大数据之HBase

时间 2019-11-13

标签数据 hbase 栏目 Hadoop 繁體版

原文原文链接

Author: Lijbhtml

HBase是一个分布式的、面向列的开源数据库，该技术来源于 Fay Chang 所撰写的Google论文“Bigtable：一个结构化数据的分布式存储系统”。就像Bigtable利用了Google文件系统（File System）所提供的分布式数据存储同样，HBase在Hadoop之上提供了相似于Bigtable的能力。HBase是Apache的Hadoop项目的子项目。HBase不一样于通常的关系数据库，它是一个适合于非结构化数据存储的数据库。另外一个不一样的是HBase基于列的而不是基于行的模式。java

Hbase和HDFS之间关系？node

由于HDFS文件系统虽然支持海量数据存储，可是不擅长对单条记录作高效的管理（查询、修改、删除，增长）不支持对海量数据作随机读写。HBase是构建在HDFS上的一款NoSQL数据库，实现了对HDFS上的数据的高效管理，可以实现对海量数据的随机读写，实现行级别数据管理。shell

HBase数据库特色数据库

大 hbase一张表规模通常是在数亿行*数百万列且每一列具有上千个版本
稀疏，HBase没有固定的表结构，在一行记录中，能够有任意多个列存在（提高磁盘利用率）。
HBase没有数据类型，全部的类型都是以字节数组形式存在。
该数据和常规的数据库最大的区别是在底层对表中记录管理形式上有很大的区别，由于绝大多数数据库都是面向行存储的模式，致使了系统的IO利用率低。在HBase中采用面向列存储的形式，极大的提高系统的IO利用率。

行存储和列存储apache

行存储centos

列存储数组

HBase环境搭建bash

确保hadoop能正常运行（HDFS），必须配置 HADOOP_HOME架构
安装zookeeper（管理hbase服务）

[root@CentOS ~]# tar -zxf zookeeper-3.4.6.tar.gz -C /usr/ [root@CentOS ~]# vi /usr/zookeeper-3.4.6/conf/zoo.cfg tickTime=2000 dataDir=/root/zkdata clientPort=2181 [root@CentOS ~]# mkdir /root/zkdata [root@CentOS 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 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 [root@centos ~]# jps 1612 SecondaryNameNode 1348 NameNode 1742 QuorumPeerMain //zookeeper 1437 DataNode
安装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

<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> [root@centos ~]# vi /usr/hbase-1.2.4/conf/regionservers

centos

[root@centos ~]# vi .bashrc

HBASE_MANAGES_ZK=false HBASE_HOME=/usr/hbase-1.2.4 HADOOP_HOME=/usr/hadoop-2.6.0 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 export HBASE_MANAGES_ZK
启动HBase

[root@centos ~]# start-hbase.sh [root@centos ~]# jps 1612 SecondaryNameNode 2102 HRegionServer //负责实际表数据的读写操做 1348 NameNode 2365 Jps 1978 HMaster //相似namenode管理表相关元数据、管理ResgionServer 1742 QuorumPeerMain 1437 DataNode

能够访问：http://centos:16010

HBase Shell命令

链接Hbase

[root@centos ~]# 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 2017

hbase(main):001:0>
查看系统状态

hbase(main):001:0> status 1 active master, 0 backup masters, 1 servers, 0 dead, 2.0000 average load
查看当前系统版本

hbase(main):006:0> version 1.2.4, rUnknown, Wed Feb 15 18:58:00 CST 2017

namespace操做（数据库）

查看系统数据库

hbase(main):003:0> list_namespace NAMESPACE default
hbase
建立namespace

hbase(main):006:0> create_namespace 'baizhi',{'author'=>'zs'} 0 row(s) in 0.3260 seconds
查看namespace的表

hbase(main):004:0> list_namespace_tables 'hbase' TABLE
meta
namespace
查看建库详情

hbase(main):008:0> describe_namespace 'baizhi' DESCRIPTION
{NAME => 'baizhi', author => 'zs'} 1 row(s) in 0.0550 seconds
修改namespace

hbase(main):010:0> alter_namespace 'baizhi',{METHOD => 'set','author'=> 'wangwu'} 0 row(s) in 0.2520 seconds hbase(main):011:0> describe_namespace 'baizhi' DESCRIPTION
{NAME => 'baizhi', author => 'wangwu'} 1 row(s) in 0.0030 seconds hbase(main):012:0> alter_namespace 'baizhi',{METHOD => 'unset',NAME => 'author'} 0 row(s) in 0.0550 seconds hbase(main):013:0> describe_namespace 'baizhi' DESCRIPTION
{NAME => 'baizhi'}
1 row(s) in 0.0080 seconds
删除namespace

hbase(main):020:0> drop_namespace 'baizhi' 0 row(s) in 0.0730 seconds

HBase不容许删除有表的数据库

table相关操做（DDL操做）

建立表

hbase(main):023:0> create 't_user','cf1','cf2' 0 row(s) in 1.2880 seconds

=> Hbase::Table - t_user hbase(main):024:0> create 'baizhi:t_user',{NAME=>'cf1',VERSIONS=>3},{NAME=>'cf2',TTL=>3600} 0 row(s) in 1.2610 seconds

=> Hbase::Table - baizhi:t_user
查看建表详情

hbase(main):026:0> describe 'baizhi:t_user' Table baizhi:t_user is ENABLED
baizhi:t_user
COLUMN FAMILIES DESCRIPTION
{NAME => 'cf1', DATA_BLOCK_ENCODING => 'NONE', BLOOMFILTER => 'ROW', REPLICATION_SCOPE => '0', VERSIONS => '3', COMPRESSION =

'NONE', MIN_VERSIONS => '0', TTL => 'FOREVER', KEEP_DELETED_CELLS => 'FALSE', BLOCKSIZE => '65536', IN_MEMORY => 'false', B LOCKCACHE => 'true'}
{NAME => 'cf2', DATA_BLOCK_ENCODING => 'NONE', BLOOMFILTER => 'ROW', REPLICATION_SCOPE => '0', VERSIONS => '1', COMPRESSION = 'NONE', MIN_VERSIONS => '0', TTL => '3600 SECONDS (1 HOUR)', KEEP_DELETED_CELLS => 'FALSE', BLOCKSIZE => '65536', IN_MEMORY => 'false', BLOCKCACHE => 'true'}
2 row(s) in 0.0240 seconds
判断表是否存在

hbase(main):030:0> exists 't_user' Table t_user does exist
0 row(s) in 0.0250 seconds
enable/is_enabled/enable_all (相似disable、disable_all、is_disabled)

hbase(main):036:0> enable 't_user' 0 row(s) in 0.0220 seconds

hbase(main):037:0> is_enabled 't_user' true
0 row(s) in 0.0090 seconds hbase(main):035:0> enable_all 't_.*' t_user
Enable the above 1 tables (y/n)? y 1 tables successfully enabled
drop表

hbase(main):038:0> disable 't_user' 0 row(s) in 2.2930 seconds

hbase(main):039:0> drop 't_user' 0 row(s) in 1.2670 seconds
展现全部用户表(没法查看系统表hbase下的表)

hbase(main):042:0> list 'baizhi:.*' TABLE
baizhi:t_user
1 row(s) in 0.0050 seconds

=> ["baizhi:t_user"] hbase(main):043:0> list TABLE
baizhi:t_user
1 row(s) in 0.0050 seconds
获取一个表的引用

hbase(main):002:0> t=get_table 'baizhi:t_user' 0 row(s) in 0.0440 seconds
修改表参数

hbase(main):008:0> alter 'baizhi:t_user',{ NAME => 'cf2', TTL => 60 } Updating all regions with the new schema... 1/1 regions updated. Done. 0 row(s) in 2.7000 seconds

表的DML操做

Put指令

hbase(main):010:0> put 'baizhi:t_user',1,'cf1:name','zhangsan' 0 row(s) in 0.2060 seconds

hbase(main):011:0> t = get_table 'baizhi:t_user' 0 row(s) in 0.0010 seconds

hbase(main):012:0> t.put 1,'cf1:age','18' 0 row(s) in 0.0500 seconds
Get指令

hbase(main):017:0> get 'baizhi:t_user',1 COLUMN CELL
cf1:age timestamp=1536996547967, value=21
cf1:name timestamp=1536996337398, value=zhangsan
2 row(s) in 0.0680 seconds hbase(main):019:0> get 'baizhi:t_user',1,{COLUMN =>'cf1', VERSIONS=>10} COLUMN CELL
cf1:age timestamp=1536996547967, value=21 cf1:age timestamp=1536996542980, value=20 cf1:age timestamp=1536996375890, value=18 cf1:name timestamp=1536996337398, value=zhangsan 4 row(s) in 0.0440 seconds

hbase(main):020:0> get 'baizhi:t_user',1,{COLUMN =>'cf1:age', VERSIONS=>10} COLUMN CELL
cf1:age timestamp=1536996547967, value=21 cf1:age timestamp=1536996542980, value=20 cf1:age timestamp=1536996375890, value=18
3 row(s) in 0.0760 seconds

hbase(main):021:0> get 'baizhi:t_user',1,{COLUMN =>'cf1:age', TIMESTAMP => 1536996542980 } COLUMN CELL cf1:age timestamp=1536996542980, value=20
1 row(s) in 0.0260 seconds

hbase(main):025:0> get 'baizhi:t_user',1,{TIMERANGE => [1536996375890,1536996547967]} COLUMN CELL
cf1:age timestamp=1536996542980, value=20 1 row(s) in 0.0480 seconds

hbase(main):026:0> get 'baizhi:t_user',1,{TIMERANGE => [1536996375890,1536996547967],VERSIONS=>10} COLUMN CELL cf1:age timestamp=1536996542980, value=20 cf1:age timestamp=1536996375890, value=18 2 row(s) in 0.0160 seconds
scan

hbase(main):004:0> scan 'baizhi:t_user' ROW COLUMN+CELL
1 column=cf1:age, timestamp=1536996547967, value=21 1 column=cf1:height, timestamp=1536997284682, value=170 1 column=cf1:name, timestamp=1536996337398, value=zhangsan 1 column=cf1:salary, timestamp=1536997158586, value=15000
1 column=cf1:weight, timestamp=1536997311001, value=\x00\x00\x00\x00\x00\x00\x00\x05
2 column=cf1:age, timestamp=1536997566506, value=18 2 column=cf1:name, timestamp=1536997556491, value=lisi
2 row(s) in 0.0470 seconds hbase(main):009:0> scan 'baizhi:t_user', {STARTROW => '1',LIMIT=>1} ROW COLUMN+CELL 1 column=cf1:age, timestamp=1536996547967, value=21 1 column=cf1:height, timestamp=1536997284682, value=170 1 column=cf1:name, timestamp=1536996337398, value=zhangsan 1 column=cf1:salary, timestamp=1536997158586, value=15000 1 column=cf1:weight, timestamp=1536997311001, value=\x00\x00\x00\x00\x00\x00\x00\x05
1 row(s) in 0.0280 seconds

hbase(main):011:0> scan 'baizhi:t_user', {COLUMNS=>'cf1:age',TIMESTAMP=>1536996542980} ROW COLUMN+CELL 1 column=cf1:age, timestamp=1536996542980, value=20 1 row(s) in 0.0330 seconds
delete/deleteall

hbase(main):013:0> scan 'baizhi:t_user', {COLUMNS=>'cf1:age',VERSIONS=>3} ROW COLUMN+CELL
1 column=cf1:age, timestamp=1536996547967, value=21 1 column=cf1:age, timestamp=1536996542980, value=20 1 column=cf1:age, timestamp=1536996375890, value=18 2 column=cf1:age, timestamp=1536997566506, value=18
2 row(s) in 0.0150 seconds

hbase(main):014:0> delete 'baizhi:t_user',1,'cf1:age',1536996542980 0 row(s) in 0.0920 seconds

hbase(main):015:0> scan 'baizhi:t_user', {COLUMNS=>'cf1:age',VERSIONS=>3} ROW COLUMN+CELL 1 column=cf1:age, timestamp=1536996547967, value=21 2 column=cf1:age, timestamp=1536997566506, value=18
2 row(s) in 0.0140 seconds

hbase(main):016:0> delete 'baizhi:t_user',1,'cf1:age' 0 row(s) in 0.0170 seconds

hbase(main):017:0> scan 'baizhi:t_user', {COLUMNS=>'cf1:age',VERSIONS=>3} ROW COLUMN+CELL
2 column=cf1:age, timestamp=1536997566506, value=18
1 row(s) in 0.0170 seconds

hbase(main):019:0> deleteall 'baizhi:t_user',1 0 row(s) in 0.0200 seconds

hbase(main):020:0> get 'baizhi:t_user',1 COLUMN CELL 0 row(s) in 0.0200 seconds
truncate

hbase(main):022:0> truncate 'baizhi:t_user' Truncating 'baizhi:t_user' table (it may take a while):
- Disabling table...
- Truncating table... 0 row(s) in 4.0040 seconds

HBase java API

maven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-common</artifactId> <version>1.2.4</version> </dependency> <dependency> <groupId>org.apache.hbase</groupId> <artifactId>hbase-protocol</artifactId> <version>1.2.4</version> </dependency> <dependency> <groupId>org.apache.hbase</groupId> <artifactId>hbase-server</artifactId> <version>1.2.4</version> </dependency>
建立Connection和Admin对象

private Connection conn; private Admin admin; @Before public void before() throws IOException { Configuration config= HBaseConfiguration.create(); //由于HMaster和HRegionServer都将信息注册在zookeeper中 config.set("hbase.zookeeper.quorum","centos"); conn= ConnectionFactory.createConnection(config);
```
admin=conn.getAdmin();
```
} @After public void after() throws IOException { admin.close(); conn.close(); }
建立Namespace

NamespaceDescriptor nd=NamespaceDescriptor.create("zpark") .addConfiguration("author","zhangsan") .build(); admin.createNamespace(nd);
建立表

//create 'zpark:t_user',{NAME=>'cf1',VERIONS=>3},,{NAME=>'cf2',TTL=>10} TableName tname=TableName.valueOf("zpark:t_user"); //建立表的描述 HTableDescriptor t_user=new HTableDescriptor(tname);

//构建列簇 HColumnDescriptor cf1=new HColumnDescriptor("cf1"); cf1.setMaxVersions(3);

HColumnDescriptor cf2=new HColumnDescriptor("cf2"); cf2.setTimeToLive(10);

//添加列簇 t_user.addFamily(cf1); t_user.addFamily(cf2);

admin.createTable(t_user);
插入数据

TableName tname=TableName.valueOf("zpark:t_user"); Table t_user = conn.getTable(tname);

String[] company={"www.baizhi.com","www.sina.com"}; for(int i=0;i<1000;i++){ String com=company[new Random().nextInt(2)]; String rowKey=com; if(i<10){ rowKey+=":00"+i; }else if(i<100){ rowKey+=":0"+i; }else if(i<1000){ rowKey+=":"+i; } Put put=new Put(rowKey.getBytes()); put.addColumn("cf1".getBytes(),"name".getBytes(),("user"+i).getBytes()); put.addColumn("cf1".getBytes(),"age".getBytes(), Bytes.toBytes(i)); put.addColumn("cf1".getBytes(),"salary".getBytes(),Bytes.toBytes(5000+1000*i)); put.addColumn("cf1".getBytes(),"company".getBytes(),com.getBytes());
```
t_user.put(put);
```
} t_user.close();
批量插入

TableName tname=TableName.valueOf("zpark:t_user"); String[] company={"www.baizhi.com","www.sina.com"}; BufferedMutator mutator=conn.getBufferedMutator(tname); for(int i=0;i<1000;i++){ String com=company[new Random().nextInt(2)]; String rowKey=com; if(i<10){ rowKey+=":00"+i; }else if(i<100){ rowKey+=":0"+i; }else if(i<1000){ rowKey+=":"+i; } Put put=new Put(rowKey.getBytes()); put.addColumn("cf1".getBytes(),"name".getBytes(),("user"+i).getBytes()); put.addColumn("cf1".getBytes(),"age".getBytes(), Bytes.toBytes(i)); put.addColumn("cf1".getBytes(),"salary".getBytes(),Bytes.toBytes(5000+1000*i)); put.addColumn("cf1".getBytes(),"company".getBytes(),com.getBytes()); mutator.mutate(put); } mutator.close(); mutator.close();
修改数据

TableName tname=TableName.valueOf("zpark:t_user"); Table t_user = conn.getTable(tname);

Put put=new Put("www.baizhi.com:000".getBytes()); put.addColumn("cf1".getBytes(),"name".getBytes(),("zhangsan").getBytes());

t_user.put(put); t_user.close();
查询一条记录

TableName tname=TableName.valueOf("zpark:t_user"); Table t_user = conn.getTable(tname);

Get get=new Get("www.sina.com:002".getBytes()); //表示一行数据，涵盖n个cell Result result = t_user.get(get);

String name = Bytes.toString(result.getValue("cf1".getBytes(), "name".getBytes())); Integer age = Bytes.toInt(result.getValue("cf1".getBytes(), "age".getBytes())); Integer salary = Bytes.toInt(result.getValue("cf1".getBytes(), "salary".getBytes())); System.out.println(name+","+age+","+salary);

t_user.close();
查询多条

TableName tname=TableName.valueOf("zpark:t_user"); Table t_user = conn.getTable(tname);

Scan scan=new Scan(); // scan.setStartRow("www.baizhi.com:000".getBytes()); // scan.setStopRow("www.taizhi.com:020".getBytes()); Filter filter1=new PrefixFilter("www.baizhi.com:00".getBytes()); Filter filter2=new PrefixFilter("www.sina.com:00".getBytes()); FilterList filter=new FilterList(FilterList.Operator.MUST_PASS_ONE,filter1,filter2); scan.setFilter(filter);

ResultScanner resultScanner = t_user.getScanner(scan); for (Result result : resultScanner) { String rowKey=Bytes.toString(result.getRow()); String name = Bytes.toString(result.getValue("cf1".getBytes(), "name".getBytes())); Integer age = Bytes.toInt(result.getValue("cf1".getBytes(), "age".getBytes())); Integer salary = Bytes.toInt(result.getValue("cf1".getBytes(), "salary".getBytes())); System.out.println(rowKey+" => "+ name+","+age+","+salary); } t_user.close();
查询多条+Filter

TableName tname=TableName.valueOf("zpark:t_user"); Table t_user = conn.getTable(tname);

Scan scan=new Scan(); // scan.setStartRow("www.baizhi.com:000".getBytes()); // scan.setStopRow("www.taizhi.com:020".getBytes()); Filter filter1=new PrefixFilter("www.baizhi.com:00".getBytes()); Filter filter2=new PrefixFilter("www.sina.com:00".getBytes()); FilterList filter=new FilterList(FilterList.Operator.MUST_PASS_ALL,filter1,filter2); scan.setFilter(filter);

ResultScanner resultScanner = t_user.getScanner(scan); for (Result result : resultScanner) { String rowKey=Bytes.toString(result.getRow()); String name = Bytes.toString(result.getValue("cf1".getBytes(), "name".getBytes())); Integer age = Bytes.toInt(result.getValue("cf1".getBytes(), "age".getBytes())); Integer salary = Bytes.toInt(result.getValue("cf1".getBytes(), "salary".getBytes())); System.out.println(rowKey+" => "+ name+","+age+","+salary); } t_user.close();

HBase和MapReduce集成

public class CustomJobSubmiter extends Configured implements Tool {
    public int run(String[] args) throws Exception {

        //建立Job
        Configuration config = HBaseConfiguration.create(getConf());
        Job job = Job.getInstance(config);
        job.setJarByClass(CustomJobSubmiter.class);     // class that contains mapper

        //设置输入、输出格式
        job.setInputFormatClass(TableInputFormat.class);
        job.setOutputFormatClass(TableOutputFormat.class);

        TableMapReduceUtil.initTableMapperJob(
                "zpark:t_user",
                new Scan(),
                UserMapper.class,
                Text.class,
                DoubleWritable.class,
                job
        );
        TableMapReduceUtil.initTableReducerJob(
                "zpark:t_user_count",
                UserReducer.class,
                job
        );
        job.setCombinerClass(UserCombiner.class);
        job.waitForCompletion(true);
        
        
        return 0;
    }

    public static void main(String[] args) throws Exception {
        ToolRunner.run(new CustomJobSubmiter(),args);
    }
    public static class UserMapper extends TableMapper<Text, CountWritable>{
        private Text k=new Text();
        private DoubleWritable v=new DoubleWritable();
        @Override
        protected void map(ImmutableBytesWritable key, Result value, Context context) throws IOException, InterruptedException {
            String company= Bytes.toString(value.getValue("cf1".getBytes(),"company".getBytes()));
            Double salary= Bytes.toInt(value.getValue("cf1".getBytes(),"salary".getBytes()))*1.0;
            k.set(company);
            v.set(salary);
            context.write(k,new CountWritable(1,salary,salary,salary));
        }
    }
    public static class UserCombiner extends Reducer<Text, CountWritable,Text, CountWritable>{
        @Override
        protected void reduce(Text key, Iterable<CountWritable> values, Context context) throws IOException, InterruptedException {
            int total=0;
            double tatalSalary=0.0;
            double avgSalary=0.0;
            double maxSalary=0.0;
            double minSalary=Integer.MAX_VALUE;
            for (CountWritable value : values) {
                tatalSalary+=value.getTatalSalary();
                total+=value.getTotal();
                if(minSalary>value.getMinSalary()){
                    minSalary=value.getMinSalary();
                }
                if(maxSalary<value.getMaxSalary()){
                    maxSalary=value.getMaxSalary();
                }
            }
            
            context.write(key,new CountWritable(total,tatalSalary,maxSalary,minSalary));
        }
    }
    public static class UserReducer extends TableReducer<Text, CountWritable, NullWritable>{
        @Override
        protected void reduce(Text key, Iterable<CountWritable> values, Context context) throws IOException, InterruptedException {
            int total=0;
            double tatalSalary=0.0;
            double avgSalary=0.0;
            double maxSalary=0.0;
            double minSalary=Integer.MAX_VALUE;
            for (CountWritable value : values) {
                tatalSalary+=value.getTatalSalary();
                total+=value.getTotal();
                if(minSalary>value.getMinSalary()){
                    minSalary=value.getMinSalary();
                }
                if(maxSalary<value.getMaxSalary()){
                    maxSalary=value.getMaxSalary();
                }
            }
            avgSalary=tatalSalary/total;

            Put put=new Put(key.getBytes());
            put.addColumn("cf1".getBytes(),"taotal".getBytes(),(total+"").getBytes());
            put.addColumn("cf1".getBytes(),"tatalSalary".getBytes(),(tatalSalary+"").getBytes());
            put.addColumn("cf1".getBytes(),"maxSalary".getBytes(),(maxSalary+"").getBytes());
            put.addColumn("cf1".getBytes(),"minSalary".getBytes(),(minSalary+"").getBytes());
            put.addColumn("cf1".getBytes(),"avgSalary".getBytes(),(avgSalary+"").getBytes());

            context.write(null,put);

        }
    }
}

public class CountWritable implements Writable {
    int total=0;
    double tatalSalary=0.0;
    double maxSalary=0.0;
    double minSalary=Integer.MAX_VALUE;

    public CountWritable(int total, double tatalSalary, double maxSalary, double minSalary) {
        this.total = total;
        this.tatalSalary = tatalSalary;
        this.maxSalary = maxSalary;
        this.minSalary = minSalary;
    }

    public CountWritable() {
    }

    public void write(DataOutput out) throws IOException {
        out.writeInt(total);
        out.writeDouble(tatalSalary);
        out.writeDouble(maxSalary);
        out.writeDouble(minSalary);
    }

    public void readFields(DataInput in) throws IOException {
        total=in.readInt();
        tatalSalary=in.readDouble();
        maxSalary=in.readDouble();
        minSalary=in.readDouble();
    }
    //....
}

HBase架构

HBase宏观架构

RegionServer架构

Region架构图

参考：http://www.blogjava.net/DLevin/archive/2015/08/22/426877.html

http://www.blogjava.net/DLevin/archive/2015/08/22/426950.html

HBase集群构建

确保HDFS正常运行（HDFS - HA）
配置安装HBase

[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

<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> [root@centos ~]# vi /usr/hbase-1.2.4/conf/regionservers CentOSA CentOSB CentOSC [root@CentOSX ~]# vi .bashrc HBASE_MANAGES_ZK=false HBASE_HOME=/usr/hbase-1.2.4 HADOOP_HOME=/usr/hadoop-2.6.0 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 export HBASE_MANAGES_ZK

[root@CentOSX ~]# source .bashrc
启动HBase [root@CentOSX ~]# hbase-daemon.sh start master [root@CentOSX ~]# hbase-daemon.sh start regionserver