一步一步跟我学习hadoop(7)----hadoop连接mysql数据库运行数据读写数据库操作

    为了方便 MapReduce 直接訪问关系型数据库（Mysql,Oracle）。Hadoop提供了DBInputFormat和DBOutputFormat两个类。通过DBInputFormat类把数据库表数据读入到HDFS，依据DBOutputFormat类把MapReduce产生的结果集导入到数据库表中。

    执行MapReduce时候报错：java.io.IOException: com.mysql.jdbc.Driver，通常是因为程序找不到mysql驱动包。解决方法是让每一个tasktracker执行MapReduce程序时都能够找到该驱动包。

加入包有两种方式：

（1）在每一个节点下的${HADOOP_HOME}/lib下加入该包。重新启动集群，通常是比較原始的方法。

（2）a)把包传到集群上： hadoop fs -put mysql-connector-java-5.1.0- bin.jar /hdfsPath/

       b)在mr程序提交job前，加入语句：DistributedCache.addFileToClassPath(new Path(“/hdfsPath/mysql- connector-java-5.1.0-bin.jar”),conf);

mysql数据库存储到hadoop hdfs

mysql表创建和数据初始化

DROP TABLE IF EXISTS `wu_testhadoop`;
CREATE TABLE `wu_testhadoop` (
  `id` int(11) NOT NULL AUTO_INCREMENT,
  `title` varchar(255) DEFAULT NULL,
  `content` varchar(255) DEFAULT NULL,
  PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=3 DEFAULT CHARSET=utf8;

-- ----------------------------
-- Records of wu_testhadoop
-- ----------------------------
INSERT INTO `wu_testhadoop` VALUES ('1', '123', '122312');
INSERT INTO `wu_testhadoop` VALUES ('2', '123', '123456');

定义hadoop数据訪问

mysql表创建完成后，我们须要定义hadoop訪问mysql的规则。

hadoop提供了org.apache.hadoop.io.Writable接口来实现简单的高效的可序列化的协议，该类基于DataInput和DataOutput来实现相关的功能。

hadoop对数据库訪问也提供了org.apache.hadoop.mapred.lib.db.DBWritable接口，当中write方法用于对PreparedStatement对象设定值，readFields方法用于对从数据库读取出来的对象进行列的值绑定。

以上两个接口的使用例如以下（内容是从源代码得来）

writable

 public class MyWritable implements Writable {
       // Some data     
       private int counter;
       private long timestamp;
       
       public void write(DataOutput out) throws IOException {
         out.writeInt(counter);
         out.writeLong(timestamp);
       }
       
       public void readFields(DataInput in) throws IOException {
         counter = in.readInt();
         timestamp = in.readLong();
       }
       
       public static MyWritable read(DataInput in) throws IOException {
         MyWritable w = new MyWritable();
         w.readFields(in);
         return w;
       }
     }
 

DBWritable

public class MyWritable implements Writable, DBWritable {
   // Some data     
   private int counter;
   private long timestamp;
       
   //Writable#write() implementation
   public void write(DataOutput out) throws IOException {
     out.writeInt(counter);
     out.writeLong(timestamp);
   }
       
   //Writable#readFields() implementation
   public void readFields(DataInput in) throws IOException {
     counter = in.readInt();
     timestamp = in.readLong();
   }
       
   public void write(PreparedStatement statement) throws SQLException {
     statement.setInt(1, counter);
     statement.setLong(2, timestamp);
   }
       
   public void readFields(ResultSet resultSet) throws SQLException {
     counter = resultSet.getInt(1);
     timestamp = resultSet.getLong(2);
   } 
 }

数据库相应的实现

package com.wyg.hadoop.mysql.bean;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.sql.SQLException;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapred.lib.db.DBWritable;

public class DBRecord implements Writable, DBWritable{
	private int id;
	private String title;
	private String content;
	public int getId() {
		return id;
	}

	public void setId(int id) {
		this.id = id;
	}

	public String getTitle() {
		return title;
	}

	public void setTitle(String title) {
		this.title = title;
	}

	public String getContent() {
		return content;
	}

	public void setContent(String content) {
		this.content = content;
	}

	@Override
	public void readFields(ResultSet set) throws SQLException {
		this.id = set.getInt("id");
		this.title = set.getString("title");
		this.content = set.getString("content");
	}

	@Override
	public void write(PreparedStatement pst) throws SQLException {
		pst.setInt(1, id);
		pst.setString(2, title);
		pst.setString(3, content);
	}

	@Override
	public void readFields(DataInput in) throws IOException {
		this.id = in.readInt();
		this.title = Text.readString(in);
		this.content = Text.readString(in);
	}

	@Override
	public void write(DataOutput out) throws IOException {
		out.writeInt(this.id);
		Text.writeString(out, this.title);
		Text.writeString(out, this.content);
	}

	@Override
	public String toString() {
		 return this.id + " " + this.title + " " + this.content;  
	}
}

实现Map/Reduce

package com.wyg.hadoop.mysql.mapper;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;

import com.wyg.hadoop.mysql.bean.DBRecord;

@SuppressWarnings("deprecation")
public class DBRecordMapper extends MapReduceBase implements Mapper<LongWritable, DBRecord, LongWritable, Text>{

	@Override
	public void map(LongWritable key, DBRecord value,
			OutputCollector<LongWritable, Text> collector, Reporter reporter)
			throws IOException {
		collector.collect(new LongWritable(value.getId()), new Text(value.toString()));  
	}
	
}

測试hadoop连接mysql并将数据存储到hdfs

package com.wyg.hadoop.mysql.db;
import java.io.IOException;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.lib.IdentityReducer;
import org.apache.hadoop.mapred.lib.db.DBConfiguration;
import org.apache.hadoop.mapred.lib.db.DBInputFormat;

import com.wyg.hadoop.mysql.bean.DBRecord;
import com.wyg.hadoop.mysql.mapper.DBRecordMapper;

public class DBAccess {
      public static void main(String[] args) throws IOException {
             JobConf conf = new JobConf(DBAccess.class);
             conf.setOutputKeyClass(LongWritable.class);
             conf.setOutputValueClass(Text.class);
             conf.setInputFormat(DBInputFormat.class);
             Path path = new Path("hdfs://192.168.44.129:9000/user/root/dbout");
             FileOutputFormat.setOutputPath(conf, path);
             DBConfiguration.configureDB(conf,"com.mysql.jdbc.Driver", "jdbc:mysql://你的ip:3306/数据库名","username","password");
             String [] fields = {"id", "title", "content"};
             DBInputFormat.setInput(conf, DBRecord.class, "wu_testhadoop",
                        null, "id", fields);
             conf.setMapperClass(DBRecordMapper.class);
             conf.setReducerClass(IdentityReducer.class);
             JobClient.runJob(conf);
      }
}

运行程序，结果例如以下：

15/08/11 16:46:18 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
15/08/11 16:46:18 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
15/08/11 16:46:18 WARN mapred.JobClient: No job jar file set.  User classes may not be found. See JobConf(Class) or JobConf#setJar(String).
15/08/11 16:46:19 INFO mapred.JobClient: Running job: job_local_0001
15/08/11 16:46:19 INFO mapred.MapTask: numReduceTasks: 1
15/08/11 16:46:19 INFO mapred.MapTask: io.sort.mb = 100
15/08/11 16:46:19 INFO mapred.MapTask: data buffer = 79691776/99614720
15/08/11 16:46:19 INFO mapred.MapTask: record buffer = 262144/327680
15/08/11 16:46:19 INFO mapred.MapTask: Starting flush of map output
15/08/11 16:46:19 INFO mapred.MapTask: Finished spill 0
15/08/11 16:46:19 INFO mapred.TaskRunner: Task:attempt_local_0001_m_000000_0 is done. And is in the process of commiting
15/08/11 16:46:19 INFO mapred.LocalJobRunner: 
15/08/11 16:46:19 INFO mapred.TaskRunner: Task 'attempt_local_0001_m_000000_0' done.
15/08/11 16:46:19 INFO mapred.LocalJobRunner: 
15/08/11 16:46:19 INFO mapred.Merger: Merging 1 sorted segments
15/08/11 16:46:19 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 48 bytes
15/08/11 16:46:19 INFO mapred.LocalJobRunner: 
15/08/11 16:46:19 INFO mapred.TaskRunner: Task:attempt_local_0001_r_000000_0 is done. And is in the process of commiting
15/08/11 16:46:19 INFO mapred.LocalJobRunner: 
15/08/11 16:46:19 INFO mapred.TaskRunner: Task attempt_local_0001_r_000000_0 is allowed to commit now
15/08/11 16:46:19 INFO mapred.FileOutputCommitter: Saved output of task 'attempt_local_0001_r_000000_0' to hdfs://192.168.44.129:9000/user/root/dbout
15/08/11 16:46:19 INFO mapred.LocalJobRunner: reduce > reduce
15/08/11 16:46:19 INFO mapred.TaskRunner: Task 'attempt_local_0001_r_000000_0' done.
15/08/11 16:46:20 INFO mapred.JobClient:  map 100% reduce 100%
15/08/11 16:46:20 INFO mapred.JobClient: Job complete: job_local_0001
15/08/11 16:46:20 INFO mapred.JobClient: Counters: 14
15/08/11 16:46:20 INFO mapred.JobClient:   FileSystemCounters
15/08/11 16:46:20 INFO mapred.JobClient:     FILE_BYTES_READ=34606
15/08/11 16:46:20 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=69844
15/08/11 16:46:20 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=30
15/08/11 16:46:20 INFO mapred.JobClient:   Map-Reduce Framework
15/08/11 16:46:20 INFO mapred.JobClient:     Reduce input groups=2
15/08/11 16:46:20 INFO mapred.JobClient:     Combine output records=0
15/08/11 16:46:20 INFO mapred.JobClient:     Map input records=2
15/08/11 16:46:20 INFO mapred.JobClient:     Reduce shuffle bytes=0
15/08/11 16:46:20 INFO mapred.JobClient:     Reduce output records=2
15/08/11 16:46:20 INFO mapred.JobClient:     Spilled Records=4
15/08/11 16:46:20 INFO mapred.JobClient:     Map output bytes=42
15/08/11 16:46:20 INFO mapred.JobClient:     Map input bytes=2
15/08/11 16:46:20 INFO mapred.JobClient:     Combine input records=0
15/08/11 16:46:20 INFO mapred.JobClient:     Map output records=2
15/08/11 16:46:20 INFO mapred.JobClient:     Reduce input records=2

同一时候能够看到hdfs文件系统多了一个dbout的文件夹，里边的文件保存了数据库相应的数据，内容保存例如以下

1	1 123 122312
2	2 123 123456

hdfs数据导入到mysql

    hdfs文件存储到mysql，也须要上边的DBRecord类作为辅助。由于数据库的操作都是通过DBInput和DBOutput来进行的；

    首先须要定义map和reduce的实现（map用以对hdfs的文档进行解析，reduce解析map的输出并输出）

package com.wyg.hadoop.mysql.mapper;

import java.io.IOException;
import java.io.DataInput;
import java.io.DataOutput;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.sql.SQLException;
import java.util.Iterator;

import org.apache.hadoop.filecache.DistributedCache;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import com.wyg.hadoop.mysql.bean.DBRecord;

public class WriteDB {
    // Map处理过程
    public static class Map extends MapReduceBase implements

            Mapper<Object, Text, Text, DBRecord> {
        private final static DBRecord one = new DBRecord();

        private Text word = new Text();

        @Override

        public void map(Object key, Text value,

            OutputCollector<Text, DBRecord> output, Reporter reporter)

                throws IOException {

            String line = value.toString();
            String[] infos = line.split(" ");
            String id = infos[0].split("	")[1];
            one.setId(new Integer(id));
            one.setTitle(infos[1]);
            one.setContent(infos[2]);
            word.set(id);
            output.collect(word, one);
        }

    }

    public static class Reduce extends MapReduceBase implements
		    Reducer<Text, DBRecord, DBRecord, Text> {
		@Override
		public void reduce(Text key, Iterator<DBRecord> values,
				OutputCollector<DBRecord, Text> collector, Reporter reporter)
				throws IOException {
			DBRecord record = values.next();
		    collector.collect(record, new Text());
		}
	}
}

測试hdfs导入数据到数据库

package com.wyg.hadoop.mysql.db;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.lib.db.DBConfiguration;
import org.apache.hadoop.mapred.lib.db.DBInputFormat;
import org.apache.hadoop.mapred.lib.db.DBOutputFormat;

import com.wyg.hadoop.mysql.bean.DBRecord;
import com.wyg.hadoop.mysql.mapper.WriteDB;

public class DBInsert {
	public static void main(String[] args) throws Exception {

		 

        JobConf conf = new JobConf(WriteDB.class);
        // 设置输入输出类型

        conf.setInputFormat(TextInputFormat.class);
        conf.setOutputFormat(DBOutputFormat.class);

        // 不加这两句，通只是，可是网上给的样例没有这两句。
        //Text, DBRecord
        conf.setMapOutputKeyClass(Text.class);
        conf.setMapOutputValueClass(DBRecord.class);
        conf.setOutputKeyClass(Text.class);
        conf.setOutputValueClass(DBRecord.class);
        // 设置Map和Reduce类
        conf.setMapperClass(WriteDB.Map.class);
        conf.setReducerClass(WriteDB.Reduce.class);
        // 设置输如文件夹
        FileInputFormat.setInputPaths(conf, new Path("hdfs://192.168.44.129:9000/user/root/dbout"));
        // 建立数据库连接
        DBConfiguration.configureDB(conf,"com.mysql.jdbc.Driver", "jdbc:mysql://数据库ip:3306/数据库名称","username","password");
        String[] fields = {"id","title","content" };
        DBOutputFormat.setOutput(conf, "wu_testhadoop", fields);
        JobClient.runJob(conf);
    }

}

測试结果例如以下

15/08/11 18:10:15 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
15/08/11 18:10:15 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
15/08/11 18:10:15 WARN mapred.JobClient: No job jar file set.  User classes may not be found. See JobConf(Class) or JobConf#setJar(String).
15/08/11 18:10:15 INFO mapred.FileInputFormat: Total input paths to process : 1
15/08/11 18:10:15 INFO mapred.JobClient: Running job: job_local_0001
15/08/11 18:10:15 INFO mapred.FileInputFormat: Total input paths to process : 1
15/08/11 18:10:15 INFO mapred.MapTask: numReduceTasks: 1
15/08/11 18:10:15 INFO mapred.MapTask: io.sort.mb = 100
15/08/11 18:10:15 INFO mapred.MapTask: data buffer = 79691776/99614720
15/08/11 18:10:15 INFO mapred.MapTask: record buffer = 262144/327680
15/08/11 18:10:15 INFO mapred.MapTask: Starting flush of map output
15/08/11 18:10:16 INFO mapred.MapTask: Finished spill 0
15/08/11 18:10:16 INFO mapred.TaskRunner: Task:attempt_local_0001_m_000000_0 is done. And is in the process of commiting
15/08/11 18:10:16 INFO mapred.LocalJobRunner: hdfs://192.168.44.129:9000/user/root/dbout/part-00000:0+30
15/08/11 18:10:16 INFO mapred.TaskRunner: Task 'attempt_local_0001_m_000000_0' done.
15/08/11 18:10:16 INFO mapred.LocalJobRunner: 
15/08/11 18:10:16 INFO mapred.Merger: Merging 1 sorted segments
15/08/11 18:10:16 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 40 bytes
15/08/11 18:10:16 INFO mapred.LocalJobRunner: 
15/08/11 18:10:16 INFO mapred.TaskRunner: Task:attempt_local_0001_r_000000_0 is done. And is in the process of commiting
15/08/11 18:10:16 INFO mapred.LocalJobRunner: reduce > reduce
15/08/11 18:10:16 INFO mapred.TaskRunner: Task 'attempt_local_0001_r_000000_0' done.
15/08/11 18:10:16 INFO mapred.JobClient:  map 100% reduce 100%
15/08/11 18:10:16 INFO mapred.JobClient: Job complete: job_local_0001
15/08/11 18:10:16 INFO mapred.JobClient: Counters: 14
15/08/11 18:10:16 INFO mapred.JobClient:   FileSystemCounters
15/08/11 18:10:16 INFO mapred.JobClient:     FILE_BYTES_READ=34932
15/08/11 18:10:16 INFO mapred.JobClient:     HDFS_BYTES_READ=60
15/08/11 18:10:16 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=70694
15/08/11 18:10:16 INFO mapred.JobClient:   Map-Reduce Framework
15/08/11 18:10:16 INFO mapred.JobClient:     Reduce input groups=2
15/08/11 18:10:16 INFO mapred.JobClient:     Combine output records=0
15/08/11 18:10:16 INFO mapred.JobClient:     Map input records=2
15/08/11 18:10:16 INFO mapred.JobClient:     Reduce shuffle bytes=0
15/08/11 18:10:16 INFO mapred.JobClient:     Reduce output records=2
15/08/11 18:10:16 INFO mapred.JobClient:     Spilled Records=4
15/08/11 18:10:16 INFO mapred.JobClient:     Map output bytes=34
15/08/11 18:10:16 INFO mapred.JobClient:     Map input bytes=30
15/08/11 18:10:16 INFO mapred.JobClient:     Combine input records=0
15/08/11 18:10:16 INFO mapred.JobClient:     Map output records=2
15/08/11 18:10:16 INFO mapred.JobClient:     Reduce input records=2

測试之前我对原有表进行了清空处理，能够看到运行后数据库里边加入了两条内容；

下次在运行的时候会报错，属于正常情况，原因在于我们导入数据的时候对id进行赋值了，假设忽略id。是能够一直加入的；

源代码下载地址

源代码已上传，下载地址为download.csdn.net/detail/wuyinggui10000/8974585