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# Partitioning and Shuffling
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Partitioning strategies and shuffle operations for controlling data distribution and optimizing performance across cluster nodes in Spark applications.
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## Capabilities
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### Partitioner
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Abstract base class defining how elements are partitioned by key across cluster nodes.
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```scala { .api }
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/**
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 * An object that defines how the elements in a key-value pair RDD are partitioned by key
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 */
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abstract class Partitioner extends Serializable {
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  /** Return the number of partitions in this partitioner */
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  def numPartitions: Int
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  /** Return the partition id for the given key */
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  def getPartition(key: Any): Int
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  /** Test whether this partitioner is equal to another object */
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  override def equals(other: Any): Boolean
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  /** Return a hash code for this partitioner */
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  override def hashCode: Int
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}
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// Built-in partitioners
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class HashPartitioner(partitions: Int) extends Partitioner {
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  require(partitions >= 0, s"Number of partitions ($partitions) cannot be negative.")
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  def numPartitions: Int = partitions
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  def getPartition(key: Any): Int = key match {
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    case null => 0
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    case _ => Utils.nonNegativeMod(key.hashCode, numPartitions)
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  }
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  override def equals(other: Any): Boolean = other match {
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    case h: HashPartitioner => h.numPartitions == numPartitions
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    case _ => false
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  }
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  override def hashCode: Int = numPartitions
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}
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class RangePartitioner[K : Ordering : ClassTag, V](
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  partitions: Int,
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  rdd: RDD[(K, V)],
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  ascending: Boolean = true) extends Partitioner {
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  def numPartitions: Int = partitions
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  def getPartition(key: Any): Int = {
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    val k = key.asInstanceOf[K]
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    // Binary search to find the partition
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    // Implementation details...
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  }
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}
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```
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### Partitioning Methods (RDD)
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Methods for controlling RDD partitioning and data distribution.
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```scala { .api }
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// Partitioning methods available on RDDs
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def partitions: Array[Partition]
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def getNumPartitions: Int
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def partitioner: Option[Partitioner]
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// Repartitioning methods
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def repartition(numPartitions: Int): RDD[T]
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def coalesce(numPartitions: Int, shuffle: Boolean = false): RDD[T]
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// Key-value partitioning (available on pair RDDs)
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def partitionBy(partitioner: Partitioner): RDD[(K, V)]
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def repartitionAndSortWithinPartitions(partitioner: Partitioner): RDD[(K, V)]
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// Partition-level operations
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def mapPartitions[U: ClassTag](f: Iterator[T] => Iterator[U], preservesPartitioning: Boolean = false): RDD[U]
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def mapPartitionsWithIndex[U: ClassTag](f: (Int, Iterator[T]) => Iterator[U], preservesPartitioning: Boolean = false): RDD[U]
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def foreachPartition(f: Iterator[T] => Unit): Unit
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def glom(): RDD[Array[T]]
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// Partition sampling and inspection
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def sample(withReplacement: Boolean, fraction: Double, seed: Long = Utils.random.nextLong): RDD[T]
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def takeSample(withReplacement: Boolean, num: Int, seed: Long = Utils.random.nextLong): Array[T]
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```
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**Usage Examples:**
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```scala
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import org.apache.spark.{SparkContext, SparkConf, HashPartitioner, RangePartitioner}
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val sc = new SparkContext(new SparkConf().setAppName("Partitioning Example").setMaster("local[*]"))
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// Create sample data
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val data = sc.parallelize(1 to 1000, numSlices = 4) // 4 initial partitions
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val keyValueData = data.map(x => (x % 10, x)) // (key, value) pairs
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// Check current partitioning
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println(s"Number of partitions: ${keyValueData.getNumPartitions}")
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println(s"Current partitioner: ${keyValueData.partitioner}")
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// Hash partitioning
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val hashPartitioned = keyValueData.partitionBy(new HashPartitioner(8))
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println(s"Hash partitioned: ${hashPartitioned.getNumPartitions} partitions")
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// Range partitioning
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val rangePartitioned = keyValueData.partitionBy(new RangePartitioner(6, keyValueData))
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println(s"Range partitioned: ${rangePartitioned.getNumPartitions} partitions")
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// Repartitioning vs coalescing
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val repartitioned = data.repartition(8) // Triggers shuffle
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val coalesced = data.coalesce(2) // May avoid shuffle
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// Inspect partition contents
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val partitionContents = keyValueData.glom().collect()
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partitionContents.zipWithIndex.foreach { case (partition, index) =>
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  println(s"Partition $index: ${partition.take(5).mkString(", ")}...")
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}
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// Process partitions individually
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val partitionSums = data.mapPartitions { iterator =>
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  val sum = iterator.sum
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  Iterator(sum)
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}
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```
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### Custom Partitioners
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Creating custom partitioning strategies for specific use cases.
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```scala { .api }
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// Example custom partitioners
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class DomainPartitioner(numPartitions: Int) extends Partitioner {
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  override def numPartitions: Int = numPartitions
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  override def getPartition(key: Any): Int = {
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    val domain = key.toString
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    domain match {
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      case d if d.endsWith(".com") => 0
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      case d if d.endsWith(".org") => 1
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      case d if d.endsWith(".edu") => 2
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      case _ => 3
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    }
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  }
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}
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class GeographicPartitioner(regions: Map[String, Int]) extends Partitioner {
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  override def numPartitions: Int = regions.size
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  override def getPartition(key: Any): Int = {
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    val location = key.toString
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    regions.getOrElse(location.substring(0, 2), 0) // First 2 chars as region
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  }
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}
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class CustomRangePartitioner[K](
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  ranges: Array[K],
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  numPartitions: Int)(implicit ord: Ordering[K]) extends Partitioner {
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  override def numPartitions: Int = numPartitions
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  override def getPartition(key: Any): Int = {
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    val k = key.asInstanceOf[K]
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    val partition = ranges.indexWhere(ord.gteq(k, _))
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    if (partition < 0) ranges.length - 1 else partition
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  }
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}
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```
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**Custom Partitioner Examples:**
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```scala
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// Domain-based partitioning for web logs
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val webLogs = sc.textFile("web-logs.txt")
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  .map(parseLogEntry) // Returns (domain, logEntry)
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  .partitionBy(new DomainPartitioner(4))
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// Geographic partitioning for location data
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val regionMap = Map("US" -> 0, "EU" -> 1, "AS" -> 2, "OT" -> 3)
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val locationData = sc.textFile("locations.txt")
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  .map(parseLocation) // Returns (country, locationInfo)
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  .partitionBy(new GeographicPartitioner(regionMap))
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// Custom range partitioning for time series data
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val timeRanges = Array(
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  "2023-01-01", "2023-04-01", "2023-07-01", "2023-10-01"
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)
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val timeSeriesData = sc.textFile("timeseries.txt")
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  .map(parseTimeEntry) // Returns (date, data)
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  .partitionBy(new CustomRangePartitioner(timeRanges, 4))
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```
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### Shuffle Operations
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Operations that trigger data shuffling across the cluster.
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```scala { .api }
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// Operations that typically trigger shuffles:
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// Repartitioning operations
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def repartition(numPartitions: Int): RDD[T] // Always shuffles
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def partitionBy(partitioner: Partitioner): RDD[(K, V)] // Shuffles if different partitioner
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// Aggregation operations (on key-value RDDs)
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def groupByKey(): RDD[(K, Iterable[V])] // Shuffles
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def reduceByKey(func: (V, V) => V): RDD[(K, V)] // Shuffles but with pre-aggregation
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def aggregateByKey[U](zeroValue: U)(seqOp: (U, V) => U, combOp: (U, U) => U): RDD[(K, U)] // Shuffles
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def combineByKey[C](createCombiner: V => C, mergeValue: (C, V) => C, mergeCombiners: (C, C) => C): RDD[(K, C)] // Shuffles
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// Join operations
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def join[W](other: RDD[(K, W)]): RDD[(K, (V, W))] // Shuffles both RDDs
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def leftOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (V, Option[W]))] // Shuffles
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def rightOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], W))] // Shuffles
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def fullOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], Option[W]))] // Shuffles
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def cogroup[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] // Shuffles
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// Sorting operations
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def sortByKey(ascending: Boolean = true): RDD[(K, V)] // Shuffles
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def sortBy[K](f: T => K, ascending: Boolean = true): RDD[T] // Shuffles
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// Set operations
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def distinct(): RDD[T] // Shuffles
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def intersection(other: RDD[T]): RDD[T] // Shuffles
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def subtract(other: RDD[T]): RDD[T] // Shuffles
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```
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## Performance Optimization Strategies
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### Minimizing Shuffles
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```scala
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// INEFFICIENT: Multiple shuffles
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val inefficientPipeline = data
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  .groupByKey() // Shuffle 1
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  .mapValues(_.sum) // No shuffle
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  .filter(_._2 > 100) // No shuffle
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  .sortByKey() // Shuffle 2
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// EFFICIENT: Fewer shuffles with better operations
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val efficientPipeline = data
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  .reduceByKey(_ + _) // Shuffle 1 with pre-aggregation
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  .filter(_._2 > 100) // No shuffle
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  .sortByKey() // Shuffle 2
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// MOST EFFICIENT: Single shuffle with repartitionAndSortWithinPartitions
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val mostEfficientPipeline = data
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  .reduceByKey(_ + _) // Shuffle 1 with pre-aggregation
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  .filter(_._2 > 100) // No shuffle
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  .repartitionAndSortWithinPartitions(new HashPartitioner(8)) // Combined repartition + sort
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```
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### Partition-Aware Operations
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```scala
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// Leverage partitioning for efficient joins
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val users = sc.textFile("users.txt")
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  .map(parseUser) // (userId, userInfo)
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  .partitionBy(new HashPartitioner(8))
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  .cache() // Cache partitioned data
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val orders = sc.textFile("orders.txt")
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  .map(parseOrder) // (userId, orderInfo)
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  .partitionBy(new HashPartitioner(8)) // Same partitioner as users
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// Efficient join - no shuffle needed since both RDDs have same partitioner
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val userOrders = users.join(orders)
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// Efficient grouped operations
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val userStats = orders
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  .mapValues(order => (1, order.amount)) // (userId, (count, amount))
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  .reduceByKey((a, b) => (a._1 + b._1, a._2 + b._2)) // Pre-aggregated
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  .mapValues(stats => (stats._1, stats._2 / stats._1)) // (count, avgAmount)
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```
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### Advanced Partitioning Patterns
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```scala
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// Multi-level partitioning for hierarchical data
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class HierarchicalPartitioner(
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  primaryPartitions: Int,
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  secondaryPartitions: Int) extends Partitioner {
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  override def numPartitions: Int = primaryPartitions * secondaryPartitions
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  override def getPartition(key: Any): Int = {
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    val (primary, secondary) = key.asInstanceOf[(String, String)]
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    val primaryPartition = math.abs(primary.hashCode) % primaryPartitions
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    val secondaryPartition = math.abs(secondary.hashCode) % secondaryPartitions
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    primaryPartition * secondaryPartitions + secondaryPartition
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  }
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}
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// Usage for multi-dimensional data
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val hierarchicalData = sc.textFile("hierarchical.txt")
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  .map(parseHierarchical) // Returns ((region, category), data)
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  .partitionBy(new HierarchicalPartitioner(4, 4)) // 16 total partitions
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// Skew-aware partitioning
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class SkewAwarePartitioner[K](
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  rdd: RDD[(K, _)],
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  numPartitions: Int,
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  sampleFraction: Double = 0.1) extends Partitioner {
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  // Sample data to identify skewed keys
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  private val keyFrequencies = rdd.sample(false, sampleFraction)
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    .map(_._1)
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    .countByValue()
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  private val heavyKeys = keyFrequencies
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    .filter(_._2 > keyFrequencies.values.sum / numPartitions * 2)
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    .keySet
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  override def numPartitions: Int = numPartitions
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  override def getPartition(key: Any): Int = {
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    val k = key.asInstanceOf[K]
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    if (heavyKeys.contains(k)) {
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      // Distribute heavy keys across multiple partitions
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      (k.hashCode() & Integer.MAX_VALUE) % (numPartitions / 2)
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    } else {
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      // Regular partitioning for normal keys
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      ((k.hashCode() & Integer.MAX_VALUE) % (numPartitions / 2)) + (numPartitions / 2)
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    }
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  }
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}
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```
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### Monitoring Shuffle Performance
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```scala
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// Monitor shuffle operations
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def analyzeShuffleMetrics(sc: SparkContext): Unit = {
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  val statusTracker = sc.statusTracker
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  statusTracker.getActiveStageIds().foreach { stageId =>
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    statusTracker.getStageInfo(stageId) match {
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      case Some(stageInfo) =>
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        println(s"Stage $stageId:")
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        println(s"  Shuffle Read Bytes: ${stageInfo.shuffleReadBytes}")
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        println(s"  Shuffle Write Bytes: ${stageInfo.shuffleWriteBytes}")
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        println(s"  Shuffle Read Records: ${stageInfo.shuffleReadRecords}")
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        println(s"  Shuffle Write Records: ${stageInfo.shuffleWriteRecords}")
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      case None =>
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        println(s"No info available for stage $stageId")
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    }
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  }
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}
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// Custom shuffle metrics collection
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def trackShuffleOperations[T](rdd: RDD[T], operationName: String): RDD[T] = {
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  val startTime = System.currentTimeMillis()
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  val result = rdd.cache() // Force evaluation
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  result.count() // Trigger action
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  val endTime = System.currentTimeMillis()
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  println(s"Operation '$operationName' took ${endTime - startTime}ms")
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  println(s"Result partitions: ${result.getNumPartitions}")
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  result
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}
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// Usage
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val shuffledData = trackShuffleOperations(
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  originalData.groupByKey(),
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  "groupByKey operation"
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)
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```
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## Advanced Optimization Techniques
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### Broadcast Hash Joins
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```scala
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// Optimize small table joins using broadcast
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def broadcastHashJoin[K, V, W](
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  largeRDD: RDD[(K, V)],
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  smallRDD: RDD[(K, W)])(implicit sc: SparkContext): RDD[(K, (V, W))] = {
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  // Collect small RDD to driver and broadcast
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  val smallMap = smallRDD.collectAsMap()
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  val broadcastSmallMap = sc.broadcast(smallMap)
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  // Perform map-side join
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  largeRDD.flatMap { case (key, value) =>
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    broadcastSmallMap.value.get(key) match {
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      case Some(smallValue) => Some((key, (value, smallValue)))
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      case None => None
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    }
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  }
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}
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// Usage
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val largeTx = sc.textFile("large-transactions.txt").map(parseTx)
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val smallLookup = sc.textFile("small-lookup.txt").map(parseLookup)
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val joined = broadcastHashJoin(largeTx, smallLookup)
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```
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### Dynamic Partition Pruning
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```scala
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// Prune partitions based on predicates
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def prunePartitions[T](
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  rdd: RDD[T],
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  partitionPredicate: Int => Boolean): RDD[T] = {
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  val filteredPartitions = rdd.partitions.zipWithIndex
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    .filter { case (_, index) => partitionPredicate(index) }
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    .map(_._1)
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  new PartitionPrunedRDD(rdd, filteredPartitions)
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}
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// Custom RDD that only processes selected partitions
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class PartitionPrunedRDD[T](
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  parent: RDD[T],
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  selectedPartitions: Array[Partition]) extends RDD[T](parent) {
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  override def compute(split: Partition, context: TaskContext): Iterator[T] = {
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    parent.compute(split, context)
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  }
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  override protected def getPartitions: Array[Partition] = selectedPartitions
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}
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```
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### Partition-Level Caching Strategy
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```scala
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// Smart caching based on partition access patterns
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class SmartCacheManager[T](rdd: RDD[T]) {
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  private var accessCounts = Array.fill(rdd.getNumPartitions)(0L)
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  private var cachedPartitions = Set.empty[Int]
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  def accessPartition(partitionId: Int): Unit = {
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    accessCounts(partitionId) += 1
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    // Cache frequently accessed partitions
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    if (accessCounts(partitionId) > 10 && !cachedPartitions.contains(partitionId)) {
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      cachePartition(partitionId)
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    }
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  }
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  private def cachePartition(partitionId: Int): Unit = {
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    // Implementation would use custom caching logic
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    cachedPartitions += partitionId
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    println(s"Cached partition $partitionId")
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  }
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  def getAccessStats: Array[Long] = accessCounts.clone()
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}
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```
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## Best Practices
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### Choosing the Right Partitioner
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```scala
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// Guidelines for partitioner selection
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def choosePartitioner[K, V](
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  rdd: RDD[(K, V)],
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  operationType: String,
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  dataCharacteristics: Map[String, Any]): Partitioner = {
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  val numPartitions = dataCharacteristics.getOrElse("partitions", 200).asInstanceOf[Int]
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  val isSkewed = dataCharacteristics.getOrElse("skewed", false).asInstanceOf[Boolean]
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  val isSorted = dataCharacteristics.getOrElse("sorted", false).asInstanceOf[Boolean]
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  (operationType, isSkewed, isSorted) match {
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    case ("join", false, _) => new HashPartitioner(numPartitions)
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    case ("join", true, _) => new SkewAwarePartitioner(rdd, numPartitions)
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    case ("sort", _, false) => new RangePartitioner(numPartitions, rdd)
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    case ("sort", _, true) => new HashPartitioner(numPartitions) // Already sorted
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    case ("groupBy", false, _) => new HashPartitioner(numPartitions)
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    case ("groupBy", true, _) => new SkewAwarePartitioner(rdd, numPartitions)
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    case _ => new HashPartitioner(numPartitions) // Default
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  }
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}
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```
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### Partition Size Guidelines
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```scala
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// Calculate optimal partition count
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def calculateOptimalPartitions(
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  dataSize: Long,
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  targetPartitionSize: Long = 128 * 1024 * 1024, // 128MB
492
  maxPartitions: Int = 2000): Int = {
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  val calculatedPartitions = (dataSize / targetPartitionSize).toInt
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  val cores = Runtime.getRuntime.availableProcessors()
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  val minPartitions = cores * 2 // At least 2 partitions per core
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  math.min(maxPartitions, math.max(minPartitions, calculatedPartitions))
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}
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// Monitor partition sizes
502
def analyzePartitionSizes[T](rdd: RDD[T]): Unit = {
503
  val partitionSizes = rdd.mapPartitions { iter =>
504
    Iterator(iter.size)
505
  }.collect()
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507
  val avgSize = partitionSizes.sum.toDouble / partitionSizes.length
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  val maxSize = partitionSizes.max
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  val minSize = partitionSizes.min
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  val skewRatio = maxSize.toDouble / avgSize
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512
  println(s"Partition Analysis:")
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  println(s"  Count: ${partitionSizes.length}")
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  println(s"  Average size: $avgSize")
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  println(s"  Max size: $maxSize")
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  println(s"  Min size: $minSize")
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  println(s"  Skew ratio: $skewRatio")
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519
  if (skewRatio > 2.0) {
520
    println("  WARNING: High partition skew detected!")
521
  }
522
}
523
```
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### Memory-Efficient Partitioning
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527
```scala
528
// Memory-aware partition processing
529
def processPartitionsWithMemoryControl[T, U](
530
  rdd: RDD[T],
531
  processFunc: Iterator[T] => Iterator[U],
532
  maxMemoryPerPartition: Long = 512 * 1024 * 1024): RDD[U] = { // 512MB
533
  
534
  rdd.mapPartitions { partition =>
535
    val runtime = Runtime.getRuntime
536
    val initialMemory = runtime.totalMemory() - runtime.freeMemory()
537
    
538
    val bufferedPartition = partition.grouped(1000) // Process in batches
539
    
540
    bufferedPartition.flatMap { batch =>
541
      val currentMemory = runtime.totalMemory() - runtime.freeMemory()
542
      
543
      if (currentMemory - initialMemory > maxMemoryPerPartition) {
544
        System.gc() // Suggest garbage collection
545
        Thread.sleep(100) // Allow GC to run
546
      }
547
      
548
      processFunc(batch.iterator)
549
    }
550
  }
551
}
552
```