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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.
