Hbase编程入门之MapReduce

Tips：如果用Eclipse开发，需要加入hadoop所有的jar包以及HBase三个jar包（hbase，zooKooper，protobuf-java）。

下面介绍一下，用mapreduce怎样操作HBase，主要对HBase中的数据进行读取。

案例一：

首先先介绍下如何上传数据，还是以最熟悉到wordcount案例开始，我们的目的是将wordcount的结果存储到Hbase而不是HDFS下。

给出代码：

package test1;

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.HColumnDescriptor;
import org.apache.hadoop.hbase.HTableDescriptor;
import org.apache.hadoop.hbase.client.HBaseAdmin;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.hbase.mapreduce.TableOutputFormat;
import org.apache.hadoop.hbase.mapreduce.TableReducer;
import org.apache.hadoop.hbase.util.Bytes;

public class WordCountHBase {

public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{

private final static IntWritable one = new IntWritable(1);
private Text word = new Text();

//map函数没有改变
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
}

map函数没有改变

//Reduce类,主要是将键值传到HBase表中
public static class IntSumReducer
extends TableReducer <Text,IntWritable,ImmutableBytesWritable> {
private IntWritable result = new IntWritable();

public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
Put put = new Put(key.getBytes());	//put实例化，每一个词存一行
//列族为content，列修饰符为count，列值为数目
put.add(Bytes.toBytes("content"), Bytes.toBytes("count"), Bytes.toBytes(String.valueOf(sum)));
context.write(new ImmutableBytesWritable(key.getBytes()), put);
}
}

由上面可知IntSumReducer继承自TableReduce，在hadoop里面TableReducer继承Reducer类。它的原型为：TableReducer<KeyIn,Values,KeyOut>可以看出，HBase里面是读出的Key类型是ImmutableBytesWritable，意为不可变类型，因为HBase里所有数据都是用字符串存储的。

@SuppressWarnings("deprecation")
public static void main(String[] args) throws Exception {
String tablename  = "wordcount";

//实例化Configuration，注意不能用 new HBaseConfiguration()了。
Configuration conf = HBaseConfiguration.create();

HBaseAdmin admin = new HBaseAdmin(conf);
if(admin.tableExists(tablename)){
System.out.println("table exists! recreating ...");
admin.disableTable(tablename);
admin.deleteTable(tablename);
}
HTableDescriptor htd = new HTableDescriptor(tablename);
HColumnDescriptor hcd = new HColumnDescriptor("content");
htd.addFamily(hcd);		//创建列族
admin.createTable(htd);	//创建表

String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length != 1) {
System.err.println("Usage: wordcount <in> <out>"+otherArgs.length);
System.exit(2);
}
Job job = new Job(conf, "word count");
job.setJarByClass(WordCountHBase.class);
job.setMapperClass(TokenizerMapper.class);
//job.setCombinerClass(IntSumReducer.class);

FileInputFormat.addInputPath(job, new Path(otherArgs[0]));

//此处的TableMapReduceUtil注意要用hadoop.hbase.mapreduce包中的，而不是hadoop.hbase.mapred包中的
TableMapReduceUtil.initTableReducerJob(tablename, IntSumReducer.class, job);

//key和value到类型设定最好放在initTableReducerJob函数后面，否则会报错
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);

System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}

在job配置的时候没有设置 job.setReduceClass(); 而是用 TableMapReduceUtil.initTableReducerJob(tablename, IntSumReducer.class, job); 来执行reduce类。

需要注意的是此处的TableMapReduceUtil是hadoop.hbase.mapreduce包中的，而不是hadoop.hbase.mapred包中的，否则会报错。

案例二：

下面再介绍下如何进行读取，读取数据时比较简单，编写Mapper函数，读取<key,value>值就行了，Reducer函数直接输出得到的结果就行了。

package test1;

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.HColumnDescriptor;
import org.apache.hadoop.hbase.HTableDescriptor;
import org.apache.hadoop.hbase.client.HBaseAdmin;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.client.Result;
import org.apache.hadoop.hbase.client.Scan;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.hbase.mapreduce.TableMapper;
import org.apache.hadoop.hbase.mapreduce.TableOutputFormat;
import org.apache.hadoop.hbase.mapreduce.TableReducer;
import org.apache.hadoop.hbase.util.Bytes;

import test1.WordCount.IntSumReducer;

import com.sun.corba.se.impl.encoding.OSFCodeSetRegistry.Entry;

public class ReadHBase {

public static class TokenizerMapper
extends TableMapper<Text, Text>{

public void map(ImmutableBytesWritable row, Result values, Context context
) throws IOException, InterruptedException {
StringBuffer sb = new StringBuffer("");
for(java.util.Map.Entry<byte[],byte[]> value : values.getFamilyMap(
"content".getBytes()).entrySet()){
String str = new String(value.getValue());	//将字节数组转换成String类型，需要new String();
if(str != null){
sb.append(new String(value.getKey()));
sb.append(":");
sb.append(str);
}
context.write(new Text(row.get()), new Text(new String(sb)));

}

}
}

map函数继承到TableMapper接口，从result中读取查询结果。

public static class IntSumReducer
extends Reducer <Text,Text,Text,Text> {
private Text result = new Text();

public void reduce(Text key, Iterable<Text> values,
Context context
) throws IOException, InterruptedException {

for (Text val : values) {
result.set(val);
context.write(key,result);
}

}
}

reduce函数没有改变，直接输出到文件中即可

@SuppressWarnings("deprecation")
public static void main(String[] args) throws Exception {
String tablename  = "wordcount";

//实例化Configuration，注意不能用 new HBaseConfiguration()了。
Configuration conf = HBaseConfiguration.create();

String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length != 2) {
System.err.println("Usage: wordcount <in> <out>"+otherArgs.length);
System.exit(2);
}
Job job = new Job(conf, "word count");
job.setJarByClass(ReadHBase.class);
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
job.setReducerClass(IntSumReducer.class);

//此处的TableMapReduceUtil注意要用hadoop.hbase.mapreduce包中的，而不是hadoop.hbase.mapred包中的
Scan scan = new Scan(args[0].getBytes());
TableMapReduceUtil.initTableMapperJob(tablename, scan, TokenizerMapper.class, Text.class, Text.class, job);

System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}

其中我输入的两个参数分别是“aa ouput” 分别是开始查找的行（这里为从“aa”行开始找），和输出文件到存储路径（这里为存到HDFS目录到output文件夹下）

要注意的是，在JOB的配置中需要实现initTableMapperJob方法。与第一个例子类似，

在job配置的时候不用设置 job.setMapperClass(); 而是用 TableMapReduceUtil.initTableMapperJob(tablename, scan, TokenizerMapper.class, Text.class, Text.class, job);来执行mapper类。Scan实例是查找的起始行。