下面说说JAVA API 提供的这些类的功能和他们之间有什么样的联系。
1.HBaseConfiguration
关系：org.apache.hadoop.hbase.HBaseConfiguration
作用：通过此类可以对HBase进行配置
用法实例： Configuration config = HBaseConfiguration.create();
说明： HBaseConfiguration.create() 默认会从classpath 中查找 hbase-site.xml 中的配置信息，初始化 Configuration。
2.HBaseAdmin 类
关系：org.apache.hadoop.hbase.client.HBaseAdmin
作用：提供接口关系HBase 数据库中的表信息
用法：HBaseAdmin admin = new HBaseAdmin(config);
3.Descriptor类
关系：org.apache.hadoop.hbase.HTableDescriptor
作用：HTableDescriptor 类包含了表的名字以及表的列族信息
用法：HTableDescriptor htd =new HTableDescriptor(tablename);
构造一个表描述符指定TableName对象。
Htd.addFamily(new HColumnDescriptor(“myFamily”));
将列家族给定的描述符
4.HTable
关系：org.apache.hadoop.hbase.client.HTable
作用：HTable 和 HBase 的表通信
用法：HTable tab = new HTable(config,Bytes.toBytes(tablename));
ResultScanner sc = tab.getScanner(Bytes.toBytes(“familyName”));
说明：获取表内列族 familyNme 的所有数据。
5.Put
关系：org.apache.hadoop.hbase.client.Put
作用：获取单个行的数据
用法：HTable table = new HTable(config,Bytes.toBytes(tablename));
Put put = new Put(row);
p.add(family,qualifier,value);
说明：向表 tablename 添加 “family,qualifier,value”指定的值。
6.Get
关系：org.apache.hadoop.hbase.client.Get
作用：获取单个行的数据
用法：HTable table = new HTable(config,Bytes.toBytes(tablename));
Get get = new Get(Bytes.toBytes(row));
Result result = table.get(get);
说明：获取 tablename 表中 row 行的对应数据
7.ResultScanner
关系：Interface
作用：获取值的接口
用法：ResultScanner scanner = table.getScanner(Bytes.toBytes(family));
For(Result rowResult : scanner){
Bytes[] str = rowResult.getValue(family,column);
}
说明：循环获取行中列值。
例1 HBase之读取HDFS数据写入HBase
package org.hadoop.hbase;import java.io.IOException;import java.util.StringTokenizer;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.Path;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.TableReducer;import org.apache.hadoop.hbase.util.Bytes;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.lib.input.FileInputFormat;import org.apache.hadoop.util.GenericOptionsParser;public class WordCountHbaseWriter { public static class WordCountHbaseMapper extends Mapper { private final static IntWritable one = new IntWritable(1); private Text word = new Text(); 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);// 输出 为 } } } public static class WordCountHbaseReducer extends TableReducer { public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) {// 遍历求和 sum += val.get(); } 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);// 输出求和后的 } } public static void main(String[] args){ String tablename = "wordcount"; Configuration conf = HBaseConfiguration.create(); conf.set("hbase.zookeeper.quorum", "192.168.1.139"); conf.set("hbase.zookeeper.property.clientPort", "2191"); HBaseAdmin admin = null; try { 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 tcd = new HColumnDescriptor("content"); htd.addFamily(tcd);//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: WordCountHbaseWriter "); System.exit(2); } Job job = new Job(conf, "WordCountHbaseWriter"); job.setNumReduceTasks(2); job.setJarByClass(WordCountHbaseWriter.class); //使用WordCountHbaseMapper类完成Map过程； job.setMapperClass(WordCountHbaseMapper.class); TableMapReduceUtil.initTableReducerJob(tablename, WordCountHbaseReducer.class, job); //设置任务数据的输入路径； FileInputFormat.addInputPath(job, new Path(otherArgs[0])); //设置了Map过程的输出类型，其中设置key的输出类型为Text； job.setOutputKeyClass(Text.class); //设置了Map过程的输出类型，其中设置value的输出类型为IntWritable； job.setOutputValueClass(IntWritable.class); //调用job.waitForCompletion(true) 执行任务，执行成功后退出； System.exit(job.waitForCompletion(true) ? 0 : 1); } catch (Exception e) { e.printStackTrace(); } finally{ if(admin!=null) try { admin.close(); } catch (IOException e) { e.printStackTrace(); } } }} 例2 HBase之读取HBase数据写入HDFS
package org.hadoop.hbase;import java.io.IOException;import java.util.Map.Entry;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.Path;import org.apache.hadoop.hbase.HBaseConfiguration;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.io.Text;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import org.apache.hadoop.util.GenericOptionsParser;public class WordCountHbaseReader { public static class WordCountHbaseReaderMapper extends TableMapper { @Override protected void map(ImmutableBytesWritable key,Result value,Context context) throws IOException, InterruptedException { StringBuffer sb = new StringBuffer(""); for(Entry entry:value.getFamilyMap("content".getBytes()).entrySet()){ String str = new String(entry.getValue()); //将字节数组转换为String类型 if(str != null){ sb.append(new String(entry.getKey())); sb.append(":"); sb.append(str); } context.write(new Text(key.get()), new Text(new String(sb))); } }} public static class WordCountHbaseReaderReduce extends Reducer { private Text result = new Text(); @Override protected void reduce(Text key, Iterable values,Context context) throws IOException, InterruptedException { for(Text val:values){ result.set(val); context.write(key, result); } } } public static void main(String[] args) throws Exception { String tablename = "wordcount"; Configuration conf = HBaseConfiguration.create(); conf.set("hbase.zookeeper.quorum", "192.168.1.139"); conf.set("hbase.zookeeper.property.clientPort", "2191"); String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); if (otherArgs.length != 1) { System.err.println("Usage: WordCountHbaseReader "); System.exit(2); } Job job = new Job(conf, "WordCountHbaseReader"); job.setJarByClass(WordCountHbaseReader.class); //设置任务数据的输出路径； FileOutputFormat.setOutputPath(job, new Path(otherArgs[0])); job.setReducerClass(WordCountHbaseReaderReduce.class); Scan scan = new Scan(); TableMapReduceUtil.initTableMapperJob(tablename,scan,WordCountHbaseReaderMapper.class, Text.class, Text.class, job); //调用job.waitForCompletion(true) 执行任务，执行成功后退出； System.exit(job.waitForCompletion(true) ? 0 : 1); }} 程序中用到hadoop的相关JAR包（如下图）及hbase所有jar包
如果上面的API还不能满足你的要求，可以到下面这个网站里面Hbase全部API介绍