做网站怎么在图片里面插字,柳市最好的网站建设公司,做母婴产品哪个网站做的好,做网站推广哪些文章目录 环境启动一、爬取数据1.1、歌单信息1.2、每首歌前20条评论1.3、排行榜 二、搭建环境1.1、搭建JAVA1.2、配置hadoop1.3、配置Hadoop环境#xff1a;YARN1.4、MYSQL1.5、HIVE(数据仓库)1.6、Sqoop#xff08;关系数据库数据迁移#xff09; 三、hadoop配置内存四、导… 文章目录 环境启动一、爬取数据1.1、歌单信息1.2、每首歌前20条评论1.3、排行榜 二、搭建环境1.1、搭建JAVA1.2、配置hadoop1.3、配置Hadoop环境YARN1.4、MYSQL1.5、HIVE(数据仓库)1.6、Sqoop关系数据库数据迁移 三、hadoop配置内存四、导入数据到hive 环境启动
启动hadoop图形化界面
cd /opt/server/hadoop-3.1.0/sbin/./start-dfs.sh
./start-yarn.sh# 或者
./start-all.sh启动hive
hive一、爬取数据
1.1、歌单信息
CREATE TABLE playlist (PlaylistID INT AUTO_INCREMENT PRIMARY KEY,Type VARCHAR(255),Title VARCHAR(255),PlayCount VARCHAR(255),Contributor VARCHAR(255)
);# _*_ coding : utf-8 _*_
# Time : 2023/11/15 10:26
# Author : Laptoy
# File : 01_playlist
# Project : finalDesign
import requests
import time
from bs4 import BeautifulSoup
import pymysqldb_connection pymysql.connect(hostlocalhost,userroot,passwordroot,databasemusic
)
cursor db_connection.cursor()headers {user-agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36
}types [华语, 欧美, 日语, 韩语, 粤语]for type in types:# 按类型获取歌单for i in range(0, 1295, 35):url https://music.163.com/discover/playlist/?cat type orderhotlimit35offset str(i)response requests.get(urlurl, headersheaders)html response.textsoup BeautifulSoup(html, html.parser)# 获取包含歌单详情页网址的标签ids soup.select(.dec a)# 获取包含歌单索引页信息的标签lis soup.select(#m-pl-container li)print(len(lis))print(类型, 标题, 播放量, 歌单贡献者, 歌单链接)for j in range(len(lis)):# 标准歌单类型type type# 获取歌单标题,替换英文分割符title ids[j][title].replace(,, )# 获取歌单播放量playCount lis[j].select(.nb)[0].get_text()# 获取歌单贡献者名字contributor lis[j].select(p)[1].select(a)[0].get_text()# 输出歌单索引页信息print(type, title, playCount, contributor)insert_query INSERT INTO playlist (Type, Title, PlayCount, Contributor) VALUES (%s, %s, %s, %s)playlist_data (type, title, playCount, contributor)cursor.execute(insert_query, playlist_data)db_connection.commit()time.sleep(0.1)
cursor.close()
db_connection.close()1.2、每首歌前20条评论
CREATE TABLE comment (song_id varchar(20),song_name varchar(255),comment varchar(255),nickname varchar(50)
) ENGINE InnoDB CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci ROW_FORMAT Dynamic;# _*_ coding : utf-8 _*_
# Time : 2023/11/15 15:09
# Author : Laptoy
# File : ces
# Project : finalDesign
import requests
from Crypto.Cipher import AES
from lxml import etree
from binascii import b2a_base64
import json
import time
import pymysql
from pymysql.converters import escape_stringheaders {user-agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36
}
e 010001
f 00e0b509f6259df8642dbc35662901477df22677ec152b5ff68ace615bb7b725152b3ab17a876aea8a5aa76d2e417629ec4ee341f56135fccf695280104e0312ecbda92557c93870114af6c9d05c4f7f0c3685b7a46bee255932575cce10b424d813cfe4875d3e82047b97ddef52741d546b8e289dc6935b3ece0462db0a22b8e7g 0CoJUm6Qyw8W8jud
# 随机值
i vDIsXMJJZqADRVBPdef get_163():# 热歌榜URLtoplist_url https://music.163.com/discover/toplist?id3778678response requests.get(toplist_url, headersheaders)html response.content.decode()html etree.HTML(html)namelist html.xpath(//div[idsong-list-pre-cache]/ul[classf-hide]/li)# 可选择保存到文件# f open(./wangyi_hotcomments.txt,modea,encodingutf-8)for name in namelist:song_name name.xpath(./a/text())[0]song_id name.xpath(./a/href)[0].split()[1]content get_hotConmments(song_id)print(song_name, song_id)save_mysql(song_id, song_name, content)# f.writelines(song_idsong_name)# f.write(\n)# f.write(str(content))# f.close()def get_encSecKey():encSecKey 516070c7404b42f34c24ef20b659add657c39e9c52125e9e9f7f5441b4381833a407e5ed302cac5d24beea1c1629b17ccb86e0d9d57f6508db5fb7a6df660089ac57b093d19421d386101676a1c8d1e312e099a3463f81fbe91f28211f9eccccfbfc64148fdd65e2b9f5fcf439a865b95fb656e36f75091957f0a1d39ca8ddd3return encSecKeydef get_params(data):first enconda_params(data, g)second enconda_params(first, i)return second# 加密params
def enconda_params(data, key):d 16 - len(data) % 16data chr(d) * ddata data.encode(utf-8)aes AES.new(keykey.encode(utf-8), IV0102030405060708.encode(utf-8), modeAES.MODE_CBC)bs aes.encrypt(data)# b64解码params b2a_base64(bs).decode(utf-8)# params b64decode(bs)return paramsdef get_hotConmments(id):# print(id)# 提交的信息data {cursor: -1,offset: 0,orderType: 1,pageNo: 1,pageSize: 20,rid: fR_SO_4_{id},threadId: fR_SO_4_{id}}post_data {params: get_params(json.dumps(data)),encSecKey: get_encSecKey()}# 获取评论的URLsong_url https://music.163.com/weapi/comment/resource/comments/get?csrf_tokence10dc34c626dc6aef3e07c86be16d70response requests.post(urlsong_url, datapost_data, headersheaders)# time.sleep(1)json_dict json.loads(response.content)# print(json_dict)hotcontent {}for content in json_dict[data][hotComments]:content_text content[content]content_id content[user][nickname]hotcontent[content_id] content_textreturn hotcontent# 保存到MySQL数据库
def save_mysql(song_id, song_name, content):connect pymysql.Connect(hostlocalhost,port3306,userroot,passwdroot,dbmusic,# charsetutf8mb4)cursor connect.cursor()# sql inster into music_163 velues(%d,%s,%s,%s)sql INSERT INTO comment(song_id, song_name, comment,nickname)VALUES(%d, %s, %s, %s)for nikename in content:data (int(song_id), escape_string(song_name), escape_string(content[nikename]), escape_string(nikename))print(data)cursor.execute(sql % data)connect.commit()if __name__ __main__:get_163()1.3、排行榜
CREATE TABLE chart (Chart varchar(255),Rank varchar(255),Title varchar(255),Times varchar(255),Singer varchar(255)
) ENGINE InnoDB CHARACTER SET utf8 COLLATE utf8_general_ci ROW_FORMAT Dynamic;# _*_ coding : utf-8 _*_
# Time : 2023/11/15 14:20
# Author : Laptoy
# File : 02_musicChart
# Project : finalDesign
from selenium import webdriver
from selenium.webdriver.common.by import By
import pymysql
import timedb_connection pymysql.connect(hostlocalhost,userroot,passwordroot,databasemusic
)
cursor db_connection.cursor()driver webdriver.Chrome()
ids [19723756, 3779629, 2884035, 3778678]
charts [飙升榜, 新歌榜, 原创榜, 热歌榜]for id, chart in zip(ids, charts):driver.get(https://music.163.com/#/discover/toplist?id id)driver.switch_to.frame(contentFrame)time.sleep(1)divs driver.find_elements(By.XPATH, //*[classg-wrap12]//tr[contains(id,1)])for div in divs:# 榜单类型chart chart# 标题title div.find_element(By.XPATH, .//div[classttc]//b).get_attribute(title)# 排名rank div.find_element(By.XPATH, .//span[classnum]).text# 时长times div.find_element(By.XPATH, .//span[classu-dur ]).text# 歌手singer div.find_element(By.XPATH, ./td/div[classtext]/span).get_attribute(title)print(chart, title, rank, times, singer)insert_query INSERT INTO chart(chart, title, rank, times,singer) VALUES (%s, %s, %s, %s, %s)chart_data (chart, title, rank, times, singer)cursor.execute(insert_query, chart_data)db_connection.commit()time.sleep(1)
cursor.close()
db_connection.close()二、搭建环境
1.1、搭建JAVA
mkdir /opt/tools
mkdir /opt/servertar -zvxf jdk-8u131-linux-x64.tar.gz -C /opt/servervim /etc/profile# 文件末尾增加
export JAVA_HOME/opt/server/jdk1.8.0_131
export PATH${JAVA_HOME}/bin:$PATHsource /etc/profilejava -version1、配置免密登录
vim /etc/hosts
# 文件末尾增加
192.168.88.110 [主机名]ssh-keygen -t rsacd ~/.ssh
cat id_rsa.pub authorized_keys
chmod 600 authorized_keys1.2、配置hadoop
tar -zvxf hadoop-3.1.0.tar.gz -C /opt/server/# 进入/opt/server/hadoop-3.1.0/etc/hadoop
vim hadoop-env.sh
# 文件添加
export JAVA_HOME/opt/server/jdk1.8.0_131vim core-site.xml
configurationproperty!--指定 namenode 的 hdfs 协议文件系统的通信地址--namefs.defaultFS/namevaluehdfs://[主机名]:8020/value/propertyproperty!--指定 hadoop 数据文件存储目录--namehadoop.tmp.dir/namevalue/home/hadoop/data/value/property
/configurationhdfs-site.xml
configurationproperty!--由于我们这里搭建是单机版本所以指定 dfs 的副本系数为 1--namedfs.replication/namevalue1/value/property
/configurationvim workers
# 配置所有从属节点的主机名或 IP 地址由于是单机版本所以指定本机即可
server1、关闭防火墙
# 查看防火墙状态
sudo firewall-cmd --state
# 关闭防火墙:
sudo systemctl stop firewalld
# 禁止开机启动
sudo systemctl disable firewalld2、初始化
cd /opt/server/hadoop-3.1.0/bin
./hdfs namenode -format3、配置启动用户
cd /opt/server/hadoop-3.1.0/sbin/
# 编辑start-dfs.sh、stop-dfs.sh,在顶部加入以下内容
# 编辑start-all.sh、stop-all.sh,在顶部加入以下内容
HDFS_DATANODE_USERroot
HDFS_DATANODE_SECURE_USERhdfs
HDFS_NAMENODE_USERroot
HDFS_SECONDARYNAMENODE_USERroot4、启动
cd /opt/server/hadoop-3.1.0/sbin/
./start-dfs.shjps5、访问
192.168.88.110:98706、配置环境变量方便启动
vim /etc/profile
export HADOOP_HOME/opt/server/hadoop-3.1.0
export PATH$PATH:${HADOOP_HOME}/bin:${HADOOP_HOME}/sbin
source /etc/profile1.3、配置Hadoop环境YARN
# 进入/opt/server/hadoop-3.1.0/etc/hadoop
vim mapred-site.xmlconfigurationpropertynamemapreduce.framework.name/namevalueyarn/value/propertypropertynameyarn.app.mapreduce.am.env/namevalueHADOOP_MAPRED_HOME${HADOOP_HOME}/value/propertypropertynamemapreduce.map.env/namevalueHADOOP_MAPRED_HOME${HADOOP_HOME}/value/propertypropertynamemapreduce.reduce.env/namevalueHADOOP_MAPRED_HOME${HADOOP_HOME}/value/property
/configurationvim yarn-site.xmlconfigurationproperty!--配置 NodeManager 上运行的附属服务。需要配置成 mapreduce_shuffle 后才可以在Yarn 上运行 MapRedvimuce 程序。--nameyarn.nodemanager.aux-services/namevaluemapreduce_shuffle/value/property
/configurationcd /opt/server/hadoop-3.1.0/sbin/
# start-yarn.sh stop-yarn.sh在两个文件顶部添加以下内容
YARN_RESOURCEMANAGER_USERroot
HADOOP_SECURE_DN_USERyarn
YARN_NODEMANAGER_USERroot./start-yarn.sh1.4、MYSQL
# 用于存放安装包
mkdir /opt/tools
# 用于存放解压后的文件
mkdir /opt/server卸载Centos7自带mariadb
# 查找
rpm -qa|grep mariadb
# mariadb-libs-5.5.52-1.el7.x86_64
# 卸载
rpm -e mariadb-libs-5.5.52-1.el7.x86_64 --nodeps# 创建mysql安装包存放点
mkdir /opt/server/mysql
# 解压
tar xvf mysql-5.7.34-1.el7.x86_64.rpm-bundle.tar -C /opt/server/mysql/# 安装依赖
yum -y install libaio
yum -y install libncurses*
yum -y install perl perl-devel
# 切换到安装目录
cd /opt/server/mysql/
# 安装
rpm -ivh mysql-community-common-5.7.34-1.el7.x86_64.rpm
rpm -ivh mysql-community-libs-5.7.34-1.el7.x86_64.rpm
rpm -ivh mysql-community-client-5.7.34-1.el7.x86_64.rpm
rpm -ivh mysql-community-server-5.7.34-1.el7.x86_64.rpm#启动mysql
systemctl start mysqld.service
#查看生成的临时root密码
cat /var/log/mysqld.log | grep password# 登录mysql
mysql -u root -p
Enter password: #输入在日志中生成的临时密码
# 更新root密码 设置为root
set global validate_password_policy0;
set global validate_password_length1;
set passwordpassword(root);grant all privileges on *.* to root % identified by root;
# 刷新
flush privileges;#mysql的启动和关闭 状态查看
systemctl stop mysqld
systemctl status mysqld
systemctl start mysqld
#建议设置为开机自启动服务
systemctl enable mysqld
#查看是否已经设置自启动成功
systemctl list-unit-files | grep mysqld1.5、HIVE(数据仓库)
# 切换到安装包目录
cd /opt/tools
# 解压到/root/server目录
tar -zxvf apache-hive-3.1.2-bin.tar.gz -C /opt/server/# 上传mysql-connector-java-5.1.38.jar到下面目录
cd /opt/server/apache-hive-3.1.2-bin/lib配置文件
cd /opt/server/apache-hive-3.1.2-bin/conf
cp hive-env.sh.template hive-env.sh
vim hive-env.sh
# 加入以下内容
HADOOP_HOME/opt/server/hadoop-3.1.0cd /opt/server/apache-hive-3.1.2-bin/conf
vim hive-site.xml?xml version1.0?
?xml-stylesheet typetext/xsl hrefconfiguration.xsl?
configuration!-- 存储元数据mysql相关配置 /etc/hosts --propertynamejavax.jdo.option.ConnectionURL/namevalue jdbc:mysql://[主机名]:3306/hive?
createDatabaseIfNotExisttrueamp;useSSLfalseamp;useUnicodetrueamp;chara
cterEncodingUTF-8/value/propertypropertynamejavax.jdo.option.ConnectionDriverName/namevaluecom.mysql.jdbc.Driver/value/propertypropertynamejavax.jdo.option.ConnectionUserName/namevalueroot/value/propertypropertynamejavax.jdo.option.ConnectionPassword/namevalueroot/value/property
/configuration初始化表
cd /opt/server/apache-hive-3.1.2-bin/bin
./schematool -dbType mysql -initSchema1.6、Sqoop关系数据库数据迁移
1、拉取sqoop
# /opt/tools
wget https://archive.apache.org/dist/sqoop/1.4.7/sqoop-1.4.7.bin__hadoop-2.6.0.tar.gztar -zxvf sqoop-1.4.7.bin__hadoop-2.6.0.tar.gz -C /opt/server/2、配置
cd /opt/server/sqoop-1.4.7.bin__hadoop-2.6.0/conf
cp sqoop-env-template.sh sqoop-env.shvim sqoop-env.sh
# 加入以下内容
export HADOOP_COMMON_HOME/opt/server/hadoop-3.1.0
export HADOOP_MAPRED_HOME/opt/server/hadoop-3.1.0
export HIVE_HOME/opt/server/apache-hive-3.1.2-bin3、加入mysql的jdbc驱动包
cd /opt/server/sqoop-1.4.7.bin__hadoop-2.6.0/lib
# mysql-connector-java-5.1.38.jar三、hadoop配置内存
修改yarn-site.xml
configuration!-- Site specific YARN configuration properties --propertynameyarn.nodemanager.aux-services/namevaluemapreduce_shuffle/value/propertypropertynameyarn.nodemanager.vmem-pmem-ratio/namevalue4/value/property
/configuration重启
cd /opt/server/hadoop-3.1.0/sbin
./stop-all.sh
./start-all.sh四、导入数据到hive
1、hive创建数据库
create database music;
use music;2、hive创建数据表
# -- 将数据当做一列放入表中后续再使用sql进行分割处理
CREATE TABLE chart_content(content STRING
);
CREATE TABLE playlist_content (content STRING
);3、hive加载csv文件进hive表
load data local inpath /opt/data/chart.csv into table chart_content;
load data local inpath /opt/data/playlist.csv into table playlist;4、创建表
CREATE TABLE chart (Chart string,Rank string,Title string,Times string,Singer string
);CREATE TABLE playlist (PlaylistID string,Type string,Title string,PlayCount string,Contributor string
);CREATE TABLE playlist (PlaylistID string,Type string,Title string,PlayCount string,Contributor string
)
row format delimited
fields terminated by ,;5、将数据插入表中去掉,
INSERT INTO TABLE chart
SELECTsplit(content, ,)[0] AS Chart,split(content, ,)[1] AS Rank,split(content, ,)[2] AS Title,split(content, ,)[3] AS Times,split(content, ,)[4] AS Singer
FROM chart_content;INSERT INTO TABLE playlist
SELECTsplit(content, ,)[0] AS PlaylistID,split(content, ,)[1] AS Type,split(content, ,)[2] AS Title,split(content, ,)[3] AS PlayCount,split(content, ,)[4] AS Contributor
FROM playlist_content;SELECTPlaylistID,Type,Title,CAST(PlayCount AS int) AS PlayCount,Contributor
FROM playlist;SELECTREGEXP_REPLACE(Contributor, , )
FROM playlist;
