Hbase

概述

Hbase是一个基于Hadoop之上的数据库服务，该数据库是一个分布式、可扩展的大的数据仓库。当您需要对大数据进行随机，实时读/写访问时，请使用Apache HBase™（HDFS虽然可以存储海量数据，但是对数据的管理粒度比较粗糙，只支持对文件的上传下载，并不支持对文件内容行记录级别的修改）。Apache HBase是一个开源，分布式，版本化，非关系型数据库，模仿了谷歌的Bigtable，正如Bigtable利用Google文件系统提供的分布式数据存储一样，Apache HBase在Hadoop和HDFS之上提供类似Bigtable的功能。

HBase和HDFS关系&区别？

Hbase是构建在HDFS之上的一个数据库服务，能够使得用户通过HBase数据库服务间接的操作HDFS，能够使得用户对HDFS上的数据实现CRUD操作（细粒度操作）。

Hbase特性-官方

• 线性和模块化扩展。
• 严格一致 reads 和 writes.
• 表的自动和可配置分片（自动分区）
• RegionServers之间的自动故障转移支持。
• 方便的基类，用于使用Apache HBase表支持Hadoop MapReduce作业。
• 易于使用的Java API，用于客户端访问。
• Block cache 和 Bloom Filters 以进行实时查询。

列存储

NoSQL:泛指非关系型数据通常包含以下类型：key-value型、文档型-JSON、基于列型、图形关系存储。每一种NoSQL产品彼此之间没有任何关系，差异很大基本上彼此之间不能够相互替换。

基于列型使用场景：

hbase支持存储数十亿级别的数据，但是Hbase不支持复杂查询和事物操作。因此Hbase虽然存储海量数据，但是基于海量数据的查询是非常有限的。

列存储和行存储区别？

Hbase安装

• 安装好HDFS，并且保证HDFS正常运行
• 必须配置HADOOP_HOME，因为HBase需要通过该变量定位HADOOP服务
• 安装Zookeeper（存储集群的元数据信息HMaster和HRegionServer）

[root@CentOS ~]# tar -zxf zookeeper-3.4.6.tar.gz  -C /usr/
[root@CentOS ~]# mkdir zkdata
[root@CentOS ~]# touch /usr/zookeeper-3.4.6/conf/zoo.cfg
[root@CentOS ~]# vi /usr/zookeeper-3.4.6/conf/zoo.cfg
tickTime=2000
dataDir=/root/zkdata
clientPort=2181
[root@CentOS ~]# /usr/zookeeper-3.4.6/bin/zkServer.sh start zoo.cfg
JMX enabled by default
Using config: /usr/zookeeper-3.4.6/bin/../conf/zoo.cfg
Starting zookeeper ... STARTED
[root@CentOS ~]# /usr/zookeeper-3.4.6/bin/zkServer.sh status zoo.cfg
JMX enabled by default
Using config: /usr/zookeeper-3.4.6/bin/../conf/zoo.cfg
Mode: standalone

• 安装配置hbase

[root@CentOS ~]# tar -zxf hbase-1.2.4-bin.tar.gz -C /usr/
[root@CentOS ~]# vi /usr/hbase-1.2.4/conf/hbase-site.xml<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration><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>
</configuration>

修改regionservers文本配置文件

[root@CentOS ~]# vi /usr/hbase-1.2.4/conf/regionservers
CentOS

配置*.bashrc*文件

[root@CentOS ~]# vi .bashrcHADOOP_CLASSPATH=/root/mysql-connector-java-5.1.46.jar
HADOOP_HOME=/usr/hadoop-2.6.0
JAVA_HOME=/usr/java/latest
PATH=$PATH:$JAVA_HOME/bin:$HADOOP_HOME/bin:$HADOOP_HOME/sbin
CLASSPATH=.
HBASE_MANAGES_ZK=false
export JAVA_HOME
export PATH
export CLASSPATH
export HADOOP_HOME
export HADOOP_CLASSPATH
export HBASE_MANAGES_ZK
[root@CentOS ~]# source .bashrc

启动HBase服务

[root@CentOS ~]# cd /usr/hbase-1.2.4/
[root@CentOS hbase-1.2.4]# ./bin/start-hbase.sh
[root@CentOS hbase-1.2.4]# jps
1667 DataNode
1844 SecondaryNameNode
1429 QuorumPeerMain
2533 Jps
2245 HRegionServer
2118 HMaster
1578 NameNode

地址栏输入：http://ip:16010 查看启动UI 界面

Hbase Shell

• 链接Hbase Shell

[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-2.6.0/share/hadoop/common/lib/slf4j-log4j12-1.7.5.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<RETURN>' for list of supported commands.
Type "exit<RETURN>" to leave the HBase Shell
Version 1.2.4, rUnknown, Wed Feb 15 18:58:00 CST 2017hbase(main):001:0>

用户可以通过help查看系统脚本命令hbase(main):001:0> help

常用命令：

status, table_help, version, whoami

hbase(main):003:0> status
1 active master, 0 backup masters, 1 servers, 0 dead, 2.0000 average load
hbase(main):004:0> whoami
root (auth:SIMPLE)groups: roothbase(main):005:0> version
1.2.4, rUnknown, Wed Feb 15 18:58:00 CST 2017

namespace:命名空间等价传统数据中的database

alter_namespace, create_namespace, describe_namespace, drop_namespace, list_namespace, list_namespace_tables

hbase(main):007:0> create_namespace 'baizhi',{'user'=>'zs'}
0 row(s) in 0.3920 secondshbase(main):009:0> alter_namespace 'baizhi', {METHOD => 'set', 'sex' => 'true'}
0 row(s) in 0.1430 secondshbase(main):010:0> describe_namespace 'baizhi'
DESCRIPTION
{NAME => 'baizhi', sex => 'true', user => 'zs'}
1 row(s) in 0.0050 secondshbase(main):011:0>  alter_namespace 'baizhi',{METHOD => 'unset', NAME=>'sex'}
0 row(s) in 0.1140 secondshbase(main):013:0> list_namespace
NAMESPACE
baizhi
default
hbase
3 row(s) in 0.1790 secondshbase(main):015:0> list_namespace '^b.*'
NAMESPACE
baizhi
1 row(s) in 0.0160 secondshbase(main):016:0> list_namespace_tables 'hbase'
TABLE
meta
namespace
2 row(s) in 0.1510 seconds

ddl命名：data define languge 数据定义命令，涵盖建表、建库命令

alter, alter_async, alter_status, create, describe, disable, disable_all, drop, drop_all, enable, enable_all, exists, get_table, is_disabled, is_enabled, list, locate_region, show_filters

hbase(main):019:0> create 'baizhi:t_user',{NAME=>'cf1',VERSIONS=>3},{NAME=>'cf2',TTL=>300}
0 row(s) in 2.9600 seconds=> Hbase::Table - baizhi:t_userhbase(main):024:0> list
TABLE
baizhi:t_user
1 row(s) in 0.0560 seconds=> ["baizhi:t_user"]hbase(main):028:0> disable_all 'baizhi:t_u.*'
baizhi:t_userDisable the above 1 tables (y/n)?
y
1 tables successfully disabledhbase(main):029:0> drop
drop             drop_all         drop_namespace
hbase(main):029:0> drop_all 'baizhi:t_u.*'
baizhi:t_userDrop the above 1 tables (y/n)?
y
1 tables successfully droppedhbase(main):030:0> list
TABLE
0 row(s) in 0.0070 seconds=> []hbase(main):032:0> exists 'baizhi:t_user'
Table baizhi:t_user does not exist
0 row(s) in 0.0210 seconds

dml data manage language 数据管理语言，通常是一些数据库的CRUD操作

append, count, delete, deleteall, get, get_counter, get_splits, incr, put, scan, truncate, truncate_preserve

hbase(main):001:0> count 'baizhi:t_user'
0 row(s) in 1.8630 seconds=> 0hbase(main):002:0> t = get_table 'baizhi:t_user'
0 row(s) in 0.0000 seconds=> Hbase::Table - baizhi:t_user
hbase(main):003:0> t.count
0 row(s) in 0.1140 seconds
=> 0

put

hbase(main):004:0> put 'baizhi:t_user','001','cf1:name','zhangsan'
0 row(s) in 0.7430 secondshbase(main):005:0> put 'baizhi:t_user','001','cf1:age',18
0 row(s) in 0.1120 seconds
# 修改
hbase(main):006:0> put 'baizhi:t_user','001','cf1:age',20 
0 row(s) in 0.0720 seconds

get

hbase(main):008:0> get 'baizhi:t_user','001'
COLUMN                              CELLcf1:age                            timestamp=1553961219305, value=20cf1:name                           timestamp=1553961181804, value=zhangsanhbase(main):009:0> get 'baizhi:t_user','001',{COLUMN=>'cf1',VERSIONS=>3}
COLUMN                              CELLcf1:age                            timestamp=1553961219305, value=20cf1:age                            timestamp=1553961198084, value=18cf1:name                           timestamp=1553961181804, value=zhangsan
3 row(s) in 0.1540 secondshbase(main):010:0> get 'baizhi:t_user','001',{COLUMN=>'cf1',TIMESTAMP=>1553961198084}
COLUMN                              CELLcf1:age                            timestamp=1553961198084, value=18
1 row(s) in 0.0900 secondshbase(main):015:0> get 'baizhi:t_user','001',{COLUMN=>'cf1',TIMERANGE=>[1553961198084,1553961219306],VERSIONS=>3}
COLUMN                              CELLcf1:age                            timestamp=1553961219305, value=20cf1:age                            timestamp=1553961198084, value=18
2 row(s) in 0.0180 secondshbase(main):018:0> get 'baizhi:t_user','001',{COLUMN=>'cf1',FILTER => "ValueFilter(=, 'binary:zhangsan')"}
COLUMN                              CELLcf1:name                           timestamp=1553961181804, value=zhangsan
1 row(s) in 0.0550 secondshbase(main):019:0> get 'baizhi:t_user','001',{COLUMN=>'cf1',FILTER => "ValueFilter(=, 'substring:zhang')"}
COLUMN                              CELLcf1:name                           timestamp=1553961181804, value=zhangsan
1 row(s) in 0.0780 seconds

delete/deleteall

# 删除指定版本之前的所以cell
hbase(main):027:0> delete 'baizhi:t_user','001','cf1:age',1553961899630
0 row(s) in 0.1020 seconds
# 删除cf1：age的所有单元格
hbase(main):031:0> delete 'baizhi:t_user','001','cf1:age'
0 row(s) in 0.0180 secondshbase(main):034:0> deleteall 'baizhi:t_user','001'
0 row(s) in 0.0360 secondshbase(main):035:0> t.count
0 row(s) in 0.0450 seconds
=> 0
hbase(main):036:0> get 'baizhi:t_user','001',{COLUMN=>'cf1',VERSIONS=>3}
COLUMN                              CELL
0 row(s) in 0.0130 seconds

scan

hbase(main):045:0> scan 'baizhi:t_user'
ROW                                 COLUMN+CELL001                                column=cf1:age, timestamp=1553962118964, value=21001                                column=cf1:name, timestamp=1553962147916, value=zs002                                column=cf1:age, timestamp=1553962166894, value=19002                                column=cf1:name, timestamp=1553962157743, value=ls003                                column=cf1:name, timestamp=1553962203754, value=zl005                                column=cf1:age, timestamp=1553962179379, value=19005                                column=cf1:name, timestamp=1553962192054, value=wwhbase(main):054:0> scan 'baizhi:t_user',{ LIMIT => 2,STARTROW=>"003",REVERSED=>true}
ROW                                 COLUMN+CELL003                                column=cf1:name, timestamp=1553962203754, value=zl002                                column=cf1:age, timestamp=1553962166894, value=19002                                column=cf1:name, timestamp=1553962157743, value=lshbase(main):058:0> scan 'baizhi:t_user',{ LIMIT => 2,STARTROW=>"003",REVERSED=>true,VERSIONS=>3,TIMERANGE=>[1553962157743,1553962203790]}
ROW                                 COLUMN+CELL003                                column=cf1:name, timestamp=1553962203754, value=zl002                                column=cf1:age, timestamp=1553962166894, value=19002                                column=cf1:name, timestamp=1553962157743, value=ls
2 row(s) in 0.0810 seconds

truncate- 截断表

hbase(main):072:0> truncate 'baizhi:t_user'
Truncating 'baizhi:t_user' table (it may take a while):- Disabling table...

Java API操作HBase

maven

<dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-common</artifactId><version>2.6.0</version>
</dependency><dependency><groupId>org.apache.hbase</groupId><artifactId>hbase-client</artifactId><version>1.2.4</version>
</dependency>

创建和Hbase链接参数

private static Admin admin;//负责执行DDL
private static Connection conn;//负责执行DML
static {try {Configuration conf = new Configuration();conf.set("hbase.zookeeper.quorum","CentOS");conn= ConnectionFactory.createConnection(conf);admin=conn.getAdmin();} catch (IOException e) {e.printStackTrace();}
}
public static void close() throws IOException {admin.close();conn.close();
}

Namespace操作

//创建
NamespaceDescriptor nd = NamespaceDescriptor.create("zpark").addConfiguration("user","zhansgan").build();
admin.createNamespace(nd);
//查看
NamespaceDescriptor[] nds = admin.listNamespaceDescriptors();
for (NamespaceDescriptor nd : nds) {System.out.println(nd.getName());
}
//删除admin.deleteNamespace("zpark");

Table先关操作(重点)

TableName tname=TableName.valueOf("zpark:t_user");
HTableDescriptor td = new HTableDescriptor(tname);//构建cf1、cf2
HColumnDescriptor cf1 = new HColumnDescriptor("cf1");
cf1.setMaxVersions(3);
//设置ROW+COL索引方式，比默认ROW占用更多的内存信息
cf1.setBloomFilterType(BloomType.ROWCOL);HColumnDescriptor cf2 = new HColumnDescriptor("cf2");
//设置失效时常5min
cf2.setTimeToLive(300);
cf2.setInMemory(true);//设置column family
td.addFamily(cf1);
td.addFamily(cf2);admin.createTable(td);

数据的DML（重点）

//2.447 秒
TableName tname = TableName.valueOf("zpark:t_user");
Table table = conn.getTable(tname);
//构建PUT指令
for(int i=0;i<1000;i++){DecimalFormat df = new DecimalFormat("0000");String rowKey = df.format(i);Put put=new Put(rowKey.getBytes());put.addColumn("cf1".getBytes(),"name".getBytes(), Bytes.toBytes("USER"+rowKey));put.addColumn("cf1".getBytes(),"age".getBytes(), Bytes.toBytes(i+""));put.addColumn("cf1".getBytes(),"sex".getBytes(), Bytes.toBytes((i%4==0)+""));put.addColumn("cf1".getBytes(),"salary".getBytes(), Bytes.toBytes(1000+(i/100.0)*100+""));table.put(put);
}
table.close();

批量插入

TableName tname = TableName.valueOf("zpark:t_user");
BufferedMutator bufferedMutator=conn.getBufferedMutator(tname);
//构建PUT指令 0.549 秒
long begin=System.currentTimeMillis();
for(int i=0;i<1000;i++){DecimalFormat df = new DecimalFormat("0000");String rowKey = df.format(i);Put put=new Put(rowKey.getBytes());put.addColumn("cf1".getBytes(),"name".getBytes(), Bytes.toBytes("USER"+rowKey));put.addColumn("cf1".getBytes(),"age".getBytes(), Bytes.toBytes(i+""));put.addColumn("cf1".getBytes(),"sex".getBytes(), Bytes.toBytes((i%4==0)+""));put.addColumn("cf1".getBytes(),"salary".getBytes(), Bytes.toBytes(1000+(i/100.0)*100+""));bufferedMutator.mutate(put);if(i%500==0){bufferedMutator.flush();}
}
long end=System.currentTimeMillis();
bufferedMutator.close();
System.out.println(((end-begin)/1000.0)+" 秒");

GET

TableName tname = TableName.valueOf("zpark:t_user");
Table table = conn.getTable(tname);Get get=new Get("0010".getBytes());Result result = table.get(get);
while (result.advance()){Cell cell = result.current();String row = Bytes.toString(CellUtil.cloneRow(cell));String cf = Bytes.toString(CellUtil.cloneFamily(cell));String col = Bytes.toString(CellUtil.cloneQualifier(cell));String v = Bytes.toString(CellUtil.cloneValue(cell));long ts=cell.getTimestamp();System.out.println(row+"=>"+cf+":"+col+"\t"+v+" ts:"+ts);
}
table.close();

TableName tname = TableName.valueOf("zpark:t_user");
Table table = conn.getTable(tname);Get get=new Get("0010".getBytes());Result result = table.get(get);
String row=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 sex = Bytes.toString(result.getValue("cf1".getBytes(), "sex".getBytes()));
String salary = Bytes.toString(result.getValue("cf1".getBytes(), "salary".getBytes()));
System.out.println(row+"\t"+name+" "+age+" "+sex+" "+salary);
table.close();

Scan

TableName tname = TableName.valueOf("zpark:t_user");
Table table = conn.getTable(tname);
Scan scan = new Scan();scan.setStartRow("0000".getBytes());
scan.setStopRow("0200".getBytes());
scan.addFamily("cf1".getBytes());
Filter filter1=new RowFilter(CompareFilter.CompareOp.EQUAL,new RegexStringComparator("09$"));
Filter filter2=new RowFilter(CompareFilter.CompareOp.EQUAL,new SubstringComparator("80"));
FilterList filter=new FilterList(FilterList.Operator.MUST_PASS_ONE,filter1,filter2);
scan.setFilter(filter);ResultScanner rs = table.getScanner(scan);for (Result result : rs) {String row=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 sex = Bytes.toString(result.getValue("cf1".getBytes(), "sex".getBytes()));String salary = Bytes.toString(result.getValue("cf1".getBytes(), "salary".getBytes()));System.out.println(row+"\t"+name+" "+age+" "+sex+" "+salary);
}table.close();

MapReduce 集成 Hbase（重点）

Jar包依赖

Hbase 0.90.x版本以后，程序可以自主解决运行时依赖，底层通过conf.set(“tmpjars”,’…’),所以用户无需使用-libjars参数，但是用户需要解决系统的提交依赖，因为系统如果读取HBase上的数据在任务初期需要计算任务切片，此时需要配置HADOOP_CLASSPATH

[root@CentOS ~]# vi .bashrcHADOOP_HOME=/usr/hadoop-2.6.0
JAVA_HOME=/usr/java/latest
PATH=$PATH:$JAVA_HOME/bin:$HADOOP_HOME/bin:$HADOOP_HOME/sbin
CLASSPATH=.
HBASE_MANAGES_ZK=false
export JAVA_HOME
export PATH
export CLASSPATH
export HADOOP_HOME
export HBASE_MANAGES_ZK
HADOOP_CLASSPATH=/root/mysql-connector-java-5.1.46.jar:`/usr/hbase-1.2.4/bin/hbase classpath`
export HADOOP_CLASSPATH
[root@CentOS ~]# source .bashrc

Maven

<dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-common</artifactId><version>2.6.0</version>
</dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-hdfs</artifactId><version>2.6.0</version>
</dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-mapreduce-client-jobclient</artifactId><version>2.6.0</version>
</dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-mapreduce-client-core</artifactId><version>2.6.0</version>
</dependency>
<!--Hbase依赖-->
<dependency><groupId>org.apache.hbase</groupId><artifactId>hbase-client</artifactId><version>1.2.4</version>
</dependency><dependency><groupId>org.apache.hbase</groupId><artifactId>hbase-server</artifactId><version>1.2.4</version>
</dependency>

任务提交

public class CustomJobsubmitter extends Configured implements Tool {public int run(String[] args) throws Exception {//1.创建Job实例Configuration conf = getConf();//开启Map端压缩conf.setBoolean("mapreduce.map.output.compress",true);conf.setClass("mapreduce.map.output.compress.codec", GzipCodec.class, CompressionCodec.class);//设置hbase的链接参数conf.set("hbase.zookeeper.quorum","CentOS");Job job=Job.getInstance(conf);job.setJarByClass(CustomJobsubmitter.class);//2.设置数据读入和写出格式化job.setInputFormatClass(TableInputFormat.class);job.setOutputFormatClass(TableOutputFormat.class);Scan scan = new Scan();scan.addFamily("cf1".getBytes());TableMapReduceUtil.initTableMapperJob("zpark:t_user",scan,UserMapper.class,Text.class,DoubleWritable.class,job);TableMapReduceUtil.initTableReducerJob("zpark:t_result",UserReducer.class,job);job.setNumReduceTasks(1);job.setCombinerClass(UserCombiner.class);job.waitForCompletion(true);return 0;}public static void main(String[] args) throws Exception {ToolRunner.run(new CustomJobsubmitter(),args);}
}

UserMapper

public class UserMapper extends TableMapper<Text, DoubleWritable> {@Overrideprotected void map(ImmutableBytesWritable key, Result value, Context context) throws IOException, InterruptedException {String sex = Bytes.toString(value.getValue("cf1".getBytes(), "sex".getBytes()));Double salary = Double.parseDouble(Bytes.toString(value.getValue("cf1".getBytes(), "salary".getBytes())));context.write(new Text(sex),new DoubleWritable(salary));}
}

UserReduce

public class UserReducer extends TableReducer<Text, DoubleWritable,NullWritable> {@Overrideprotected void reduce(Text key, Iterable<DoubleWritable> values, Context context) throws IOException, InterruptedException {double totalSalary=0.0;for (DoubleWritable value : values) {totalSalary+=value.get();}Put put =new Put(key.getBytes());put.addColumn("cf1".getBytes(),"totalSalary".getBytes(), Bytes.toBytes(totalSalary+""));context.write(null,put);}
}

UserCombiner

public class UserCombiner extends Reducer<Text, DoubleWritable,Text, DoubleWritable> {@Overrideprotected void reduce(Text key, Iterable<DoubleWritable> values, Context context) throws IOException, InterruptedException {double totalSalary=0.0;for (DoubleWritable value : values) {totalSalary+=value.get();}context.write(key,new DoubleWritable(totalSalary));}
}

HBase集群构建

• 保证所有物理主机的时钟同步，否则集群搭建失败

[root@CentOSX ~]# date -s '2019-04-01 16:24:00'
Mon Apr  1 16:24:00 CST 2019
[root@CentOSX ~]# clock -w

• 确保HDFS正常启动（参考HDFS集群构建）
• 搭建HBase集群

[root@CentOSX ~]# tar -zxf hbase-1.2.4-bin.tar.gz -C /usr/
[root@CentOSX ~]# vi /usr/hbase-1.2.4/conf/hbase-site.xml<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration><property><name>hbase.rootdir</name><value>hdfs://mycluster/hbase</value></property><property><name>hbase.cluster.distributed</name><value>true</value></property><property><name>hbase.zookeeper.quorum</name><value>CentOSA,CentOSB,CentOSC</value></property><property><name>hbase.zookeeper.property.clientPort</name><value>2181</value></property>
</configuration>

• 修改RegionServers

[root@CentOSX ~]# vi /usr/hbase-1.2.4/conf/regionservers
CentOSA
CentOSB
CentOSC

• 修改环境变量

[root@CentOS ~]# vi .bashrcHADOOP_HOME=/usr/hadoop-2.6.0
JAVA_HOME=/usr/java/latest
PATH=$PATH:$JAVA_HOME/bin:$HADOOP_HOME/bin:$HADOOP_HOME/sbin
CLASSPATH=.
export JAVA_HOME
export PATH
export CLASSPATH
export HADOOP_HOMEHBASE_MANAGES_ZK=false
HADOOP_CLASSPATH=`/usr/hbase-1.2.4/bin/hbase classpath`
export HBASE_MANAGES_ZK
export HADOOP_CLASSPATH[root@CentOS ~]# source .bashrc

• 启动Hbase服务

[root@CentOSX hbase-1.2.4]# ./bin/hbase-daemon.sh start master
[root@CentOSX hbase-1.2.4]# ./bin/hbase-daemon.sh start regionserver

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