1.HBase读写的方式概况
主要分为:
- 纯Java API读写HBase的方式;
- Spark读写HBase的方式;
- Flink读写HBase的方式;
- HBase通过Phoenix读写的方式;
第一种方式是HBase自身提供的比较原始的高效操作方式,而第二、第三则分别是Spark、Flink集成HBase的方式,最后一种是第三方插件Phoenix集成的JDBC方式,Phoenix集成的JDBC操作方式也能在Spark、Flink中调用。
注意:
这里我们使用HBase2.1.2版本,以下代码都是基于该版本开发的。
2. 纯Java API读写HBase
2.1 连接HBase
这里我们采用静态方式连接HBase,不同于2.1.2之前的版本,无需创建HBase线程池,HBase2.1.2提供的代码已经封装好,只需创建调用即可:
/**
* 声明静态配置
*/
static Configuration conf = null;
static Connection conn = null;
static {
conf = HBaseConfiguration.create();
conf.set("hbase.zookeeper.quorum", "hadoop01,hadoop02,hadoop03");
conf.set("hbase.zookeeper.property.client", "2181");
try{
conn = ConnectionFactory.createConnection(conf);
}catch (Exception e){
e.printStackTrace();
}
}
2.2 创建HBase的表
创建HBase表,是通过Admin来执行的,表和列簇则是分别通过TableDescriptorBuilder和ColumnFamilyDescriptorBuilder来构建。
/**
* 创建只有一个列簇的表
* @throws Exception
*/
public static void createTable() throws Exception{
Admin admin = conn.getAdmin();
if (!admin.tableExists(TableName.valueOf("test"))){
TableName tableName = TableName.valueOf("test");
//表描述器构造器
TableDescriptorBuilder tdb = TableDescriptorBuilder.newBuilder(tableName);
//列族描述器构造器
ColumnFamilyDescriptorBuilder cdb = ColumnFamilyDescriptorBuilder.newBuilder(Bytes.toBytes("user"));
//获得列描述器
ColumnFamilyDescriptor cfd = cdb.build();
//添加列族
tdb.setColumnFamily(cfd);
//获得表描述器
TableDescriptor td = tdb.build();
//创建表
admin.createTable(td);
}else {
System.out.println("表已存在");
}
//关闭连接
}
2.3 HBase表添加数据
通过put api来添加数据
/**
* 添加数据(多个rowKey,多个列族)
* @throws Exception
*/
public static void insertMany() throws Exception{
Table table = conn.getTable(TableName.valueOf("test"));
List<Put> puts = new ArrayList<Put>();
Put put1 = new Put(Bytes.toBytes("rowKey1"));
put1.addColumn(Bytes.toBytes("user"), Bytes.toBytes("name"), Bytes.toBytes("wd"));
Put put2 = new Put(Bytes.toBytes("rowKey2"));
put2.addColumn(Bytes.toBytes("user"), Bytes.toBytes("age"), Bytes.toBytes("25"));
Put put3 = new Put(Bytes.toBytes("rowKey3"));
put3.addColumn(Bytes.toBytes("user"), Bytes.toBytes("weight"), Bytes.toBytes("60kg"));
Put put4 = new Put(Bytes.toBytes("rowKey4"));
put4.addColumn(Bytes.toBytes("user"), Bytes.toBytes("sex"), Bytes.toBytes("男"));
puts.add(put1);
puts.add(put2);
puts.add(put3);
puts.add(put4);
table.put(puts);
table.close();
}
2.4 删除HBase的列簇或列
/**
* 根据rowKey删除一行数据、或者删除某一行的某个列簇,或者某一行某个列簇某列
* @param tableName
* @param rowKey
* @throws Exception
*/
public static void deleteData(TableName tableName, String rowKey, String rowKey, String columnFamily, String columnName) throws Exception{
Table table = conn.getTable(tableName);
Delete delete = new Delete(Bytes.toBytes(rowKey));
//①根据rowKey删除一行数据
table.delete(delete);
//②删除某一行的某一个列簇内容
delete.addFamily(Bytes.toBytes(columnFamily));
//③删除某一行某个列簇某列的值
delete.addColumn(Bytes.toBytes(columnFamily), Bytes.toBytes(columnName));
table.close();
}
2.5 更新HBase表的列
使用Put api直接替换掉即可
/**
* 根据RowKey , 列簇, 列名修改值
* @param tableName
* @param rowKey
* @param columnFamily
* @param columnName
* @param columnValue
* @throws Exception
*/
public static void updateData(TableName tableName, String rowKey, String columnFamily, String columnName, String columnValue) throws Exception{
Table table = conn.getTable(tableName);
Put put1 = new Put(Bytes.toBytes(rowKey));
put1.addColumn(Bytes.toBytes(columnFamily), Bytes.toBytes(columnName), Bytes.toBytes(columnValue));
table.put(put1);
table.close();
}
2.6 HBase查询
HBase查询分为get、scan、scan和filter结合。filter过滤器又分为RowFilter(rowKey过滤器)、SingleColumnValueFilter(列值过滤器)、ColumnPrefixFilter(列名前缀过滤器)。
/**
* 根据rowKey查询数据
* @param tableName
* @param rowKey
* @throws Exception
*/
public static void getResult(TableName tableName, String rowKey) throws Exception{
Table table = conn.getTable(tableName);
//获得一行
Get get = new Get(Bytes.toBytes(rowKey));
Result set = table.get(get);
Cell[] cells = set.rawCells();
for (Cell cell: cells){
System.out.println(Bytes.toString(cell.getQualifierArray(), cell.getQualifierOffset(), cell.getQualifierLength()) + "::" +
Bytes.toString(cell.getValueArray(), cell.getValueOffset(), cell.getValueLength()));
}
table.close();
}
//过滤器 LESS < LESS_OR_EQUAL <= EQUAL = NOT_EQUAL <> GREATER_OR_EQUAL >= GREATER > NO_OP 排除所有
/**
* @param tableName
* @throws Exception
*/
public static void scanTable(TableName tableName) throws Exception{
Table table = conn.getTable(tableName);
//①全表扫描
Scan scan1 = new Scan();
ResultScanner rscan1 = table.getScanner(scan1);
//②rowKey过滤器
Scan scan2 = new Scan();
//str$ 末尾匹配,相当于sql中的 %str ^str开头匹配,相当于sql中的str%
RowFilter filter = new RowFilter(CompareOperator.EQUAL, new RegexStringComparator("Key1$"));
scan2.setFilter(filter);
ResultScanner rscan2 = table.getScanner(scan2);
//③列值过滤器
Scan scan3 = new Scan();
//下列参数分别为列族,列名,比较符号,值
SingleColumnValueFilter filter3 = new SingleColumnValueFilter(Bytes.toBytes("author"), Bytes.toBytes("name"),
CompareOperator.EQUAL, Bytes.toBytes("spark"));
scan3.setFilter(filter3);
ResultScanner rscan3 = table.getScanner(scan3);
//列名前缀过滤器
Scan scan4 = new Scan();
ColumnPrefixFilter filter4 = new ColumnPrefixFilter(Bytes.toBytes("name"));
scan4.setFilter(filter4);
ResultScanner rscan4 = table.getScanner(scan4);
//过滤器集合
Scan scan5 = new Scan();
FilterList list = new FilterList(FilterList.Operator.MUST_PASS_ALL);
SingleColumnValueFilter filter51 = new SingleColumnValueFilter(Bytes.toBytes("author"), Bytes.toBytes("name"),
CompareOperator.EQUAL, Bytes.toBytes("spark"));
ColumnPrefixFilter filter52 = new ColumnPrefixFilter(Bytes.toBytes("name"));
list.addFilter(filter51);
list.addFilter(filter52);
scan5.setFilter(list);
ResultScanner rscan5 = table.getScanner(scan5);
for (Result rs : rscan){
String rowKey = Bytes.toString(rs.getRow());
System.out.println("row key :" + rowKey);
Cell[] cells = rs.rawCells();
for (Cell cell: cells){
System.out.println(Bytes.toString(cell.getFamilyArray(), cell.getFamilyOffset(), cell.getFamilyLength()) + "::"
+ Bytes.toString(cell.getQualifierArray(), cell.getQualifierOffset(), cell.getQualifierLength()) + "::"
+ Bytes.toString(cell.getValueArray(), cell.getValueOffset(), cell.getValueLength()));
}
System.out.println("-------------------------------------------");
}
}
3.总结
HBase连接的几种方式(二)spark篇 查看Spark上读写HBase
HBase读写的几种方式(三)flink篇 查看flink上读写HBase
github地址:
https://github.com/SwordfallYeung/HBaseDemo
参考资料:
https://hbase.apache.org/book.html
来源:oschina
链接:https://my.oschina.net/u/4359728/blog/3304783