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：
EQUAL 相等GREATER 大于GREATER_OR_EQUAL 大于等于LESS 小于LESS_OR_EQUAL 小于等于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：
BinaryComparator 匹配完整字节数组 BinaryPrefixComparator 匹配字节数组前缀 BitComparatorNullComparatorRegexStringComparator 正则表达式匹配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：
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FilterList list = new FilterList(FilterList.Operator.MUST_PASS_ONE); 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：
java代码测试：
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Table table = connection.getTable(TableName.valueOf("user")); SingleColumnValueFilter scvf= new SingleColumnValueFilter(Bytes.toBytes("account"), Bytes.toBytes("name"), CompareOp.EQUAL,"zhangsan".getBytes()); scvf.setFilterIfMissing(true); Scan scan = new Scan(); scan.setFilter(scvf); ResultScanner resultScanner = table.getScanner(scan); for (Result result : resultScanner) { List<Cell> 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
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[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。
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[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] <span style="color:#ff0000;">[row:zhangsan_1495527850824],[family:account],[qualifier:name],[value:zhangsan],[time:1495556648729]</span> [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：
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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"))); Scan scan = new Scan(); 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
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[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)
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Table table = connection.getTable(TableName.valueOf("user")); QualifierFilter ff = new QualifierFilter( CompareOp.EQUAL , new BinaryComparator(Bytes.toBytes("name"))); Scan scan = new Scan(); scan.setFilter(ff); ResultScanner resultScanner = table.getScanner(scan);
测试结果：只返回 name 的列内容
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[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)
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Table table = connection.getTable(TableName.valueOf("user")); ColumnPrefixFilter ff = new ColumnPrefixFilter(Bytes.toBytes("name")); Scan scan = new Scan(); scan.setFilter(ff); ResultScanner resultScanner = table.getScanner(scan);
返回结果：
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[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
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byte[][] prefixes = new byte[][] {Bytes.toBytes("name"), Bytes.toBytes("age")}; MultipleColumnPrefixFilter ff = new MultipleColumnPrefixFilter(prefixes); Scan scan = new Scan(); scan.setFilter(ff); ResultScanner rs = table.getScanner(scan);
结果:
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[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：
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Table table = connection.getTable(TableName.valueOf("user")); byte[] startColumn = Bytes.toBytes("a"); byte[] endColumn = Bytes.toBytes("d"); 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
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[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：
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Table table = connection.getTable(TableName.valueOf("user")); RowFilter rf = new RowFilter(CompareOp.EQUAL , new SubstringComparator("zhangsan")); Scan scan = new Scan(); scan.setFilter(rf); ResultScanner rs = table.getScanner(scan);
结果：
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[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：
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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,
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[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：
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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：
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[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
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[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] <strong><span style="color:#ff0000;">[row:zhangsan_1495527850824],[family:account],[qualifier:country],[value:china] [row:zhangsan_1495527850824],[family:account],[qualifier:name],[value:zhangsan]</span></strong> [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
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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。
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[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“
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[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] <strong><span style="color:#ff0000;">[row:zhangsan_1495527850759],[family:account],[qualifier:passport],[value:5689879898]</span></strong> [row:zhangsan_1495527850824],[family:account],[qualifier:country],[value:china] <strong><span style="color:#ff0000;">[row:zhangsan_1495527850824],[family:account],[qualifier:name],[value:zhangsan]</span></strong> [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
