05 RDD编程
一、词频统计：
1读文本文件生成RDD lines
lines=sc.textFile("file:///usr/local/spark/mycode/rdd/word.txt") lines.foreach(print)
2将一行一行的文本分割成单词 words flatmap()
words=lines.flatMap(lambda line:line.split()) words.foreach(print)
3全部转换为小写 lower()
words1=lines.map(lambda word:word.lower()) words1.foreach(print)
4去掉长度小于3的单词 filter()
word=words.filter(lambda words:len(words)>2) words.foreach(print)
5去掉停用词
with open("/usr/local/spark/mycode/rdd/stopwords.txt") as f: stops=f.read().split() lines.flatMap(lambda line:line.split()).filter(lambda word:word not in stops).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.lower(),1)).reduceByKey(lambda a,b:a+b).sortBy(lambda word:word[1],False).collect()
10.结果文件保存 saveAsTextFile(out_url)
lines = sc.textFile('file:///usr/local/spark/mycode/rdd/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:///usr/local/spark/mycode/rdd/out_url")
二、学生课程分数案例
总共有多少学生？map(), distinct(), count()
lines=sc.textFile("file:///usr/local/spark/mycode/rdd/chapter4-data01.txt")lines.map(lambda line:line.split(',')[0]).distinct().count()
开设了多少门课程？
lines.map(lambda line:line.split(',')[1]).distinct().count()
每个学生选修了多少门课？map(), countByKey()
name = lines.map(lambda line:line.split(',')).map(lambda line:(line[0],(line[1],line[2])))
name.take(5)
name.count()
name.countByKey()
每门课程有多少个学生选？map(), countByValue()
name=lines.map(lambda line:line.split(',')).map(lambda line:line[1])name.countByValue()
Tom选修了几门课？每门课多少分？filter(), map() RDD
Tom=lines.filter(lambda line:'Tom' in line).map(lambda line:line.split(','))Tom.collect()
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[2])).lookup('Tom')
Tom的成绩按分数大小排序。filter(), map(), sortBy()
Tom.sortBy(lambda word:word[2],False).collect()
Tom的平均分。map(),lookup(),mean()
from numpy import meantomList=lines.map(lambda line:line.split(',')).map(lambda line:(line[0],line[2])).lookup('Tom')mean([int(x) for x in tomList])
生成（课程，分数）RDD，观察keys(),values()
course_rev.saveAsTextFile("file:///usr/local/spark/mycode/rdd/out_url")
words = lines.map(lambda x:x.split(',')).map(lambda x:(x[1],x[2]))
words.keys().take(5)
words.values().take(5)
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)
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]))
course.take(10)
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])))
course_rev.take(10)
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)
