hue查询hbase操作相关参考

淡淡的烟草味﹌ 2022-04-25 01:24 260阅读 0赞

# hbase基于hue的查询语法 #

## 登录地址 ##

。。。。。。

## 界面操作说明 ##

1.  进入hue中的hbase  
    ![xM0YIPc.png][]
2.  进入表的查询界面  
    ![ALYLFBK.png][]
3.  界面说明  
    ![74GokZt.png][]

## 查询语句 ##

`,`表示结束查询，可以不加

### 主键查询 ###

1.  输入主键 `rowkey1,rowkey2`  
    说明：只输入主键查询  
    例1：`00000051|1538229142`  
    例2：`00000051|1538229142,00000051|1538230148`  
    ![oLUzJoU.png][]
2.  根据主键的前缀查询 `row_prefix*,`  
    说明：根据主键的前几位进行模糊查询，默认只显示一条数据；通过 `+n-1` 来显示n条数据  
    例1：`00000051|*`  
    例2：显示10条数据 `00000051|* +9`  
    ![8deP28C.png][]  
    ![9lSq58i.png][]

### 查询列族和列名 ###

`[colFamily:col1,colFamily:col2,colFamily:,col3,col_prefix*+(n-1),col1 to col2]`  
说明：colFamily表示列族,col表示列名, `colFamily:` 表示显示该列族下所有列, `<col_prefix>*+(n-1)` 根据列名模糊查询,`col1 to col2`显示col1到col2之间的列  
**例1**：`00000051|* +9 [i:ts,i:path]`  
![GHmDyTT.png][]  
**例2**：`00000051|* +9 [i:p*+3]`  
![VxFTjkY.png][]  
**例3**：`00000051|1538229142,00000051|1538230148[elapse to path]`  
![oENVmG2.png][]

### 根据条件过滤查询 ###

`{filter() AND/OR filter()}`  
个人意见：substring筛选时使用=来筛选，否则会出错;binary筛选时可以用=、>=、<=等操作

*  根据主键进行过滤
    
     *  `RowFilter(=,'substring:111')` 主键中包含111
     *  `RowFilter(=,'binary:111')` 主键等于111
     *  `PrefixFilter('user1')` 主键的前缀是user1  
        ![XcDZxeB.png][]
 *  根据列进行过滤
    
     *  列名过滤
        
         *  `QualifierFilter (=, 'substring:p')` 列名中包含p
         *  `QualifierFilter (=, 'binary:p')` 列名等于p
         *  `MultipleColumnPrefixFilter('a','b','e')` 列名的前缀是a或者b或者e
         *  `ColumnPrefixFilter('c2')` 列名的前缀是c2  
            ![Y1UN3a1.png][]
     *  列值过滤
        
         *  `SingleColumnValueFilter('i', 'path', =, 'substring:student')` 列族为i,列名为path,列值包含student
         *  `SingleColumnValueFilter('i', 'path', =, 'binary:student')` 列族为i,列名为path,列值等于student
         *  `ValueFilter(=,'substring:111')` 列值中包含111
         *  `ValueFilter(=,'binary:111')` 列值等于111  
            ![qZe3pvJ.png][]  
            ![laumBV7.png][]  
            以上过滤器是大部分常用的过滤器，在hue-hbase中另有一些过滤器未在此文档中描述。

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

以下是我自己整理的一部分参考语法：

hbase中有单纯根据列值查询的ValueFilter和根据列名和列值查询的SingleColumnValueFilter，可根据需要选择。  
下表中是按照根据列名和列值来进行查询的参考

<table> 
 <thead> 
  <tr> 
   <th>SQL</th> 
   <th>hue_hbase</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>select col1,col2</td> 
   <td>[col1,col2]</td> 
  </tr> 
  <tr> 
   <td>where col1="student"</td> 
   <td>SingleColumnValueFilter('i', 'col1', =, 'binary:student')</td> 
  </tr> 
  <tr> 
   <td>where col1 like "%body%"</td> 
   <td>SingleColumnValueFilter('i', 'col1', =, 'substring:body')</td> 
  </tr> 
  <tr> 
   <td>where col1&gt;=1.9</td> 
   <td>SingleColumnValueFilter('i', 'col1', &gt;=, 'binary:1.9')</td> 
  </tr> 
  <tr> 
   <td>where col1="student" AND col2&gt;1.9</td> 
   <td>SingleColumnValueFilter('i', 'col1', =, 'binary:student') AND SingleColumnValueFilter('i', 'col2', &gt;, 'binary:1.9')</td> 
  </tr> 
  <tr> 
   <td>where col1="student" OR col2&gt;1.9</td> 
   <td>SingleColumnValueFilter('i', 'col1', =, 'binary:student') OR SingleColumnValueFilter('i', 'col2', &gt;, 'binary:1.9')</td> 
  </tr> 
 </tbody> 
</table>

参考：[https://www.cnblogs.com/fengzzi/p/10033093.html][https_www.cnblogs.com_fengzzi_p_10033093.html]

[https://blog.csdn.net/u012551524/article/details/78918295?utm\_source=blogxgwz3][https_blog.csdn.net_u012551524_article_details_78918295_utm_source_blogxgwz3]

[xM0YIPc.png]: https://i.imgur.com/xM0YIPc.png
[ALYLFBK.png]: https://i.imgur.com/ALYLFBK.png
[74GokZt.png]: https://i.imgur.com/74GokZt.png
[oLUzJoU.png]: https://i.imgur.com/oLUzJoU.png
[8deP28C.png]: https://i.imgur.com/8deP28C.png
[9lSq58i.png]: https://i.imgur.com/9lSq58i.png
[GHmDyTT.png]: https://i.imgur.com/GHmDyTT.png
[VxFTjkY.png]: https://i.imgur.com/VxFTjkY.png
[oENVmG2.png]: https://i.imgur.com/oENVmG2.png
[XcDZxeB.png]: https://i.imgur.com/XcDZxeB.png
[Y1UN3a1.png]: https://i.imgur.com/Y1UN3a1.png
[qZe3pvJ.png]: https://i.imgur.com/qZe3pvJ.png
[laumBV7.png]: https://i.imgur.com/laumBV7.png
[https_www.cnblogs.com_fengzzi_p_10033093.html]: https://www.cnblogs.com/fengzzi/p/10033093.html
[https_blog.csdn.net_u012551524_article_details_78918295_utm_source_blogxgwz3]: https://blog.csdn.net/u012551524/article/details/78918295?utm_source=blogxgwz3