毕设：《基于hive的音乐数据分析系统的设计与实现》

本文介绍: dd

文章目录

环境启动
一、爬取数据
二、搭建环境
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 pymysql

db_connection = pymysql.connect(
    host="localhost",
    user="root",
    password="root",
    database="music"
)
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 + '&order=hot&limit=35&offset=' + str(i)
        response = requests.get(url=url, headers=headers)
        html = response.text
        soup = 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()

在这里插入图片描述

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_string

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'
}
e = '010001'
f = '00e0b509f6259df8642dbc35662901477df22677ec152b5ff68ace615bb7b725152b3ab17a876aea8a5aa76d2e417629ec4ee341f56135fccf695280104e0312ecbda92557c93870114af6c9d05c4f7f0c3685b7a46bee255932575cce10b424d813cfe4875d3e82047b97ddef52741d546b8e289dc6935b3ece0462db0a22b8e7'

g = '0CoJUm6Qyw8W8jud'
# 随机值
i = 'vDIsXMJJZqADRVBP'


def get_163():
    # 热歌榜URL
    toplist_url = 'https://music.163.com/discover/toplist?id=3778678'

    response = requests.get(toplist_url, headers=headers)
    html = response.content.decode()
    html = etree.HTML(html)
    namelist = html.xpath("//div[@id='song-list-pre-cache']/ul[@class='f-hide']/li")
    # 可选择保存到文件
    # f = open('./wangyi_hotcomments.txt',mode='a',encoding='utf-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_id+song_name)
        # f.write('n')
        # f.write(str(content))
    # f.close()


def get_encSecKey():
    encSecKey = "516070c7404b42f34c24ef20b659add657c39e9c52125e9e9f7f5441b4381833a407e5ed302cac5d24beea1c1629b17ccb86e0d9d57f6508db5fb7a6df660089ac57b093d19421d386101676a1c8d1e312e099a3463f81fbe91f28211f9eccccfbfc64148fdd65e2b9f5fcf439a865b95fb656e36f75091957f0a1d39ca8ddd3"
    return encSecKey

def 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) % 16
    data += chr(d) * d
    data = data.encode('utf-8')
    aes = AES.new(key=key.encode('utf-8'), IV='0102030405060708'.encode('utf-8'), mode=AES.MODE_CBC)
    bs = aes.encrypt(data)
    # b64解码
    params = b2a_base64(bs).decode('utf-8')
    # params = b64decode(bs)
    return params


def get_hotConmments(id):
    # print(id)
    # 提交的信息
    data = {
        'cursor': '-1',
        'offset': '0',
        'orderType': '1',
        'pageNo': '1',
        'pageSize': '20',
        'rid': f'R_SO_4_{id}',
        'threadId': f'R_SO_4_{id}'
    }
    post_data = {
        'params': get_params(json.dumps(data)),
        'encSecKey': get_encSecKey()
    }
    # 获取评论的URL
    song_url = 'https://music.163.com/weapi/comment/resource/comments/get?csrf_token=ce10dc34c626dc6aef3e07c86be16d70'

    response = requests.post(url=song_url, data=post_data, headers=headers)
    # 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_text

    return hotcontent


# 保存到MySQL数据库
def save_mysql(song_id, song_name, content):
    connect = pymysql.Connect(
        host='localhost',
        port=3306,
        user='root',
        passwd='root',
        db='music',
        # charset='utf8mb4'
    )
    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()

在这里插入图片描述

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 time

db_connection = pymysql.connect(
    host="localhost",
    user="root",
    password="root",
    database="music"
)
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, '//*[@class="g-wrap12"]//tr[contains(@id,"1")]')

    for div in divs:
        # 榜单类型
        chart = chart
        # 标题
        title = div.find_element(By.XPATH, './/div[@class="ttc"]//b').get_attribute('title')
        # 排名
        rank = div.find_element(By.XPATH, './/span[@class="num"]').text
        # 时长
        times = div.find_element(By.XPATH, './/span[@class="u-dur "]').text
        # 歌手
        singer = div.find_element(By.XPATH, './td/div[@class="text"]/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/server

tar -zvxf jdk-8u131-linux-x64.tar.gz -C /opt/server

vim /etc/profile

# 文件末尾增加
export JAVA_HOME=/opt/server/jdk1.8.0_131
export PATH=${JAVA_HOME}/bin:$PATH

source /etc/profile

java -version

1、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 rsa

cd ~/.ssh
cat id_rsa.pub >> authorized_keys
chmod 600 authorized_keys

1.2、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_131

vim core-site.xml

<configuration>
    <property>
        <!--指定 namenode 的 hdfs 协议文件系统的通信地址-->
        <name>fs.defaultFS</name>
        <value>hdfs://[主机名]:8020</value>
    </property>
    <property>
        <!--指定 hadoop 数据文件存储目录-->
        <name>hadoop.tmp.dir</name>
        <value>/home/hadoop/data</value>
    </property>
</configuration>

hdfs-site.xml

<configuration>
    <property>
        <!--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-->
        <name>dfs.replication</name>
        <value>1</value>
    </property>
</configuration>

vim 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：
server

1、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 firewalld

2、初始化

cd /opt/server/hadoop-3.1.0/bin
./hdfs namenode -format

在这里插入图片描述

3、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_USER=root
HDFS_DATANODE_SECURE_USER=hdfs
HDFS_NAMENODE_USER=root
HDFS_SECONDARYNAMENODE_USER=root

4、启动

cd /opt/server/hadoop-3.1.0/sbin/
./start-dfs.sh

jps

在这里插入图片描述
5、访问

192.168.88.110:9870

在这里插入图片描述
6、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/profile

1.3、配置Hadoop环境：YARN

# 进入/opt/server/hadoop-3.1.0/etc/hadoop
vim mapred-site.xml

<configuration>
    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>
    <property>
        <name>yarn.app.mapreduce.am.env</name>
        <value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value>
    </property>
    <property>
        <name>mapreduce.map.env</name>
        <value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value>
    </property>
    <property>
        <name>mapreduce.reduce.env</name>
        <value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value>
    </property>
</configuration>

vim yarn-site.xml

<configuration>
    <property>
        <!--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。-->
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
</configuration>

cd /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_USER=root
HADOOP_SECURE_DN_USER=yarn
YARN_NODEMANAGER_USER=root

./start-yarn.sh

在这里插入图片描述

1.4、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

在这里插入图片描述

# 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_policy=0;
set global validate_password_length=1;
set password=password('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 mysqld

1.5、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-3.1.0

cd /opt/server/apache-hive-3.1.2-bin/conf
vim hive-site.xml

<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
    <!-- 存储元数据mysql相关配置 /etc/hosts -->
    <property>
        <name>javax.jdo.option.ConnectionURL</name>
        <value> jdbc:mysql://[主机名]:3306/hive?
createDatabaseIfNotExist=true&amp;useSSL=false&amp;useUnicode=true&amp;chara
cterEncoding=UTF-8</value>
    </property>
    <property>
        <name>javax.jdo.option.ConnectionDriverName</name>
        <value>com.mysql.jdbc.Driver</value>
    </property>
    <property>
        <name>javax.jdo.option.ConnectionUserName</name>
        <value>root</value>
    </property>
    <property>
        <name>javax.jdo.option.ConnectionPassword</name>
        <value>root</value>
    </property>
</configuration>

初始化表

cd /opt/server/apache-hive-3.1.2-bin/bin
./schematool -dbType mysql -initSchema

在这里插入图片描述

1.6、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.gz

tar -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.sh

vim 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-bin

3、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 -->
    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
    <property>
        <name>yarn.nodemanager.vmem-pmem-ratio</name>
        <value>4</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`
SELECT
  split(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`
SELECT
  split(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`;

在这里插入图片描述

SELECT
  PlaylistID,
  Type,
  Title,
  CAST(PlayCount AS int) AS PlayCount,
  Contributor
FROM playlist;

SELECT
    REGEXP_REPLACE(Contributor, '"', '')
FROM playlist;

原文地址:https://blog.csdn.net/apple_53947466/article/details/134417437

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hadoop hive

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JAVA

1.2、配置hadoop

1.3、配置Hadoop环境：YARN

1.4、MYSQL

1.5、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

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