1.使用HBase提供的TableOutputFormat，原理是通过一个Mapreduce作业将数据导入HBase
2.另一种方式就是使用HBase原生Client API
本文就是示范如何通过MapReduce作业从一个文件读取数据并写入到HBase中。
首先启动Hadoop与HBase，然后创建一个空表，用于后面导入数据：
hbase(main):006:0> create 'mytable','cf' 0 row(s) in 10.8310 seconds => Hbase::Table - mytable hbase(main):007:0> list TABLE mytable 1 row(s) in 0.1220 seconds => ["mytable"] hbase(main):008:0> scan 'mytable' ROW COLUMN+CELL 0 row(s) in 0.2130 seconds
一、示例程序
下面的示例程序通过 TableOutputFormat 将HDFS上具有一定格式的文本数据导入到HBase中。
首先创建MapReduce作业，目录结构如下：
Hdfs2HBase/
├── classes
└── src
├── Hdfs2HBase.java
├── Hdfs2HBaseMapper.java
└── Hdfs2HBaseReducer.java
Hdfs2HBaseMapper.java
package com.lisong.hdfs2hbase;
import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class Hdfs2HBaseMapper extends Mapper<LongWritable, Text, Text, Text> {
public void map(LongWritable key, Text line, Context context) throws IOException,InterruptedException {
String lineStr = line.toString();
int index = lineStr.indexOf(":");
String rowkey = lineStr.substring(0, index);
String left = lineStr.substring(index+1);
context.write(new Text(rowkey), new Text(left));
}
}
Hdfs2HBaseReducer.java
package com.lisong.hdfs2hbase;
import java.io.IOException;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.util.Bytes;
public class Hdfs2HBaseReducer extends Reducer<Text, Text, ImmutableBytesWritable, Put> {
public void reduce(Text rowkey, Iterable<Text> value, Context context) throws IOException,InterruptedException {
String k = rowkey.toString();
for(Text val : value) {
// 设置行键值
Put put = new Put(k.getBytes());
String[] strs = val.toString().split(":");
String family = strs[0];
String qualifier = strs[1];
String v = strs[2];
// 设置列簇、列名和列值
put.add(family.getBytes(), qualifier.getBytes(), v.getBytes());
context.write(new ImmutableBytesWritable(k.getBytes()), put);
}
}
}
Hdfs2HBase.java
package com.lisong.hdfs2hbase;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.mapreduce.TableOutputFormat;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
public class Hdfs2HBase {
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if(otherArgs.length != 2) {
System.err.println("Usage: wordcount <infile> <table>");
System.exit(2);
}
Job job = new Job(conf, "hdfs2hbase");
job.setJarByClass(Hdfs2HBase.class);
job.setMapperClass(Hdfs2HBaseMapper.class);
job.setReducerClass(Hdfs2HBaseReducer.class);
job.setMapOutputKeyClass(Text.class); // +
job.setMapOutputValueClass(Text.class); // +
job.setOutputKeyClass(ImmutableBytesWritable.class);
job.setOutputValueClass(Put.class);
// 以表输出的格式
job.setOutputFormatClass(TableOutputFormat.class);
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
job.getConfiguration().set(TableOutputFormat.OUTPUT_TABLE, otherArgs[1]);
System.exit(job.waitForCompletion(true)?0:1);
}
}
编译
$ javac -d classes/ src/*.java
打包
$ jar -cvf hdfs2hbase.jar classes
运行
创建一个 data.txt 文件，内容如下（列族是建表时创建的列族 cf ）：
r1:cf:c1:value1 r2:cf:c2:value2 r3:cf:c3:value3
将文件复制到hdfs上：
$ hadoop/bin/hadoop fs -put data.txt /hbase
把HBase的jar包加到 hadoop-env.sh 中。
TEMP=`ls /home/hadoop/hbase/lib/*.jar` HBASE_JARS=`echo $TEMP | sed 's/ /:/g'` HADOOP_CLASSPATH=$HBASE_JARS
运行MapReduce作业：
$ hadoop/bin/hadoop jar Hdfs2HBase/hdfs2hbase.jar com.lisong.hdfs2hbase.Hdfs2HBase /hbase/data.txt mytable
查询HBase表，验证数据是否已导入：
hbase(main):001:0> scan 'mytable' ROW COLUMN+CELL r1 column=cf:c1, timestamp=1439223857492, value=value1 r2 column=cf:c2, timestamp=1439223857492, value=value2 r3 column=cf:c3, timestamp=1439223857492, value=value3 3 row(s) in 1.3820 seconds
可以看到，数据导入成功！
由于需要频繁的与存储数据的RegionServer通信，占用资源较大，一次性入库大量数据时，TableOutputFormat效率并不好。
二、拓展-TableReducer
我们可以将 Hdfs2HBaseReducer.java 代码改成下面这样，作用是一样的：
package com.lisong.hdfs2hbase;
import java.io.IOException;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.hbase.mapreduce.TableReducer;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.util.Bytes;
public class Hdfs2HBaseReducer extends TableReducer<Text, Text, ImmutableBytesWritable> {
public void reduce(Text rowkey, Iterable<Text> value, Context context) throws IOException,InterruptedException {
String k = rowkey.toString();
for(Text val : value) {
Put put = new Put(k.getBytes());
String[] strs = val.toString().split(":");
String family = strs[0];
String qualifier = strs[1];
String v = strs[2];
put.add(family.getBytes(), qualifier.getBytes(), v.getBytes());
context.write(new ImmutableBytesWritable(k.getBytes()), put);
}
}
}
这里直接 继承了 TableReducer ， TableReducer是部分特例化的 Reducer ，它只有三个类型参数：输入Key/Value是对应Mapper的输出，输出Key可以是任意的类型，但是输出Value必须是一个 Put 或 Delete 实例。
转自： http://www.tuicool.com/articles/jInQ3y2