搭建hadoop和hive分析脚本-Toy模板网

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#实现云主机自身使用root用户ssh访问hadoop000免密登陆

搭建环境

vim /etc/hosts
hostnamectl set-hostname hadoop000
bash
ssh hadoop000
hdfs namenode -format
start-all.sh
jps
systemctl start mysqld.service
schematool -dbType mysql -initSchema

hive

hdfs操作

创建路径

上传

#创建文件，删除，
hadoop fs -mkdir HDFS://phonemodel
    hdfs dfs -mkdir -p /phonemodel/usergender
hadoop fs -touchz /phonemodel/userage/part-r-00000
hadoop fs -rm -r /phonemodel/userage/000000_0
hadoop fs -cat /phonemodel/userage/*

#-r删除文件
#上传-f强制覆盖
hadoop fs -put  -f /root/phonemodel/gender_age_train.csv /phonemodel/
hadoop fs -put /root/phonemodel/phone_brand_device_model.csv /phonemodel/
hadoop fs -put /root/demo/part-r-00000  /phonemodel/usergender/
hadoop fs -put /root/1/part-r-00000  /phonemodel/agegender/


#究极版
hadoop fs -mkdir -p /phonemodel/agegender && hadoop fs -put /root/1/part-r-00000 /phonemodel/agegender/
/root/internetlogs/journal.log上传至HDFS文件系统/input/下
hadoop fs -mkdir -p /input/ && hadoop fs -put /root/internetlogs/journal.log /input/

INSERT OVERWRITE DIRECTORY '/root/theft/result06'
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
SELECT COUNT(*) 
FROM THEFT
WHERE case_subtype = '盗窃居民小区车辆';

HIVE数据库操作

创库

建表

导入数据

#建库
CREATE demo IF NOT EXISTS hive;

create database demo;
#创建表
CREATE EXTERNAL TABLE IF NOT EXISTS user_age (
  device_id STRING,
  gender STRING,
  age INT,
  age_group STRING
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE
LOCATION 'hdfs://phonemodel/';




#导入数据-- 加载数据到表中，上传local，还是inpath
LOAD DATA INPATH 'hdfs://your_input_data_path' INTO TABLE user_data;

LOAD DATA INPATH '/phonemodel/gender_age_train.csv' INTO TABLE user_data;
LOAD DATA INPATH '/phonemodel/gender_age_train.csv' INTO TABLE user_data;
LOAD DATA INPATH '/phonemodel/phone_brand_device_model.csv' INTO TABLE user_data;

导出文件

#输出路径

先选


INSERT OVERWRITE DIRECTORY '/phonemodel/userage/' 
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ' '



SELECT age, COUNT(*) AS user_count
FROM user_age
GROUP BY age;

insert overwrite local directory '/root/theft/resulte1' #只写到文件夹
row format delimited fields terminated by '\t'
select count(report_time)num from project.theft
where substr(report_time,1,8)='2821年g5月'；

数据分析校验输出

Hive会自动生成输出文件，并将结果写入这些文件中。你不需要提前创建part-r-00000文件，Hive会根据配置自动创建文件并命名


#计算百分比
INSERT OVERWRITE DIRECTORY '/phonemodel/usergender'
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ' '
SELECT gender, COUNT(*) AS gender_count,
       CONCAT(ROUND(COUNT(*) / t.total_count * 100, 2), '%') AS percentage
FROM user_age
CROSS JOIN (SELECT COUNT(*) AS total_count FROM user_age) t
GROUP BY gender;


SELECT gender, COUNT(*) AS gender_count,
       ROUND(COUNT(*) * 100 / (SELECT COUNT(*) FROM gender_age_train), 2) AS gender_percentage
FROM gender_age_train
GROUP BY gender;
M 64.18%
F 35.82%

联合输出

SELECT CONCAT(age, ':', gender) AS age_gender, COUNT(*) AS user_count
FROM gender_age_train
GROUP BY CONCAT(age, ':', gender);

hadoop

#配置windows
cmd

C:\WINDOWS\system32\drivers\etc
172.18.39.140 hadoop000 #改成外网ip

创建链接
包-类



import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class PageViewCount {

    public static class PageViewMapper extends Mapper<Object, Text, Text, IntWritable> {
        private final static IntWritable ONE = new IntWritable(1);
        private Text page = new Text();

        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            String line = value.toString();
            StringTokenizer tokenizer = new StringTokenizer(line);
            if (tokenizer.hasMoreTokens()) {
                String pageName = tokenizer.nextToken();
                page.set(pageName);
                context.write(page, ONE);
            }
        }
    }

    public static class PageViewReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
        private IntWritable result = new IntWritable();

        public void reduce(Text key, Iterable<IntWritable> values, Context context)
                throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable val : values) {
                sum += val.get();
            }
            result.set(sum);
            context.write(key, result);
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "Page View Count");
        job.setJarByClass(PageViewCount.class);
        job.setMapperClass(PageViewMapper.class);
        job.setCombinerClass(PageViewReducer.class);
        job.setReducerClass(PageViewReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        FileInputFormat.addInputPath(job, new Path("<输入文件路径>")); // 替换为实际的输入文件路径
        FileOutputFormat.setOutputPath(job, new Path("/root/internetlogs/pv")); // 输出到指定目录
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

$ javac -classpath $(hadoop classpath) PageViewCount.java $ jar cf pv.jar PageViewCount*.class

$ hadoop jar pv.jar PageViewCount <输入文件路径>

import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class BrowserIdentificationCount {

    public static class BrowserIdentificationMapper extends Mapper<Object, Text, Text, IntWritable> {
        private final static IntWritable ONE = new IntWritable(1);
        private Text browserIdentification = new Text();

        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            String line = value.toString();
            StringTokenizer tokenizer = new StringTokenizer(line);
            if (tokenizer.hasMoreTokens()) {
                String browserId = tokenizer.nextToken(); // 提取浏览器标识
                browserIdentification.set(browserId);
                context.write(browserIdentification, ONE);
            }
        }
    }

    public static class BrowserIdentificationReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
        private IntWritable result = new IntWritable();

        public void reduce(Text key, Iterable<IntWritable> values, Context context)
                throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable val : values) {
                sum += val.get();
            }
            result.set(sum);
            context.write(key, result);
        }
    }

    
Step 4. 定义主类, 描述 Job 并提交 Job
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "Browser Identification Count");
        job.setJarByClass(BrowserIdentificationCount.class);
        job.setMapperClass(BrowserIdentificationMapper.class);
        job.setCombinerClass(BrowserIdentificationReducer.class);
        job.setReducerClass(BrowserIdentificationReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        FileInputFormat.addInputPath(job, new Path("<输入文件路径>")); // 替换为实际的输入文件路径
        FileOutputFormat.setOutputPath(job, new Path("/root/internetlogs/browser")); // 输出到指定目录
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}


hadoop jar hadoop_hdfs_operate‐1.0‐SNAPSHOT.jar

WordCountMapper.java

public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
    /**
     * Map阶段的业务逻辑需写在自定义的map()方法中
     * MapTask会对每一行输入数据调用一次我们自定义的map()方法
     */
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        //（1）将MapTask传给我们的文本内容先转换成String
        String line = value.toString();  
        //（2）根据空格将这一行切分成单词
        String[] words = line.split(" ");
        //（3）将单词输出为<单词，1>
        for (String word : words) {
            // 将单词作为key，将次数1作为value，以便后续的数据分发，可以根据单词分发，将相同单词分发到同一个ReduceTask中
            context.write(new Text(word), new IntWritable(1));
        }
    }
}

WordCountReducer.java

public class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    /**
     * <Deer,1><Deer,1><Deer,1><Deer,1><Deer,1>
     * <Car,1><Car,1><Car,1><Car,1>
     * 框架在Map处理完成之后，将所有key-value对缓存起来，进行分组，然后传递一个组<key,values{}>，调用一次reduce()方法
     * <Deer,{1,1,1,1,1,1.....}>
     * 入参key，是一组相同单词kv对的key
     */
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context)
            throws IOException, InterruptedException {
        //做每个单词的结果汇总
        int sum = 0;
        for (IntWritable v : values) {
            sum += v.get();
        }
        //写出最后的结果
        context.write(key, new IntWritable(sum));
    }
}

WordCount Driver

public class WordCount {
    /**
     * 该MR程序运行的入口，相当于YARN集群（分配运算资源）的客户端
     */
    public static void main(String[] args) throws Exception {
        // （1）创建配置文件对象
        Configuration conf = new Configuration();

        // （2）新建一个 job 任务
        Job job = Job.getInstance(conf);

        // （3）将 job 所用到的那些类（class）文件，打成jar包 （打成jar包在集群运行必须写）
        job.setJarByClass(WordCount.class);

        // （4）指定 mapper 类和 reducer 类
        job.setMapperClass(WordCountMapper.class);
        job.setReducerClass(WordCountReducer.class);

        // （5）指定 MapTask 的输出key-value类型（可以省略）
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);

        // （6）指定 ReduceTask 的输出key-value类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        // （7）指定该 mapreduce 程序数据的输入和输出路径
        Path inPath=new Path("/wordcount/input");
        Path outpath=new Path("/wordcount/output");
        FileSystem fs=FileSystem.get(conf);
        if(fs.exists(outpath)){
            fs.delete(outpath,true);
        }
        FileInputFormat.setInputPaths(job,inPath);
        FileOutputFormat.setOutputPath(job, outpath);

        // （8）最后给YARN来运行，等着集群运行完成返回反馈信息，客户端退出
        boolean waitForCompletion = job.waitForCompletion(true);
        System.exit(waitForCompletion ? 0 : 1);
    }
}

流量

hdfs dfs -put /root/internetlogs/journal.log /input/

PageViewCount是一个Java类，它是用于实现页面访问量统计的MapReduce程序的主类。在这个类中，包含了Map函数和Reduce函数的定义，以及配置作业的相关信息。这个类负责设置作业的输入和输出路径，指定Map和Reduce函数的实现类，以及其他作业配置参数。在main方法中，创建了一个作业实例并运行它

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;

public class PageViewCount {
    public static class PageViewMapper extends Mapper<Object, Text, Text, IntWritable> {
        private final static IntWritable one = new IntWritable(1);
        private Text resource = new Text();

        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            String[] parts = value.toString().split(" ");
            if (parts.length >= 4) {
                String resourcePath = parts[3];
                resource.set(resourcePath);
                context.write(resource, one);
            }
        }
    }

    public static class PageViewReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
        private IntWritable result = new IntWritable();

        public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable value : values) {
                sum += value.get();
            }
            result.set(sum);
            context.write(key, result);
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "PageViewCount");
        job.setJarByClass(PageViewCount.class);
        job.setMapperClass(PageViewMapper.class);
        job.setCombinerClass(PageViewReducer.class);
        job.setReducerClass(PageViewReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        FileInputFormat.addInputPath(job, new Path("/input/journal.log"));
        FileOutputFormat.setOutputPath(job, new Path("/output/pv"));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

#编译
hadoop com.sun.tools.javac.Main PageViewCount.java




#执行jar包

hadoop jar pv.jar PageViewCount /input/journal.log /output/pv

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

public class IPCountMapper extends Mapper<Object, Text, Text, IntWritable> {
    private final static IntWritable one = new IntWritable(1);
    private Text ipAddress = new Text();

    public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
        String[] parts = value.toString().split(" ");
        if (parts.length >= 1) {
            String ip = parts[0];
            ipAddress.set(ip);
            context.write(ipAddress, one);
        }
    }
}



import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

public class IPCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    private IntWritable result = new IntWritable();

    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        int sum = 0;
        for (IntWritable value : values) {
            sum += value.get();
        }
        result.set(sum);
        context.write(key, result);
    }
}
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class IPCount {
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "IPCount");
        job.setJarByClass(IPCount.class);
        job.setMapperClass(IPCountMapper.class);
        job.setCombinerClass(IPCountReducer.class);
        job.setReducerClass(IPCountReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        FileInputFormat.addInputPath(job, new Path("/input/journal.log"));
        FileOutputFormat.setOutputPath(job, new Path("/output/ip"));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}
Step 5: 编译和打包程序，将其生成为一个JAR文件。

Step 6: 将journal.log文件上传到HDFS的输入目录，例如/input/journal.log。

Step 7: 运行MapReduce作业，将结果保存到指定的输出目录，例如/output/ip。

Step 8: 将结果从HDFS中复制到本地目录/root/internetlogs/ip/下的part-00000文件。

打包执行

在hdfs文件系统上创建输入文件的目录

上传输入文件到目录中，上传后查看文件是否在该目录下存在文章来源地址https://www.toymoban.com/news/detail-603845.html

javac -classpath /usr/local/hadoop/share/hadoop/common/hadoop-common-2.7.1.jar:/usr/local/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-client-core-2.7.1.jar:/usr/local/maven/lib/commons-cli-1.2.jar WordCount.java
 jar -cvf WordCount/WordCount.jar *.class
hadoop fs -mkdir /input
hadoop fs -put 1.txt /input
./hadoop jar /usr/local/hadoop/bin/WordCount/WordCount.jar org.apache.hadoop.examples.WordCount /input /output
收起

mysql

#见表
CREATE TABLE attendance (
  id INT NOT NULL,
  check_date DATE NOT NULL,
  emp_id INT NOT NULL,
  clock_in TIMESTAMP,
  clock_out TIMESTAMP,
  PRIMARY KEY (id),
  UNIQUE KEY uk_attendance (check_date, emp_id)
);

#导入数据，用navicat

LOAD DATA local INFILE '/root/mysq1/employee/employee.csv'
INTO TABLE employee
FIELDS TERMINATED BY
LINES TERMINATED BY '\n'
IGNORE 1 ROWS;

4. 查询“系乐”和“姚欣然”的共同好友信息（ID），结果存入视图table4。(字段：user_name1,user_name2,friend_id)（8.80 / 10分）
操作环境: qingjiao

CREATE VIEW table4 AS
SELECT u1.user_name AS user_name1, u2.user_name AS f_name, f.friend_id
FROM friend f
JOIN user u1 ON f.user_id = u1.user_id
JOIN user u2 ON f.friend_id = u2.user_id
WHERE (u1.user_name = '系乐' OR u1.user_name = '姚欣然')
  AND (u2.user_name = '系乐' OR u2.user_name = '姚欣然');

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