Hadoop综合大作业

1.用Hive对爬虫大作业产生的文本文件（或者英文词频统计下载的英文长篇小说）词频统计。

    启动hadoop

    

   Hdfs上创建文件夹并查看

   

  上传nover.txt文件至hdfs

   

启动Hive

hive

创建原始文档表

create table novels(line string);

导入文件内容到表novels并查看

load data inpath '/user/hadoop/fileinput/novel.txt' overwrite into table novels;
select * from novels;

 

用HQL进行词频统计，结果放在表word_count里

create table word_count as select word,count(1) as count from (se
lect explode(split(line,' ')) as word from novels) word group by word order by word;

查看统计结果

show tables;
select * from word_count;

 

2.用Hive对爬虫大作业产生的csv文件进行数据分析，写一篇博客描述你的分析过程和分析结果。

# 爬取环球科技网新闻信息

import requests
from bs4 import BeautifulSoup
from datetime import datetime
import jieba
import pandas

newsurl = 'http://tech.huanqiu.com/internet/'

def sort(text):
    str = '''一！“”，。？；’"',.、：
'''
    for s in str:
        text = text.replace(s, ' ')
    wordlist = list(jieba.cut(text))
    exclude = {'这', 'u3000', '
', 'xa0', '的', '_', ' ', '将', '在', '是', '了', '一', '还', '也', '《', '》', '(', ')'}
    set2 = set(wordlist) - exclude
    dict = {}
    for key in set2:
        dict[key] = wordlist.count(key)
    dictlist = list(dict.items())
    dictlist.sort(key=lambda x: x[1], reverse=True)
    print("top5关键词:")
    for i in range(5):
        print(dictlist[i])


def getContent(url):
    res = requests.get(url)
    res.encoding = 'utf-8'
    soup2 = BeautifulSoup(res.text, 'html.parser')
    for news in soup2.select('.l_a'):
        if len(news.select('.author'))>0:
            author=news.select('.author')[0].text
            print("作者",author)
    if len(soup2.select('.la_con'))>0:
        content = soup2.select('.la_con')[0].text
        print("正文:", content)
        sort(content)


def getNewDetails(newsurl):
    pagelist = []
    res = requests.get(newsurl)
    res.encoding = 'utf-8'
    soup = BeautifulSoup(res.text, 'html.parser')
    for news in soup.select('.item'):
        #  print(news)
        newsDict = {}
        newsDict['title'] = news.select('a')[0].attrs['title']
        href= news.select('a')[0].attrs['href']
        newsDict['brief'] = news.select('h5')[0].text.rstrip('[详细]')
        newsDict['time'] = news.select('h6')[0].text
        dt= datetime.strptime(newsDict['time'], '%Y-%m-%d %H:%M')
        # print("新闻标题:", title)
        # print("链接:", a)
        # print("内容简介:", brief)
        # print("时间:", dt)
        pagelist.append(newsDict)
        getContent(href)
        print('
')
    pan = pandas.DataFrame(pagelist)
    pan.to_csv('data.csv')
    # break


res = requests.get(newsurl)
res.encoding = 'utf-8'
soup = BeautifulSoup(res.text, 'html.parser')
getNewDetails(newsurl)
# for total in soup.select('#pages'):
#     all=int(total.select('a')[0].text.rstrip('条'))  #获取总条数计算总页数
#     #print(all)
#     if(all%60==0):
#         totalpages=all//60
#     else:
#         totalpages=all//60+1
#     print(totalpages)
#     for i in range(1,totalpages+1):     #所有页面的新闻信息
#         PageUrl = newsurl + '{}.html'.format(i)
#         getNewDetails(PageUrl)

产生的csv文件

传到虚拟机并查看

上传到hdfs,启动hive

导入数据到docs表并查看