有时候我们在项目中会遇到输入结果集很大,但是输出结果很小,比如一些 pv、uv 数据,然后为了实时查询的需求,或者一些 OLAP 的需求,我们需要 mapreduce 与 mysql 进行数据的交互,而这些特性正是 hbase 或者 hive 目前亟待改进的地方。
推荐阅读:
使用Hadoop构建MapReduce应用 http://www.linuxidc.com/Linux/2013-07/87423.htm
采用MapReduce与Hadoop进行大数据分析 http://www.linuxidc.com/Linux/2013-07/87312.htm
MapReduce作业提交源码分析 http://www.linuxidc.com/Linux/2012-09/71217.htm
Hadoop应用:剖解MapReduce http://www.linuxidc.com/Linux/2012-05/59686.htm
Ubuntu下配置 Eclipse 编译、开发 Hadoop(MapReduce)源代码 http://www.linuxidc.com/Linux/2011-07/38819.htm
好了言归正传,简单的说说背景、原理以及需要注意的地方:
1、为了方便 MapReduce 直接访问关系型数据库(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,根据DBOutputFormat类把MapReduce产生的结果集导入到数据库表中。
2、由于0.20版本对DBInputFormat和DBOutputFormat支持不是很好,该例用了0.19版本来说明这两个类的用法。
至少在我的 0.20.203 中的 org.apache.hadoop.mapreduce.lib 下是没见到 db 包,所以本文也是以老版的 API 来为例说明的。
3、运行MapReduce时候报错:java.io.IOException: com.mysql.jdbc.Driver,一般是由于程序找不到mysql驱动包。解决方法是让每个tasktracker运行MapReduce程序时都可以找到该驱动包。
添加包有两种方式:
(1)在每个节点下的${HADOOP_HOME}/lib下添加该包。重启集群,一般是比较原始的方法。
(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,添加语句:DistributedCache.addFileToClassPath(new Path(“/hdfsPath/mysql- connector-java- 5.1.0-bin.jar”), conf);
(3)虽然API用的是0.19的,但是使用0.20的API一样可用,只是会提示方法已过时而已。
4、测试数据:
CREATE TABLE `t` (
`id` int DEFAULT NULL,
`name` varchar(10) DEFAULT NULL
) ENGINE=InnoDB DEFAULT CHARSET=utf8;
CREATE TABLE `t2` (
`id` int DEFAULT NULL,
`name` varchar(10) DEFAULT NULL
) ENGINE=InnoDB DEFAULT CHARSET=utf8;
insert into t values (1,"june"),(2,"decli"),(3,"hello"),
(4,"june"),(5,"decli"),(6,"hello"),(7,"june"),
(8,"decli"),(9,"hello"),(10,"june"),
(11,"june"),(12,"decli"),(13,"hello");
5、代码:
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 java.util.Iterator;
import org.apache.hadoop.filecache.DistributedCache;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
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 org.apache.hadoop.mapred.lib.IdentityReducer;
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 org.apache.hadoop.mapred.lib.db.DBWritable;
/**
* Function: 测试 mr 与 mysql 的数据交互,此测试用例将一个表中的数据复制到另一张表中
* 实际当中,可能只需要从 mysql 读,或者写到 mysql 中。
* date: 2013-7-29 上午2:34:04 <br/>
* @author june
*/
public class Mysql2Mr {
// DROP TABLE IF EXISTS `hadoop`.`studentinfo`;
// CREATE TABLE studentinfo (
// id INTEGER NOT NULL PRIMARY KEY,
// name VARCHAR(32) NOT NULL);
public static class StudentinfoRecord implements Writable, DBWritable {
int id;
String name;
public StudentinfoRecord() {
}
public void readFields(DataInput in) throws IOException {
this.id = in.readInt();
this.name = Text.readString(in);
}
public String toString() {
return new String(this.id + " " + this.name);
}
@Override
public void write(PreparedStatement stmt) throws SQLException {
stmt.setInt(1, this.id);
stmt.setString(2, this.name);
}
@Override
public void readFields(ResultSet result) throws SQLException {
this.id = result.getInt(1);
this.name = result.getString(2);
}
@Override
public void write(DataOutput out) throws IOException {
out.writeInt(this.id);
Text.writeString(out, this.name);
}
}
// 记住此处是静态内部类,要不然你自己实现无参构造器,或者等着抛异常:
// Caused by: java.lang.NoSuchMethodException: DBInputMapper.<init>()
// http://stackoverflow.com/questions/7154125/custom-mapreduce-input-format-cant-find-constructor
// 网上脑残式的转帖,没见到一个写对的。。。
public static class DBInputMapper extends MapReduceBase implements
Mapper<LongWritable, StudentinfoRecord, LongWritable, Text> {
public void map(LongWritable key, StudentinfoRecord value,
OutputCollector<LongWritable, Text> collector, Reporter reporter) throws IOException {
collector.collect(new LongWritable(value.id), new Text(value.toString()));
}
}
public static class MyReducer extends MapReduceBase implements
Reducer<LongWritable, Text, StudentinfoRecord, Text> {
@Override
public void reduce(LongWritable key, Iterator<Text> values,
OutputCollector<StudentinfoRecord, Text> output, Reporter reporter) throws IOException {
String[] splits = values.next().toString().split(" ");
StudentinfoRecord r = new StudentinfoRecord();
r.id = Integer.parseInt(splits[0]);
r.name = splits[1];
output.collect(r, new Text(r.name));
}
}
public static void main(String[] args) throws IOException {
JobConf conf = new JobConf(Mysql2Mr.class);
DistributedCache.addFileToClassPath(new Path("/tmp/mysql-connector-java-5.0.8-bin.jar"), conf);
conf.setMapOutputKeyClass(LongWritable.class);
conf.setMapOutputValueClass(Text.class);
conf.setOutputKeyClass(LongWritable.class);
conf.setOutputValueClass(Text.class);
conf.setOutputFormat(DBOutputFormat.class);
conf.setInputFormat(DBInputFormat.class);
// // mysql to hdfs
// conf.setReducerClass(IdentityReducer.class);
// Path outPath = new Path("/tmp/1");
// FileSystem.get(conf).delete(outPath, true);
// FileOutputFormat.setOutputPath(conf, outPath);
DBConfiguration.configureDB(conf, "com.mysql.jdbc.Driver", "jdbc:mysql://192.168.1.101:3306/test",
"root", "root");
String[] fields = { "id", "name" };
// 从 t 表读数据
DBInputFormat.setInput(conf, StudentinfoRecord.class, "t", null, "id", fields);
// mapreduce 将数据输出到 t2 表
DBOutputFormat.setOutput(conf, "t2", "id", "name");
// conf.setMapperClass(org.apache.hadoop.mapred.lib.IdentityMapper.class);
conf.setMapperClass(DBInputMapper.class);
conf.setReducerClass(MyReducer.class);
JobClient.runJob(conf);
}
}
接下来请看第二页: http://www.linuxidc.com/Linux/2013-07/88117p2.htm