HBase使用

1 create ‘table‘,{NAME=>‘dataset‘,DATA_BLOCK_ENCODING=>‘PREFIX‘} # 指定表名/列簇/压缩方式
2 create ‘table‘,{NAME=>‘1‘},{NAME=>‘2‘}
3 alter ‘table‘,‘family‘ # 添加列簇

1 disable ‘table‘ # 删除表
2 drop ‘table‘     
3  
4 alter ‘table‘,{NAME=>‘1‘,METHOD=>‘delete‘} # 删除列簇
5 delete ‘tebale‘,‘row‘,‘family:coloumn‘ # 删除列delete <table>,<rowkey>,<family:column>
6 deleteall ‘table‘,‘row‘ # 删除行deleteall <table>,<rowkey>,<family:column>
7 eg：
8 deleteall ‘annotation_task‘,‘oilT2My9Asrsi85CV0M.6.xj8upd8kbypm7vIQsoE‘
9 deleteall ‘annotation_task‘,"oilT2My9Asrsi85CV0M.\x5Cx00\x5Cx00\x5Cx00\x5Cx06.xj8upd8kbypm7vIQsoE" (双引号)

1 put <table>,<rowkey>,<family:column>,<value>,<timestamp>
2 put ‘table‘,‘sfsfsf‘,‘id:lisi‘,‘1993‘ # column 可以临时创建，通过:指定

1 count ‘table‘，{INTERVAL => 100, CACHE => 500} #表中有多少行，每100条显示一次，缓存区为500
2 get ‘table‘,‘row‘,‘family:column‘
3  
4 scan ‘table‘,{COLUM=>‘info‘} # 扫描info这个列簇
5 scan ‘table‘,{COLUMNS=>‘info:birthday‘} # 扫描指定列
6 scan ‘table‘, {STARTROW => ‘Sariel‘, LIMIT=>1, VERSIONS=>1}

1 scan ‘table‘, { STARTROW => ‘rowKey‘, LIMIT=>1, VERSIONS=>1}

1 scan ‘table‘, FILTER=>"ValueFilter(=,‘binary:26’)"
2 scan ‘member‘, FILTER=>"ValueFilter(=,‘substring:6‘)" # 值包含6这个值
3 scan ‘member‘, FILTER=>"ColumnPrefixFilter(‘birth‘) # 列名中的前缀为birth
4 scan ‘table‘,FILTER=>"PrefixFilter(‘rowPrefix‘)" # 过滤扫描rowkey
5 scan ‘member‘, FILTER=>"ColumnPrefixFilter(‘birth‘) AND ValueFilter ValueFilter(=,‘substring:1988‘)" # 多重条件过滤
6 scan ‘hbase:meta‘,FILTER=>"PrefixFilter(‘table‘)" # 获取指定table的region信息

1 exists ‘table‘ # 判断表名是否存在
2 disable ‘table‘ # 修改表结构，先disable，再enable
3 alter ‘table‘,{NAME=>‘1‘,TTL=>‘18888‘} 
4 ebale ‘table‘    

 1 create ‘sample_set_lemon‘,
 2 {NAME => ‘s‘, DATA_BLOCK_ENCODING => ‘FAST_DIFF‘}, 
 3 {NAME => ‘l‘, DATA_BLOCK_ENCODING => ‘FAST_DIFF‘}, 
 4 METADATA => { 
 5     ‘lemon.autoindex.enabled‘ => ‘true‘, 
 6     ‘lemon.index.enabled‘ => ‘true‘, 
 7     ‘lemon.index.regions‘ => ‘1‘,
 8     ‘lemon.update.enabled‘ =>‘true‘, 
 9     ‘lemon.index.meta‘ => ‘{"indexes":[
10         {"nameType":"E","family":"s","column":"sample_id","termExtractor":"com.huaweicloud.gaia.annotation.lemon.DatasetQualifierValueExtractor"},
11         {"nameType":"E","family":"s","column":"sample_name","termExtractor":"com.huaweicloud.gaia.annotation.lemon.DatasetQualifierValueMd5Extractor"},
12         {"nameType":"E","family":"s","column":"sample_dir","termExtractor":"com.huaweicloud.gaia.annotation.lemon.DatasetQualifierValueMd5Extractor"},
13         {"nameType":"E","family":"s","column":"sample_size","termExtractor":"com.huaweicloud.gaia.annotation.lemon.SampleSizeExtractor"},
14         {"nameType":"E","family":"s","column":"sample_time","termExtractor":"com.huaweicloud.gaia.annotation.lemon.SampleDateExtractor"},
15         {"nameType":"E","family":"s","column":"sample_status","termExtractor":"com.huaweicloud.gaia.annotation.lemon.DatasetQualifierValueExtractor"},
16         {"nameType":"E","family":"s","column":"annotation_status","termExtractor":"com.huaweicloud.gaia.annotation.lemon.DatasetQualifierValueExtractor"},
17         {"nameType":"E","family":"s","column":"annotated_by","termExtractor":"com.huaweicloud.gaia.annotation.lemon.DatasetQualifierValueMappingExtractor"},
18         {"nameType":"E","family":"s","column":"reviewer","termExtractor":"com.huaweicloud.gaia.annotation.lemon.DatasetQualifierValueMappingExtractor"},
19         {"nameType":"E","family":"s","column":"review_score","termExtractor":"com.huaweicloud.gaia.annotation.lemon.DatasetQualifierValueExtractor"},
20         {"nameType":"E","family":"s","column":"create_time","termExtractor":"com.huaweicloud.gaia.annotation.lemon.SampleDateExtractor"},
21         {"nameType":"E","family":"s","column":"update_time","termExtractor":"com.huaweicloud.gaia.annotation.lemon.SampleDateExtractor"},
22         {"nameType":"E","family":"s","column":"metadata","termExtractor":"com.huaweicloud.gaia.annotation.lemon.SampleMetadataExtractor"},
23         {"nameType":"F","family":"l","termExtractor":"com.huaweicloud.gaia.annotation.lemon.SampleLabelsExtractor"}
24     ]}‘
25 }