Hbase小结

(一).Hbase基本介绍

1.hbase是建立的hdfs之上，提供高可靠性、高性能、列存储、可伸缩、实时读写的数据库系统

2.hbase特点:
　　HBase中的存储一切皆是字节
　　HBase的RowKey会按照字节顺序排序，并且添加索引
　　HBase会按照row数量自动切割成Region，保持负载均衡与冗余

3.hbase存储结构:
　　RowKey：是Byte array，是表中每条记录的“主键”，方便快速查找，Rowkey的设计非常重要；
　　Column Family：列族，拥有一个名称(string)，包含一个或者多个相关列;同一列族下的列具有相同的属性
　　Column：属于某一个columnfamily，familyName:columnName，每条记录可动态添加；
　　Cell：其中timestamp是时间戳,value是rowkey对应列的值

　　hbase(main):009:0> scan 'User'

　　ROW                                       COLUMN+CELL

　　id001 column=personInfo:name, timestamp=1502368030841, value=xiaoming
　　id001 column=personInfo:age, timestamp=1502368069926, value=18
　　id001 column=personInfo:sex, timestamp=1502368093636, value=man

(二).Hbase常用命令

1.进入shell: hbase shell

[hadoop@indb-3-136-hzifc bin]$ echo $HBASE_HOME

/data/program/hbase

[hadoop@indb-3-136-hzifc bin]$ /data/program/hbase/bin/hbase shell


2.查看所有表: list

hbase(main):003:0> list
T
TABLE
S
SYSTEM.CATALOG
S
SYSTEM.FUNCTION
S
SYSTEM.SEQUENCE
S
SYSTEM.STATS
T
TEST.USER
U
User

6 row(s) in 0.0340 seconds


3.查看某个表详情: describe

hbase(main):004:0> describe 'User'
T
Table User is ENABLED
U
User
C
COLUMN FAMILIES DESCRIPTION
{
{NAME => 'info', BLOOMFILTER => 'ROW', VERSIONS => '1', IN_MEMORY => 'false', KEEP_DELETED_CELLS => 'FALSE',

DATA_BLOCK_ENCODING => 'NONE', TTL => 'FORE
V
VER', COMPRESSION => 'NONE', MIN_VERSIONS => '0', BLOCKCACHE => 'true', BLOCKSIZE => '65536', REPLICATION_SCOPE =>

'0'}

1 row(s) in 0.1410 seconds



4.创建表: create

语法：create <table>, {NAME => <family>, VERSIONS => <VERSIONS>}
创建一个User表，可以一个或多个info列族

hbase(main):002:0> create 'User','info1'
0 row(s) in 1.5890 seconds


5.删除指定的列族: delete

语法: alter 表名,'delete' =>'列族'

hbase(main):002:0> alter 'User', 'delete' => 'info'
U
Updating all regions with the new schema...

1/1 regions updated.
D
Done.

0 row(s) in 2.5340 seconds



6.插入数据: put

语法：put <table>,<rowkey>,<family:column>,<value>

hbase(main):005:0> put 'User', 'row1', 'info:name', 'xiaoming'
0 row(s) in 0.1200 seconds

hbase(main):006:0> put 'User', 'row2', 'info:age', '18'
0 row(s) in 0.0170 seconds

hbase(main):007:0> put 'User', 'row3', 'info:sex', 'man'
0 row(s) in 0.0030 seconds




7.根据rowKey查询某个记录: get

语法：get <table>,<rowkey>,[<family:column>,....]

hbase(main):008:0> get 'User', 'row2'

COLUMN CELL

info:age timestamp=1502368069926, value=18
1 row(s) in 0.0280 seconds

hbase(main):028:0> get 'User', 'row3', 'info:sex'

COLUMN CELL

info:sex timestamp=1502368093636, value=man

hbase(main):036:0> get 'User', 'row1', {COLUMN => 'info:name'}

COLUMN CELL

info:name timestamp=1502368030841, value=xiaoming

1 row(s) in 0.0120 seconds



8.查询所有记录: scan

语法：scan <table>, {COLUMNS => [ <family:column>,.... ], LIMIT => num}

扫描所记录
hbase(main):009:0> scan 'User'

ROW COLUMN+CELL

row1 column=info:name, timestamp=1502368030841, value=xiaoming

row2 column=info:age, timestamp=1502368069926, value=18
row3 column=info:sex, timestamp=1502368093636, value=man

3 row(s) in 0.0380 seconds

扫描前2条
hbase(main):037:0> scan 'User', {LIMIT => 2}
R
ROW COLUMN+CELL

row1 column=info:name, timestamp=1502368030841, value=xiaoming

row2 column=info:age, timestamp=1502368069926, value=18
2 row(s) in 0.0170 seconds

范围查询
hbase(main):011:0> scan 'User', {STARTROW => 'row2'}
R
ROW COLUMN+CELL

row2 column=info:age, timestamp=1502368069926, value=18
row3 column=info:sex, timestamp=1502368093636, value=man

2 row(s) in 0.0170 seconds

hbase(main):012:0> scan 'User', {STARTROW => 'row2', ENDROW => 'row2'}
R
ROW COLUMN+CELL

row2 column=info:age, timestamp=1502368069926, value=18
1 row(s) in 0.0110 seconds

hbase(main):013:0> scan 'User', {STARTROW => 'row2', ENDROW => 'row3'}
R
ROW COLUMN+CELL

row2 column=info:age, timestamp=1502368069926, value=18
1 row(s) in 0.0120 seconds

另外，还可以添加TIMERANGE和FITLER等高级功能
STARTROW,ENDROW必须大写，否则报错;查询结果不包含等于ENDROW的结果集

9.统计表记录数: count

语法：count <table>, {INTERVAL => intervalNum, CACHE => cacheNum}

INTERVAL设置多少行显示一次及对应的rowkey，默认1000；CACHE每次去取的缓存区大小，默认是10，调整该参数可提高查询速度
hbase(main):020:0> count 'User'
3 row(s) in 0.0360 seconds




10.删除: delete

删除列
hbase(main):008:0> delete 'User', 'row1', 'info:age'
0 row(s) in 0.0290 seconds

删除所行
hbase(main):014:0> deleteall 'User', 'row2'
0 row(s) in 0.0090 seconds

清空表中所有数据
hbase(main):016:0> truncate 'User'
T
Truncating 'User' table (it may take a while):

- Disabling table...

- Truncating table...

0 row(s) in 3.6610 seconds


11.查看表是否存在: exists

hbase(main):022:0> exists 'User'
T
Table User does exist

0 row(s) in 0.0150 seconds


12.禁用表: disable

hbase(main):014:0> disable 'User'
0 row(s) in 2.2660 seconds



13.启用表: enable

hbase(main):017:0> enable 'User'
0 row(s) in 1.3470 seconds



14.删除表: drop

删除前，必须先disable

hbase(main):031:0> disable 'TEST.USER'
0 row(s) in 2.2640 seconds
hbase(main):033:0> drop 'TEST.USER'
0 row(s) in 1.2490 seconds

(三).scala操作hbase的api

import org.apache.hadoop.hbase.{HTableDescriptor,HColumnDescriptor,HBaseConfiguration,TableName}
import org.apache.hadoop.hbase.client.{ConnectionFactory,Put,Get,Delete,Scan}
import org.apache.hadoop.hbase.util.Bytes
import scala.collection.JavaConversions._
import java.util



val conf=HBaseConfiguration.create()
//Connection 的创建是个重量级的工作，线程安全，是操作hbase的入口
val conn=ConnectionFactory.createConnection(conf)
//从Connection获得 Admin 对象(相当于以前的 HAdmin)
val admin=conn.getAdmin
//本例将操作的表名
val userTable=TableName.valueOf("user_score_table")


val cf1="scoreInfo"
val cf2="addressInfo"
val cn1="math"
val cn2="physics"
val cn3="Addr"


if(admin.tableExists(userTable)){
  println("Table exists!")
  //admin.disableTable(userTable)
  //admin.deleteTable(userTable)
  //exit()
}else{
  val tableDesc=new HTableDescriptor(userTable)
  tableDesc.addFamily(new HColumnDescriptor("scoreInfo".getBytes))
  tableDesc.addFamily(new HColumnDescriptor("addressInfo".getBytes))
  admin.createTable(tableDesc)
  println("Create table success!")
}



//插入一条rowkey 为 IromMan 的数据
val p=new Put("IromMan".getBytes())
//为put操作指定 column 和 value （以前的 put.add 方法被弃用了）
p.addColumn(cf1.getBytes,cn1.getBytes,"98".getBytes) // scoreInfo:math  98
p.addColumn(cf1.getBytes,cn2.getBytes,"87".getBytes) // scoreInfo:physics  87
p.addColumn(cf2.getBytes,cn3.getBytes,"Beijing".getBytes) // addressInfo
table.put(p)


//按rowkey查询数据
val listGet=new util.ArrayList[Get]
val get=new Get(Bytes.toBytes("id002_Thor"))
val get2=new Get(Bytes.toBytes("id003_jack"))
listGet.add(get)
listGet.add(get2)
val resultArr=myTable.get(listGet).flatMap(z=>{
  val cellArr=z.rawCells()
  val valueArr=cellArr.map(n=>(Bytes.toString(z.getRow()),(Bytes.toString(CellUtil.cloneQualifier(n)),Bytes.toString(CellUtil.cloneValue(n)))))
  valueArr
})


userTable.close()
conn.close()
【推广】 免费学中医，健康全家人
原文地址：https://www.cnblogs.com/ShyPeanut/p/11265075.html