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)