Hbase的Filter详解使用

参数基础
有两个参数类在各类Filter中经常出现,统一介绍下:
(1)比较运算符  CompareFilter.CompareOp
比较运算符用于定义比较关系, 可以有以下几类值供选择:
  1. EQUAL                                  相等
  2. GREATER                              大于
  3. GREATER_OR_EQUAL           大于等于
  4. LESS                                      小于
  5. LESS_OR_EQUAL                  小于等于
  6. NOT_EQUAL                        不等于

(2)比较器   ByteArrayComparable
通过比较器可以实现多样化目标匹配效果,比较器 有以下子类可以使用:
  1. BinaryComparator               匹配完整字节数组 
  2. BinaryPrefixComparator     匹配字节数组前缀 
  3. BitComparator
  4. NullComparator
  5. RegexStringComparator    正则表达式匹配
  6. SubstringComparator        子串匹配
1,FilterList
FilterList 代表一个过滤器链 ,它可以包含一组即将应用于目标数据集的过滤器 ,过滤器间具有“与”   FilterList.Operator.MUST_PASS_ALL   和“或”  FilterList.Operator.MUST_PASS_ONE  关系。
官网实例代码, 两个 或” 关系的 过滤器 的写法:
FilterList list = new FilterList(FilterList.Operator.MUST_PASS_ONE);   //数据只要满足一组过滤器中的一个就可以
SingleColumnValueFilter filter1 = new SingleColumnValueFilter(cf,column,CompareOp.EQUAL,Bytes.toBytes("my value"));
list.add(filter1);
SingleColumnValueFilter filter2 = new SingleColumnValueFilter(cf,column,CompareOp.EQUAL,Bytes.toBytes("my other value"));
list.add(filter2);
Scan scan = new Scan();
scan.setFilter(list);
2,列值过滤器--SingleColumnValueFilter
SingleColumnValueFilter 用于测试列值相等 (CompareOp.EQUAL ), 不等 (CompareOp.NOT_EQUAL),或单侧范围 (e.g., CompareOp.GREATER)。
构造函数:
(1)比较的关键字是一个字符数组
SingleColumnValueFilter(byte[] family, byte[] qualifier, CompareFilter.CompareOp compareOp, byte[] value)
(2)比较的关键字是一个比较器(比较器下一小节做介绍)
SingleColumnValueFilter(byte[] family, byte[] qualifier, CompareFilter.CompareOp compareOp, ByteArrayComparable comparator)
注意:根据列的值来决定这一行数据是否返回,落脚点在行,而不是列。我们可以设置filter.setFilterIfMissing(true);如果为true,当这一列不存在时,不会返回,如果为false,当这一列不存在时,会返回所有的列信息
测试表user内容如下:
Hbase的Filter详解使用_第1张图片
java代码测试:
Table table = connection.getTable(TableName.valueOf("user"));
        SingleColumnValueFilter scvf= new SingleColumnValueFilter(Bytes.toBytes("account"), Bytes.toBytes("name"), 
       		 CompareOp.EQUAL,"zhangsan".getBytes());
        scvf.setFilterIfMissing(true); //默认为false, 没有此列的数据也会返回 ,为true则只返回name=lisi的数据
        Scan scan = new Scan();
        scan.setFilter(scvf);
        ResultScanner resultScanner = table.getScanner(scan);
        for (Result result : resultScanner) {
			 List cells= result.listCells();	
			 for (Cell cell : cells) {
				 String row = Bytes.toString(result.getRow());
				 String family1 = Bytes.toString(CellUtil.cloneFamily(cell));
				 String qualifier = Bytes.toString(CellUtil.cloneQualifier(cell));
				 String value = Bytes.toString(CellUtil.cloneValue(cell));
				 System.out.println("[row:"+row+"],[family:"+family1+"],[qualifier:"+qualifier+"]"
				 		+ ",[value:"+value+"],[time:"+cell.getTimestamp()+"]");
			}
		}
如果setFilterIfMissing(true), 有匹配只会返回当前列所在的行数据,基于行的数据 country 也返回了,因为他么你的rowkey是相同的
[row:zhangsan_1495527850824],[family:account],[qualifier:country],[value:china],[time:1495636452285]
[row:zhangsan_1495527850824],[family:account],[qualifier:name],[value:zhangsan],[time:1495556648729]

如果setFilterIfMissing(false),有匹配的列的值相同会返回,没有此列的 name的也会返回,, 不匹配的name则不会返回。
下面 红色是匹配列内容的会返回,其他的不是account:name列也会返回,, name=lisi的不会返回,因为不匹配。
[row:lisi_1495527849910],[family:account],[qualifier:idcard],[value:42963319861234561230],[time:1495556647872]
[row:lisi_1495527850111],[family:account],[qualifier:password],[value:123451231236],[time:1495556648013]
[row:lisi_1495527850114],[family:address],[qualifier:city],[value:黄埔],[time:1495556648017]
[row:lisi_1495527850136],[family:address],[qualifier:province],[value:shanghai],[time:1495556648041]
[row:lisi_1495527850144],[family:info],[qualifier:age],[value:21],[time:1495556648045]
[row:lisi_1495527850154],[family:info],[qualifier:sex],[value:女],[time:1495556648056]
[row:lisi_1495527850159],[family:userid],[qualifier:id],[value:002],[time:1495556648060]
[row:wangwu_1495595824517],[family:userid],[qualifier:id],[value:009],[time:1495624624131]
[row:zhangsan_1495527850759],[family:account],[qualifier:idcard],[value:9897645464646],[time:1495556648664]
[row:zhangsan_1495527850759],[family:account],[qualifier:passport],[value:5689879898],[time:1495636370056]
[row:zhangsan_1495527850824],[family:account],[qualifier:country],[value:china],[time:1495636452285]
[row:zhangsan_1495527850824],[family:account],[qualifier:name],[value:zhangsan],[time:1495556648729]
[row:zhangsan_1495527850951],[family:address],[qualifier:province],[value:guangdong],[time:1495556648855]
[row:zhangsan_1495527850975],[family:info],[qualifier:age],[value:100],[time:1495556648878]
[row:zhangsan_1495527851080],[family:info],[qualifier:sex],[value:男],[time:1495556648983]
[row:zhangsan_1495527851095],[family:userid],[qualifier:id],[value:001],[time:1495556648996]


3 键值元数据
由于HBase 采用键值对保存内部数据,键值元数据过滤器评估一行的键 (ColumnFamily:Qualifiers) 是否存在  

3.1. 基于列族过滤数据的FamilyFilter
构造函数:
FamilyFilter(CompareFilter.CompareOp familyCompareOp, ByteArrayComparable familyComparator)
代码如下:
   public static ResultScanner getDataFamilyFilter(String tableName,String family) throws IOException{
    	Table table = connection.getTable(TableName.valueOf("user"));
        FamilyFilter ff = new FamilyFilter(CompareOp.EQUAL , 
        		new BinaryComparator(Bytes.toBytes("account")));   //表中不存在account列族,过滤结果为空
//		 new BinaryPrefixComparator(value) //匹配字节数组前缀
//		 new RegexStringComparator(expr) // 正则表达式匹配
//		 new SubstringComparator(substr)// 子字符串匹配 
        Scan scan = new Scan();
        // 通过scan.addFamily(family)  也可以实现此操作
        scan.setFilter(ff);
        ResultScanner resultScanner = table.getScanner(scan);
    	return resultScanner;
    }
测试结果:查询的都是account列簇的内容
[row:lisi_1495527849910],[family:account],[qualifier:idcard],[value:42963319861234561230],[time:1495556647872]
[row:lisi_1495527850081],[family:account],[qualifier:name],[value:lisi],[time:1495556647984]
[row:lisi_1495527850111],[family:account],[qualifier:password],[value:123451231236],[time:1495556648013]
[row:zhangsan_1495527850759],[family:account],[qualifier:idcard],[value:9897645464646],[time:1495556648664]
[row:zhangsan_1495527850759],[family:account],[qualifier:passport],[value:5689879898],[time:1495636370056]
[row:zhangsan_1495527850824],[family:account],[qualifier:country],[value:china],[time:1495636452285]
[row:zhangsan_1495527850824],[family:account],[qualifier:name],[value:zhangsan],[time:1495556648729]

3.2. 基于限定符Qualifier(列)过滤数据的QualifierFilter
构造函数:
QualifierFilter(CompareFilter.CompareOp op, ByteArrayComparable qualifierComparator)
	Table table = connection.getTable(TableName.valueOf("user"));
    	QualifierFilter ff = new QualifierFilter(
                CompareOp.EQUAL , new BinaryComparator(Bytes.toBytes("name")));
//		 new BinaryPrefixComparator(value) //匹配字节数组前缀
//		 new RegexStringComparator(expr) // 正则表达式匹配
//		 new SubstringComparator(substr)// 子字符串匹配 
        Scan scan = new Scan();
        // 通过scan.addFamily(family)  也可以实现此操作
        scan.setFilter(ff);
        ResultScanner resultScanner = table.getScanner(scan);
测试结果:只返回 name 的列内容
[row:lisi_1495527850081],[family:account],[qualifier:name],[value:lisi],[time:1495556647984]
[row:zhangsan_1495527850824],[family:account],[qualifier:name],[value:zhangsan],[time:1495556648729]

3.3. 基于列名(即Qualifier)前缀过滤数据的ColumnPrefixFilter  ( 该功能用QualifierFilter也能实现 )
构造函数:
ColumnPrefixFilter(byte[] prefix) 
Table table = connection.getTable(TableName.valueOf("user"));
    	 ColumnPrefixFilter ff = new ColumnPrefixFilter(Bytes.toBytes("name"));
        Scan scan = new Scan();
        // 通过QualifierFilter的 newBinaryPrefixComparator也可以实现
        scan.setFilter(ff);
        ResultScanner resultScanner = table.getScanner(scan);

返回结果:
[row:lisi_1495527850081],[family:account],[qualifier:name],[value:lisi],[time:1495556647984]
[row:zhangsan_1495527850824],[family:account],[qualifier:name],[value:zhangsan],[time:1495556648729]

3.4. 基于多个列名(即Qualifier)前缀过滤数据的MultipleColumnPrefixFilter
MultipleColumnPrefixFilter 和 ColumnPrefixFilter 行为差不多,但可以指定多个前缀
byte[][] prefixes = new byte[][] {Bytes.toBytes("name"), Bytes.toBytes("age")};
        //返回所有行中以name或者age打头的列的数据
        MultipleColumnPrefixFilter ff = new MultipleColumnPrefixFilter(prefixes);

        Scan scan = new Scan();
        scan.setFilter(ff);
        ResultScanner rs = table.getScanner(scan);  
结果:
[row:lisi_1495527850081],[family:account],[qualifier:name],[value:lisi],[time:1495556647984]
[row:lisi_1495527850144],[family:info],[qualifier:age],[value:21],[time:1495556648045]
[row:zhangsan_1495527850824],[family:account],[qualifier:name],[value:zhangsan],[time:1495556648729]
[row:zhangsan_1495527850975],[family:info],[qualifier:age],[value:100],[time:1495556648878]

3.5. 基于列范围过滤数据ColumnRangeFilter
构造函数:
ColumnRangeFilter(byte[] minColumn, boolean minColumnInclusive, byte[] maxColumn, boolean maxColumnInclusive)
参数解释:
  • minColumn - 列范围的最小值,如果为空,则没有下限;
  • minColumnInclusive - 列范围是否包含minColumn 
  • maxColumn - 列范围最大值,如果为空,则没有上限;
  • maxColumnInclusive - 列范围是否包含maxColumn 。
代码:
Table table = connection.getTable(TableName.valueOf("user"));
    	byte[] startColumn = Bytes.toBytes("a");
        byte[] endColumn = Bytes.toBytes("d");
        //返回所有列中从a到d打头的范围的数据,
        ColumnRangeFilter ff = new ColumnRangeFilter(startColumn, true, endColumn, true);
        Scan scan = new Scan();
        scan.setFilter(ff);
        ResultScanner rs = table.getScanner(scan);  
结果:返回列名开头是a 到  d的所有列数据
[row:lisi_1495527850114],[family:address],[qualifier:city],[value:黄埔],[time:1495556648017]
[row:lisi_1495527850144],[family:info],[qualifier:age],[value:21],[time:1495556648045]
[row:zhangsan_1495527850824],[family:account],[qualifier:country],[value:china],[time:1495636452285]
[row:zhangsan_1495527850975],[family:info],[qualifier:age],[value:100],[time:1495556648878]

4. RowKey
当需要根据行键特征查找一个范围的行数据时,使用Scan的 startRow和stopRow会更高效,但是, startRow和stopRow只能匹配行键的开始字符,而不能匹配中间包含的字符
        byte[] startColumn = Bytes.toBytes("azha");
        byte[] endColumn = Bytes.toBytes("dddf");
        Scan scan = new Scan(startColumn,endColumn);
当需要针对行键进行更复杂的过滤时,可以使用 RowFilter:
构造函数:
RowFilter(CompareFilter.CompareOp rowCompareOp, ByteArrayComparable rowComparator)

代码:
Table table = connection.getTable(TableName.valueOf("user"));
    	RowFilter rf = new RowFilter(CompareOp.EQUAL , 
                new SubstringComparator("zhangsan"));
		//		 new BinaryPrefixComparator(value) //匹配字节数组前缀
		//		 new RegexStringComparator(expr) // 正则表达式匹配
		//		 new SubstringComparator(substr)// 子字符串匹配 
        Scan scan = new Scan();
        scan.setFilter(rf);
        ResultScanner rs = table.getScanner(scan); 
结果:
[row:zhangsan_1495527850759],[family:account],[qualifier:idcard],[value:9897645464646],[time:1495556648664]
[row:zhangsan_1495527850759],[family:account],[qualifier:passport],[value:5689879898],[time:1495636370056]
[row:zhangsan_1495527850824],[family:account],[qualifier:country],[value:china],[time:1495636452285]
[row:zhangsan_1495527850824],[family:account],[qualifier:name],[value:zhangsan],[time:1495556648729]
[row:zhangsan_1495527850951],[family:address],[qualifier:province],[value:guangdong],[time:1495556648855]
[row:zhangsan_1495527850975],[family:info],[qualifier:age],[value:100],[time:1495556648878]
[row:zhangsan_1495527851080],[family:info],[qualifier:sex],[value:男],[time:1495556648983]
[row:zhangsan_1495527851095],[family:userid],[qualifier:id],[value:001],[time:1495556648996]

5.PageFilter
指定页面行数,返回对应行数的结果集。
需要注意的是,该过滤器并不能保证返回的结果行数小于等于指定的页面行数,因为过滤器是分别作用到各个region server的,它只能保证当前region返回的结果行数不超过指定页面行数。
构造函数:
PageFilter(long pageSize)
代码:
Table table = connection.getTable(TableName.valueOf("user"));
        PageFilter pf = new PageFilter(2L);
        Scan scan = new Scan();
        scan.setFilter(pf);
        scan.setStartRow(Bytes.toBytes("zhangsan_"));
        ResultScanner rs = table.getScanner(scan);

结果:返回的结果实际上有四条,因为这数据来自不同RegionServer, 
[row:zhangsan_1495527850759],[family:account],[qualifier:idcard],[value:9897645464646],[time:1495556648664]
[row:zhangsan_1495527850759],[family:account],[qualifier:passport],[value:5689879898],[time:1495636370056]
[row:zhangsan_1495527850824],[family:account],[qualifier:country],[value:china],[time:1495636452285]
[row:zhangsan_1495527850824],[family:account],[qualifier:name],[value:zhangsan],[time:1495556648729]

6.SkipFilter
根据整行中的每个列来做过滤,只要存在一列不满足条件,整行都被过滤掉。
例如,如果一行中的所有列代表的是不同物品的重量,则真实场景下这些数值都必须大于零,我们希望将那些包含任意列值为0的行都过滤掉。
在这个情况下,我们结合ValueFilter和SkipFilter共同实现该目的:
scan.setFilter(new SkipFilter(new ValueFilter(CompareOp.NOT_EQUAL, new BinaryComparator(Bytes.toBytes(0))));
构造函数:
SkipFilter(Filter filter) 
代码:
Table table = connection.getTable(TableName.valueOf("user"));
    	SkipFilter sf = new SkipFilter(new ValueFilter(CompareOp.NOT_EQUAL,
                new BinaryComparator(Bytes.toBytes("zhangsan"))));
        Scan scan = new Scan();
        scan.setFilter(sf);
        ResultScanner rs = table.getScanner(scan); 
结果:
[row:lisi_1495527849910],[family:account],[qualifier:idcard],[value:42963319861234561230],[time:1495556647872]
[row:lisi_1495527850081],[family:account],[qualifier:name],[value:lisi],[time:1495556647984]
[row:lisi_1495527850111],[family:account],[qualifier:password],[value:123451231236],[time:1495556648013]
[row:lisi_1495527850114],[family:address],[qualifier:city],[value:黄埔],[time:1495556648017]
[row:lisi_1495527850136],[family:address],[qualifier:province],[value:shanghai],[time:1495556648041]
[row:lisi_1495527850144],[family:info],[qualifier:age],[value:21],[time:1495556648045]
[row:lisi_1495527850154],[family:info],[qualifier:sex],[value:女],[time:1495556648056]
[row:lisi_1495527850159],[family:userid],[qualifier:id],[value:002],[time:1495556648060]
[row:wangwu_1495595824517],[family:userid],[qualifier:id],[value:009],[time:1495624624131]
[row:zhangsan_1495527850759],[family:account],[qualifier:idcard],[value:9897645464646],[time:1495556648664]
[row:zhangsan_1495527850759],[family:account],[qualifier:passport],[value:5689879898],[time:1495636370056]
[row:zhangsan_1495527850951],[family:address],[qualifier:province],[value:guangdong],[time:1495556648855]
[row:zhangsan_1495527850975],[family:info],[qualifier:age],[value:100],[time:1495556648878]
[row:zhangsan_1495527851080],[family:info],[qualifier:sex],[value:男],[time:1495556648983]
[row:zhangsan_1495527851095],[family:userid],[qualifier:id],[value:001],[time:1495556648996]
和原来数据相比  列值为name的 zhagnsan的所在行的 rowkey   为   zhangsan_1495527850824 在上面结果中是过滤了
[row:lisi_1495527849910],[family:account],[qualifier:idcard],[value:42963319861234561230]
[row:lisi_1495527850081],[family:account],[qualifier:name],[value:lisi]
[row:lisi_1495527850111],[family:account],[qualifier:password],[value:123451231236]
[row:lisi_1495527850114],[family:address],[qualifier:city],[value:黄埔]
[row:lisi_1495527850136],[family:address],[qualifier:province],[value:shanghai]
[row:lisi_1495527850144],[family:info],[qualifier:age],[value:21]
[row:lisi_1495527850154],[family:info],[qualifier:sex],[value:女]
[row:lisi_1495527850159],[family:userid],[qualifier:id],[value:002]
[row:wangwu_1495595824517],[family:userid],[qualifier:id],[value:009]
[row:zhangsan_1495527850759],[family:account],[qualifier:idcard],[value:9897645464646]
[row:zhangsan_1495527850759],[family:account],[qualifier:passport],[value:5689879898]
[row:zhangsan_1495527850824],[family:account],[qualifier:country],[value:china]
[row:zhangsan_1495527850824],[family:account],[qualifier:name],[value:zhangsan]
[row:zhangsan_1495527850951],[family:address],[qualifier:province],[value:guangdong]
[row:zhangsan_1495527850975],[family:info],[qualifier:age],[value:100]
[row:zhangsan_1495527851080],[family:info],[qualifier:sex],[value:男]
[row:zhangsan_1495527851095],[family:userid],[qualifier:id],[value:001]



7. FirstKeyOnlyFilter
该过滤器仅仅返回每一行中的第一个cell的值, 可以用于高效的执行行数统计操作。
构造函数:
public FirstKeyOnlyFilter()

代码
Table table = connection.getTable(TableName.valueOf("user"));
    	 FirstKeyOnlyFilter fkof = new FirstKeyOnlyFilter();
    	    Scan scan = new Scan();
    	    scan.setFilter(fkof);
    	    ResultScanner rs = table.getScanner(scan); 

结果: 看着返回数据还没明白, 仅仅返回每一行中的第一个cell的值可以用于高效的执行行数统计操作。
[row:lisi_1495527849910],[family:account],[qualifier:idcard],[value:42963319861234561230],[time:1495556647872]
[row:lisi_1495527850081],[family:account],[qualifier:name],[value:lisi],[time:1495556647984]
[row:lisi_1495527850111],[family:account],[qualifier:password],[value:123451231236],[time:1495556648013]
[row:lisi_1495527850114],[family:address],[qualifier:city],[value:黄埔],[time:1495556648017]
[row:lisi_1495527850136],[family:address],[qualifier:province],[value:shanghai],[time:1495556648041]
[row:lisi_1495527850144],[family:info],[qualifier:age],[value:21],[time:1495556648045]
[row:lisi_1495527850154],[family:info],[qualifier:sex],[value:女],[time:1495556648056]
[row:lisi_1495527850159],[family:userid],[qualifier:id],[value:002],[time:1495556648060]
[row:wangwu_1495595824517],[family:userid],[qualifier:id],[value:009],[time:1495624624131]
[row:zhangsan_1495527850759],[family:account],[qualifier:idcard],[value:9897645464646],[time:1495556648664]
[row:zhangsan_1495527850824],[family:account],[qualifier:country],[value:china],[time:1495636452285]
[row:zhangsan_1495527850951],[family:address],[qualifier:province],[value:guangdong],[time:1495556648855]
[row:zhangsan_1495527850975],[family:info],[qualifier:age],[value:100],[time:1495556648878]
[row:zhangsan_1495527851080],[family:info],[qualifier:sex],[value:男],[time:1495556648983]
[row:zhangsan_1495527851095],[family:userid],[qualifier:id],[value:001],[time:1495556648996]
对比原数据“
[row:lisi_1495527849910],[family:account],[qualifier:idcard],[value:42963319861234561230]
[row:lisi_1495527850081],[family:account],[qualifier:name],[value:lisi]
[row:lisi_1495527850111],[family:account],[qualifier:password],[value:123451231236]
[row:lisi_1495527850114],[family:address],[qualifier:city],[value:黄埔]
[row:lisi_1495527850136],[family:address],[qualifier:province],[value:shanghai]
[row:lisi_1495527850144],[family:info],[qualifier:age],[value:21]
[row:lisi_1495527850154],[family:info],[qualifier:sex],[value:女]
[row:lisi_1495527850159],[family:userid],[qualifier:id],[value:002]
[row:wangwu_1495595824517],[family:userid],[qualifier:id],[value:009]
[row:zhangsan_1495527850759],[family:account],[qualifier:idcard],[value:9897645464646]
[row:zhangsan_1495527850759],[family:account],[qualifier:passport],[value:5689879898]
[row:zhangsan_1495527850824],[family:account],[qualifier:country],[value:china]
[row:zhangsan_1495527850824],[family:account],[qualifier:name],[value:zhangsan]
[row:zhangsan_1495527850951],[family:address],[qualifier:province],[value:guangdong]
[row:zhangsan_1495527850975],[family:info],[qualifier:age],[value:100]
[row:zhangsan_1495527851080],[family:info],[qualifier:sex],[value:男]
[row:zhangsan_1495527851095],[family:userid],[qualifier:id],[value:001]
对比一下明显,rowkey相同的只会返回第一个rowkey的所在cell数据

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