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
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
- BitComparator 不常用
- NullComparator 不常用
- RegexStringComparator 匹配正则表达式
- SubstringComparator 匹配子字符串
1.多重过滤器--FilterList(Shell不支持)
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。
//结合过滤器，获取所有age在15到30之间的行 private static void scanFilter() throws IOException, UnsupportedEncodingException { Configuration conf = HBaseConfiguration.create(); conf.set("hbase.rootdir", "hdfs://ncst:9000/hbase"); conf.set("hbase.zookeeper.quorum", "ncst"); HTable ht = new HTable(conf, "users"); // And FilterList filterList = new FilterList(FilterList.Operator.MUST_PASS_ALL); // >=15 SingleColumnValueFilter filter1 = new SingleColumnValueFilter("info".getBytes(), "age".getBytes(), CompareOp.GREATER_OR_EQUAL, "15".getBytes()); // =<30 SingleColumnValueFilter filter2 = new SingleColumnValueFilter("info".getBytes(), "age".getBytes(), CompareOp.LESS_OR_EQUAL, "30".getBytes()); filterList.addFilter(filter1); filterList.addFilter(filter2); Scan scan = new Scan(); // set Filter scan.setFilter(filterList); ResultScanner rs = ht.getScanner(scan); for(Result result : rs){ for(Cell cell : result.rawCells()){ System.out.println(new String(CellUtil.cloneRow(cell))+" " +new String(CellUtil.cloneFamily(cell))+" " +new String(CellUtil.cloneQualifier(cell))+" " +new String(CellUtil.cloneValue(cell),"UTF-8")+" " +cell.getTimestamp()); } } ht.close(); }
2. 列值过滤器--SingleColumnValueFilter
用于测试列值相等(CompareOp.EQUAL ),不等(CompareOp.NOT_EQUAL),或单侧范围 (如CompareOp.GREATER)。构造函数：
2.1.比较的关键字是一个字符数组(Shell不支持?)
SingleColumnValueFilter(byte[] family, byte[] qualifier, CompareFilter.CompareOp compareOp, byte[] value)
//SingleColumnValueFilter例子 private static void scanFilter01() throws IOException, UnsupportedEncodingException { Configuration conf = HBaseConfiguration.create(); conf.set("hbase.rootdir", "hdfs://ncst:9000/hbase"); conf.set("hbase.zookeeper.quorum", "ncst"); HTable ht = new HTable(conf, "users"); SingleColumnValueFilter scvf = new SingleColumnValueFilter("info".getBytes(), "age".getBytes(), CompareOp.EQUAL, "18".getBytes()); Scan scan = new Scan(); scan.setFilter(scvf); ResultScanner rs = ht.getScanner(scan); for(Result result : rs){ for(Cell cell : result.rawCells()){ System.out.println(new String(CellUtil.cloneRow(cell))+" " +new String(CellUtil.cloneFamily(cell))+" " +new String(CellUtil.cloneQualifier(cell))+" " +new String(CellUtil.cloneValue(cell),"UTF-8")+" " +cell.getTimestamp()); } } ht.close(); }
2.2.比较的关键字是一个比较器ByteArrayComparable
SingleColumnValueFilter(byte[] family, byte[] qualifier, CompareFilter.CompareOp compareOp, ByteArrayComparable comparator)
//SingleColumnValueFilter例子2 -- RegexStringComparator private static void scanFilter02() throws IOException, UnsupportedEncodingException { Configuration conf = HBaseConfiguration.create(); conf.set("hbase.rootdir", "hdfs://ncst:9000/hbase"); conf.set("hbase.zookeeper.quorum", "ncst"); HTable ht = new HTable(conf, "users");
//值比较的正则表达式 -- RegexStringComparator //匹配info:age值以"4"结尾 RegexStringComparator comparator = new RegexStringComparator(".4"); //第四个参数不一样 SingleColumnValueFilter scvf = new SingleColumnValueFilter("info".getBytes(), "age".getBytes(), CompareOp.EQUAL, comparator); Scan scan = new Scan(); scan.setFilter(scvf); ResultScanner rs = ht.getScanner(scan); for(Result result : rs){ for(Cell cell : result.rawCells()){ System.out.println(new String(CellUtil.cloneRow(cell))+" " +new String(CellUtil.cloneFamily(cell))+" " +new String(CellUtil.cloneQualifier(cell))+" " +new String(CellUtil.cloneValue(cell),"UTF-8")+" " +cell.getTimestamp()); } } ht.close(); }
hbase(main):032:0> scan 'users',{FILTER=>"SingleColumnValueFilter('info','age',=,'regexstring:.4')"} ROW COLUMN+CELL xiaoming01 column=address:contry, timestamp=1442000277200, value=xE4xB8xADxE5x9BxBD xiaoming01 column=address:country, timestamp=1442000228945, value=xE4xB8xADxE5x9BxBD xiaoming01 column=info:age, timestamp=1441998917568, value=24 xiaoming02 column=info:age, timestamp=1441998917594, value=24 xiaoming03 column=info:age, timestamp=1441998919607, value=24 3 row(s) in 0.0130 seconds
//SingleColumnValueFilter例子2 -- SubstringComparator private static void scanFilter03() throws IOException, UnsupportedEncodingException { Configuration conf = HBaseConfiguration.create(); conf.set("hbase.rootdir", "hdfs://ncst:9000/hbase"); conf.set("hbase.zookeeper.quorum", "ncst"); HTable ht = new HTable(conf, "users"); //检测一个子串是否存在于值中(大小写不敏感) -- SubstringComparator //过滤age值中包含'4'的RowKey SubstringComparator comparator = new SubstringComparator("4"); //第四个参数不一样 SingleColumnValueFilter scvf = new SingleColumnValueFilter("info".getBytes(), "age".getBytes(), CompareOp.EQUAL, comparator); Scan scan = new Scan(); scan.setFilter(scvf); ResultScanner rs = ht.getScanner(scan); for(Result result : rs){ for(Cell cell : result.rawCells()){ System.out.println(new String(CellUtil.cloneRow(cell))+" " +new String(CellUtil.cloneFamily(cell))+" " +new String(CellUtil.cloneQualifier(cell))+" " +new String(CellUtil.cloneValue(cell),"UTF-8")+" " +cell.getTimestamp()); } } ht.close(); }
hbase(main):033:0> scan 'users',{FILTER=>"SingleColumnValueFilter('info','age',=,'substring:4')"} ROW COLUMN+CELL xiaoming01 column=address:contry, timestamp=1442000277200, value=xE4xB8xADxE5x9BxBD xiaoming01 column=address:country, timestamp=1442000228945, value=xE4xB8xADxE5x9BxBD xiaoming01 column=info:age, timestamp=1441998917568, value=24 xiaoming02 column=info:age, timestamp=1441998917594, value=24 xiaoming03 column=info:age, timestamp=1441998919607, value=24 3 row(s) in 0.0180 seconds
3.列名过滤器
由于HBase采用键值对保存内部数据，列名过滤器过滤一行的列名(ColumnFamily：Qualifiers)是否存在 , 对应前节所述列值的情况。
3.1.基于Columun Family列族过滤数据的FamilyFilter
FamilyFilter(CompareFilter.CompareOp familyCompareOp, ByteArrayComparable familyComparator)
注意：
1.如果希望查找的是一个已知的列族，则使用 scan.addFamily(family); 比使用过滤器效率更高.
2.由于目前HBase对多列族支持不完善，所以该过滤器目前用途不大.
//基于列族过滤数据的FamilyFilter private static void scanFilter04() throws IOException, UnsupportedEncodingException { Configuration conf = HBaseConfiguration.create(); conf.set("hbase.rootdir", "hdfs://ncst:9000/hbase"); conf.set("hbase.zookeeper.quorum", "ncst"); HTable ht = new HTable(conf, "users"); //过滤 = 'address'的列族 //FamilyFilter familyFilter = new FamilyFilter(CompareOp.EQUAL, new BinaryComparator("address".getBytes())); //过滤以'add'开头的列族 FamilyFilter familyFilter = new FamilyFilter(CompareOp.EQUAL, new BinaryPrefixComparator("add".getBytes())); Scan scan = new Scan(); scan.setFilter(familyFilter); ResultScanner rs = ht.getScanner(scan); for(Result result : rs){ for(Cell cell : result.rawCells()){ System.out.println(new String(CellUtil.cloneRow(cell))+" " +new String(CellUtil.cloneFamily(cell))+" " +new String(CellUtil.cloneQualifier(cell))+" " +new String(CellUtil.cloneValue(cell),"UTF-8")+" " +cell.getTimestamp()); } } ht.close(); }
hbase(main):021:0> scan 'users',{FILTER=>"FamilyFilter(=,'binaryprefix:add')"} ROW COLUMN+CELL xiaoming column=address:city, timestamp=1441997498965, value=hangzhou xiaoming column=address:contry, timestamp=1441997498911, value=china xiaoming column=address:province, timestamp=1441997498939, value=zhejiang xiaoming01 column=address:contry, timestamp=1442000277200, value=xE4xB8xADxE5x9BxBD xiaoming01 column=address:country, timestamp=1442000228945, value=xE4xB8xADxE5x9BxBD zhangyifei column=address:city, timestamp=1441997499108, value=jieyang zhangyifei column=address:contry, timestamp=1441997499077, value=china zhangyifei column=address:province, timestamp=1441997499093, value=guangdong zhangyifei column=address:town, timestamp=1441997500711, value=xianqiao 3 row(s) in 0.0400 seconds
3.2.基于Qualifier列名过滤数据的QualifierFilter
QualifierFilter(CompareFilter.CompareOp op, ByteArrayComparable qualifierComparator)
说明：该过滤器应该比FamilyFilter更常用！
//基于Qualifier(列名)过滤数据的QualifierFilter private static void scanFilter05() throws IOException, UnsupportedEncodingException { Configuration conf = HBaseConfiguration.create(); conf.set("hbase.rootdir", "hdfs://ncst:9000/hbase"); conf.set("hbase.zookeeper.quorum", "ncst"); HTable ht = new HTable(conf, "users"); //过滤列名 = 'age'所有RowKey //QualifierFilter qualifierFilter = new QualifierFilter(CompareOp.EQUAL, new BinaryComparator("age".getBytes())); //过滤列名 以'age'开头 所有RowKey(包含age) //QualifierFilter qualifierFilter = new QualifierFilter(CompareOp.EQUAL, new BinaryPrefixComparator("age".getBytes())); //过滤列名 包含'age' 所有RowKey(包含age) //QualifierFilter qualifierFilter = new QualifierFilter(CompareOp.EQUAL, new SubstringComparator("age")); //过滤列名 符合'.ge'正则表达式 所有RowKey QualifierFilter qualifierFilter = new QualifierFilter(CompareOp.EQUAL, new RegexStringComparator(".ge")); Scan scan = new Scan(); scan.setFilter(qualifierFilter); ResultScanner rs = ht.getScanner(scan); for(Result result : rs){ for(Cell cell : result.rawCells()){ System.out.println(new String(CellUtil.cloneRow(cell))+" " +new String(CellUtil.cloneFamily(cell))+" " +new String(CellUtil.cloneQualifier(cell))+" " +new String(CellUtil.cloneValue(cell),"UTF-8")+" " +cell.getTimestamp()); } } ht.close(); }
hbase(main):020:0> scan 'users',{FILTER=>"QualifierFilter(=,'regexstring:.ge')"} ROW COLUMN+CELL xiaoming column=info:age, timestamp=1441997971945, value=38 xiaoming01 column=info:age, timestamp=1441998917568, value=24 xiaoming02 column=info:age, timestamp=1441998917594, value=24 xiaoming03 column=info:age, timestamp=1441998919607, value=24 zhangyifei column=info:age, timestamp=1442247255446, value=18 5 row(s) in 0.0460 seconds
3.3.基于列名前缀过滤数据的ColumnPrefixFilter(该功能用QualifierFilter也能实现)
ColumnPrefixFilter(byte[] prefix)
注意：一个列名是可以出现在多个列族中的，该过滤器将返回所有列族中匹配的列。
//ColumnPrefixFilter例子 private static void scanFilter06() throws IOException, UnsupportedEncodingException { Configuration conf = HBaseConfiguration.create(); conf.set("hbase.rootdir", "hdfs://ncst:9000/hbase"); conf.set("hbase.zookeeper.quorum", "ncst"); HTable ht = new HTable(conf, "users"); //匹配 以'ag'开头的所有的列 ColumnPrefixFilter columnPrefixFilter = new ColumnPrefixFilter("ag".getBytes()); Scan scan = new Scan(); scan.setFilter(columnPrefixFilter); ResultScanner rs = ht.getScanner(scan); for(Result result : rs){ for(Cell cell : result.rawCells()){ System.out.println(new String(CellUtil.cloneRow(cell))+" " +new String(CellUtil.cloneFamily(cell))+" " +new String(CellUtil.cloneQualifier(cell))+" " +new String(CellUtil.cloneValue(cell),"UTF-8")+" " +cell.getTimestamp()); } } ht.close(); }
hbase(main):018:0> scan 'users',{FILTER=>"ColumnPrefixFilter('ag')"} ROW COLUMN+CELL xiaoming column=info:age, timestamp=1441997971945, value=38 xiaoming01 column=info:age, timestamp=1441998917568, value=24 xiaoming02 column=info:age, timestamp=1441998917594, value=24 xiaoming03 column=info:age, timestamp=1441998919607, value=24 zhangyifei column=info:age, timestamp=1442247255446, value=18 5 row(s) in 0.0280 seconds
3.4.基于多个列名前缀过滤数据的MultipleColumnPrefixFilter
MultipleColumnPrefixFilter 和 ColumnPrefixFilter 行为差不多，但可以指定多个前缀。
//MultipleColumnPrefixFilter例子 private static void scanFilter07() throws IOException, UnsupportedEncodingException { Configuration conf = HBaseConfiguration.create(); conf.set("hbase.rootdir", "hdfs://ncst:9000/hbase"); conf.set("hbase.zookeeper.quorum", "ncst"); HTable ht = new HTable(conf, "users"); //匹配 以'a'或者'c'开头 所有的列{二维数组} byte[][] prefixes =new byte[][]{"a".getBytes(), "c".getBytes()}; MultipleColumnPrefixFilter multipleColumnPrefixFilter = new MultipleColumnPrefixFilter(prefixes ); Scan scan = new Scan(); scan.setFilter(multipleColumnPrefixFilter); ResultScanner rs = ht.getScanner(scan); for(Result result : rs){ for(Cell cell : result.rawCells()){ System.out.println(new String(CellUtil.cloneRow(cell))+" " +new String(CellUtil.cloneFamily(cell))+" " +new String(CellUtil.cloneQualifier(cell))+" " +new String(CellUtil.cloneValue(cell),"UTF-8")+" " +cell.getTimestamp()); } } ht.close(); }
hbase(main):017:0> scan 'users',{FILTER=>"MultipleColumnPrefixFilter('a','c')"} ROW COLUMN+CELL xiaoming column=address:city, timestamp=1441997498965, value=hangzhou xiaoming column=address:contry, timestamp=1441997498911, value=china xiaoming column=info:age, timestamp=1441997971945, value=38 xiaoming column=info:company, timestamp=1441997498889, value=alibaba xiaoming01 column=address:contry, timestamp=1442000277200, value=xE4xB8xADxE5x9BxBD xiaoming01 column=address:country, timestamp=1442000228945, value=xE4xB8xADxE5x9BxBD xiaoming01 column=info:age, timestamp=1441998917568, value=24 xiaoming02 column=info:age, timestamp=1441998917594, value=24 xiaoming03 column=info:age, timestamp=1441998919607, value=24 zhangyifei column=address:city, timestamp=1441997499108, value=jieyang zhangyifei column=address:contry, timestamp=1441997499077, value=china zhangyifei column=info:age, timestamp=1442247255446, value=18 zhangyifei column=info:company, timestamp=1441997499039, value=alibaba 5 row(s) in 0.0430 seconds
3.5.基于列范围(不是行范围)过滤数据ColumnRangeFilter
- 可用于获得一个范围的列，例如，如果你的一行中有百万个列，但是你只希望查看列名从bbbb到dddd的范围
- 该方法从 HBase 0.92 版本开始引入
- 一个列名是可以出现在多个列族中的，该过滤器将返回所有列族中匹配的列
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
//ColumnRangeFilter例子 private static void scanFilter08() throws IOException, UnsupportedEncodingException { Configuration conf = HBaseConfiguration.create(); conf.set("hbase.rootdir", "hdfs://ncst:9000/hbase"); conf.set("hbase.zookeeper.quorum", "ncst"); HTable ht = new HTable(conf, "users"); //匹配 以'a'开头到以'c'开头(不包含c) 所有的列 ColumnRangeFilter columnRangeFilter = new ColumnRangeFilter("a".getBytes(), true, "c".getBytes(), false); Scan scan = new Scan(); scan.setFilter(columnRangeFilter); ResultScanner rs = ht.getScanner(scan); for(Result result : rs){ for(Cell cell : result.rawCells()){ System.out.println(new String(CellUtil.cloneRow(cell))+" " +new String(CellUtil.cloneFamily(cell))+" " +new String(CellUtil.cloneQualifier(cell))+" " +new String(CellUtil.cloneValue(cell),"UTF-8")+" " +cell.getTimestamp()); } } ht.close(); }
hbase(main):016:0> scan 'users',{FILTER=>"ColumnRangeFilter('a',true,'c',false)"} ROW COLUMN+CELL xiaoming column=info:age, timestamp=1441997971945, value=38 xiaoming column=info:birthday, timestamp=1441997498851, value=1987-06-17 xiaoming01 column=info:age, timestamp=1441998917568, value=24 xiaoming02 column=info:age, timestamp=1441998917594, value=24 xiaoming03 column=info:age, timestamp=1441998919607, value=24 zhangyifei column=info:age, timestamp=1442247255446, value=18 zhangyifei column=info:birthday, timestamp=1441997498990, value=1987-4-17 5 row(s) in 0.0340 seconds
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。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)
//RowFilter例子 private static void scanFilter09() throws IOException, UnsupportedEncodingException { Configuration conf = HBaseConfiguration.create(); conf.set("hbase.rootdir", "hdfs://ncst:9000/hbase"); conf.set("hbase.zookeeper.quorum", "ncst"); HTable ht = new HTable(conf, "users"); //匹配 行键包含'01' 所有的行 RowFilter rowFilter = new RowFilter(CompareOp.EQUAL, new SubstringComparator("01")); Scan scan = new Scan(); scan.setFilter(rowFilter); ResultScanner rs = ht.getScanner(scan); for(Result result : rs){ for(Cell cell : result.rawCells()){ System.out.println(new String(CellUtil.cloneRow(cell))+" " +new String(CellUtil.cloneFamily(cell))+" " +new String(CellUtil.cloneQualifier(cell))+" " +new String(CellUtil.cloneValue(cell),"UTF-8")+" " +cell.getTimestamp()); } } ht.close(); }
hbase(main):013:0> scan 'users',{FILTER=>"RowFilter(=,'substring:01')"} ROW COLUMN+CELL xiaoming01 column=address:contry, timestamp=1442000277200, value=xE4xB8xADxE5x9BxBD xiaoming01 column=address:country, timestamp=1442000228945, value=xE4xB8xADxE5x9BxBD xiaoming01 column=info:age, timestamp=1441998917568, value=24 1 row(s) in 0.0190 seconds
5.PageFilter(Shell不支持?)
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)
//PageFilter例子 private static void scanFilter10() throws IOException, UnsupportedEncodingException { Configuration conf = HBaseConfiguration.create(); conf.set("hbase.rootdir", "hdfs://ncst:9000/hbase"); conf.set("hbase.zookeeper.quorum", "ncst"); HTable ht = new HTable(conf, "users"); //从RowKey为 "xiaoming" 开始，取3行(包含xiaoming) PageFilter pageFilter = new PageFilter(3L); Scan scan = new Scan(); scan.setStartRow("xiaoming".getBytes()); scan.setFilter(pageFilter); ResultScanner rs = ht.getScanner(scan); for(Result result : rs){ for(Cell cell : result.rawCells()){ System.out.println(new String(CellUtil.cloneRow(cell))+" " +new String(CellUtil.cloneFamily(cell))+" " +new String(CellUtil.cloneQualifier(cell))+" " +new String(CellUtil.cloneValue(cell),"UTF-8")+" " +cell.getTimestamp()); } } ht.close(); }
注意：由于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，所以更好的返回指定行数的办法是ResultScanner.next(int nbRows)，即：
//上面Demo的改动版
private static void scanFilter11() throws IOException, UnsupportedEncodingException { Configuration conf = HBaseConfiguration.create(); conf.set("hbase.rootdir", "hdfs://ncst:9000/hbase"); conf.set("hbase.zookeeper.quorum", "ncst"); HTable ht = new HTable(conf, "users"); //从RowKey为 "xiaoming" 开始，取3行(包含xiaoming) //PageFilter pageFilter = new PageFilter(3L); Scan scan = new Scan(); scan.setStartRow("xiaoming".getBytes()); //scan.setFilter(pageFilter); ResultScanner rs = ht.getScanner(scan); //指定返回3行数据 for(Result result : rs.next(3)){ for(Cell cell : result.rawCells()){ System.out.println(new String(CellUtil.cloneRow(cell))+" " +new String(CellUtil.cloneFamily(cell))+" " +new String(CellUtil.cloneQualifier(cell))+" " +new String(CellUtil.cloneValue(cell),"UTF-8")+" " +cell.getTimestamp()); } } ht.close(); }
6.SkipFilter(Shell不支持)
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。
构造函数：SkipFilter(Filter filter)
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))));
//SkipFilter例子 private static void scanFilter12() throws IOException, UnsupportedEncodingException { Configuration conf = HBaseConfiguration.create(); conf.set("hbase.rootdir", "hdfs://ncst:9000/hbase"); conf.set("hbase.zookeeper.quorum", "ncst"); HTable ht = new HTable(conf, "users"); //跳过列值中包含"24"的所有列 SkipFilter skipFilter = new SkipFilter(new ValueFilter(CompareOp.NOT_EQUAL, new BinaryComparator("24".getBytes()))); Scan scan = new Scan(); scan.setFilter(skipFilter); ResultScanner rs = ht.getScanner(scan); for(Result result : rs){ for(Cell cell : result.rawCells()){ System.out.println(new String(CellUtil.cloneRow(cell))+" " +new String(CellUtil.cloneFamily(cell))+" " +new String(CellUtil.cloneQualifier(cell))+" " +new String(CellUtil.cloneValue(cell),"UTF-8")+" " +cell.getTimestamp()); } } ht.close(); }
7.Utility--FirstKeyOnlyFilter
该过滤器仅仅返回每一行中第一个cell的值，可以用于高效的执行行数统计操作。估计实战意义不大。
构造函数：public FirstKeyOnlyFilter()
//FirstKeyOnlyFilter例子 private static void scanFilter12() throws IOException, UnsupportedEncodingException { Configuration conf = HBaseConfiguration.create(); conf.set("hbase.rootdir", "hdfs://ncst:9000/hbase"); conf.set("hbase.zookeeper.quorum", "ncst"); HTable ht = new HTable(conf, "users"); //返回每一行中的第一个cell的值 FirstKeyOnlyFilter firstKeyOnlyFilter = new FirstKeyOnlyFilter(); Scan scan = new Scan(); scan.setFilter(firstKeyOnlyFilter); ResultScanner rs = ht.getScanner(scan); int i = 0; for(Result result : rs){ for(Cell cell : result.rawCells()){ System.out.println(new String(CellUtil.cloneRow(cell))+" " +new String(CellUtil.cloneFamily(cell))+" " +new String(CellUtil.cloneQualifier(cell))+" " +new String(CellUtil.cloneValue(cell),"UTF-8")+" " +cell.getTimestamp()); i++; } } //输出总的行数 System.out.println(i); ht.close(); }
hbase(main):009:0> scan 'users',{FILTER=>'FirstKeyOnlyFilter()'} ROW COLUMN+CELL xiaoming column=address:city, timestamp=1441997498965, value=hangzhou xiaoming01 column=address:contry, timestamp=1442000277200, value=xE4xB8xADxE5x9BxBD xiaoming02 column=info:age, timestamp=1441998917594, value=24 xiaoming03 column=info:age, timestamp=1441998919607, value=24 zhangyifei column=address:city, timestamp=1441997499108, value=jieyang 5 row(s) in 0.0240 seconds