spark編程模型（十五）之RDD鍵值轉換操作（Transformation Operation）——cogroup、join

cogroup

• 參數爲1個RDD
  
  • def cogroup[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))]
  • def cogroup[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W]))]
  • def cogroup[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (Iterable[V], Iterable[W]))]
• 參數爲2個RDD
  
  • def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)]): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))]
  • def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)], numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))]
  • def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)], partitioner: Partitioner): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))]
• 參數爲3個RDD
  
  • def cogroup[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)]): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))]
  • def cogroup[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)], numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))]
  • def cogroup[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)], partitioner: Partitioner): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))]
• cogroup相當於SQL中的全外關聯full outer join，返回左右RDD中的記錄，關聯不上的爲空
• 參數numPartitions用於指定結果的分區數

• 參數partitioner用於指定分區函數

  var rdd1 = sc.makeRDD(Array(("A","1"),("B","2"),("C","3")),2)
  var rdd2 = sc.makeRDD(Array(("A","a"),("C","c"),("D","d")),2)
  
  scala> var rdd3 = rdd1.cogroup(rdd2)
  rdd3: org.apache.spark.rdd.RDD[(String, (Iterable[String], Iterable[String]))] = MapPartitionsRDD[12] at cogroup at :25
  
  scala> rdd3.partitions.size
  res3: Int = 2
  
  scala> rdd3.collect
  res1: Array[(String, (Iterable[String], Iterable[String]))] = Array(
  (B,(CompactBuffer(2),CompactBuffer())), 
  (D,(CompactBuffer(),CompactBuffer(d))), 
  (A,(CompactBuffer(1),CompactBuffer(a))), 
  (C,(CompactBuffer(3),CompactBuffer(c)))
  )
  
  
  scala> var rdd4 = rdd1.cogroup(rdd2,3)
  rdd4: org.apache.spark.rdd.RDD[(String, (Iterable[String], Iterable[String]))] = MapPartitionsRDD[14] at cogroup at :25
  
  scala> rdd4.partitions.size
  res5: Int = 3
  
  scala> rdd4.collect
  res6: Array[(String, (Iterable[String], Iterable[String]))] = Array(
  (B,(CompactBuffer(2),CompactBuffer())), 
  (C,(CompactBuffer(3),CompactBuffer(c))), 
  (A,(CompactBuffer(1),CompactBuffer(a))), 
  (D,(CompactBuffer(),CompactBuffer(d))))
  
  var rdd1 = sc.makeRDD(Array(("A","1"),("B","2"),("C","3")),2)
  var rdd2 = sc.makeRDD(Array(("A","a"),("C","c"),("D","d")),2)
  var rdd3 = sc.makeRDD(Array(("A","A"),("E","E")),2)
  
  scala> var rdd4 = rdd1.cogroup(rdd2,rdd3)
  rdd4: org.apache.spark.rdd.RDD[(String, (Iterable[String], Iterable[String], Iterable[String]))] = 
  MapPartitionsRDD[17] at cogroup at :27
  
  scala> rdd4.partitions.size
  res7: Int = 2
  
  scala> rdd4.collect
  res9: Array[(String, (Iterable[String], Iterable[String], Iterable[String]))] = Array(
  (B,(CompactBuffer(2),CompactBuffer(),CompactBuffer())), 
  (D,(CompactBuffer(),CompactBuffer(d),CompactBuffer())), 
  (A,(CompactBuffer(1),CompactBuffer(a),CompactBuffer(A))), 
  (C,(CompactBuffer(3),CompactBuffer(c),CompactBuffer())), 
  (E,(CompactBuffer(),CompactBuffer(),CompactBuffer(E))))
  

join

• def join[W](other: RDD[(K, W)]): RDD[(K, (V, W))]
• def join[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (V, W))]
• def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))]
• join相當於SQL中的內關聯join，只返回兩個RDD根據K可以關聯上的結果，join只能用於兩個RDD之間的關聯，如果要多個RDD關聯，多關聯幾次即可
• 參數numPartitions用於指定結果的分區數

• 參數partitioner用於指定分區函數

  var rdd1 = sc.makeRDD(Array(("A","1"),("B","2"),("C","3")),2)
  var rdd2 = sc.makeRDD(Array(("A","a"),("C","c"),("D","d")),2)
  
  scala> rdd1.join(rdd2).collect
  res10: Array[(String, (String, String))] = Array((A,(1,a)), (C,(3,c)))