Spark编程之基本的RDD算子之cogroup，groupBy，groupByKey

Spark编程之基本的RDD算子之cogroup，groupBy，groupByKey

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• 1) cogroup [Pair], groupWith [Pair]

首先来看一下它的api。

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]))]
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]))]
def groupWith[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))]
def groupWith[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)]): RDD[(K, (Iterable[V], IterableW1], Iterable[W2]))]

这个表示将多个RDD中的同一个key对应的不同的value组合到一起。它可以允许同时group三个key-value类型的键值对的数据。

val a = sc.parallelize(List(1, 2, 1, 3), 1)
val b = a.map((_, "b")) //这个将原来的数据变为一个键值对类型。
val c = a.map((_, "c"))

b.cogroup(c).collect
res7: Array[(Int, (Iterable[String], Iterable[String]))] = Array(
(2,(ArrayBuffer(b),ArrayBuffer(c))),
(3,(ArrayBuffer(b),ArrayBuffer(c))),
(1,(ArrayBuffer(b, b),ArrayBuffer(c, c))) // 可以看到它首先是将同一个rdd里面的有相同的key的值放在一个ArrayBuffer里面。最后再和同一个key相关。

val d = a.map((_, "d"))
b.cogroup(c, d).collect //它同样允许同时操作多个rdd值,操作时同样和之前类似
res9: Array[(Int, (Iterable[String], Iterable[String], Iterable[String]))] = Array(
(2,(ArrayBuffer(b),ArrayBuffer(c),ArrayBuffer(d))),
(3,(ArrayBuffer(b),ArrayBuffer(c),ArrayBuffer(d))),
(1,(ArrayBuffer(b, b),ArrayBuffer(c, c),ArrayBuffer(d, d)))
)

val x = sc.parallelize(List((1, "apple"), (2, "banana"), (3, "orange"), (4, "kiwi")), 2)
val y = sc.parallelize(List((5, "computer"), (1, "laptop"), (1, "desktop"), (4, "iPad")), 2)

//在这个里面x有的键y是可能没有的。
x.cogroup(y).collect
//可以从结果里看到，x有key为2，而y没有，则cogroup之后，y那边的ArrayBuffer是空。
res23: Array[(Int, (Iterable[String], Iterable[String]))] = Array(
(4,(ArrayBuffer(kiwi),ArrayBuffer(iPad))), 
(2,(ArrayBuffer(banana),ArrayBuffer())), 
(3,(ArrayBuffer(orange),ArrayBuffer())),
(1,(ArrayBuffer(apple),ArrayBuffer(laptop, desktop))),
(5,(ArrayBuffer(),ArrayBuffer(computer))))

• 2) groupBy

def groupBy[K: ClassTag](f: T => K): RDD[(K, Iterable[T])]
def groupBy[K: ClassTag](f: T => K, numPartitions: Int): RDD[(K, Iterable[T])]
def groupBy[K: ClassTag](f: T => K, p: Partitioner): RDD[(K, Iterable[T])]

groupBy算子接收一个函数，这个函数返回的值作为key，然后通过这个key来对里面的元素进行分组。

val a = sc.parallelize(1 to 9, 3)
a.groupBy(x => { if (x % 2 == 0) "even" else "odd" }).collect
//返回的even或者odd字符串作为key来group RDD里面的值，
res42: Array[(String, Seq[Int])] = Array((even,ArrayBuffer(2, 4, 6, 8)), (odd,ArrayBuffer(1, 3, 5, 7, 9)))

val a = sc.parallelize(1 to 9, 3)
def myfunc(a: Int) : Int =
{
  a % 2
}
a.groupBy(myfunc).collect //同样的，返回的是0的时候，表示的是偶数值，返回的是1的时候表示的是奇数。
res3: Array[(Int, Seq[Int])] = Array((0,ArrayBuffer(2, 4, 6, 8)), (1,ArrayBuffer(1, 3, 5, 7, 9)))

class MyPartitioner extends Partitioner {
def numPartitions: Int = 2
def getPartition(key: Any): Int =
{
    key match
    {
case null     => 0
case key: Int => key          % numPartitions
case _        => key.hashCode % numPartitions
    }
  }
override def equals(other: Any): Boolean =
  {
    other match
    {
case h: MyPartitioner => true
case _                => false
    }
  }
}
val a = sc.parallelize(1 to 9, 3)
val p = new MyPartitioner()
val b = a.groupBy((x:Int) => { x }, p)
val c = b.mapWith(i => i)((a, b) => (b, a))
c.collect
res42: Array[(Int, (Int, Seq[Int]))] = Array((0,(4,ArrayBuffer(4))), (0,(2,ArrayBuffer(2))), (0,(6,ArrayBuffer(6))), (0,(8,ArrayBuffer(8))), (1,(9,ArrayBuffer(9))), (1,(3,ArrayBuffer(3))), (1,(1,ArrayBuffer(1))), (1,(7,ArrayBuffer(7))), (1,(5,ArrayBuffer(5))))

例子来源:groupBy

• 3) groupByKey [Pair]

def groupByKey(): RDD[(K, Iterable[V])]
def groupByKey(numPartitions: Int): RDD[(K, Iterable[V])]
def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])]

这个算子和group类似，不过和它不同的是他不接收一个函数，而是直接将键值对类型的数据的key作为group的key 值。同样的，他也可以接收其他参数比如说partitioner。

val a = sc.parallelize(List("dog", "tiger", "lion", "cat", "spider", "eagle"), 2)
val b = a.keyBy(_.length) //将字符串的长度作为key值。
b.groupByKey.collect //根据相同key值来进行group操作

res11: Array[(Int, Seq[String])] = Array((4,ArrayBuffer(lion)), (6,ArrayBuffer(spider)), (3,ArrayBuffer(dog, cat)), (5,ArrayBuffer(tiger, eagle)))