Spark 中 map 与 flatMap 的区别

通过一个实验来看Spark 中
map 与 flatMap 的区别。

步骤一：将测试数据放到hdfs上面
hadoopdfs -put data1/test1.txt /tmp/test1.txt
该测试数据有两行文本：



步骤二：在Spark中创建一个RDD来读取hdfs文件/tmp/test1.txt



步骤三：查看map函数的返回值
得到map函数返回的RDD: 



查看map函数的返回值——文件中的每一行数据返回了一个数组对象



步骤四：查看flatMap函数的返回值
得到flatMap函数返回的RDD:



查看flatMap函数的返回值——文件中的所有行数据仅返回了一个数组对象



总结：
- Spark 中 map函数会对每一条输入进行指定的操作，然后为每一条输入返回一个对象；
- 而flatMap函数则是两个操作的集合——正是“先映射后扁平化”：
   操作1：同map函数一样：对每一条输入进行指定的操作，然后为每一条输入返回一个对象
   操作2：最后将所有对象合并为一个对象

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-bash-4.1# sh /opt/hadoop-2.7.2/sbin/start-all.sh

-bash-4.1# sh /opt/spark-2.1.1-hadoop2.7/sbin/start-all.sh

-bash-4.1# spark-shell

scala> sc

res1: org.apache.spark.SparkContext = org.apache.spark.SparkContext@23d07ad3

scala> rdd=sc.parallelize([1,2,3])

<console>:1: error: illegal start of simple expression

rdd=sc.parallelize([1,2,3])

                   ^

scala> import org.apache.spark.{SparkConf, SparkContext}

import org.apache.spark.{SparkConf, SparkContext}

scala> val textFile = sc.textFile("/root/spark_function_test")

textFile: org.apache.spark.rdd.RDD[String] = /root/spark_function_test MapPartitionsRDD[1] at textFile at <console>:25

scala> var mapResult = textFile.map(line => line.splt("\\s+"))

<console>:27: error: value splt is not a member of String

       var mapResult = textFile.map(line => line.splt("\\s+"))

                                                 ^

scala> var mapResult = textFile.map(line => line.split("\\s+"))

mapResult: org.apache.spark.rdd.RDD[Array[String]] = MapPartitionsRDD[2] at map at <console>:27

scala> mapResult.collect

res2: Array[Array[String]] = Array(Array(This, is, 1st, line), Array(We, have, 2nd, line, too))

scala> var flatMapResult = textFile.flatmap(line => line.split("\\s+"))

<console>:27: error: value flatmap is not a member of org.apache.spark.rdd.RDD[String]

       var flatMapResult = textFile.flatmap(line => line.spl
87f1
it("\\s+"))

                                    ^

scala> var flatMapResult = textFile.flatMap(line => line.split("\\s+"))

flatMapResult: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[3] at flatMap at <console>:27

scala> flatMapResult.colect

<console>:30: error: value colect is not a member of org.apache.spark.rdd.RDD[String]

       flatMapResult.colect

                     ^

scala> flatMapResult.collect

res4: Array[String] = Array(This, is, 1st, line, We, have, 2nd, line, too)