org.apache.hadoop.hbase.MasterNotRunningException解决策略

执行HBase时常会遇到个错误，我就有这种经历。 

ERROR: org.apache.hadoop.hbase.MasterNotRunningException: Retried 7 times

检查日志：org.apache.hadoop.ipc.RPC$VersionMismatch: Protocol org.apache.hadoop.hdfs.protocol.ClientProtocol version mismatch. (client = 42, server = 41)

假设是这个错误，说明RPC协议不一致所造成的，解决方法：将hbase/lib文件夹下的hadoop-core的jar文件删除。将hadoop文件夹下的hadoop-0.20.2-core.jar复制到hbase/lib以下。然后又一次启动hbase就可以。

另外一种错误是：没有启动hadoop。先启用hadoop，再启用hbase。

在Eclipse开发中，须要增加hadoop全部的jar包以及HBase二个jar包（hbase，zooKooper）。

1. 建表，通过HBaseAdmin类中的create静态方法来创建表。
2. HTable类是操作表，比如，静态方法put能够插入数据，该类初始化时能够传递一个行键，静态方法getScanner()能够获得某一列上的全部数据。返回Result类,Result类中有个静态方法getFamilyMap（）能够获得以列名为key，值为value，这刚好与hadoop中map结果是一样的。

3. package test;
   import java.io.IOException;
   import java.util.Map;
   import org.apache.hadoop.conf.Configuration;
   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.HTable;
   import org.apache.hadoop.hbase.client.Put;
   import org.apache.hadoop.hbase.client.Result;
   
   public class Htable {
   
       /**
        * @param args
        */
       public static void main(String[] args) throws IOException {
           // TODO Auto-generated method stub
           Configuration hbaseConf = HBaseConfiguration.create();
           HBaseAdmin admin = new HBaseAdmin(hbaseConf);
           HTableDescriptor htableDescriptor = new HTableDescriptor("table"
                   .getBytes());  //set the name of table
           htableDescriptor.addFamily(new HColumnDescriptor("fam1")); //set the name of column clusters
           admin.createTable(htableDescriptor); //create a table 
           HTable table = new HTable(hbaseConf, "table"); //get instance of table.
           for (int i = 0; i < 3; i++) {   //for is number of rows
               Put putRow = new Put(("row" + i).getBytes()); //the ith row
               putRow.add("fam1".getBytes(), "col1".getBytes(), "vaule1"
                       .getBytes());  //set the name of column and value.
               putRow.add("fam1".getBytes(), "col2".getBytes(), "vaule2"
                       .getBytes());
               putRow.add("fam1".getBytes(), "col3".getBytes(), "vaule3"
                       .getBytes());
               table.put(putRow);
           }
           for(Result result: table.getScanner("fam1".getBytes())){//get data of column clusters 
               for(Map.Entry<byte[], byte[]> entry : result.getFamilyMap("fam1".getBytes()).entrySet()){//get collection of result
                   String column = new String(entry.getKey());
                   String value = new String(entry.getValue());
                   System.out.println(column+","+value);
               }
           }
           admin.disableTable("table".getBytes()); //disable the table
           admin.deleteTable("table".getBytes());  //drop the tbale
       }
   }

   以上代码不难看懂。

以下介绍一下，用mapreduce如何操作HBase。主要对HBase中的数据进行读取。

如今有一些大的文件，须要存入HBase中。其思想是先把文件传到HDFS上，利用map阶段读取<key,value>对，可在reduce把这些键值对上传到HBase中。

package test;

import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class MapperClass extends Mapper<LongWritable,Text,Text,Text>{
        public void map(LongWritable key,Text value,Context context)thorws IOException{
            String[] items = value.toString().split(" ");
            String k = items[0];
            String v = items[1];         
            context.write(new Text(k), new Text(v));
    }

}

Reduce类,主要是将键值传到HBase表中

package test;

import java.io.IOException;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.TableReducer;
import org.apache.hadoop.io.Text;

public class ReducerClass extends TableReducer<Text,Text,ImmutableBytesWritable>{
    public void reduce(Text key,Iterable<Text> values,Context context){
        String k = key.toString();
        StringBuffer str=null;
        for(Text value: values){
            str.append(value.toString());
        }
        String v = new String(str); 
        Put putrow = new Put(k.getBytes());
        putrow.add("fam1".getBytes(), "name".getBytes(), v.getBytes());     
    }
}

由上面可知ReducerClass继承TableReduce，在hadoop里面ReducerClass继承Reducer类。它的原型为：TableReducer<KeyIn,Values,KeyOut>能够看出，HBase里面是读出的Key类型是ImmutableBytesWritable。

Map，Reduce。以及Job的配置分离。比較清晰,mahout也是採用这样的构架。

package test;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.util.Tool;

public class Driver extends Configured implements Tool{

    @Override
    public static void run(String[] arg0) throws Exception {
        // TODO Auto-generated method stub
        Configuration conf = HBaseConfiguration.create();
        conf.set("hbase.zookeeper.quorum.", "localhost");  
        
        Job job = new Job(conf,"Hbase");
        job.setJarByClass(TxtHbase.class);
        
        Path in = new Path(arg0[0]);
        
        job.setInputFormatClass(TextInputFormat.class);
        FileInputFormat.addInputPath(job, in);
        
        job.setMapperClass(MapperClass.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(Text.class);
        
        TableMapReduceUtil.initTableReducerJob("table", ReducerClass.class, job);
        
       job.waitForCompletion(true);
    }
    
}

Driver中job配置的时候没有设置 job.setReduceClass(); 而是用 TableMapReduceUtil.initTableReducerJob("tab1", THReducer.class, job); 来运行reduce类。

主函数

package test;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.util.ToolRunner;

public class TxtHbase {
    public static void main(String [] args) throws Exception{

        Driver.run(new Configuration(),new THDriver(),args); 

    } 
}

读取数据时比較简单，编写Mapper函数，读取<key,value>值即可了。

package test;

import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.client.Result;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapred.TableMap;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;

public class MapperClass extends MapReduceBase implements
        TableMap<Text, Text> {
    static final String NAME = "GetDataFromHbaseTest";
    private Configuration conf;

    public void map(ImmutableBytesWritable row, Result values,
            OutputCollector<Text, Text> output, Reporter reporter)
            throws IOException {
        StringBuilder sb = new StringBuilder();
        for (Entry<byte[], byte[]> value : values.getFamilyMap(
                "fam1".getBytes()).entrySet()) {
            String cell = value.getValue().toString();
            if (cell != null) {
                sb.append(new String(value.getKey())).append(new String(cell));
            }
        }
        output.collect(new Text(row.get()), new Text(sb.toString()));
    }

要实现这种方法 initTableMapJob(String table, String columns, Class<?

extends TableMap> mapper, Class<? extends org.apache.hadoop.io.WritableComparable> outputKeyClass, Class<? extends org.apache.hadoop.io.Writable> outputValueClass, org.apache.hadoop.mapred.JobConf job, boolean addDependencyJars)。

package test;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.util.Tool;

public class Driver extends Configured implements Tool{

    @Override
    public static void run(String[] arg0) throws Exception {
        // TODO Auto-generated method stub
        Configuration conf = HBaseConfiguration.create();
        conf.set("hbase.zookeeper.quorum.", "localhost");  
        Job job = new Job(conf,"Hbase");
        job.setJarByClass(TxtHbase.class);
        job.setInputFormatClass(TextInputFormat.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(Text.class);

        TableMapReduceUtilinitTableMapperJob("table", args0[0],MapperClass.class, job); 
        job.waitForCompletion(true); }
 }

主函数

package test;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.util.ToolRunner;

public class TxtHbase {
    public static void main(String [] args) throws Exception{

        Driver.run(new Configuration(),new THDriver(),args); 

    } 
}