Working with key/value Pairs

Motivation

• Pair RDDs are a useful building block in many programs, as they expose operations that allow u to act on each key in parallel or regroup data across network.
• Eg: pair RDDs have a reduceByKey() method that can aggeragate data separately for each key; join() method that can merge two RDDs together by grouping elements with the same key.

Creating Pair RDDs

• Many formats we loading from will directly return pair RDDs for their k/v values.
• By turning a regular RDD into a pair RDD  --> Using map() function

val pairs = lines.map(x => (x.split(" ")(0), x))

Transformation on Pair RDDs

• 我们同样可以给Spark传送函数，不过由于pair RDDs包含的是元组tuple，所以我们要传送的函数式操作在tuples之上的。实际上Pair RDDs就是RDDs of Tuple2 object。

Aggregations

• reduceByKey()和reduce()很相似：它们都接收一个函数并使用该函数来combine values。它们的不同在于：

1. 1. reduceByKey()并行地为数据集中每个key运行reduce操作。
   2. reduceByKey()属于transformation，它返回一个新的RDD。这样做是考虑到数据集有大量的keys。

<Spark><Programming><Key/Value Pairs><RDD>

原文：http://www.cnblogs.com/wttttt/p/6827870.html