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。