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# Utilities
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Graph loading, generation, and utility functions for creating test graphs, importing data, performance optimization, and working with GraphX efficiently.
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## Capabilities
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### Graph Loading
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Load graphs from various file formats and data sources.
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```scala { .api }
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object GraphLoader {
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  /**
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   * Load graph from edge list file format
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   * @param sc SparkContext  
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   * @param path Path to edge list file (each line: "srcId dstId" or "srcId dstId weight")
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   * @param canonicalOrientation Whether to orient edges canonically (srcId < dstId)
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   * @param numEdgePartitions Number of edge partitions (-1 for default)
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   * @param edgeStorageLevel Storage level for edges
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   * @param vertexStorageLevel Storage level for vertices
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   * @returns Graph with integer vertex and edge attributes
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   */
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  def edgeListFile(
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    sc: SparkContext,
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    path: String,
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    canonicalOrientation: Boolean = false,
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    numEdgePartitions: Int = -1,
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    edgeStorageLevel: StorageLevel = StorageLevel.MEMORY_ONLY,
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    vertexStorageLevel: StorageLevel = StorageLevel.MEMORY_ONLY
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  ): Graph[Int, Int]
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}
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```
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**Usage Examples:**
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```scala
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import org.apache.spark.graphx._
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// Load graph from edge list file
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// File format: each line contains "srcId dstId" or "srcId dstId weight"  
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val graph = GraphLoader.edgeListFile(sc, "path/to/edges.txt")
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// Load with canonical orientation (srcId < dstId)
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val canonicalGraph = GraphLoader.edgeListFile(
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  sc, 
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  "path/to/edges.txt",
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  canonicalOrientation = true
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)
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// Load with custom partitioning and storage  
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val optimizedGraph = GraphLoader.edgeListFile(
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  sc,
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  "hdfs://cluster/large-graph.txt", 
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  canonicalOrientation = false,
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  numEdgePartitions = 100,
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  edgeStorageLevel = StorageLevel.MEMORY_AND_DISK,
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  vertexStorageLevel = StorageLevel.MEMORY_ONLY
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)
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```
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### Graph Generators
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Generate synthetic graphs for testing, benchmarking, and algorithm development.
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```scala { .api }
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object GraphGenerators {
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  /** Default RMAT parameters for realistic graph generation */
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  val RMATa: Double = 0.45
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  val RMATb: Double = 0.15  
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  val RMATd: Double = 0.25
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  // RMATc = 1.0 - RMATa - RMATb - RMATd = 0.15
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  /**
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   * Generate log-normal degree distribution graph
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   * @param sc SparkContext
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   * @param numVertices Number of vertices  
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   * @param numEParts Number of edge partitions (-1 for default)
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   * @param mu Mean of underlying normal distribution
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   * @param sigma Standard deviation of underlying normal distribution  
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   * @param seed Random seed for reproducibility
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   * @returns Graph with long vertex attributes and integer edge attributes
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   */
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  def logNormalGraph(
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    sc: SparkContext,
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    numVertices: Int,
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    numEParts: Int = -1,
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    mu: Double = 4.0,
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    sigma: Double = 1.3,
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    seed: Long = -1
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  ): Graph[Long, Int]
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  /**
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   * Generate R-MAT graph with realistic structure
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   * @param sc SparkContext
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   * @param requestedNumVertices Desired number of vertices (will be rounded up to power of 2)
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   * @param numEdges Number of edges to generate
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   * @param a Probability of edge in top-left quadrant
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   * @param b Probability of edge in top-right quadrant  
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   * @param c Probability of edge in bottom-left quadrant
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   * @param d Probability of edge in bottom-right quadrant (a+b+c+d should equal 1.0)
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   * @param seed Random seed
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   * @param numEParts Number of edge partitions
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   * @returns R-MAT graph
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   */
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  def rmatGraph(
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    sc: SparkContext,
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    requestedNumVertices: Int,
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    numEdges: Int, 
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    a: Double = RMATa,
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    b: Double = RMATb,
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    c: Double = 1.0 - RMATa - RMATb - RMATd, 
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    d: Double = RMATd,
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    seed: Long = -1,
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    numEParts: Int = -1  
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  ): Graph[Int, Int]
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  /**
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   * Generate star graph (one central vertex connected to all others)
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   * @param sc SparkContext
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   * @param nverts Number of vertices (including center)
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   * @param numEParts Number of edge partitions
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   * @returns Star graph with center at vertex 0
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   */
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  def starGraph(
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    sc: SparkContext, 
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    nverts: Int,
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    numEParts: Int = -1
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  ): Graph[Int, Int]
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  /**
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   * Generate 2D grid graph  
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   * @param sc SparkContext
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   * @param rows Number of rows in grid
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   * @param cols Number of columns in grid  
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   * @returns Grid graph with vertices connected to adjacent cells
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   */
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  def gridGraph(
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    sc: SparkContext,
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    rows: Int, 
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    cols: Int
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  ): Graph[(Int, Int), Double]
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}
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```
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**Usage Examples:**
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```scala
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import org.apache.spark.graphx.util.GraphGenerators
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// Generate log-normal degree distribution graph (realistic social networks)
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val socialGraph = GraphGenerators.logNormalGraph(sc, numVertices = 1000)
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// Generate R-MAT graph with default parameters  
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val rmatGraph = GraphGenerators.rmatGraph(sc, requestedNumVertices = 1024, numEdges = 5000)
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// Generate custom R-MAT with different parameters
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val customRMAT = GraphGenerators.rmatGraph(
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  sc, 
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  requestedNumVertices = 2048,
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  numEdges = 10000,
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  a = 0.57, b = 0.19, c = 0.19, d = 0.05  // More skewed distribution
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)
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// Generate star graph for testing centrality algorithms
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val starGraph = GraphGenerators.starGraph(sc, nverts = 100)
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// Generate grid graph for spatial algorithms
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val gridGraph = GraphGenerators.gridGraph(sc, rows = 10, cols = 10)
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// Use generated graphs for testing
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val pageRanks = rmatGraph.pageRank(0.001).vertices
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val components = socialGraph.connectedComponents().vertices
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```
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### Graph Utilities and Optimization
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Utility functions for graph optimization, serialization, and performance tuning.
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```scala { .api }
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object GraphXUtils {
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  /**
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   * Register GraphX classes with Kryo serialization for better performance
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   * @param conf SparkConf to modify
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   */
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  def registerKryoClasses(conf: SparkConf): Unit
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}
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class PeriodicGraphCheckpointer[VD: ClassTag, ED: ClassTag](
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  checkpointInterval: Int,
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  sc: SparkContext
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) {
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  /**
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   * Update the graph, managing checkpointing and persistence automatically
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   * @param graph New graph to manage
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   */  
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  def update(graph: Graph[VD, ED]): Unit
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  /**
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   * Checkpoint the current graph if needed
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   */
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  def checkpoint(): Unit
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  /**
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   * Clean up all cached/checkpointed graphs
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   */  
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  def deleteAllCheckpoints(): Unit
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}
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object BytecodeUtils {
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  /**
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   * Test whether a closure invokes a specific method (for optimization)
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   * @param closure Function closure to analyze  
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   * @param targetClass Class containing the target method
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   * @param targetMethod Name of method to check for
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   * @returns Whether the closure calls the target method
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   */
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  def invokedMethod(
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    closure: AnyRef,
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    targetClass: Class[_], 
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    targetMethod: String
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  ): Boolean
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}
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```
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### Partition Strategies
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Different strategies for distributing edges across partitions to optimize performance.
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```scala { .api }
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trait PartitionStrategy {
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  /**
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   * Determine which partition an edge should be assigned to
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   * @param src Source vertex ID
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   * @param dst Destination vertex ID  
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   * @param numParts Total number of partitions
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   * @returns Partition ID for this edge
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   */
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  def getPartition(src: VertexId, dst: VertexId, numParts: PartitionID): PartitionID
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}
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object PartitionStrategy {
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  /**
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   * 2D edge partitioning with vertex replication bound of √numParts
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   * Provides good load balancing and communication efficiency
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   */
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  val EdgePartition2D: PartitionStrategy
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  /**
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   * Hash partitioning by source vertex only  
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   * Simple but can lead to load imbalance
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   */
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  val EdgePartition1D: PartitionStrategy
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  /**
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   * Random partitioning that colocates same-direction edges
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   * Good for undirected graphs
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   */  
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  val RandomVertexCut: PartitionStrategy
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  /**
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   * Random partitioning that colocates all edges between vertex pairs
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   * Reduces communication for algorithms using both edge directions
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   */
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  val CanonicalRandomVertexCut: PartitionStrategy
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  /**
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   * Get partition strategy by string name
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   * @param s Strategy name ("EdgePartition1D", "EdgePartition2D", etc.)
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   * @returns Corresponding PartitionStrategy
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   */
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  def fromString(s: String): PartitionStrategy
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}
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```
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**Usage Examples:**
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```scala
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// Optimize graph partitioning for better performance
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val optimizedGraph = graph.partitionBy(PartitionStrategy.EdgePartition2D)
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// Use different strategies based on graph characteristics  
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val strategy = if (graph.numVertices > 1000000) {
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  PartitionStrategy.EdgePartition2D  // Better for large graphs
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} else {
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  PartitionStrategy.RandomVertexCut  // Simpler for small graphs  
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}
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val partitionedGraph = graph.partitionBy(strategy)
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// Get strategy from configuration
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val strategyName = "EdgePartition1D"
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val configuredStrategy = PartitionStrategy.fromString(strategyName)
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```
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### Performance Optimization Utilities  
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Helper functions and patterns for optimizing GraphX performance.
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```scala { .api }
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class GraphOps[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]) {
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  /**
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   * Convert multiple edges between vertices to single edges
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   * @param merge Function to combine edge attributes  
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   * @returns Graph with merged parallel edges
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   */
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  def groupEdges(merge: (ED, ED) => ED): Graph[VD, ED]
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  /**
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   * Remove self-loops (edges from vertex to itself)
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   * @returns Graph without self-loops
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   */
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  def removeSelfEdges(): Graph[VD, ED]
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  /**
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   * Pick a random vertex ID from the graph
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   * @returns Random vertex ID
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   */
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  def pickRandomVertex(): VertexId
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  /**
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   * Filter graph with preprocessing optimization
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   * @param preprocess Function to optimize graph before filtering
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   * @param epred Edge predicate
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   * @param vpred Vertex predicate  
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   * @returns Filtered graph
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   */
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  def filter[VD2: ClassTag, ED2: ClassTag](
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    preprocess: Graph[VD, ED] => Graph[VD2, ED2],
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    epred: EdgeTriplet[VD2, ED2] => Boolean,
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    vpred: (VertexId, VD2) => Boolean
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  ): Graph[VD, ED]
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}
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```
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### TripletFields Optimization
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Control which triplet fields are accessed to optimize message passing performance.
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```scala { .api }
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class TripletFields(val useSrc: Boolean, val useDst: Boolean, val useEdge: Boolean) {
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  // Java class for specifying which EdgeTriplet/EdgeContext fields are accessed
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}
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object TripletFields {
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  /** No fields are accessed */
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  val None: TripletFields
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  /** Only edge attribute is accessed */  
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  val EdgeOnly: TripletFields
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  /** Source vertex and edge attributes are accessed */
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  val Src: TripletFields
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  /** Destination vertex and edge attributes are accessed */
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  val Dst: TripletFields
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  /** All fields are accessed (default) */
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  val All: TripletFields
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}
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```
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**Usage Examples:**
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```scala
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// Optimize aggregateMessages with TripletFields
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val inDegrees = graph.aggregateMessages[Int](
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  sendMsg = ctx => ctx.sendToDst(1),  // Only sending to destination
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  mergeMsg = (a, b) => a + b,
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  tripletFields = TripletFields.None  // No triplet fields needed
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)
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val weightedInDegrees = graph.aggregateMessages[Double](
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  sendMsg = ctx => ctx.sendToDst(ctx.attr),  // Using edge attribute
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  mergeMsg = (a, b) => a + b,
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  tripletFields = TripletFields.EdgeOnly  // Only edge attribute needed
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)
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val neighborSum = graph.aggregateMessages[Double](
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  sendMsg = ctx => ctx.sendToDst(ctx.srcAttr),  // Using source attribute
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  mergeMsg = (a, b) => a + b, 
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  tripletFields = TripletFields.Src  // Only source attribute needed
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)
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```
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## Performance Optimization Patterns
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### Graph Construction Optimization
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```scala
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// Efficient graph construction for large datasets
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def buildLargeGraph(vertices: RDD[(VertexId, String)], edges: RDD[Edge[Double]]): Graph[String, Double] = {
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  // Partition edges for better locality
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  val partitionedEdges = edges.partitionBy(new HashPartitioner(100))
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  // Use appropriate storage levels
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  val graph = Graph(
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    vertices, 
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    partitionedEdges,
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    defaultVertexAttr = "Unknown",
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    edgeStorageLevel = StorageLevel.MEMORY_AND_DISK_SER,
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    vertexStorageLevel = StorageLevel.MEMORY_ONLY
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  )
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  // Apply efficient partitioning strategy
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  graph.partitionBy(PartitionStrategy.EdgePartition2D).cache()
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}
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```
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### Iterative Algorithm Optimization
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```scala
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// Optimize for iterative algorithms
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def optimizeForIterativeAlgorithms[VD: ClassTag, ED: ClassTag](
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  graph: Graph[VD, ED]
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): Graph[VD, ED] = {
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  graph
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    .partitionBy(PartitionStrategy.EdgePartition2D)  // Better load balancing
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    .cache()                                         // Cache for multiple iterations
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    .checkpoint()                                    // Checkpoint for fault tolerance
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}
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// Use with iterative algorithms  
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val optimizedGraph = optimizeForIterativeAlgorithms(graph)
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val pageRanks = optimizedGraph.pageRank(0.001)
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val components = optimizedGraph.connectedComponents()
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// Clean up when done
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optimizedGraph.unpersist()
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```
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### Memory Management
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```scala
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// Manage memory usage for large graphs
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def memoryEfficientProcessing[VD: ClassTag, ED: ClassTag](
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  graph: Graph[VD, ED]
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): Unit = {
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  // Use serialized storage for large graphs
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  val efficientGraph = graph.persist(StorageLevel.MEMORY_AND_DISK_SER)
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  try {
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    // Process graph
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    val results = efficientGraph.pageRank(0.001)
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    // Process results immediately  
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    results.vertices.foreachPartition { iter =>
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      iter.foreach { case (id, rank) => 
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        // Process each result
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      }
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    }
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  } finally {
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    // Always clean up
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    efficientGraph.unpersist(blocking = false)
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  }
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}
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```
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### Kryo Serialization Setup
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```scala
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// Configure Spark for optimal GraphX performance
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def configureSparkForGraphX(appName: String): SparkContext = {
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  val conf = new SparkConf()
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    .setAppName(appName)
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    .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
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    .set("spark.kryo.registrator", "org.apache.spark.graphx.GraphKryoRegistrator")
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    .set("spark.locality.wait", "0s")  // Disable locality wait for GraphX
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    .set("spark.sql.adaptive.enabled", "false")  // Can interfere with GraphX
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  // Register GraphX classes with Kryo
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  GraphXUtils.registerKryoClasses(conf)
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  new SparkContext(conf)
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}
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```
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## Common Utility Patterns
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### Graph Validation and Debugging
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```scala
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// Validate graph structure and properties
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def validateGraph[VD, ED](graph: Graph[VD, ED]): Unit = {
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  println(s"Vertices: ${graph.numVertices}")  
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  println(s"Edges: ${graph.numEdges}")
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  // Check for self-loops
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  val selfLoops = graph.edges.filter(e => e.srcId == e.dstId).count()
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  println(s"Self-loops: $selfLoops")
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  // Check degree distribution
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  val degrees = graph.degrees.map(_._2)
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  val maxDegree = degrees.max()
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  val avgDegree = degrees.mean()
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  println(s"Max degree: $maxDegree, Average degree: $avgDegree")
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  // Check connectivity  
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  val components = graph.connectedComponents().vertices.map(_._2).distinct().count()
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  println(s"Connected components: $components")
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}
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```
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### Graph Format Conversion
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```scala
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// Convert between different graph representations
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def convertEdgeListToAdjacencyList[ED](graph: Graph[Long, ED]): RDD[(VertexId, Array[VertexId])] = {
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  graph.collectNeighborIds(EdgeDirection.Out)
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}
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def saveGraphToEdgeList[VD, ED](graph: Graph[VD, ED], path: String): Unit = {
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  graph.edges
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    .map(edge => s"${edge.srcId} ${edge.dstId}")
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    .saveAsTextFile(path)
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}
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def loadGraphFromAdjacencyList(sc: SparkContext, path: String): Graph[Int, Int] = {
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  val adjacencyList = sc.textFile(path).map { line =>
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    val parts = line.split("\\s+")
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    val src = parts(0).toLong
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    val neighbors = parts.tail.map(_.toLong)
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    (src, neighbors)
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  }
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  val edges = adjacencyList.flatMap { case (src, neighbors) =>
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    neighbors.map(dst => Edge(src, dst, 1))
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  }
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  Graph.fromEdges(edges, defaultValue = 0)
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}
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```