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紫金优化网站制作,服务器租用,公司网站建设需要显示什么,企业型商务网站制作MapReduce 读写数据库经常听到小伙伴吐槽 MapReduce 计算的结果无法直接写入数据库， 实际上 MapReduce 是有操作数据库实现的本案例代码将实现 MapReduce 数据库读写操作和将数据表中数据复制到另外一张数据表中准备数据表 create database htu; use htu; creat…

MapReduce 读写数据库

经常听到小伙伴吐槽 MapReduce 计算的结果无法直接写入数据库，
实际上 MapReduce 是有操作数据库实现的
本案例代码将实现 MapReduce 数据库读写操作和将数据表中数据复制到另外一张数据表中

准备数据表

create database htu;
use htu;
create table word(name varchar(255) comment '单词',count int comment '数量'
);
create table new_word(name varchar(255) comment '单词',count int comment '数量'
);

数据库持久化类

package com.lihaozhe.db;import lombok.AllArgsConstructor;
import lombok.Getter;
import lombok.NoArgsConstructor;
import lombok.Setter;
import org.apache.hadoop.mapreduce.lib.db.DBWritable;import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.sql.SQLException;/*** @author 李昊哲* @version 1.0.0* @create 2023/11/7*/
@Getter
@Setter
@NoArgsConstructor
@AllArgsConstructor
public class Word implements DBWritable {/*** 单词*/private String name;/*** 单词数量*/private int count;@Overridepublic String toString() {return this.name + "\t" + this.count;}@Overridepublic void write(PreparedStatement pst) throws SQLException {pst.setString(1, this.name);pst.setInt(2, this.count);}@Overridepublic void readFields(ResultSet rs) throws SQLException {this.name = rs.getString(1);this.count = rs.getInt(2);}
}

MapReduce 将数据写入数据库

package com.lihaozhe.db;import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.db.DBConfiguration;
import org.apache.hadoop.mapreduce.lib.db.DBOutputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import java.io.IOException;/*** @author 李昊哲* @version 1.0* @create 2023-11-7*/
public class Write {public static class WordMapper extends Mapper<LongWritable, Text, Word, NullWritable> {@Overrideprotected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Word, NullWritable>.Context context) throws IOException, InterruptedException {String[] split = value.toString().split("\t");Word word = new Word();word.setName(split[0]);word.setCount(Integer.parseInt(split[1]));context.write(word, NullWritable.get());}}public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {// 设置环境变量 hadoop 用户名 为 rootSystem.setProperty("HADOOP_USER_NAME", "root");// 参数配置对象Configuration conf = new Configuration();// 配置JDBC 参数DBConfiguration.configureDB(conf,"com.mysql.cj.jdbc.Driver","jdbc:mysql://spark03:3306/htu?useUnicode=true&createDatabaseIfNotExist=true&characterEncoding=UTF8&useSSL=false&serverTimeZone=Asia/Shanghai","root", "Lihaozhe!!@@1122");// 跨平台提交conf.set("mapreduce.app-submission.cross-platform", "true");// 本地运行// conf.set("mapreduce.framework.name", "local");// 设置默认文件系统为 本地文件系统// conf.set("fs.defaultFS", "file:///");// 声明Job对象 就是一个应用Job job = Job.getInstance(conf, "write db");// 指定当前Job的驱动类// 本地提交 注释该行job.setJarByClass(Write.class);// 本地提交启用该行// job.setJar("D:\\work\\河南师范大学\\2023\\bigdata2023\\Hadoop\\code\\hadoop\\target\\hadoop.jar");// 指定当前Job的 Mapperjob.setMapperClass(WordMapper.class);// 指定当前Job的 Combiner 注意：一定不能影响最终计算结果 否则 不使用// job.setCombinerClass(WordCountReduce.class);// 指定当前Job的 Reducer// job.setReducerClass(WordCountReduce.class);// 设置 reduce 数量为 零job.setNumReduceTasks(0);// 设置 map 输出 key 的数据类型job.setMapOutputValueClass(WordMapper.class);// 设置 map 输出 value 的数据类型job.setMapOutputValueClass(NullWritable.class);// 设置最终输出 key 的数据类型// job.setOutputKeyClass(Text.class);// 设置最终输出 value 的数据类型// job.setOutputValueClass(NullWritable.class);// 定义 map 输入的路径 注意：该路径默认为hdfs路径FileInputFormat.addInputPath(job, new Path("/wordcount/result/part-r-00000"));// 定义 reduce 输出数据持久化的路径 注意：该路径默认为hdfs路径
//        Path dst = new Path("/video/ods");
//        // 保护性代码 如果 reduce 输出目录已经存在则删除 输出目录
//        DistributedFileSystem dfs = new DistributedFileSystem();
//        String nameService = conf.get("dfs.nameservices");
//        String hdfsRPCUrl = "hdfs://" + nameService + ":" + 8020;
//        dfs.initialize(URI.create(hdfsRPCUrl), conf);
//        if (dfs.exists(dst)) {
//            dfs.delete(dst, true);
//        }//        FileSystem fs = FileSystem.get(conf);
//        if (fs.exists(dst)) {
//            fs.delete(dst, true);
//        }
//        FileOutputFormat.setOutputPath(job, dst);// 设置输出类job.setOutputFormatClass(DBOutputFormat.class);// 配置将数据写入表DBOutputFormat.setOutput(job, "word", "name", "count");// 提交 job// job.submit();System.exit(job.waitForCompletion(true) ? 0 : 1);}
}

MapReduce 从数据库读取数据

注意：
由于集群环境导致 MapTask数量不可控可导致最终输出文件可能不止一个，
可以在代码使用 conf.set(“mapreduce.job.maps”, “1”) 设置 MapTask 数量

package com.lihaozhe.db;import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hdfs.DistributedFileSystem;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.db.DBConfiguration;
import org.apache.hadoop.mapreduce.lib.db.DBInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import java.io.IOException;
import java.net.URI;/*** @author 李昊哲* @version 1.0* @create 2023-11-7*/
public class Read {public static class WordMapper extends Mapper<LongWritable, Word, Word, NullWritable> {@Overrideprotected void map(LongWritable key, Word value, Mapper<LongWritable, Word, Word, NullWritable>.Context context) throws IOException, InterruptedException {context.write(value, NullWritable.get());}}public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {// 设置环境变量 hadoop 用户名 为 rootSystem.setProperty("HADOOP_USER_NAME", "root");// 参数配置对象Configuration conf = new Configuration();// 配置JDBC 参数DBConfiguration.configureDB(conf,"com.mysql.cj.jdbc.Driver","jdbc:mysql://spark03:3306/htu?useUnicode=true&createDatabaseIfNotExist=true&characterEncoding=UTF8&useSSL=false&serverTimeZone=Asia/Shanghai","root", "Lihaozhe!!@@1122");// 跨平台提交conf.set("mapreduce.app-submission.cross-platform", "true");// 设置 MapTask 数量conf.set("mapreduce.job.maps", "1");// 本地运行// conf.set("mapreduce.framework.name", "local");// 设置默认文件系统为 本地文件系统// conf.set("fs.defaultFS", "file:///");// 声明Job对象 就是一个应用Job job = Job.getInstance(conf, "read db");// 指定当前Job的驱动类// 本地提交 注释该行job.setJarByClass(Read.class);// 本地提交启用该行// job.setJar("D:\\work\\河南师范大学\\2023\\bigdata2023\\Hadoop\\code\\hadoop\\target\\hadoop.jar");// 指定当前Job的 Mapperjob.setMapperClass(WordMapper.class);// 指定当前Job的 Combiner 注意：一定不能影响最终计算结果 否则 不使用// job.setCombinerClass(WordCountReduce.class);// 指定当前Job的 Reducer// job.setReducerClass(WordCountReduce.class);// 设置 reduce 数量为 零job.setNumReduceTasks(0);// 设置 map 输出 key 的数据类型job.setMapOutputValueClass(Word.class);// 设置 map 输出 value 的数据类型job.setMapOutputValueClass(NullWritable.class);// 设置最终输出 key 的数据类型// job.setOutputKeyClass(Text.class);// 设置最终输出 value 的数据类型// job.setOutputValueClass(NullWritable.class);// 设置输入类job.setInputFormatClass(DBInputFormat.class);// 配置将数据写入表DBInputFormat.setInput(job, Word.class,"select name,count from word","select count(*) from word");// 定义 map 输入的路径 注意：该路径默认为hdfs路径// FileInputFormat.addInputPath(job, new Path("/wordcount/result/part-r-00000"));// 定义 reduce 输出数据持久化的路径 注意：该路径默认为hdfs路径Path dst = new Path("/wordcount/db");// 保护性代码 如果 reduce 输出目录已经存在则删除 输出目录DistributedFileSystem dfs = new DistributedFileSystem();String nameService = conf.get("dfs.nameservices");String hdfsRPCUrl = "hdfs://" + nameService + ":" + 8020;dfs.initialize(URI.create(hdfsRPCUrl), conf);if (dfs.exists(dst)) {dfs.delete(dst, true);}//        FileSystem fs = FileSystem.get(conf);
//        if (fs.exists(dst)) {
//            fs.delete(dst, true);
//        }FileOutputFormat.setOutputPath(job, dst);// 提交 job// job.submit();System.exit(job.waitForCompletion(true) ? 0 : 1);}
}

MapReduce 实现数据库表复制

MapReduce 实现将数据库一张数据表的数据复制到另外一张数据表中

package com.lihaozhe.db;import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.db.DBConfiguration;
import org.apache.hadoop.mapreduce.lib.db.DBInputFormat;
import org.apache.hadoop.mapreduce.lib.db.DBOutputFormat;import java.io.IOException;/*** @author 李昊哲* @version 1.0* @create 2023-11-7*/
public class Copy {public static class RWMapper extends Mapper<LongWritable, Word, Word, NullWritable> {@Overrideprotected void map(LongWritable key, Word value, Mapper<LongWritable, Word, Word, NullWritable>.Context context) throws IOException, InterruptedException {context.write(value, NullWritable.get());}}public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {System.setProperty("HADOOP_USER_NAME", "root");// 参数配置对象Configuration conf = new Configuration();// 配置JDBC 参数DBConfiguration.configureDB(conf,"com.mysql.cj.jdbc.Driver","jdbc:mysql://spark03:3306/htu?useUnicode=true&createDatabaseIfNotExist=true&characterEncoding=UTF8&useSSL=false&serverTimeZone=Asia/Shanghai","root", "Lihaozhe!!@@1122");// 跨平台提交conf.set("mapreduce.app-submission.cross-platform", "true");// 设置 MapTask 数量// conf.set("mapreduce.job.maps", "1");// 声明Job对象 就是一个应用Job job = Job.getInstance(conf, "read db");job.setJarByClass(Read.class);job.setMapperClass(Read.WordMapper.class);job.setNumReduceTasks(0);job.setMapOutputValueClass(Word.class);job.setMapOutputValueClass(NullWritable.class);// 设置输入类job.setInputFormatClass(DBInputFormat.class);// 配置将数据写入表DBInputFormat.setInput(job, Word.class,"select name,count from word","select count(*) from word");// 设置输出类job.setOutputFormatClass(DBOutputFormat.class);// 配置将数据写入表DBOutputFormat.setOutput(job, "new_word", "name", "count");System.exit(job.waitForCompletion(true) ? 0 : 1);}
}