转载：https://www.cnblogs.com/zzhangyuhang/p/9001608.html
1.keys
功能：
返回所有键值对的key
示例
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val list = List("hadoop","spark","hive","spark")
val rdd = sc.parallelize(list)
val pairRdd = rdd.map(x => (x,1))
pairRdd.keys.collect.foreach(println)
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结果
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hadoop
spark
hive
spark
list: List[String] = List(hadoop, spark, hive, spark)
rdd: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[142] at parallelize at command-3434610298353610:2
pairRdd: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[143] at map at command-3434610298353610:3
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2.values
功能：
返回所有键值对的value
示例
| 1 2 3 4 |
val list = List("hadoop","spark","hive","spark")
val rdd = sc.parallelize(list)
val pairRdd = rdd.map(x => (x,1))
pairRdd.values.collect.foreach(println)
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结果
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list: List[String] = List(hadoop, spark, hive, spark)
rdd: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[145] at parallelize at command-3434610298353610:2
pairRdd: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[146] at map at command-3434610298353610:3
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3.mapValues(func)
功能：
对键值对每个value都应用一个函数，但是，key不会发生变化。
示例
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val list = List("hadoop","spark","hive","spark")
val rdd = sc.parallelize(list)
val pairRdd = rdd.map(x => (x,1))
pairRdd.mapValues(_+1).collect.foreach(println)//对每个value进行+1
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结果
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(hadoop,2)
(spark,2)
(hive,2)
(spark,2)
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