7.hbase shell命令 cmd

$HADOOP_USER_NAME

#创建命名空间
create_namespace 'bd1902'

#展示所有命名空间
list_namespace

#删除命名空间,The namespace must be empty.
drop_namespace 'IMUT'

create 't1', 'f1', 'f2', 'f3'
create 't1', {NAME => 'f1'}, {NAME => 'f2'}, {NAME => 'f3'}

#创建一张表，指定版本号为3

create 'bd1902:student', 'baseinfo', 'extrainfo'
create 'bd1902:student1', {NAME => 'baseinfo', VERSIONS => 3},{NAME => 'extrainfo',VERSIONS => 5}

create 'bd1803:employee', 'baseinfo', 'extrainfo'
create 'bd1803:employee1', {NAME => 'baseinfo', VERSIONS => 3},{NAME => 'extrainfo',VERSIONS => 5}

describe 'bd1902:student2'

describe 'bigdata:test1'

hbase 热点问题，数据倾斜
读操作 写操作
1. 默认分区
2. rowkey递增

解决热点问题：
1 预分区 （建表过程中）
2 随机产生rowkey hash 、MD5 、 SHA256

#创建表，预定义分区，在rowkey为0<= <10 10<= 20 20<= 30

create 'bd1902:student2', {NAME=>'baseinfo',VERSIONS=>3}, SPLITS => ['1000', '2000', '3000', '4000'] 四个分界点，分五个区

create 'bd1803:employee3', {NAME=>'baseinfo',VERSIONS=>3}, SPLITS => ['1000', '2000', '3000', '4000']
put 'hbase_test:teacher3','2000009','baseinfo:name','zhangsan'

#创建表，分区标准在文件中，如果rowkey以0001等开头,进行分区使用| 或者 ~ 帮助划分rowkey区域，文件放在进入hbase shell 的目录下

create 'bd1902:student3','baseinfo',{SPLITS_FILE => '/home/briup/splits.txt'}

测试ROWKEY开闭区间：(左闭右开)
put 'bd1902:student2','1000','baseinfo:name' ,'jack'

create 'bd1803:employee4','baseinfo',{SPLITS_FILE => '/home/hbase/sps.txt'}
create 'bd1803:employee4', 'baseinfo', SPLITS_FILE => 'sps.txt'

#使用HexStringSplit算法进行分区，分成10个region，适合散列字符不包含中文，适合16进制的rowkey或者前缀是16进制的rowkey （哈希算法、SHA32）

create 'bd1902:student4', 'baseinfo', {NUMREGIONS => 10, SPLITALGO => 'HexStringSplit'}

create 'bd1803:employee5', 'baseinfo', {NUMREGIONS => 10, SPLITALGO => 'HexStringSplit'}

#使用UniformSplit算法进行分区，rowkey可以包含中文，适合随机字节数组rowkey

create 'bd1902:student5', 'baseinfo', {NUMREGIONS => 5, SPLITALGO => 'UniformSplit'}

create 'bd1803:employee6', 'baseinfo', {NUMREGIONS => 5, SPLITALGO => 'UniformSplit'}
put 'bd1803:employee3', '2000', 'baseinfo:name', '张三'

JRuby （脚本）变量
#create 返回引用值
t1 = create 't1', 'f1'

#alter修改表结构--增加列族
alter 'bd1902:student' ,{NAME => 'extrainfo' ,VERSIONS => 5},{NAME => 'secret',VERSIONS => 5 }

alter 'bigdata:test2', {NAME => 'extrainfo', IN_MEMORY => true}, {NAME => 'secret', VERSIONS => 5}

#alter修改表结构--删除列族
alter 'bd1902:student',{METHOD => 'delete',NAME => 'baseinfo'}

alter 'bigdata:test2', {METHOD => 'delete',NAME => 'baseinfo'}

---------------------------------------

#插入数据 兼顾更新
Cell
put 'ns:t','r','cf:q','v'[,'t']
put 'bd1902:student1','1001','baseinfo:name','Kenvin'
put 'bd1902:student1','1001','baseinfo:gender','male'
put 'bd1902:student1','1001','baseinfo:age','40'
put 'bd1902:student1','1001','baseinfo:pos','CTO'

put 'bd1902:student1','1002','baseinfo:name','Terry'
put 'bd1902:student1','1002','baseinfo:gender','male'
put 'bd1902:student1','1002','baseinfo:age','36'
put 'bd1902:student1','1002','baseinfo:pos','Manager'
put 'bd1902:student1','2001','baseinfo:name','Wood'
put 'bd1902:student1','2001','baseinfo:gender','male'
put 'bd1902:student1','2001','baseinfo:age','32'
put 'bd1902:student1','2001','baseinfo:pos','Manager'
put 'bd1902:student1','2002','baseinfo:name','Terry'
put 'bd1902:student1','2002','baseinfo:gender','male'
put 'bd1902:student1','2002','baseinfo:age','30'
put 'bd1902:student1','2002','baseinfo:pos','Teacher'
put 'bd1902:student1','3001','baseinfo:name','Lurry'
put 'bd1902:student1','3001','baseinfo:gender','male'
put 'bd1902:student1','3001','baseinfo:age','36'
put 'bd1902:student1','3001','baseinfo:pos','Teacher'

scan 'bd1902:student1'

put 'bd1803:employee1','1001','baseinfo:gender','male'
put 'briup:employee3','2000','baseinfo:name','tom'

#插入指定timestamp
put 'hbase_test:teacher5','100000000','extrainfo:salary','5000',1488888888888

#查询
get 单行查询
scan 多行查询

#获得某一个特定值

get 't1', 'r1', ['c1', 'c2']

get 'bigdata:test1','10','baseinfo:name'

#获得前5个版本的数据
get 'bd1803:employee1','1001',{COLUMN => 'baseinfo:position',VERSIONS => 5}

#获得某个时间段数据,不一定是时间最新的数据
get 'hbase_test:teacher2', '10001', {TIMERANGE => [1479371084728, 1479373228331]}

#scan 扫描某张表 select *
scan 'bd1803:employee1'

scan 'bd1902:student1'

#scan 扫描 表中某一列
scan 'test1:student5',{COLUMNS=>'baseinfo:name'}

#scan 使用limit 进行行数限制
scan 'test1:student5',{COLUMNS=>'baseinfo:name',LIMIT=>2}

#scan 指定从某一行开始扫描
scan 'hbase_test:teacher2',{COLUMNS=>'baseinfo:name',LIMIT=>2,STARTROW=>'20001'}

#scan 扫描所有版本
scan 'bigdata:test1','10',{VERSIONS=>5}

#在hbase 对于hfile没有进行过合并操作之前
#scan 超出版本限制也能访问到
scan 'briup:employee3',{VERSIONS=>5,RAW=>true}

#scan 使用过滤器 行键前缀过滤器,只有这一个有属性
scan 'bigdata:test1', {ROWPREFIXFILTER => '10'}
scan 'bd1902:student1', {ROWPREFIXFILTER => '1002'}

#scan 使用空值行健过滤器，只返回行健
scan 'bigdata:test1',{FILTER=>'KeyOnlyFilter()'}

scan 'bigdata:test1',{FILTER=>"ColumnPrefixFilter('na') "}

1 数值 数字
2 CompareFilter.CompareOp 比较符 >
3 ByteArrayComparable binary:1000 substring:
4 byte[] ''

scan 'bd1803:employee1',{FILTER=>"RowFilter(>,'binary:1001')"}

scan 'bd1902:student1',{FILTER=>"RowFilter(>,'binary:2000')"}

#scan 使用行健过滤器,binary: 帮助数据类型转化
scan 'hbase_test:teacher2',{FILTER =>"RowFilter(!=,'binary:10001')"}

#scan 使用列名过滤器
scan 'test1:student5',{FILTER =>"QualifierFilter(>=,'binary:baseinfo:name')"}

#scan 使用子串过滤器
scan 'test1:student5',{FILTER =>"ValueFilter(=,'binary:zhao')"}

#列名前缀过滤器
scan 'test1:student5',{FILTER =>"ColumnPrefixFilter('name')"}

#scan 使用多种过滤器进行条件结合
scan 'hbase_test:teacher2',{FILTER =>"(ValueFilter(=,'binary:hello')) OR (RowFilter (>,'binary:10'))"}

#scan 使用page过滤器，限制每页展示数量
scan 'bigdata:test1',{FILTER =>org.apache.hadoop.hbase.filter.KeyOnlyFilter.new()}

#scan 使用行健过滤器，进行正则表达式的匹配
scan 'test1', {FILTER => RowFilter.new(CompareFilter::CompareOp.valueOf('EQUAL'),RegexStringComparator.new('.*ll.*'))}

scan 'bd1902:student1', {FILTER => org.apache.hadoop.hbase.filter.RowFilter.new(org.apache.hadoop.hbase.filter.CompareFilter::CompareOp.valueOf('EQUAL'),org.apache.hadoop.hbase.filter.RegexStringComparator.new('.*3.*'))}

//-----------------------

#删除数据
delete 't1','r1','c1'

#清空某张表
truncate 't1'

#disable 某张表
disable 'bigdata:test1'

#删除某张表
drop 'bigdata:test2'

#大合并 hfile
major_compact '583b13b5efb36a6ae7794d7e60b4c3a8'
major_compact 'bigdata:test2'

#小合并

#移动region
move 'ENCODED_REGIONNAME', 'SERVER_NAME'
#第一个参数指的是region最后一部分编号(逗号分隔每部分)
move 'a39dc69bd00d19e556ae17e4aeb1ebe1','datanode02,16020,1479354142616'

// 行过滤器
// 1 行健范围
ByteArrayComparable com1 = new BinaryComparator(Bytes.toBytes("briup004"));
RowFilter rf1 = new RowFilter(CompareOp.LESS, com1);
// 2 行健子串范围
ByteArrayComparable com2 = new SubstringComparator("007");
RowFilter rf2 = new RowFilter(CompareOp.EQUAL, com2);
// 3 某个列标示符的值范围
SingleColumnValueFilter scf1 = new SingleColumnValueFilter
(Bytes.toBytes("infos"), Bytes.toBytes("name"), CompareOp.LESS_OR_EQUAL, Bytes.toBytes("张三"));
// 4 匹配正则表达式
ByteArrayComparable com3 = new SubstringComparator("test.");
SingleColumnValueFilter scf2 = new SingleColumnValueFilter
(Bytes.toBytes("infos"), Bytes.toBytes("name"), CompareOp.EQUAL,com3);
// 5 匹配子串 不区分大小写
ByteArrayComparable com4 = new SubstringComparator("te");
SingleColumnValueFilter scf3 = new SingleColumnValueFilter
(Bytes.toBytes("infos"), Bytes.toBytes("name"), CompareOp.EQUAL,com4);