hbase shell操作之scan+filter

╰+攻爆jí腚メ 2022-05-26 12:21 216阅读 0赞

创建表

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1.  create 'test1', 'lf', 'sf'

lf: column family of LONG values (binary value)  
\-- sf: column family of STRING values

导入数据

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1.  put 'test1', 'user1|ts1', 'sf:c1', 'sku1'  
2.  put 'test1', 'user1|ts2', 'sf:c1', 'sku188'  
3.  put 'test1', 'user1|ts3', 'sf:s1', 'sku123'  
4.    
5.  put 'test1', 'user2|ts4', 'sf:c1', 'sku2'  
6.  put 'test1', 'user2|ts5', 'sf:c2', 'sku288'  
7.  put 'test1', 'user2|ts6', 'sf:s1', 'sku222'

一个用户（userX），在什么时间（tsX），作为rowkey  
对什么产品（value：skuXXX），做了什么操作作为列名，比如，c1: click from homepage; c2: click from ad; s1: search from homepage; b1: buy

查询案例

1、谁的值=sku188

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1.  scan 'test1', FILTER=**>**"ValueFilter(=,'binary:sku188')"  
2.    
3.  ROW                          COLUMN+CELL                                                                         
4.  user1|ts2                   column=sf:c1, timestamp=1409122354918, value=sku188

2、谁的值包含88

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1.  scan 'test1', FILTER=**>**"ValueFilter(=,'substring:88')"  
2.    
3.  ROW                          COLUMN+CELL                                                                         
4.   user1|ts2                   column=sf:c1, timestamp=1409122354918, value=sku188                                 
5.   user2|ts5                   column=sf:c2, timestamp=1409122355030, value=sku288

3、通过广告点击进来的(column为c2)值包含88的用户

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1.  scan 'test1', FILTER=**>**"ColumnPrefixFilter('c2') AND ValueFilter(=,'substring:88')"  
2.    
3.  ROW                          COLUMN+CELL                                                                         
4.   user2|ts5                   column=sf:c2, timestamp=1409122355030, value=sku288

4、通过搜索进来的(column为s)值包含123或者222的用户

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1.  scan 'test1', FILTER=**>**"ColumnPrefixFilter('s') AND ( ValueFilter(=,'substring:123') OR ValueFilter(=,'substring:222') )"  
2.    
3.  ROW                          COLUMN+CELL                                                                         
4.   user1|ts3                   column=sf:s1, timestamp=1409122354954, value=sku123                                 
5.   user2|ts6                   column=sf:s1, timestamp=1409122355970, value=sku222

5、rowkey为user1开头的

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1.  scan 'test1', FILTER =**>** "PrefixFilter ('user1')"  
2.    
3.  ROW                          COLUMN+CELL                                                                         
4.   user1|ts1                   column=sf:c1, timestamp=1409122354868, value=sku1                                   
5.   user1|ts2                   column=sf:c1, timestamp=1409122354918, value=sku188                                 
6.   user1|ts3                   column=sf:s1, timestamp=1409122354954, value=sku123

6、FirstKeyOnlyFilter: 一个rowkey可以有多个version,同一个rowkey的同一个column也会有多个的值, 只拿出key中的第一个column的第一个version  
KeyOnlyFilter: 只要key,不要value

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1.  scan 'test1', FILTER=**>**"FirstKeyOnlyFilter() AND ValueFilter(=,'binary:sku188') AND KeyOnlyFilter()"  
2.    
3.  ROW                          COLUMN+CELL                                                                         
4.   user1|ts2                   column=sf:c1, timestamp=1409122354918, value=

7、从user1|ts2开始,找到所有的rowkey以user1开头的

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1.  scan 'test1', \{ STARTROW=**>**'user1|ts2', FILTER =**>** "PrefixFilter ('user1')"\}  
2.    
3.  ROW                          COLUMN+CELL                                                                         
4.   user1|ts2                   column=sf:c1, timestamp=1409122354918, value=sku188                                 
5.   user1|ts3                   column=sf:s1, timestamp=1409122354954, value=sku123

8、从user1|ts2开始,找到所有的到rowkey以user2开头

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1.  scan 'test1', \{ STARTROW=**>**'user1|ts2', STOPROW=**>**'user2'\}  
2.    
3.  ROW                          COLUMN+CELL                                                                         
4.   user1|ts2                   column=sf:c1, timestamp=1409122354918, value=sku188                                 
5.   user1|ts3                   column=sf:s1, timestamp=1409122354954, value=sku123

9、查询rowkey里面包含ts3的

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10、查询rowkey里面包含ts的

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1.  import org.apache.hadoop.hbase.filter.CompareFilter  
2.  import org.apache.hadoop.hbase.filter.SubstringComparator  
3.  import org.apache.hadoop.hbase.filter.RowFilter  
4.  scan 'test1', \{ FILTER =**>** RowFilter.new(CompareFilter::CompareOp.valueOf('EQUAL'), SubstringComparator.new('ts'))\}  
5.    
6.  ROW                          COLUMN+CELL                                                                         
7.   user1|ts1                   column=sf:c1, timestamp=1409122354868, value=sku1                                   
8.   user1|ts2                   column=sf:c1, timestamp=1409122354918, value=sku188                                 
9.   user1|ts3                   column=sf:s1, timestamp=1409122354954, value=sku123                                 
10.  user2|ts4                   column=sf:c1, timestamp=1409122354998, value=sku2                                   
11.  user2|ts5                   column=sf:c2, timestamp=1409122355030, value=sku288                                 
12.  user2|ts6                   column=sf:s1, timestamp=1409122355970, value=sku222

加入一条测试数据

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1.  put 'test1', 'user2|err', 'sf:s1', 'sku999'

11、查询rowkey里面以user开头的，新加入的测试数据并不符合正则表达式的规则，故查询不出来

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1.  import org.apache.hadoop.hbase.filter.RegexStringComparator  
2.  import org.apache.hadoop.hbase.filter.CompareFilter  
3.  import org.apache.hadoop.hbase.filter.SubstringComparator  
4.  import org.apache.hadoop.hbase.filter.RowFilter  
5.  scan 'test1', \{ FILTER =**>** RowFilter.new(CompareFilter::CompareOp.valueOf('EQUAL'),RegexStringComparator.new('^user\\d+\\|ts\\d+$'))\}  
6.    
7.  ROW                          COLUMN+CELL                                                                         
8.   user1|ts1                   column=sf:c1, timestamp=1409122354868, value=sku1                                   
9.   user1|ts2                   column=sf:c1, timestamp=1409122354918, value=sku188                                 
10.  user1|ts3                   column=sf:s1, timestamp=1409122354954, value=sku123                                 
11.  user2|ts4                   column=sf:c1, timestamp=1409122354998, value=sku2                                   
12.  user2|ts5                   column=sf:c2, timestamp=1409122355030, value=sku288                                 
13.  user2|ts6                   column=sf:s1, timestamp=1409122355970, value=sku222

加入测试数据

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1.  put 'test1', 'user1|ts9', 'sf:b1', 'sku1'

12、b1开头的列中并且值为sku1的：

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1.  scan 'test1', FILTER=**>**"ColumnPrefixFilter('b1') AND ValueFilter(=,'binary:sku1')"  
2.    
3.  ROW                          COLUMN+CELL                                                                         
4.   user1|ts9                   column=sf:b1, timestamp=1409124908668, value=sku1

13、SingleColumnValueFilter的使用，b1开头的列中并且值为sku1的

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1.  import org.apache.hadoop.hbase.filter.CompareFilter  
2.  import org.apache.hadoop.hbase.filter.SingleColumnValueFilter  
3.  import org.apache.hadoop.hbase.filter.SubstringComparator  
4.  scan 'test1', \{ COLUMNS =**>** 'sf:b1', FILTER =**>** SingleColumnValueFilter.new(Bytes.toBytes('sf'), Bytes.toBytes('b1'), CompareFilter::CompareOp.valueOf('EQUAL'), Bytes.toBytes('sku1'))\}  
5.    
6.  ROW                          COLUMN+CELL                                                                         
7.   user1|ts9                   column=sf:b1, timestamp=1409124908668, value=sku1

hbase zkcli 的使用

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1.  hbase zkcli  
2.  ls /  
3.  \[hbase, zookeeper\]  
4.    
5.  \[zk: hadoop000:2181(CONNECTED) 1\] ls /hbase  
6.  \[meta-region-server, backup-masters, table, draining, region-in-transition, running, table-lock, master, namespace, hbaseid, online-snapshot, replication, splitWAL, recovering-regions, rs\]  
7.    
8.  \[zk: hadoop000:2181(CONNECTED) 2\] ls /hbase/table  
9.  \[member, test1, hbase:meta, hbase:namespace\]  
10.   
11. \[zk: hadoop000:2181(CONNECTED) 3\] ls /hbase/table/test1  
12. \[\]  
13.   
14. \[zk: hadoop000:2181(CONNECTED) 4\] get /hbase/table/test1  
15. ?master:60000\}l$??lPBUF  
16. cZxid = 0x107  
17. ctime = Wed Aug 27 14:52:21 HKT 2014  
18. mZxid = 0x10b  
19. mtime = Wed Aug 27 14:52:22 HKT 2014  
20. pZxid = 0x107  
21. cversion = 0  
22. dataVersion = 2  
23. aclVersion = 0  
24. ephemeralOwner = 0x0  
25. dataLength = 31  
26. numChildren = 0

[view plain]: https://blog.csdn.net/liuxiao723846/article/details/73823056#