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# Pair RDD Operations
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Advanced operations for key-value pair RDDs including grouping, joining, and aggregation operations essential for data processing workflows.
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## Capabilities
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### PairRDDFunctions
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Extra operations available on RDDs of key-value pairs through implicit conversion from RDD[(K, V)].
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```scala { .api }
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/**
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 * Extra functions available on RDDs of (key, value) pairs through an implicit conversion.
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 * These functions are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)]).
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 */
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class PairRDDFunctions[K, V](self: RDD[(K, V)])
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    (implicit kt: ClassTag[K], vt: ClassTag[V], ord: Ordering[K] = null)
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  extends Logging with Serializable {
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  // GROUPING OPERATIONS
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  /** Group values by key */
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  def groupByKey(): RDD[(K, Iterable[V])]
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  /** Group values by key with custom number of partitions */
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  def groupByKey(numPartitions: Int): RDD[(K, Iterable[V])]
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  /** Group values by key with custom partitioner */
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  def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])]
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  // REDUCTION OPERATIONS
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  /** Combine values with the same key using a function */
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  def reduceByKey(func: (V, V) => V): RDD[(K, V)]
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  /** Combine values with the same key using function and custom partitions */
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  def reduceByKey(func: (V, V) => V, numPartitions: Int): RDD[(K, V)]
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  /** Combine values with the same key using function and custom partitioner */
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  def reduceByKey(partitioner: Partitioner, func: (V, V) => V): RDD[(K, V)]
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  /** Fold values by key with zero value */
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  def foldByKey(zeroValue: V)(func: (V, V) => V): RDD[(K, V)]
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  /** Fold values by key with zero value and custom partitioner */
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  def foldByKey(zeroValue: V, partitioner: Partitioner)(func: (V, V) => V): RDD[(K, V)]
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  /** Aggregate values by key using different types for intermediate and final results */
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  def aggregateByKey[U: ClassTag](zeroValue: U)(seqOp: (U, V) => U, combOp: (U, U) => U): RDD[(K, U)]
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  /** Aggregate values by key with custom partitioner */
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  def aggregateByKey[U: ClassTag](zeroValue: U, partitioner: Partitioner)(seqOp: (U, V) => U, combOp: (U, U) => U): RDD[(K, U)]
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  /** Generic function to combine values by key using combiners */
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  def combineByKey[C](createCombiner: V => C,
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                     mergeValue: (C, V) => C,
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                     mergeCombiners: (C, C) => C): RDD[(K, C)]
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  /** Generic function to combine values by key with custom partitioner */
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  def combineByKey[C](createCombiner: V => C,
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                     mergeValue: (C, V) => C,
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                     mergeCombiners: (C, C) => C,
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                     partitioner: Partitioner): RDD[(K, C)]
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  // JOIN OPERATIONS
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  /** Inner join with another RDD */
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  def join[W](other: RDD[(K, W)]): RDD[(K, (V, W))]
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  /** Inner join with custom partitioner */
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  def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))]
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  /** Left outer join with another RDD */
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  def leftOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (V, Option[W]))]
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  /** Left outer join with custom partitioner */
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  def leftOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, Option[W]))]
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  /** Right outer join with another RDD */
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  def rightOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], W))]
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  /** Right outer join with custom partitioner */
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  def rightOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (Option[V], W))]
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  /** Full outer join with another RDD */
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  def fullOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], Option[W]))]
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  /** Full outer join with custom partitioner */
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  def fullOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (Option[V], Option[W]))]
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  /** Cogroup with another RDD (group by key across both RDDs) */
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  def cogroup[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))]
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  /** Cogroup with custom partitioner */
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  def cogroup[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (Iterable[V], Iterable[W]))]
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  /** Cogroup with two other RDDs */
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  def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)]): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))]
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  // SET OPERATIONS
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  /** Subtract by key (remove elements with keys present in other RDD) */
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  def subtractByKey[W: ClassTag](other: RDD[(K, W)]): RDD[(K, V)]
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  /** Subtract by key with custom partitioner */
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  def subtractByKey[W: ClassTag](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, V)]
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  // SORTING AND PARTITIONING
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  /** Sort RDD by key */
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  def sortByKey(ascending: Boolean = true): RDD[(K, V)]
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  /** Sort RDD by key with custom number of partitions */
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  def sortByKey(ascending: Boolean, numPartitions: Int): RDD[(K, V)]
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  /** Partition RDD by key using specified partitioner */
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  def partitionBy(partitioner: Partitioner): RDD[(K, V)]
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  // EXTRACTION OPERATIONS
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  /** Get RDD of keys only */
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  def keys: RDD[K]
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  /** Get RDD of values only */
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  def values: RDD[V]
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  /** Transform only the values (keep keys unchanged) */
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  def mapValues[U](f: V => U): RDD[(K, U)]
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  /** FlatMap only the values (keep keys unchanged) */
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  def flatMapValues[U](f: V => TraversableOnce[U]): RDD[(K, U)]
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  // ACTIONS
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  /** Count number of elements for each key */
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  def countByKey(): Map[K, Long]
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  /** Collect as a map (key -> single value, not suitable for duplicate keys) */
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  def collectAsMap(): Map[K, V]
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  /** Look up values for a specific key */
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  def lookup(key: K): Seq[V]
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  // HADOOP OUTPUT OPERATIONS
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  /** Save as Hadoop file using old MapReduce API */
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  def saveAsHadoopFile[F <: OutputFormat[K, V]](path: String,
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                                               keyClass: Class[_],
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                                               valueClass: Class[_],
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                                               outputFormatClass: Class[F],
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                                               conf: JobConf = new JobConf,
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                                               codec: Option[Class[_ <: CompressionCodec]] = None): Unit
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  /** Save as Hadoop file using new MapReduce API */
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  def saveAsNewAPIHadoopFile[F <: NewOutputFormat[K, V]](path: String,
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                                                        keyClass: Class[_],
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                                                        valueClass: Class[_],
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                                                        outputFormatClass: Class[F],
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                                                        conf: Configuration = new Configuration): Unit
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  /** Save as Hadoop dataset using old API */
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  def saveAsHadoopDataset(conf: JobConf): Unit
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  /** Save as Hadoop dataset using new API */
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  def saveAsNewAPIHadoopDataset(conf: Configuration): Unit
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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.{SparkContext, SparkConf}
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import org.apache.spark.HashPartitioner
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val sc = new SparkContext(new SparkConf().setAppName("Pair RDD Examples").setMaster("local[*]"))
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// Create pair RDDs
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val scores = sc.parallelize(Array(
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  ("Alice", 85), ("Bob", 90), ("Alice", 92), ("Charlie", 78), ("Bob", 95)
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))
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val grades = sc.parallelize(Array(
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  ("Alice", "A"), ("Bob", "A+"), ("Charlie", "B"), ("David", "C")
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))
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// Grouping operations
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val groupedScores = scores.groupByKey() // RDD[(String, Iterable[Int])]
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// Result: ("Alice", [85, 92]), ("Bob", [90, 95]), ("Charlie", [78])
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val avgScores = scores.reduceByKey(_ + _).mapValues(_ / 2) // Average scores
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// Result: ("Alice", 88.5), ("Bob", 92.5), ("Charlie", 78)
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val maxScores = scores.reduceByKey(math.max) // Maximum score per student
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// Result: ("Alice", 92), ("Bob", 95), ("Charlie", 78)
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// Aggregation with different types
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val stats = scores.aggregateByKey((0, 0))(
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  seqOp = { case ((sum, count), score) => (sum + score, count + 1) },
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  combOp = { case ((sum1, count1), (sum2, count2)) => (sum1 + sum2, count1 + count2) }
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).mapValues { case (sum, count) => sum.toDouble / count }
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// Join operations
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val joined = scores.join(grades) // Inner join
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// Result: ("Alice", (85, "A")), ("Alice", (92, "A")), ("Bob", (90, "A+")), ("Bob", (95, "A+")), ("Charlie", (78, "B"))
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val leftJoin = scores.leftOuterJoin(grades) // Left outer join
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// Includes all scores, grades as Option[String]
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val fullJoin = scores.fullOuterJoin(grades) // Full outer join
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// Includes all keys from both RDDs
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// Cogroup (group by key across multiple RDDs)
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val cogrouped = scores.cogroup(grades)
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// Result: ("Alice", ([85, 92], ["A"])), ("Bob", ([90, 95], ["A+"])), etc.
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// Set operations
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val students = sc.parallelize(Array(("Alice", 1), ("Bob", 2), ("Charlie", 3)))
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val withdrawn = sc.parallelize(Array(("Bob", "withdrawn"), ("David", "withdrawn")))
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val activeStudents = students.subtractByKey(withdrawn)
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// Result: ("Alice", 1), ("Charlie", 3)
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// Sorting
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val sortedScores = scores.sortByKey() // Sort by student name
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val sortedByScore = scores.map(_.swap).sortByKey(false).map(_.swap) // Sort by score descending
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// Key/value extraction
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val studentNames = scores.keys.distinct() // RDD[String] of unique student names
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val allScores = scores.values // RDD[Int] of all scores
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val upperCaseNames = scores.mapValues(score => s"Score: $score") // Transform values only
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// Actions
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val countsByStudent = scores.countByKey() // Map[String, Long]
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val scoreMap = scores.collectAsMap() // Map[String, Int] - warning: loses duplicates!
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val aliceScores = scores.lookup("Alice") // Seq[Int] = List(85, 92)
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sc.stop()
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```
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### Advanced Aggregation with combineByKey
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The `combineByKey` function is the most general aggregation function for pair RDDs.
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```scala { .api }
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/**
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 * Generic function to combine values by key using three functions:
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 * @param createCombiner function to create initial combiner from first value
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 * @param mergeValue function to merge a value into an existing combiner  
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 * @param mergeCombiners function to merge two combiners
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 */
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def combineByKey[C](createCombiner: V => C,
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                   mergeValue: (C, V) => C,
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                   mergeCombiners: (C, C) => C): RDD[(K, C)]
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```
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**Usage Example:**
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```scala
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// Compute mean score per student using combineByKey
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val meanScores = scores.combineByKey(
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  createCombiner = (score: Int) => (score, 1), // (sum, count)
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  mergeValue = (acc: (Int, Int), score: Int) => (acc._1 + score, acc._2 + 1),
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  mergeCombiners = (acc1: (Int, Int), acc2: (Int, Int)) => (acc1._1 + acc2._1, acc1._2 + acc2._2)
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).mapValues { case (sum, count) => sum.toDouble / count }
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// Collect all scores per student into a list (more efficient than groupByKey)
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val scoresPerStudent = scores.combineByKey(
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  createCombiner = (score: Int) => List(score),
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  mergeValue = (acc: List[Int], score: Int) => score :: acc,
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  mergeCombiners = (acc1: List[Int], acc2: List[Int]) => acc1 ::: acc2
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)
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```
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### Partitioning for Performance
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Understanding and controlling partitioning is crucial for performance in pair RDD operations.
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```scala { .api }
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/**
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 * Hash partitioner that distributes keys using hash function
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 */
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class HashPartitioner(partitions: Int) extends Partitioner {
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  def numPartitions: Int = partitions
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  def getPartition(key: Any): Int
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}
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/**
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 * Range partitioner that distributes keys by ranges  
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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[_ <: Product2[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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}
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```
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**Usage Examples:**
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```scala
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// Partition data for better join performance
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val partitioner = new HashPartitioner(4)
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val partitionedScores = scores.partitionBy(partitioner).persist()
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val partitionedGrades = grades.partitionBy(partitioner).persist()
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// Now joins will be much faster as data is co-located
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val efficientJoin = partitionedScores.join(partitionedGrades)
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// Range partitioner for sorted data
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val sortedData = scores.sortByKey() // Uses RangePartitioner internally
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```
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### Join Performance Optimizations
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Different join strategies for different data size scenarios:
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```scala
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// Broadcast join for small datasets (automatically optimized)
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val smallGrades = grades.filter(_._2 == "A+") // Small RDD
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val broadcastJoin = scores.join(smallGrades) // Spark may broadcast small RDD
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// Manual broadcast for very small lookup tables
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val gradeMap = sc.broadcast(grades.collectAsMap())
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val manualBroadcastJoin = scores.map { case (student, score) =>
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  val grade = gradeMap.value.get(student)
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  (student, (score, grade))
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}
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// Bucket join for large datasets with known partitioning
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val bucketedScores = scores.partitionBy(new HashPartitioner(10)).persist()
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val bucketedGrades = grades.partitionBy(new HashPartitioner(10)).persist()
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val bucketJoin = bucketedScores.join(bucketedGrades) // No shuffle needed
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```
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## Performance Considerations
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### Choosing the Right Operation
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- **`reduceByKey`** vs **`groupByKey`**: Always prefer `reduceByKey` when possible as it reduces data before shuffling
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- **`aggregateByKey`** vs **`combineByKey`**: Use `aggregateByKey` for simpler cases, `combineByKey` for complex transformations
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- **`cogroup`** vs multiple **joins**: Use `cogroup` when you need to join multiple RDDs by the same key
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### Partitioning Strategy
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- Use consistent partitioners across RDDs that will be joined
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- Persist partitioned RDDs when they'll be reused
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- Consider the number of unique keys when choosing partition count
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- Use `RangePartitioner` for sorted operations, `HashPartitioner` for general use
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### Memory Management
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- Be careful with `groupByKey` on high-cardinality keys (can cause OOM)
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- Use `mapValues` and `flatMapValues` to avoid shuffling keys
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- Consider using `aggregateByKey` with smaller intermediate types
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### Common Anti-patterns
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- Using `groupByKey().mapValues(_.sum)` instead of `reduceByKey(_ + _)`
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- Not partitioning RDDs that will be joined multiple times
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- Using `collectAsMap()` on RDDs with duplicate keys (data loss)
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- Performing multiple joins without consistent partitioning