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-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
8、REF:
新版 API 写法:
http://superlxw1234.iteye.com/blog/1880712
老版: