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，
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。
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); }}
6、结果：
执行两次后，你可以看到mysql结果：
mysql> select * from t2;+------+-------+| id | name |+------+-------+| 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 || 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 |+------+-------+26 rows in set (0.00 sec)mysql>
7、日志：
13/07/29 02:33:03 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.13/07/29 02:33:03 INFO filecache.TrackerDistributedCacheManager: Creating mysql-connector-java-5.0.8-bin.jar in /tmp/hadoop-june/mapred/local/archive/-8943686319031389138_-1232673160_640840668/192.168.1.101/tmp-work--8372797484204470322 with rwxr-xr-x13/07/29 02:33:03 INFO filecache.TrackerDistributedCacheManager: Cached hdfs://192.168.1.101:9000/tmp/mysql-connector-java-5.0.8-bin.jar as /tmp/hadoop-june/mapred/local/archive/-8943686319031389138_-1232673160_640840668/192.168.1.101/tmp/mysql-connector-java-5.0.8-bin.jar13/07/29 02:33:03 INFO filecache.TrackerDistributedCacheManager: Cached hdfs://192.168.1.101:9000/tmp/mysql-connector-java-5.0.8-bin.jar as /tmp/hadoop-june/mapred/local/archive/-8943686319031389138_-1232673160_640840668/192.168.1.101/tmp/mysql-connector-java-5.0.8-bin.jar13/07/29 02:33:03 INFO mapred.JobClient: Running job: job_local_000113/07/29 02:33:03 INFO mapred.MapTask: numReduceTasks: 113/07/29 02:33:03 INFO mapred.MapTask: io.sort.mb = 10013/07/29 02:33:03 INFO mapred.MapTask: data buffer = 79691776/9961472013/07/29 02:33:03 INFO mapred.MapTask: record buffer = 262144/32768013/07/29 02:33:03 INFO mapred.MapTask: Starting flush of map output13/07/29 02:33:03 INFO mapred.MapTask: Finished spill 013/07/29 02:33:03 INFO mapred.Task: Task:attempt_local_0001_m_000000_0 is done. And is in the process of commiting13/07/29 02:33:04 INFO mapred.JobClient: map 0% reduce 0%13/07/29 02:33:06 INFO mapred.LocalJobRunner: 13/07/29 02:33:06 INFO mapred.Task: Task 'attempt_local_0001_m_000000_0' done.13/07/29 02:33:06 INFO mapred.LocalJobRunner: 13/07/29 02:33:06 INFO mapred.Merger: Merging 1 sorted segments13/07/29 02:33:06 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 235 bytes13/07/29 02:33:06 INFO mapred.LocalJobRunner: 13/07/29 02:33:06 INFO mapred.Task: Task:attempt_local_0001_r_000000_0 is done. And is in the process of commiting13/07/29 02:33:07 INFO mapred.JobClient: map 100% reduce 0%13/07/29 02:33:09 INFO mapred.LocalJobRunner: reduce > reduce13/07/29 02:33:09 INFO mapred.Task: Task 'attempt_local_0001_r_000000_0' done.13/07/29 02:33:09 WARN mapred.FileOutputCommitter: Output path is null in cleanup13/07/29 02:33:10 INFO mapred.JobClient: map 100% reduce 100%13/07/29 02:33:10 INFO mapred.JobClient: Job complete: job_local_000113/07/29 02:33:10 INFO mapred.JobClient: Counters: 1813/07/29 02:33:10 INFO mapred.JobClient: File Input Format Counters 13/07/29 02:33:10 INFO mapred.JobClient: Bytes Read=013/07/29 02:33:10 INFO mapred.JobClient: File Output Format Counters 13/07/29 02:33:10 INFO mapred.JobClient: Bytes Written=013/07/29 02:33:10 INFO mapred.JobClient: FileSystemCounters13/07/29 02:33:10 INFO mapred.JobClient: FILE_BYTES_READ=121169113/07/29 02:33:10 INFO mapred.JobClient: HDFS_BYTES_READ=108170413/07/29 02:33:10 INFO mapred.JobClient: FILE_BYTES_WRITTEN=239284413/07/29 02:33:10 INFO mapred.JobClient: Map-Reduce Framework13/07/29 02:33:10 INFO mapred.JobClient: Map output materialized bytes=23913/07/29 02:33:10 INFO mapred.JobClient: Map input records=1313/07/29 02:33:10 INFO mapred.JobClient: Reduce shuffle bytes=013/07/29 02:33:10 INFO mapred.JobClient: Spilled Records=2613/07/29 02:33:10 INFO mapred.JobClient: Map output bytes=20713/07/29 02:33:10 INFO mapred.JobClient: Map input bytes=1313/07/29 02:33:10 INFO mapred.JobClient: SPLIT_RAW_BYTES=7513/07/29 02:33:10 INFO mapred.JobClient: Combine input records=013/07/29 02:33:10 INFO mapred.JobClient: Reduce input records=1313/07/29 02:33:10 INFO mapred.JobClient: Reduce input groups=1313/07/29 02:33:10 INFO mapred.JobClient: Combine output records=013/07/29 02:33:10 INFO mapred.JobClient: Reduce output records=1313/07/29 02:33:10 INFO mapred.JobClient: Map output records=13
8、REF：
新版 API 写法：
http://superlxw1234.iteye.com/blog/1880712
老版：
http://blog.csdn.net/dajuezhao/article/details/5799371
http://www.zhengmenbb.com/archives/583.htm
