Hadoop 中利用 mapreduce 读写 mysql 数据

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问题导读
1.hadoop mapreduce的通过哪两个类可以读取数据源？
2.如果没有mysql驱动包，一般会是什么问题？
3.如何添加包？

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");

复制代码

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);
}
}

复制代码

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>

复制代码

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-x
13/07/29 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.jar
13/07/29 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.jar
13/07/29 02:33:03 INFO mapred.JobClient: Running job: job_local_0001
13/07/29 02:33:03 INFO mapred.MapTask: numReduceTasks: 1
13/07/29 02:33:03 INFO mapred.MapTask: io.sort.mb = 100
13/07/29 02:33:03 INFO mapred.MapTask: data buffer = 79691776/99614720
13/07/29 02:33:03 INFO mapred.MapTask: record buffer = 262144/327680
13/07/29 02:33:03 INFO mapred.MapTask: Starting flush of map output
13/07/29 02:33:03 INFO mapred.MapTask: Finished spill 0
13/07/29 02:33:03 INFO mapred.Task: Task:attempt_local_0001_m_000000_0 is done. And is in the process of commiting
13/07/29 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 segments
13/07/29 02:33:06 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 235 bytes
13/07/29 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 commiting
13/07/29 02:33:07 INFO mapred.JobClient: map 100% reduce 0%
13/07/29 02:33:09 INFO mapred.LocalJobRunner: reduce > reduce
13/07/29 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 cleanup
13/07/29 02:33:10 INFO mapred.JobClient: map 100% reduce 100%
13/07/29 02:33:10 INFO mapred.JobClient: Job complete: job_local_0001
13/07/29 02:33:10 INFO mapred.JobClient: Counters: 18
13/07/29 02:33:10 INFO mapred.JobClient: File Input Format Counters
13/07/29 02:33:10 INFO mapred.JobClient: Bytes Read=0
13/07/29 02:33:10 INFO mapred.JobClient: File Output Format Counters
13/07/29 02:33:10 INFO mapred.JobClient: Bytes Written=0
13/07/29 02:33:10 INFO mapred.JobClient: FileSystemCounters
13/07/29 02:33:10 INFO mapred.JobClient: FILE_BYTES_READ=1211691
13/07/29 02:33:10 INFO mapred.JobClient: HDFS_BYTES_READ=1081704
13/07/29 02:33:10 INFO mapred.JobClient: FILE_BYTES_WRITTEN=2392844
13/07/29 02:33:10 INFO mapred.JobClient: Map-Reduce Framework
13/07/29 02:33:10 INFO mapred.JobClient: Map output materialized bytes=239
13/07/29 02:33:10 INFO mapred.JobClient: Map input records=13
13/07/29 02:33:10 INFO mapred.JobClient: Reduce shuffle bytes=0
13/07/29 02:33:10 INFO mapred.JobClient: Spilled Records=26
13/07/29 02:33:10 INFO mapred.JobClient: Map output bytes=207
13/07/29 02:33:10 INFO mapred.JobClient: Map input bytes=13
13/07/29 02:33:10 INFO mapred.JobClient: SPLIT_RAW_BYTES=75
13/07/29 02:33:10 INFO mapred.JobClient: Combine input records=0
13/07/29 02:33:10 INFO mapred.JobClient: Reduce input records=13
13/07/29 02:33:10 INFO mapred.JobClient: Reduce input groups=13
13/07/29 02:33:10 INFO mapred.JobClient: Combine output records=0
13/07/29 02:33:10 INFO mapred.JobClient: Reduce output records=13
13/07/29 02:33:10 INFO mapred.JobClient: Map output records=13

复制代码

MapReduce直接连接Mysql获取数据

Mysql中数据：

mysql> select * from lxw_tbls;
+---------------------+----------------+
| TBL_NAME | TBL_TYPE |
+---------------------+----------------+
| lxw_test_table | EXTERNAL_TABLE |
| lxw_t | MANAGED_TABLE |
| lxw_t1 | MANAGED_TABLE |
| tt | MANAGED_TABLE |
| tab_partition | MANAGED_TABLE |
| lxw_hbase_table_1 | MANAGED_TABLE |
| lxw_hbase_user_info | MANAGED_TABLE |
| t | EXTERNAL_TABLE |
| lxw_jobid | MANAGED_TABLE |
+---------------------+----------------+
9 rows in set (0.01 sec)
mysql> select * from lxw_tbls where TBL_NAME like 'lxw%' order by TBL_NAME;
+---------------------+----------------+
| TBL_NAME | TBL_TYPE |
+---------------------+----------------+
| lxw_hbase_table_1 | MANAGED_TABLE |
| lxw_hbase_user_info | MANAGED_TABLE |
| lxw_jobid | MANAGED_TABLE |
| lxw_t | MANAGED_TABLE |
| lxw_t1 | MANAGED_TABLE |
| lxw_test_table | EXTERNAL_TABLE |
+---------------------+----------------+
6 rows in set (0.00 sec)

复制代码

MapReduce程序代码，ConnMysql.java:

package com.lxw.study;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.net.URI;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.sql.SQLException;
import java.util.Iterator;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.filecache.DistributedCache;
import org.apache.hadoop.fs.FileSystem;
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.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.db.DBConfiguration;
import org.apache.hadoop.mapreduce.lib.db.DBInputFormat;
import org.apache.hadoop.mapreduce.lib.db.DBWritable;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class ConnMysql {
private static Configuration conf = new Configuration();
static {
conf.addResource(new Path("F:/lxw-hadoop/hdfs-site.xml"));
conf.addResource(new Path("F:/lxw-hadoop/mapred-site.xml"));
conf.addResource(new Path("F:/lxw-hadoop/core-site.xml"));
conf.set("mapred.job.tracker", "10.133.103.21:50021");
}
public static class TblsRecord implements Writable, DBWritable {
String tbl_name;
String tbl_type;
public TblsRecord() {
}
@Override
public void write(PreparedStatement statement) throws SQLException {
// TODO Auto-generated method stub
statement.setString(1, this.tbl_name);
statement.setString(2, this.tbl_type);
}
@Override
public void readFields(ResultSet resultSet) throws SQLException {
// TODO Auto-generated method stub
this.tbl_name = resultSet.getString(1);
this.tbl_type = resultSet.getString(2);
}
@Override
public void write(DataOutput out) throws IOException {
// TODO Auto-generated method stub
Text.writeString(out, this.tbl_name);
Text.writeString(out, this.tbl_type);
}
@Override
public void readFields(DataInput in) throws IOException {
// TODO Auto-generated method stub
this.tbl_name = Text.readString(in);
this.tbl_type = Text.readString(in);
}
public String toString() {
return new String(this.tbl_name + " " + this.tbl_type);
}
}
public static class ConnMysqlMapper extends Mapper<LongWritable,TblsRecord,Text,Text> {
public void map(LongWritable key,TblsRecord values,Context context)
throws IOException,InterruptedException {
context.write(new Text(values.tbl_name), new Text(values.tbl_type));
}
}
public static class ConnMysqlReducer extends Reducer<Text,Text,Text,Text> {
public void reduce(Text key,Iterable<Text> values,Context context)
throws IOException,InterruptedException {
for(Iterator<Text> itr = values.iterator();itr.hasNext();) {
context.write(key, itr.next());
}
}
}
public static void main(String[] args) throws Exception {
Path output = new Path("/user/lxw/output/");
FileSystem fs = FileSystem.get(URI.create(output.toString()), conf);
if (fs.exists(output)) {
fs.delete(output);
}
//mysql的jdbc驱动
DistributedCache.addFileToClassPath(new Path(
"hdfs://hd022-test.nh.sdo.com/user/liuxiaowen/mysql-connector-java-5.1.13-bin.jar"), conf);
DBConfiguration.configureDB(conf, "com.mysql.jdbc.Driver",
"jdbc:mysql://10.133.103.22:3306/hive", "hive", "hive");
Job job = new Job(conf,"test mysql connection");
job.setJarByClass(ConnMysql.class);
job.setMapperClass(ConnMysqlMapper.class);
job.setReducerClass(ConnMysqlReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
job.setInputFormatClass(DBInputFormat.class);
FileOutputFormat.setOutputPath(job, output);
//列名
String[] fields = { "TBL_NAME", "TBL_TYPE" };
//六个参数分别为：
//1.Job;2.Class<? extends DBWritable>
//3.表名;4.where条件
//5.order by语句;6.列名
DBInputFormat.setInput(job, TblsRecord.class,
"lxw_tbls", "TBL_NAME like 'lxw%'", "TBL_NAME", fields);
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}

复制代码

运行结果：

[lxw@hd025-test ~]$ hadoop fs -cat /user/lxw/output/part-r-00000
lxw_hbase_table_1 MANAGED_TABLE
lxw_hbase_user_info MANAGED_TABLE
lxw_jobid MANAGED_TABLE
lxw_t MANAGED_TABLE
lxw_t1 MANAGED_TABLE
lxw_test_table EXTERNAL_TABLE

复制代码

http://www.aboutyun.com/forum.php?highlight=MapReduce+MySQL&mod=viewthread&tid=7405

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