RDD编程

一、词频统计：

1.读文本文件生成RDD lines

lines = sc.textFile('file:///home/hadoop/file')
lines.collect()

 

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())
words.collect()

4.去掉长度小于3的单词 filter()

words=lines.flatMap(lambda line:line.lower().split()).filter(lambda line:len(line)>3)
words.collect()

5.去掉停用词

stops= sc.textFile('file:///home/hadoop/stopwords.txt')
stops.collect()

stop = stops.flatMap(lambda line : line.split()).collect()
stop

 

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).collect()

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)

saveword=words.map(lambda word:(word,1)).reduceByKey(lambda a,b:a+b).sortBy(lambda word:word[0])
saveword.saveAsTextFile('file:///home/hadoop/018.txt')

 

 

二、学生课程分数案例

 lines = sc.textFile('file:///home/hadoop/chapter4-data01.txt')

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[1],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[1])).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

lines.map(lambda line:line.split(',')).map(lambda line:(line[0],(line[1],line[2]))).lookup("Tom")

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()

 numpy库下载不了，这题做不了了

9.生成（课程，分数）RDD，观察keys(),values()

kf = lines.map(lambda line:line.split(',')).map(lambda line:(line[1],line[2]))
kf.take(3)

10.每个分数+5分。mapValues(func)

kf.map(lambda x:(x[0],int(x[1]))).mapValues(lambda x:x+5).take(3)

11.求每门课的选修人数及所有人的总分。combineByKey()

line2=kf.combineByKey(lambda v:(int(v),1),lambda c,v:(c[0]+int(v),c[1]+1),lambda c1,c2:(c1[0]+c2[0],c1[1]+c2[1]))
line2.take(3)

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

line2.map(lambda x:(x[0],x[1][1],round(x[1][0]/x[1][1],2))).take(5)

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。

lines3 = lines.map(lambda line:line.split(',')).map(lambda line:(line[1],(int(line[2]),1))).reduceByKey(lambda a,b:(a[0]+b[0],a[1]+b[1]))
lines3.take(5)
lines3.map(lambda x:(x[0],x[1][1],round(x[1][0]/x[1][1],2))).take(5)