partitioning strategies to the vertices RDD.
D
Utilize broadcast variables to distribute graph structure information across nodes.
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