HBase
一：hbase的理解
HBase：是一个基于hadoop的分布式，可扩展，巨大存储仓库，当用户需要对海量的数据进行实时的失效性随机的读写操作，用户可以使用hbase设计一张巨大的表，该表的规模能达到十亿行*数百万列，并且可以运行在商用的硬件集群之上。
hbase：是一个基于hadoop的分布式，可扩展，版本化的巨大的非关系型数据库。
hdfs和hbase的区别：hbase是构建在hdfs之上的一个数据库服务，能够使用户通过hbase数据库服务间接操作hdfs，使用户对hdfs上的数据实现crud操作（细粒度的操作）。
二：传统关系型数据库存在的索引问题
传统的关系型数据库索引特点：
按照搜索条件快速定位当前记录
在数据库中加载所有属性
映射返回需要的字段
而加载所有属性这一行为是多余的，这一部分io的读取对于系统而言是一种浪费，不支持稀疏存储。
解决之道：列的共现性问题—》进行分表
列存储：
将io特性相似的列归为一个簇，列簇为最小的加载单位
hbase中所有的记录都是按照一定顺序排列的：rowkey，列簇，列名，时间戳（默认返回最新的时间戳）
支持稀疏存储，null不存
缺点：值中包含rowkey、列信息、时间戳。
rowkey：等同于关系型数据库中的主键id
列簇：将io特性相似的列归为一个簇，hbase底层会以列簇作为单位进行索引。
列：列簇，列名，时间戳构成
时间戳：记录hbase中数据的版本，系统自动插入数据时间
三：hbase的安装
1.HDFS基本环境(存储)
1,安装JDK,配置环境变量JAVA_HOME
[root@CentOS ~]# rpm -ivh jdk-8u171-linux-x64.rpm
Preparing... ################################# [100%]
Updating / installing...
1:jdk1.8-2000:1.8.0_171-fcs ################################# [100%]
Unpacking JAR files...
tools.jar...
plugin.jar...
javaws.jar...
deploy.jar...
rt.jar...
jsse.jar...
charsets.jar...
localedata.jar...
[root@CentOS ~]# vi .bashrc
JAVA_HOME=/usr/java/latest
CLASSPATH=.
PATH=$PATH:$JAVA_HOME/bin
export JAVA_HOME
export CLASSPATH
export PATH
[root@CentOS ~]# source .bashrc
[root@CentOS ~]# jps
1933 Jps
2,关闭防火墙
[root@CentOS ~]# systemctl stop firewalld # 关闭 服务
[root@CentOS ~]# systemctl disable firewalld # 关闭开机自启动
Removed symlink /etc/systemd/system/multi-user.target.wants/firewalld.service.
Removed symlink /etc/systemd/system/dbus-org.fedoraproject.FirewallD1.service.
[root@CentOS ~]# firewall-cmd --state
not running
3,配置主机名和IP映射关系
[root@CentOS ~]# cat /etc/hostname
CentOS
[root@CentOS ~]# vi /etc/hosts
127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4
::1 localhost localhost.localdomain localhost6 localhost6.localdomain6
192.168.186.150 CentOS
4,配置SSH免密码登录
[root@CentOS ~]# ssh-keygen -t rsa
Generating public/private rsa key pair.
Enter file in which to save the key (/root/.ssh/id_rsa):
Created directory '/root/.ssh'.
Enter passphrase (empty for no passphrase):
Enter same passphrase again:
Your identification has been saved in /root/.ssh/id_rsa.
Your public key has been saved in /root/.ssh/id_rsa.pub.
The key fingerprint is:
SHA256:6yYiypvclJAZLU2WHvzakxv6uNpsqpwk8kzsjLv3yJA root@CentOS
The key's randomart image is:
+---[RSA 2048]----+
| .o. |
| =+ |
| o.oo |
| =. . |
| + o . S |
| o...= . |
|E.oo. + . |
|BXX+o.... |
|B#%O+o o. |
+----[SHA256]-----+
[root@CentOS ~]# ssh-copy-id CentOS
[root@CentOS ~]# ssh CentOS
Last failed login: Mon Jan 6 14:30:49 CST 2020 from centos on ssh:notty
There was 1 failed login attempt since the last successful login.
Last login: Mon Jan 6 14:20:27 2020 from 192.168.186.1
5,上传Hadoop安装包,并解压到/usr目录
[root@CentOS ~]# tar -zxf hadoop-2.9.2.tar.gz -C /usr/
6,配置HADOOP_HOME环境变量
[root@CentOS ~]# vi .bashrc
HADOOP_HOME=/usr/hadoop-2.9.2
JAVA_HOME=/usr/java/latest
CLASSPATH=.
PATH=$PATH:$JAVA_HOME/bin:$HADOOP_HOME/bin:$HADOOP_HOME/sbin
export JAVA_HOME
export CLASSPATH
export PATH
export HADOOP_HOME
[root@CentOS ~]# source .bashrc
[root@CentOS ~]# hadoop classpath #打印Hadoop的类路径
/usr/hadoop-2.9.2/etc/hadoop:/usr/hadoop-2.9.2/share/hadoop/common/lib/*:/usr/hadoop-2.9.2/share/hadoop/common/*:/usr/hadoop-2.9.2/share/hadoop/hdfs:/usr/hadoop-2.9.2/share/hadoop/hdfs/lib/*:/usr/hadoop-2.9.2/share/hadoop/hdfs/*:/usr/hadoop-2.9.2/share/hadoop/yarn:/usr/hadoop-2.9.2/share/hadoop/yarn/lib/*:/usr/hadoop-2.9.2/share/hadoop/yarn/*:/usr/hadoop-2.9.2/share/hadoop/mapreduce/lib/*:/usr/hadoop-2.9.2/share/hadoop/mapreduce/*:/usr/hadoop-2.9.2/contrib/capacity-scheduler/*.jar
7,修改core-site.xml
[root@CentOS ~]# vi /usr/hadoop-2.9.2/etc/hadoop/core-site.xml
<!--nn访问入口-->
<property>
<name>fs.defaultFS</name>
<value>hdfs://CentOS:9000</value>
</property>
<!--hdfs工作基础目录-->
<property>
<name>hadoop.tmp.dir</name>
<value>/usr/hadoop-2.9.2/hadoop-${user.name}</value>
</property>
8,修改hdfs-site.xml
[root@CentOS ~]# vi /usr/hadoop-2.9.2/etc/hadoop/hdfs-site.xml
<!--block副本因子-->
<property>
<name>dfs.replication</name>
<value>1</value>
</property>
<!--配置Sencondary namenode所在物理主机-->
<property>
<name>dfs.namenode.secondary.http-address</name>
<value>CentOS:50090</value>
</property>
<!--设置datanode最大文件操作数-->
<property>
<name>dfs.datanode.max.xcievers</name>
<value>4096</value>
</property>
<!--设置datanode并行处理能力-->
<property>
<name>dfs.datanode.handler.count</name>
<value>6</value>
</property>
9,修改slaves
[root@CentOS ~]# vi /usr/hadoop-2.9.2/etc/hadoop/slaves
CentOS
10,格式化NameNode,生成fsimage
[root@CentOS ~]# hdfs namenode -format
[root@CentOS ~]# yum install -y tree
[root@CentOS ~]# tree /usr/hadoop-2.9.2/hadoop-root/
/usr/hadoop-2.9.2/hadoop-root/
└── dfs
└── name
└── current
├── fsimage_0000000000000000000
├── fsimage_0000000000000000000.md5
├── seen_txid
└── VERSION
3 directories, 4 files
11,启动HDFS服务
[root@CentOS ~]# start-dfs.sh
2.Zookeeper安装(协调)
1,上传zookeeper的安装包,并解压在/usr目录下
[root@CentOS ~]# tar -zxf zookeeper-3.4.12.tar.gz -C /usr/
2,配置Zookepeer的zoo.cfg
[root@CentOS ~]# tar -zxf zookeeper-3.4.12.tar.gz -C /usr/
[root@CentOS ~]# cd /usr/zookeeper-3.4.12/
[root@CentOS zookeeper-3.4.12]# cp conf/zoo_sample.cfg conf/zoo.cfg
[root@CentOS zookeeper-3.4.12]# vi conf/zoo.cfg
# The number of milliseconds of each tick
tickTime=2000
# The number of ticks that the initial
# synchronization phase can take
initLimit=10
# The number of ticks that can pass between
# sending a request and getting an acknowledgement
syncLimit=5
# the directory where the snapshot is stored.
# do not use /tmp for storage, /tmp here is just
# example sakes.
dataDir=/root/zkdata
# the port at which the clients will connect
clientPort=2181
# the maximum number of client connections.
# increase this if you need to handle more clients
#maxClientCnxns=60
#
# Be sure to read the maintenance section of the
# administrator guide before turning on autopurge.
#
# http://zookeeper.apache.org/doc/current/zookeeperAdmin.html#sc_maintenance
#
# The number of snapshots to retain in dataDir
#autopurge.snapRetainCount=3
# Purge task interval in hours
# Set to "0" to disable auto purge feature
#autopurge.purgeInterval=1
3,创建zookeeper的数据目录
[root@CentOS ~]# mkdir /root/zkdata
4,启动zookeeper服务
[root@CentOS ~]# cd /usr/zookeeper-3.4.12/
[root@CentOS zookeeper-3.4.12]# ./bin/zkServer.sh start zoo.cfg
ZooKeeper JMX enabled by default
Using config: /usr/zookeeper-3.4.12/bin/../conf/zoo.cfg
Starting zookeeper ... STARTED
[root@CentOS zookeeper-3.4.12]# ./bin/zkServer.sh status zoo.cfg
ZooKeeper JMX enabled by default
Using config: /usr/zookeeper-3.4.12/bin/../conf/zoo.cfg
Mode: standalone
3.Hbase配置与安装(数据库服务)
1,上传Hbase安装包,并解压到/usr目录下
[root@CentOS ~]# tar -zxf hbase-1.2.4-bin.tar.gz -C /usr/
2,配置Hbase环境变量HBASE_HOME
[root@CentOS ~]# vi .bashrc
HBASE_HOME=/usr/hbase-1.2.4
HADOOP_HOME=/usr/hadoop-2.9.2
JAVA_HOME=/usr/java/latest
CLASSPATH=.
PATH=$PATH:$JAVA_HOME/bin:$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$HBASE_HOME/bin
export JAVA_HOME
export CLASSPATH
export PATH
export HADOOP_HOME
export HBASE_HOME
[root@CentOS ~]# source .bashrc
[root@CentOS ~]# hbase classpath # 测试Hbase是否识别Hadoop
/usr/hbase-1.2.4/conf:/usr/java/latest/lib/tools.jar:/usr/hbase-1.2.4:/usr/hbase-1.2.4/lib/activation-1.1.jar:/usr/hbase-1.2.4/lib/aopalliance-1.0.jar:/usr/hbase-1.2.4/lib/apacheds-i18n-2.0.0-M15.jar:/usr/hbase-1.2.4/lib/apacheds-kerberos-codec-2.0.0-M15.jar:/usr/hbase-1.2.4/lib/api-asn1-api-1.0.0-M20.jar:/usr/hbase-1.2.4/lib/api-util-1.0.0-M20.jar:/usr/hbase-1.2.4/lib/asm-3.1.jar:/usr/hbase-1.2.4/lib/avro-
...
1.7.4.jar:/usr/hbase-1.2.4/lib/commons-beanutils-1.7.0.jar:/usr/hbase-1.2.4/lib/commons-
2.9.2/share/hadoop/yarn/*:/usr/hadoop-2.9.2/share/hadoop/mapreduce/lib/*:/usr/hadoop-2.9.2/share/hadoop/mapreduce/*:/usr/hadoop-2.9.2/contrib/capacity-scheduler/*.jar
3,配置hbase-site.xml
[root@CentOS ~]# cd /usr/hbase-1.2.4/
[root@CentOS hbase-1.2.4]# vi conf/hbase-site.xml
<property>
<name>hbase.rootdir</name>
<value>hdfs://CentOS:9000/hbase</value>
</property>
<property>
<name>hbase.cluster.distributed</name>
<value>true</value>
</property>
<property>
<name>hbase.zookeeper.quorum</name>
<value>CentOS</value>
</property>
<property>
<name>hbase.zookeeper.property.clientPort</name>
<value>2181</value>
</property>
4,修改hbase-env.sh,将HBASE_MANAGES_ZK修改为false
[root@CentOS ~]# cd /usr/hbase-1.2.4/
[root@CentOS hbase-1.2.4]# grep -i HBASE_MANAGES_ZK conf/hbase-env.sh
# export HBASE_MANAGES_ZK=true
[root@CentOS hbase-1.2.4]# vi conf/hbase-env.sh
export HBASE_MANAGES_ZK=false
[root@CentOS hbase-1.2.4]# grep -i HBASE_MANAGES_ZK conf/hbase-env.sh
export HBASE_MANAGES_ZK=false
export HBASE_MANAGES_ZK=false告知Hbase,使用外部Zookeeper
5,启动Hbase
[root@CentOS hbase-1.2.4]# ./bin/start-hbase.sh
starting master, logging to /usr/hbase-1.2.4/logs/hbase-root-master-CentOS.out
Java HotSpot(TM) 64-Bit Server VM warning: ignoring option PermSize=128m; support was removed in 8.0
Java HotSpot(TM) 64-Bit Server VM warning: ignoring option MaxPermSize=128m; support was removed in 8.0
starting regionserver, logging to /usr/hbase-1.2.4/logs/hbase-root-1-regionserver-CentOS.out
[root@CentOS hbase-1.2.4]# jps
3090 NameNode
5027 HMaster
3188 DataNode
5158 HRegionServer
3354 SecondaryNameNode
5274 Jps
3949 QuorumPeerMain
6,验证Hbase安装是否成功
-
WebUI验证 http://192.168.186.150:16010/
-
Hbase shell验证(靠谱)
[root@CentOS hbase-1.2.4]# ./bin/hbase shell
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/usr/hbase-1.2.4/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/usr/hadoop-2.9.2/share/hadoop/common/lib/slf4j-log4j12-1.7.25.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
HBase Shell; enter ‘help’ for list of supported commands.
Type “exit” to leave the HBase Shell
Version 1.2.4, rUnknown, Wed Feb 15 18:58:00 CST 2017hbase(main):001:0> status
1 active master, 0 backup masters, 1 servers, 0 dead, 2.0000 average loadhbase(main):002:0> version
1.2.4, rUnknown, Wed Feb 15 18:58:00 CST 2017hbase(main):003:0>
-
链接HDFS查看
4.安装总结
环境搭建：
安装jdk，配置环境变量 /etc/profile
关闭防火墙 systemctl stop/disable firewalld
配置主机名和主机名映射关系
ssh免密登录
安装hadoop并配置
格式化 hdfs -namenode format
启动hdfs start-dfs.sh
安装zookeeper并配置
安装hbase
安装检测：
登录ip:16010
hbase shell
namenode:50070
四：shell命令 （重点）
1.基本命令
打开base shell
获取帮助： help help ‘get’
查看服务器状态：status
查看版本信息：verson
2.namespace数据库
alter_namespace, create_namespace, describe_namespace, drop_namespace, list_namespace, list_namespace_tables
创建namespace ：create_namespace ‘baizhi’ , {‘user’=>‘xiaoming’}
查看namespace详情：describe_namespace ‘baizhi’
修改namespace：alter_namespace ‘baizhi’, {METHOD => ‘set’, ‘user’ => ‘aaa’}
查看所有namespace：list_namespace
查看namespace中所有的表：list_namespace_tables ‘baizhi’
删除namespace：drop_namespace ‘baizhi’ //表的个数必须为空
3.表的操作
查看所有表（用户表）：list
创建表：create ‘baizhi:t_user’ , {NAME=>‘cf1’,VERSIONS=>3,BLOCKCACHE => true}
VERSIONS:保留数据版本,默认值1 TTL:列簇下列存活时间,默认是FOREVER BLOCKCACHE:是否开启缓存,用于加快读.IN_MEMORY:设置是否将列簇下所有数据加载内存中,加速读写,默认值false,BLOOMFILTER:配置布隆过滤器(一种数据文件过滤机制),默认值ROW,可选值两个ROW|ROWCOL,如果修改为ROWCOL系统需要额外开销存储列信息作为过滤文件的索引.
查看表的详情：desc ‘baizhi’
禁用表：disable_all ‘baizhi:t_user’
启用表：enabel_all ‘baizhi:t_user’
截断表（清空表）：truncate ‘baizhi:t_user’
删除表：drop ‘baizhi:t_user’
4.DML操作
put操作（添加，修改）：put ‘baizhi:t_user’ , ‘001’ , ‘cf1:name’ , ‘xiaoming’
get操作（查询）：
get 'baizhi:t_user' , '001' , {COLUMN=>'cf1' , VERSIONS=>3 , TIMESTAMP=>1553961198084}
get 'baizhi:t_user' , '001' , {COLUMN=>'cf1',TIMERANGE=[1553961198084,1553961219306],VERSIONS=>3}
delete操作（删除）：delete ‘baizhi:t_user’ , ‘001’ , ‘cf1:age’
scan操作：scan ‘baizhi:t_user’
五.java操作hbase
1.导jar包
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>2.9.2</version>
</dependency>
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-client</artifactId>
<version>1.2.4</version>
</dependency>
2.链接hbase并提供admin
private Admin admin;
private Connection conn;
@Before
public void before() throws IOException {
Configuration conf= HBaseConfiguration.create();
conf.set(HConstants.ZOOKEEPER_QUORUM,"hbase");
conn= ConnectionFactory.createConnection(conf);
admin=conn.getAdmin();
}
@After
public void after() throws IOException {
admin.close();
conn.close();
}
3.操作namespace
创建一个namespace（数据库）
@Test
public void testCreateNamespace() throws IOException {
NamespaceDescriptor nd=NamespaceDescriptor.create("zpark")
.addConfiguration("user","xiaoming")
.build();
admin.createNamespace(nd);
}
查看所有数据库
@Test
public void testListNamespace() throws IOException {
NamespaceDescriptor[] namespaceDescriptors = admin.listNamespaceDescriptors();
for (NamespaceDescriptor nd : namespaceDescriptors) {
System.out.println(nd.getName());
System.out.println(nd.getConfiguration());
}
}
删除数据库
@Test
public void testDeleteNamespace() throws IOException {
admin.deleteNamespace("zpark");
}
4.操作表table
查看所有用户表
@Test
public void testListTable() throws IOException {
HTableDescriptor[] hTableDescriptors = admin.listTables();
for (HTableDescriptor hTableDescriptor : hTableDescriptors) {
System.out.println(hTableDescriptor);
}
}
查看此namespace下的所有表
@Test
public void testListTableByNamespace() throws IOException {
HTableDescriptor[] baizhis = admin.listTableDescriptorsByNamespace("baizhi");
for (HTableDescriptor baizhi : baizhis) {
System.out.println(baizhi);
}
}
创建一个表
// 建表
@Test
public void testCreateTable() throws IOException {
// 要创建哪个表
TableName tname=TableName.valueOf("baizhi:t_user");
// 表描述
HTableDescriptor htabledescriptor=new HTableDescriptor(tname);
// 表的列簇信息
HColumnDescriptor cf1=new HColumnDescriptor("cf1");
cf1.setMaxVersions(3);
cf1.setInMemory(true);
cf1.setBlockCacheEnabled(true);
HColumnDescriptor cf2=new HColumnDescriptor("cf2");
cf2.setTimeToLive(300);
cf2.setBloomFilterType(BloomType.ROW);
htabledescriptor.addFamily(cf1);
htabledescriptor.addFamily(cf2);
// 使用admin创建表
admin.createTable(htabledescriptor);
}
删除表（前提：禁用表）
@Test
public void testDeleteTable() throws IOException {
TableName tname=TableName.valueOf("baizhi:t_user");
if(!admin.isTableDisabled(tname)){
admin.disableTable(tname);
}
admin.deleteTable(tname);
}
5.操作CRUD
put添加操作
带缓存，好用
@Test
public void testPut() throws IOException {
BufferedMutator bufferedMutator=conn.getBufferedMutator(TableName.valueOf("baizhi:t_user"));
for (int i=0;i<100;i++){
DecimalFormat decimalFormat = new DecimalFormat("000");
String rowKey=decimalFormat.format(i);
Put put = new Put(rowKey.getBytes());
put.addColumn("cf2".getBytes(),"name".getBytes(),("user"+rowKey).getBytes());
bufferedMutator.mutate(put);
if(i%50==0){
bufferedMutator.flush();
}
}
bufferedMutator.close();
}
get的两种操作
第一种:根据条件查询
@Test
public void testGet01() throws IOException {
Table table = conn.getTable(TableName.valueOf("baizhi:t_user"));
// 获取想要查询的rowkey
String rowKey="000";
// 构建查询条件
Get get=new Get(rowKey.getBytes());
get.setMaxVersions(3);
// 指定时间戳
// get.setTimeStamp(1578330481699L);
Filter filter=new QualifierFilter(CompareFilter.CompareOp.EQUAL,new SubstringComparator("me"));
get.setFilter(filter);
// 遍历查询结果
Result result = table.get(get);
CellScanner cellScanner = result.cellScanner();
while(cellScanner.advance()){
Cell cell = cellScanner.current();
String family = Bytes.toString(CellUtil.cloneFamily(cell));
String qualifier = Bytes.toString(CellUtil.cloneQualifier(cell));
String value=Bytes.toString(CellUtil.cloneValue(cell));
Long version=cell.getTimestamp();
String key=Bytes.toString(cell.getRow());
System.out.println(key+"\t"+family+"\t"+qualifier+"\t"+value+"\t"+version);
}
table.close();
}
第二种：简单遍历
@Test
public void testGet02() throws IOException {
Table table = conn.getTable(TableName.valueOf("baizhi:t_user"));
// 想要查询的rowkey
String rowKey="002";
Get get=new Get(rowKey.getBytes());
// 在这里构建查询条件
Result result = table.get(get);
// 处理查询结果
String key = Bytes.toString(result.getRow());
String name = Bytes.toString(result.getValue("cf1".getBytes(), "name".getBytes()));
String age = Bytes.toString(result.getValue("cf1".getBytes(), "age".getBytes()));
String salary = Bytes.toString(result.getValue("cf1".getBytes(), "salary".getBytes()));
String depa = Bytes.toString(result.getValue("cf1".getBytes(), "depa".getBytes()));
System.out.println(key+"\t"+name+","+age+","+salary+","+depa);
table.close();
}
删除表中的数据
@Test
public void testDeleteAll() throws IOException {
Table table = conn.getTable(TableName.valueOf("baizhi:t_user"));
String rowKey="000";
Delete delete=new Delete(rowKey.getBytes());
table.delete(delete);
table.close();
}
删除表中的某个单元格
@Test
public void testDeleteOneCell() throws IOException {
Table table = conn.getTable(TableName.valueOf("baizhi:t_user"));
String rowKey="000";
Delete delete=new Delete(rowKey.getBytes());
delete.addColumn("cf1".getBytes(),"name".getBytes(),1578330481699L);
table.delete(delete);
table.close();
}
scan浏览
@Test
public void testScan() throws IOException {
Table table = conn.getTable(TableName.valueOf("baizhi:t_user"));
Scan scan=new Scan();
// scan.addFamily("cf1".getBytes()); 指定要检索的列
// scan.setStartRow("010".getBytes()); 指定要检索的rowkey
// scan.setStopRow("020".getBytes());
Filter filter1=new RowFilter(CompareFilter.CompareOp.LESS,new BinaryComparator("010".getBytes()));
Filter filter2=new RowFilter(CompareFilter.CompareOp.GREATER,new BinaryComparator("005".getBytes()));
Filter list=new FilterList(FilterList.Operator.MUST_PASS_ALL,filter1,filter2);
scan.setFilter(list);
// 处理查询结果
ResultScanner resultScanner = table.getScanner(scan);
Iterator<Result> resultIterator = resultScanner.iterator();
while(resultIterator.hasNext()){
Result result = resultIterator.next();
String key = Bytes.toString(result.getRow());
String name = Bytes.toString(result.getValue("cf1".getBytes(), "name".getBytes()));
String age = Bytes.toString(result.getValue("cf1".getBytes(), "age".getBytes()));
String salary = Bytes.toString(result.getValue("cf1".getBytes(), "salary".getBytes()));
String depa = Bytes.toString(result.getValue("cf1".getBytes(), "depa".getBytes()));
System.out.println(key+"\t"+name+","+age+","+salary+","+depa);
}
table.close();
}
来源：CSDN
作者：origin_cx
链接：https://blog.csdn.net/origin_cx/article/details/103881759