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本篇內容介紹了“mapPartitions的簡單介紹及使用方法”的有關知識，在實際案例的操作過程中，不少人都會遇到這樣的困境，接下來就讓小編帶領大家學習一下如何處理這些情況吧！希望大家仔細閱讀，能夠學有所成！
1. mappartition簡介
/** * Return a new RDD by applying a function to each partition of this RDD. * * `preservesPartitioning` indicates whether the input function preserves the partitioner, which * should be `false` unless this is a pair RDD and the input function doesn't modify the keys. */ def mapPartitions[U: ClassTag]( f: Iterator[T] => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = withScope { val cleanedF = sc.clean(f) new MapPartitionsRDD( this, (_: TaskContext, _: Int, iter: Iterator[T]) => cleanedF(iter), preservesPartitioning) }
/** * Return a new RDD by applying a function to all elements of this RDD. */ def map[U: ClassTag](f: T => U): RDD[U] = withScope { val cleanF = sc.clean(f) new MapPartitionsRDD[U, T](this, (_, _, iter) => iter.map(cleanF)) }
val a = sc.parallelize(1 to 20, 2)def mapTerFunc(a : Int) : Int = {a*3}val mapResult = a.map(mapTerFunc)println(mapResult.collect().mkString(","))
3,6,9,12,15,18,21,24,27,30,33,36,39,42,45,48,51,54,57,60
3. mappartitions低效用法
val a = sc.parallelize(1 to 20, 2)def terFunc(iter: Iterator[Int]) : Iterator[Int] = {var res = List[Int]()while (iter.hasNext){val cur = iter.next;res.::= (cur*3) ;}res.iterator}val result = a.mapPartitions(terFunc)println(result.collect().mkString(","))
30,27,24,21,18,15,12,9,6,3,60,57,54,51,48,45,42,39,36,33
4. mappartitions高效用法
class CustomIterator(iter: Iterator[Int]) extends Iterator[Int] {
def hasNext : Boolean = {
iter.hasNext
}
def next : Int= {
val cur = iter.next
cur*3
}
}
val result = a.mapPartitions(v => new CustomIterator(v))
println(result.collect().mkString(","))
3,6,9,12,15,18,21,24,27,30,33,36,39,42,45,48,51,54,57,60
“mapPartitions的簡單介紹及使用方法”的內容就介紹到這里了，感謝大家的閱讀。如果想了解更多行業相關的知識可以關注億速云網站，小編將為大家輸出更多高質量的實用文章！
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