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）。

建表，通过HBaseAdmin类中的create静态方法来创建表。
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。

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

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,

is-external=true">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); 

    }

}

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

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