05 RDD编程

一、词频统计：

1.读文本文件生成RDD lines

>>> lines = sc.textFile('file:///home/hadoop/word.txt')

 2.将一行一行的文本分割成单词 words flatmap()

>>> words=lines.flatMap(lambda line:line.split())
>>> words.collect()

3.全部转换为小写 lower()

>>> words=lines.flatMap(lambda line:line.lower().split()).collect()
>>> words=lines.flatMap(lambda line:line.lower().split())
>>> words.collect()

4.去掉长度小于3的单词 filter()

>>> words.filter(lambda word : len(word)>3).collect()

5.去掉停用词

>>> lines = sc.textFile('file:///home/hadoop/stopwords.txt')
>>> stop = lines.flatMap(lambda line : line.split()).collect()
>>> lines = sc.textFile('file:///home/hadoop/word.txt')
>>> words=lines.flatMap(lambda line:line.lower().split()).filter(lambda word : word not in stop)
>>> words.collect()

6.转换成键值对 map()

>>> words.map(lambda word : (word,1)).collect()

7.统计词频 reduceByKey()

>>> words.map(lambda word : (word,1)).reduceByKey(lambda a,b:a+b).foreach(print)

8.按字母顺序排序 sortBy(f)

>>> words.map(lambda word : (word,1)).reduceByKey(lambda a,b:a+b).sortBy(lambda word:word[0]).collect()

9.按词频排序 sortByKey()

>>> words.map(lambda word : (word,1)).reduceByKey(lambda a,b:a+b).sortByKey().collect()

10.结果文件保存 saveAsTextFile(out_url)

lines = sc.textFile('file:///home/hadoop/chapter4-data01.txt')
course_rev = lines.map(lambda line:line.split(',')).map(lambda x:(x[1],(int(x[2]),1))).reduceByKey(lambda a,b:(a[0]+b[0],a[1]+b[1]))
course_rev.saveAsTextFile("file:///home/hadoop/out_url")

 

 

 

二、学生课程分数案例

1.总共有多少学生？map(), distinct(), count()

>>> lines.map(lambda line : line.split(',')[0]).distinct().count()

2.开设了多少门课程？

>>> lines.map(lambda line : line.split(',')[1]).distinct().count()

3.每个学生选修了多少门课？map(), countByKey()

>>> lines.map(lambda line : line.split(',')).map(lambda line:(line[0],line[2])).countByKey()

4.每门课程有多少个学生选？map(), countByValue()

>>> lines.map(lambda line : line.split(',')).map(lambda line : (line[0])).countByValue()

5。Tom选修了几门课？每门课多少分？filter(), map() RDD

>>> lines.filter(lambda line:"Tom" in line).map(lambda line:line.split(',')).collect()

6.Tom选修了几门课？每门课多少分？map(),lookup()  list

>>> Tom=lines.filter(lambda line:'Tom' in line).map(lambda line:line.split(' '))
>>> Tom.collect()

7.Tom的成绩按分数大小排序。filter(), map(), sortBy()

>>> lines.filter(lambda line:"Tom" in line).map(lambda line:line.split(',')).sortBy(lambda line:(line[2])).collect()

8.Tom的平均分。map(),lookup(),mean()

>>> import numpy as np
>>> meanlist=lines.map(lambda line:line.split(',')).map(lambda line:(line[0],line[2])).lookup("Tom")
>>> np.mean([int(x) for x in meanlist])

9. 生成（课程，分数）RDD，观察keys(),values()

>>> lines = sc.textFile('file:///home/hadoop/chapter4-data01.txt')
>>> words = lines.map(lambda x:x.split(',')).map(lambda x:(x[1],x[2]))
>>> words.keys().take(5)
>>> words.values().take(5)

 10.每个分数+5分。mapValues(func)

>>> words = words.map(lambda x:(x[0],int(x[1])))
>>> words.mapValues(lambda x:x+1).foreach(print)

11.求每门课的选修人数及所有人的总分。combineByKey()

>>>course = words.combineByKey(lambda v:(v,1),lambda c,v:(c[0]+v,c[1]+1),lambda c1,c2:(c1[0]+c2[0],c1[1]+c2[1]))

12.求每门课的选修人数及平均分，精确到2位小数。map()，round()

>>>course_rev = course.map(lambda x:(x[0],x[1][1],round(x[1][0]/x[1][1])))

13.求每门课的选修人数及平均分。用reduceByKey()实现，并比较与combineByKey()的异同。

>>> lines.map(lambda line:line.split(',')).map(lambda x:(x[1],(int(x[2]),1))).reduceByKey(lambda a,b:(a[0]+b[0],a[1]+b[1])).foreach(print)