007-spark的wordCount

测试文本内容

[hadoop@mycluster ~]$ cat /home/hadoop/wc.txt
 
 hello   me
 
 hello   you
 
 hello   china
 
 hello   you

1、读取本地或者HDFS文件

spark启动时候生成SparkContext 对象sc，通过spark的上下文对象sc读取文件

scala>
  var textFile = sc.textFile("/home/hadoop/wc.txt").collect

执行结果：textFile: Array[String] = Array(hello   me, hello       you, hello      china, hello    you)

2、执行文件

2.1 flatMap 对读取的结果通过制表符方式平摊

scala> var textFile = sc.textFile("/home/hadoop/wc.txt").flatMap(line => line.split("\t")).collect

或者

scala> var textFile = sc.textFile("/home/hadoop/wc.txt").flatMap(_.split("\t")).collect

结果：

textFile: Array[String] = Array(hello, me, hello, you, hello, china, hello, you)

2.2  map(word=>(word,1))   word表示每个单词，每个单词为1

scala> var textFile = sc.textFile("/home/hadoop/wc.txt").flatMap(line => line.split("\t")).map(word => (word,1)).collect

或者

scala> var textFile = sc.textFile("/home/hadoop/wc.txt").flatMap(_.split("\t")).map((_,1)).collect

结果：

textFile: Array[(String, Int)] = Array((hello,1), (me,1), (hello,1), (you,1), (hello,1), (china,1), (hello,1), (you,1))

2.3 执行reduceByKey函数

scala> var textFile = sc.textFile("/home/hadoop/wc.txt").flatMap(_.split("\t")).map((_,1)).reduceByKey( (a,b) => a + b ).collect

或者

var textFile = sc.textFile("/home/hadoop/wc.txt").flatMap(_.split("\t")).map((_,1)).reduceByKey( _ + _ ).collect

结果：

textFile: Array[(String, Int)] = Array((hello,4), (me,1), (you,2), (china,1))

2.4 key 字段进行排序

scala> var textFile = sc.textFile("/home/hadoop/wc.txt").flatMap(_.split("\t")).map((_,1)).reduceByKey( _ + _ ).sortByKey(true).collect   

 

 结果：

 

 textFile: Array[(String, Int)] = Array((china,4), ("hello ",1), (me,1), (you,2))

2.5 输出结果保存本地或者HDFS上

var textFile = sc.textFile("/home/hadoop/wc.txt").flatMap(_.split("\t")).map((_,1)).reduceByKey( _ + _ ).sortByKey(true).saveAsTextFile("/home/hadoop/output")

执行结果： 

[hadoop@mycluster output]$ more part-00000
(hello,4)
(me,1)
[hadoop@mycluster output]$ more part-00001
(you,2)
(china,1)

2.6 让输出结果仅生成一个文件

var textFile = sc.textFile("/home/hadoop/wc.txt").flatMap(_.split("\t")).map((_,1)).reduceByKey(_+_).repartition(1).saveAsTextFile("/home/hadoop/output")

执行结果：

[hadoop@mycluster output]$ more part-00000
(hello,4)
(me,1)
(you,2)
(china,1)

 出现次数最多的单词排在前面

var textFile = sc.textFile("hdfs://mycluster:9000/wc.txt").flatMap(_.split("\t")).map((_,1)).reduceByKey(_+_).map(x=>(x._2,x._1)).sortByKey(false).map( x => (x._2,x._1) ).collect

结果： textFile: Array[(String, Int)] = Array((hello,4), (you,2), (me,1), (china,1))

备注： 

以上就是wordcount的例子。下面给出读取hdfs上的数据的案例

var textFile = sc.textFile("hdfs://mycluster:9000/wc.txt").flatMap(_.split("\t")).map((_,1)).reduceByKey(_+_).repartition(1).saveAsTextFile("hdfs://mycluster:9000/output")

执行结果：

[hadoop@mycluster output]$ hdfs dfs -cat hdfs://mycluster:9000/output/part-00000
(hello,4)
(me,1)
(you,2)
(china,1)