HBase Java API

Apache HBase是一个开源的、分布式的、非关系型的列式数据库。

HBase位于Hadoop生态系统的结构化存储层，数据存储于分布式文件系统HDFS并且使用ZooKeeper作为协调服务。HDFS为HBase提供了高可靠性的底层存储支持，MapReduce为HBase提供了高性能的计算能力，ZooKeeper则为HBase提供了稳定的服务和失效恢复机制。

<dependency>
    <groupId>org.apache.hbase</groupId>
    <artifactId>hbase-server</artifactId>
    <version>1.3.3</version>
</dependency>

 

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package com.xc.xcspringboot.test;   import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.hbase.*; import org.apache.hadoop.hbase.client.*; import org.apache.hadoop.hbase.filter.*; import org.apache.hadoop.hbase.util.Bytes;   import java.io.IOException;   public class HBaseTest {       static Connection conn;       static {         //创建HBase配置对象         Configuration conf = HBaseConfiguration.create();         //加上这一句，就不需要将代码发布到服务器中执行了，直接eclipse中运行就可以。不加这一句，需要将代码导出jar，上传到HBase服务器执行。         conf.set("hbase.zookeeper.quorum", "172.19.25.168:2181,172.19.25.169:2181,172.19.25.170:2181");         //创建连接对象Connection         try {             conn = ConnectionFactory.createConnection(conf);         } catch (IOException e) {             e.printStackTrace();         }     }       public static void main(String[] args) throws IOException { //        createTable(); //        put(); //        get(); //        delete(); //        filterTest();         multiFilterTest();     }         static void createTable() throws IOException {         //得到数据库管理员对象         Admin admin = conn.getAdmin();         //创建表描述，并指定表名         TableName tableName = TableName.valueOf("t1");         HTableDescriptor desc = new HTableDescriptor(tableName);         //创建列族描述         HColumnDescriptor family = new HColumnDescriptor("f1");         //指定列族         desc.addFamily(family);         //创建表         admin.createTable(desc);         System.out.println("create table success!!");     }       static void put() throws IOException {         //Table负责与记录相关的操作,如增删改查等         TableName tableName = TableName.valueOf("t1");         Table table = conn.getTable(tableName);           Put put = new Put(Bytes.toBytes("row7"));// 设置rowkey         put.addColumn(Bytes.toBytes("f1"), Bytes.toBytes("name"), Bytes.toBytes("xiaoming24"));         put.addColumn(Bytes.toBytes("f1"), Bytes.toBytes("age"), Bytes.toBytes("33"));         put.addColumn(Bytes.toBytes("f1"), Bytes.toBytes("address"), Bytes.toBytes("beijing"));           Put put2 = new Put(Bytes.toBytes("row8"));// 设置rowkey         put2.addColumn(Bytes.toBytes("f1"), Bytes.toBytes("name"), Bytes.toBytes("xiaoming23333"));         put2.addColumn(Bytes.toBytes("f1"), Bytes.toBytes("age"), Bytes.toBytes("30"));         put2.addColumn(Bytes.toBytes("f1"), Bytes.toBytes("address"), Bytes.toBytes("beijing2"));           Put put3 = new Put(Bytes.toBytes("row9"));// 设置rowkey         put3.addColumn(Bytes.toBytes("f1"), Bytes.toBytes("age"), Bytes.toBytes("31"));         put3.addColumn(Bytes.toBytes("f1"), Bytes.toBytes("address"), Bytes.toBytes("beijing3"));           //执行添加数据         table.put(put);         table.put(put2);         table.put(put3);         //释放资源         table.close();         System.out.println("put data success!!");     }       static void get() throws IOException {         //获取Table对象，指定查询表名，Table负责与记录相关的操作,如增删改查等         Table table = conn.getTable(TableName.valueOf("t1"));         //创建Get对象，根据rowkey查询,rowkey=row1         Get get = new Get("row7".getBytes());         //查询数据，取得结果集         Result r = table.get(get);         //循环输出每个单元格的数据         for (Cell cell : r.rawCells()) {             //取得当前单元格所属的列族名称             String family = new String(CellUtil.cloneFamily(cell));             //取得当前单元格所属的列名称             String qualifier = new String(CellUtil.cloneQualifier(cell));             //取得当前单元格的列值             String value = new String(CellUtil.cloneValue(cell));             //输出结果             System.out.println("列：" + family + ":" + qualifier + "—————值:" + value);         }     }       static void delete() throws IOException {         //获取Table对象，指定表名，Table负责与记录相关的操作,如增删改查等         TableName tableName = TableName.valueOf("t1");         Table table = conn.getTable(tableName);         //创建删除对象Delete，根据rowkey删除一整条         Delete delete = new Delete(Bytes.toBytes("row1"));         table.delete(delete);         //释放资源         table.close();         System.out.println("delete data success!!");     }       static void filterTest() throws IOException {         Table table = conn.getTable(TableName.valueOf("t1"));         Scan scan = new Scan(); //      RegexStringComparator comp = new RegexStringComparator("you."); // 以 you 开头的字符串 //      SingleColumnValueFilter filter = new SingleColumnValueFilter(Bytes.toBytes("family"), Bytes.toBytes("qualifier"), CompareOp.EQUAL, comp);         //1. 行键过滤器：筛选出行键为row1的一行数据         Filter filter = new RowFilter(CompareFilter.CompareOp.EQUAL, new BinaryComparator(Bytes.toBytes("row2")));         //2. 列族过滤器：筛选出列族为f1的所有数据 //        Filter filter = new FamilyFilter(CompareFilter.CompareOp.EQUAL, new BinaryComparator(Bytes.toBytes("f1")));         //3. 列过滤器：筛选出列为name的所有数据 //        Filter filter = new QualifierFilter(CompareFilter.CompareOp.EQUAL, new BinaryComparator(Bytes.toBytes("name")));         //4. 值过滤器：筛选出一行中的值包含"xiaoming"的所有单元格数据 //        Filter filter = new ValueFilter(CompareFilter.CompareOp.EQUAL, new SubstringComparator("beijing"));         //5. 单列值过滤器：用一列的值决定该行是否被过滤         /*//筛选出name列不包含xiaoming的所有行数据         SingleColumnValueFilter filter = new SingleColumnValueFilter(Bytes.toBytes("f1"), Bytes.toBytes("name"), CompareFilter.CompareOp.NOT_EQUAL, new SubstringComparator("xiaoming"));         //如果某行列name不存在，那么该行将被过滤掉，false则不进行过滤，默认为false。         filter.setFilterIfMissing(true);*/           //6. 分页过滤器，未完善         /*Filter filter = new PageFilter(2);         scan.setStartRow(Bytes.toBytes("row2"));*/         scan.setFilter(filter);         ResultScanner rs = table.getScanner(scan);         for (Result res : rs) {             System.out.println(res);         }         rs.close();     }       static void multiFilterTest() throws IOException {         // 指定要查询的表t1         Table table = conn.getTable(TableName.valueOf("t1"));         Scan scan = new Scan();         // 创建过滤器1，查询年龄小于等于30岁的所有数据         SingleColumnValueFilter filter1 = new SingleColumnValueFilter(Bytes.toBytes("f1"), Bytes.toBytes("age"), CompareFilter.CompareOp.LESS_OR_EQUAL, Bytes.toBytes("30"));         filter1.setFilterIfMissing(true);         // 创建过滤器2，查询年龄大于等于18岁的所有数据         SingleColumnValueFilter filter2 = new SingleColumnValueFilter(Bytes.toBytes("f1"), Bytes.toBytes("age"), CompareFilter.CompareOp.GREATER_OR_EQUAL, Bytes.toBytes("18"));         filter2.setFilterIfMissing(true);         // 创建过滤器集合对象         FilterList filterList = new FilterList();         // 添加过滤器1         filterList.addFilter(filter1);         // 添加过滤器2         filterList.addFilter(filter2);         // 设置过滤器         scan.setFilter(filterList);         // 执行查询，得到结果集         ResultScanner rs = table.getScanner(scan);         // 输出结果，每个res代表一行数据         for (Result res : rs) {             System.out.println(res);         }         rs.close();     }   }

　　

书籍: Hadoop大数据技术开发实战 8.7 HBase Java API操作
https://gitee.com/caoyeoo0/xc-springboot/blob/hadoopApi/src/main/java/com/xc/xcspringboot/test/HBaseTest.java