hbase数据查询及过滤器详细使用，hbase数据查询
本文介绍了在hbase中简单的数据查询及过滤器（比较全）的使用，代码和命令均经过本人实测通过，辛苦的将这些整理出来为以后方便查找
创建并插入数据：
hbase(main):179:0> create 'scores','grade','course'
hbase(main):180:0> put 'scores','zhangsan01','course:art','90'
hbase(main):181:0> scan 'scores'
ROW COLUMN+CELL
zhangsan01 column=course:art, timestamp=1498003561726, value=90
1 row(s) in 0.0150 seconds
hbase(main):182:0> put 'scores','zhangsan01','course:math','99',1498003561726
（这里手动设置时间戳的时候一定不能大于你当前的系统时间，否则的话无法删除该数据，我这里手动设置数据是为了下面的DependentColumnFilter过滤器试验。你可以查看一下插入第一条数据的时间戳，再插入第二条数据的时间戳为第一条数据的时间戳）
hbase(main):183:0> put 'scores','zhangsan01','grade:','101'
问题：当我将这条插入的数据删除之后再执行put 'scores','zhangsan01','grade:','101',1498003561726后能成功却scan 'scores'后没有该条数据，而再执行put 'scores','zhangsan01','grade:','101'后scan 'scores'却能查到该条数据。如果想插入该条数据的时候手动设置时间戳的话，必须在第一次插入该条数据或者truncate后再插入。
hbase(main):184:0> put 'scores','zhangsan02','course:art','90'
hbase(main):185:0> get 'scores','zhangsan02','course:art'
COLUMN CELL
course:art timestamp=1498003601365, value=90
1 row(s) in 0.0080 seconds
hbase(main):186:0> put 'scores','zhangsan02','grade:','102',1498003601365
hbase(main):187:0> put 'scores','zhangsan02','course:math','66',1498003561726
hbase(main):188:0> put 'scores','lisi01','course:math','89',1498003561726
hbase(main):189:0> put 'scores','lisi01','course:art','89'
hbase(main):190:0> put 'scores','lisi01','grade:','201',1498003561726查询数据：
根据rowkey查询：
hbase(main):187:0> get 'scores','zhangsan01'
COLUMN CELL
course:art timestamp=1498003561726, value=90
course:math timestamp=1498003561726, value=99
grade: timestamp=1498003593575, value=101
3 row(s) in 0.0160 seconds根据列名查询：
hbase(main):188:0> scan 'scores',{COLUMNS=>'course:art'}
ROW COLUMN+CELL
lisi01 column=course:art, timestamp=1498003655021, value=89
zhangsan01 column=course:art, timestamp=1498003561726, value=90
zhangsan02 column=course:art, timestamp=1498003601365, value=90
3 row(s) in 0.0120 seconds查询两个rowkey之间的数据：
hbase(main):205:0> scan 'scores',{STARTROW=>'zhangsan01',STOPROW=>'zhangsan02'}
ROW COLUMN+CELL
zhangsan01 column=course:art, timestamp=1498003561726, value=90
zhangsan01 column=course:math, timestamp=1498003561726, value=99
zhangsan01 column=grade:, timestamp=1498003593575, value=101
1 row(s) in 0.0140 seconds查询两个rowkey且根据列名来查询：
hbase(main):206:0> scan 'scores',{COLUMNS=>'course:art',STARTROW=>'zhangsan01',STOPROW=>'zhangsan02'}
ROW COLUMN+CELL
zhangsan01 column=course:art, timestamp=1498003561726, value=90
1 row(s) in 0.0110 seconds查询指定rowkey到末尾根据列名的查询：
hbase(main):207:0> scan 'scores',{COLUMNS=>'course:art',STARTROW=>'zhangsan01',STOPROW=>'zhangsan09'}
ROW COLUMN+CELL
zhangsan01 column=course:art, timestamp=1498003561726, value=90
zhangsan02 column=course:art, timestamp=1498003601365, value=90
2 row(s) in 0.0310 seconds过滤器的使用：
引言 -- 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
BitComparator Performs a bitwise comparison, providing a BitwiseOp class with OR, and XOR operators.
NullComparator Does not compare against an actual value but whether a given one is null, or not null.
RegexStringComparator 正则表达式匹配
SubstringComparator 子串匹配
import java.io.IOException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.Cell;
import org.apache.hadoop.hbase.CellUtil;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.client.Admin;
import org.apache.hadoop.hbase.client.Connection;
import org.apache.hadoop.hbase.client.ConnectionFactory;
import org.apache.hadoop.hbase.client.Get;
import org.apache.hadoop.hbase.client.Result;
import org.apache.hadoop.hbase.client.ResultScanner;
import org.apache.hadoop.hbase.client.Scan;
import org.apache.hadoop.hbase.client.Table;
import org.apache.hadoop.hbase.filter.BinaryComparator;
import org.apache.hadoop.hbase.filter.ColumnCountGetFilter;
import org.apache.hadoop.hbase.filter.ColumnPaginationFilter;
import org.apache.hadoop.hbase.filter.ColumnPrefixFilter;
import org.apache.hadoop.hbase.filter.ColumnRangeFilter;
import org.apache.hadoop.hbase.filter.DependentColumnFilter;
import org.apache.hadoop.hbase.filter.FamilyFilter;
import org.apache.hadoop.hbase.filter.Filter;
import org.apache.hadoop.hbase.filter.FilterList;
import org.apache.hadoop.hbase.filter.FirstKeyOnlyFilter;
import org.apache.hadoop.hbase.filter.FuzzyRowFilter;
import org.apache.hadoop.hbase.filter.InclusiveStopFilter;
import org.apache.hadoop.hbase.filter.KeyOnlyFilter;
import org.apache.hadoop.hbase.filter.MultipleColumnPrefixFilter;
import org.apache.hadoop.hbase.filter.PageFilter;
import org.apache.hadoop.hbase.filter.PrefixFilter;
import org.apache.hadoop.hbase.filter.QualifierFilter;
import org.apache.hadoop.hbase.filter.RandomRowFilter;
import org.apache.hadoop.hbase.filter.RegexStringComparator;
import org.apache.hadoop.hbase.filter.RowFilter;
import org.apache.hadoop.hbase.filter.SingleColumnValueExcludeFilter;
import org.apache.hadoop.hbase.filter.SingleColumnValueFilter;
import org.apache.hadoop.hbase.filter.SkipFilter;
import org.apache.hadoop.hbase.filter.SubstringComparator;
import org.apache.hadoop.hbase.filter.TimestampsFilter;
import org.apache.hadoop.hbase.filter.ValueFilter;
import org.apache.hadoop.hbase.filter.WhileMatchFilter;
import org.apache.hadoop.hbase.filter.CompareFilter.CompareOp;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.hbase.util.Pair;
public class HbaseUtils {
public static Admin admin = null;
public static Connection conn = null;
public HbaseUtils() {
Configuration conf = HBaseConfiguration.create();
conf.set("hbase.zookeeper.quorum", "h71:2181");
conf.set("hbase.rootdir", "hdfs://h71:9000/hbase");
try {
conn = ConnectionFactory.createConnection(conf);
admin = conn.getAdmin();
} catch (IOException e) {
e.printStackTrace();
}
}
public static void main(String[] args) throws Exception {
HbaseUtils hbase = new HbaseUtils();
//1，FamilyFilter：基于“列族”来过滤数据；
// hbase.FamilyFilter("scores");
//2，QualifierFilter：基于“列名”来过滤数据；
// hbase.QualifierFilter("scores");
//3.RowFilter：基于rowkey来过滤数据；
// hbase.RowFilter("scores","zhangsan01");
//4.PrefixFilter：基于rowkey前缀来过滤数据；
// hbase.PrefixFilter("scores","zhang");
//后缀过滤数据
// hbase.HouZui("scores");
//5，ColumnPrefixFilter：基于列名前缀来过滤数据；
// hbase.ColumnPrefixFilter("scores");
//6，MultipleColumnPrefixFilter：ColumnPrefixFilter的加强版；
// hbase.MultipleColumnPrefixFilter("scores");
//7，ColumnCountGetFilter：限制每行返回多少列；
// hbase.columnCountGetFilter();
//8，ColumnPaginationFilter：对一行的所有列分页，只返回[limit, offset]范围内的列；
// hbase.ColumnPaginationFilter("scores");
//9，ColumnRangeFilter：可用于获得一个范围的列
// hbase.ColumnRangeFilter("scores");
//10，DependentColumnFilter：返回（与（符合条件[列族，列名]或[列族，列名，值]的参考列）具有相同的时间戳）的所有列，即：基于比较器过滤参考列，基于参考列的时间戳过滤其他列；
// hbase.DependentColumnFilter("scores");
//11，FirstKeyOnlyFilter：结果只返回每行的第一个值对；
// hbase.FirstKeyOnlyFilter("scores");
//12，FuzzyRowFilter：模糊row查询；
// hbase.FuzzyRowFilter("scores");
//13，InclusiveStopFilter：将stoprow也一起返回；
// hbase.InclusiveStopFilter("scores");
//14，KeyOnlyFilter：只返回行键；
// hbase.KeyOnlyFilter("scores");
//15，PageFilter： 取回XX条数据 ；
// hbase.PageFilter("scores");
//16，RandomRowFilter：随机获取一定比例（比例为参数）的数据；
// hbase.RandomRowFilter("scores");
//17，SingleColumnValueFilter：基于参考列的值来过滤数据；
// hbase.SingleColumnValueFilter("scores");
//18，ValueFilter：基于值来过滤数据；
// hbase.ValueFilter("scores");
//19，SkipFilter：当过滤器发现某一行中的一列要过滤时，就将整行数据都过滤掉；
// hbase.SkipFilter("scores");
//20，TimestampsFilter：基于时间戳来过滤数据；
// hbase.TimestampsFilter("scores");
//21，WhileMatchFilter：一旦遇到一条符合过滤条件的数据，就停止扫描；
// hbase.WhileMatchFilter("scores");
//22，FilterList：多个过滤器组合过滤。
// hbase.FilterList("scores");
}
/**
1,FamilyFilter
a,按family（列族）查找，取回所有符合条件的“family”
b,构造方法第一个参数为compareOp
c,第二个参数为WritableByteArrayComparable，有BinaryComparator, BinaryPrefixComparator,
BitComparator, NullComparator, RegexStringComparator, SubstringComparator这些类，
最常用的为BinaryComparator
*/
public void FamilyFilter(String tableName) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
Filter filter = new FamilyFilter(CompareOp.LESS_OR_EQUAL, new BinaryComparator(Bytes.toBytes("grc")));
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
}
/*
hbase(main):224:0> scan 'scores', {FILTER => "FamilyFilter(<=,'binary:grc')"}
或者
hbase(main):011:0> scan 'scores', FILTER => "FamilyFilter(<=,'binary:grc')"
ROW COLUMN+CELL
lisi01 column=course:art, timestamp=1498003655021, value=89
lisi01 column=course:math, timestamp=1498003561726, value=89
lisi01 column=grade:, timestamp=1498003561726, value=201
zhangsan01 column=course:art, timestamp=1498003561726, value=90
zhangsan01 column=course:math, timestamp=1498003561726, value=99
zhangsan01 column=grade:, timestamp=1498003593575, value=101
zhangsan02 column=course:art, timestamp=1498003601365, value=90
zhangsan02 column=course:math, timestamp=1498003561726, value=66
zhangsan02 column=grade:, timestamp=1498003601365, value=102
3 row(s) in 0.0220 seconds
*/
/**
2，QualifierFilter
类似于FamilyFilter，取回所有符合条件的“列”
构造方法第一个参数 compareOp
第二个参数为WritableByteArrayComparable
*/
public void QualifierFilter(String tableName) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
Filter filter = new QualifierFilter(CompareOp.LESS_OR_EQUAL, new BinaryComparator(Bytes.toBytes("grc")));
//这里输的参数是相应位置比大小，及当输入ms的时候，所有列名的第一位小于等于m，如果第一位相等则比较第二位的大小。一开始没理解，所以一开始参数输入math或course的时候把我整懵了。
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
}
/*
hbase(main):221:0> scan 'scores', {FILTER => "QualifierFilter(<=,'binary:b')"}
ROW COLUMN+CELL
lisi01 column=course:art, timestamp=1498003655021, value=89
lisi01 column=grade:, timestamp=1498003561726, value=201
zhangsan01 column=course:art, timestamp=1498003561726, value=90
zhangsan01 column=grade:, timestamp=1498003593575, value=101
zhangsan02 column=course:art, timestamp=1498003601365, value=90
zhangsan02 column=grade:, timestamp=1498003601365, value=102
3 row(s) in 0.0470 seconds
*/
/**
3，RowFilter
构造方法参数设置类似于FamilyFilter，符合条件的row都返回
但是通过row查询时，如果知道开始结束的row，还是用scan的start和end方法更直接并且经测试速度快一半以上
*/
public void RowFilter(String tableName, String reg) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
//这个参数EQUAL很重要，如果参数不同，查询的结果也会不同
// RowFilter filter = new RowFilter(CompareOp.EQUAL, new BinaryComparator(Bytes.toBytes(reg)));//这样写也行
// Filter filter = new RowFilter(CompareOp.EQUAL, new BinaryComparator(Bytes.toBytes(reg)));
Filter filter = new RowFilter(CompareOp.LESS_OR_EQUAL, new BinaryComparator(Bytes.toBytes(reg)));
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
/**
* 更推荐用下面的方法直接指定起止行，因为filter本质上还是会遍历全部数据，而设定起止行后会直接从指定行开始，指定行结束，效率高很多。
*/
// scan.setStartRow(Bytes.toBytes("AAAAAAAAAAAA"));
// scan.setStopRow(Bytes.toBytes( "AAAAAAAAABBB"));
}
/*
hbase(main):004:0> scan 'scores', {FILTER => "RowFilter(<=,'binary:zhangsan01')"}
ROW COLUMN+CELL
lisi01 column=course:art, timestamp=1498003655021, value=89
lisi01 column=course:math, timestamp=1498003561726, value=89
lisi01 column=grade:, timestamp=1498003561726, value=201
zhangsan01 column=course:art, timestamp=1498003561726, value=90
zhangsan01 column=course:math, timestamp=1498003561726, value=99
zhangsan01 column=grade:, timestamp=1498003593575, value=101
2 row(s) in 0.0210 seconds
*/
/**
4，PrefixFilter
取回rowkey以指定prefix开头的所有行
*/
public void PrefixFilter(String tableName, String reg) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
Filter filter = new PrefixFilter(Bytes.toBytes("zhang"));
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
}
/*
hbase(main):022:0> scan 'scores', {FILTER => org.apache.hadoop.hbase.filter.PrefixFilter.new(org.apache.hadoop.hbase.util.Bytes.toBytes('li'))}
或者
hbase(main):004:0> scan 'scores', {FILTER => "PrefixFilter('li')"}
ROW COLUMN+CELL
lisi01 column=course:art, timestamp=1489747672249, value=89
lisi01 column=course:math, timestamp=1489747666861, value=89
lisi01 column=grade:, timestamp=1489747677402, value=201
1 row(s) in 0.0110 seconds
*/
/**
由于其原生带有PrefixFilter这种对ROWKEY的前缀过滤查询，因此想着实现的后缀查询的过程中，发现这一方面相对来说还是空白。
因此，只能采用一些策略来实现，主要还是采用正则表达式的方式。
*/
public void HouZui(String tableName) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
Filter filter = new RowFilter(CompareOp.EQUAL,new RegexStringComparator(".*n01"));
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
}
/*
hbase(main):020:0> scan 'scores', {FILTER => "RowFilter(=,'regexstring:.*n01')"}
ROW COLUMN+CELL
zhangsan01 column=course:art, timestamp=1498003561726, value=90
zhangsan01 column=course:math, timestamp=1498003561726, value=99
zhangsan01 column=grade:, timestamp=1498003593575, value=101
1 row(s) in 0.0080 seconds
*/
/**
5，ColumnPrefixFilter
*/
public void ColumnPrefixFilter(String tableName) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
byte[] prefix = Bytes.toBytes("ar");
Filter filter = new ColumnPrefixFilter(prefix);
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
}
/*
hbase(main):021:0> scan 'scores', {FILTER => "ColumnPrefixFilter('ar')"}
或者
hbase(main):022:0> scan 'scores', {FILTER => org.apache.hadoop.hbase.filter.ColumnPrefixFilter.new(org.apache.hadoop.hbase.util.Bytes.toBytes('ar'))}
ROW COLUMN+CELL
lisi01 column=course:art, timestamp=1498003655021, value=89
zhangsan01 column=course:art, timestamp=1498003561726, value=90
zhangsan02 column=course:art, timestamp=1498003601365, value=90
3 row(s) in 0.0140 seconds
*/
/**
6，MultipleColumnPrefixFilter
a,返回有此前缀的所有列，
b,在byte[][]中定义所有需要的列前缀，只要满足其中一条约束就会被返回（ColumnPrefixFilter的加强版），
*/
public void MultipleColumnPrefixFilter(String tableName) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
byte[][] prefix = {Bytes.toBytes("ar"),Bytes.toBytes("ma")};
Filter filter = new MultipleColumnPrefixFilter(prefix);
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
}
/*
hbase(main):023:0> scan 'scores', {FILTER => "MultipleColumnPrefixFilter('ar','ma')"}
ROW COLUMN+CELL
lisi01 column=course:art, timestamp=1498003655021, value=89
lisi01 column=course:math, timestamp=1498003561726, value=89
zhangsan01 column=course:art, timestamp=1498003561726, value=90
zhangsan01 column=course:math, timestamp=1498003561726, value=99
zhangsan02 column=course:art, timestamp=1498003601365, value=90
zhangsan02 column=course:math, timestamp=1498003561726, value=66
3 row(s) in 0.0290 seconds
*/
/**
7，ColumnCountGetFilter
a,无法再scan中使用，只能在Get中
b,若设为0，则无法返回数据，设为几就按服务器中存储位置取回几列
c,可用size()取到列数，观察效果
*/
public void columnCountGetFilter() throws Exception {
Table table = conn.getTable(TableName.valueOf("scores"));
Get get = new Get(Bytes.toBytes("zhangsan01"));
get.setFilter(new ColumnCountGetFilter(2));
Result result = table.get(get);
//输出结果size，观察效果
System.out.println(result.size());
// byte[] value1 = result.getValue("course".getBytes(), "art".getBytes());
// byte[] value2 = result.getValue("course".getBytes(), "math".getBytes());
// System.out.println("course:art"+"-->"+new String(value1)+" "
// +"course:math"+"-->"+new String(value2));
}
/*
hbase(main):026:0> scan 'scores', {FILTER => "ColumnCountGetFilter(2)"}
ROW COLUMN+CELL
lisi01 column=course:art, timestamp=1498003655021, value=89
lisi01 column=course:math, timestamp=1498003561726, value=89
zhangsan01 column=course:art, timestamp=1498003561726, value=90
zhangsan01 column=course:math, timestamp=1498003561726, value=99
zhangsan02 column=course:art, timestamp=1498003601365, value=90
zhangsan02 column=course:math, timestamp=1498003561726, value=66
3 row(s) in 0.0120 seconds
*/
/**
8，ColumnPaginationFilter
a,limit 表示返回列数
b,offset 表示返回列的偏移量，如果为0，则全部取出，如果为1，则返回第二列及以后
*/
public void ColumnPaginationFilter(String tableName) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
Filter filter = new ColumnPaginationFilter(2,1);
scan.setFilter(filter);
// 用addFamily增加列族后，会只返回指定列族的数据
scan.addFamily(Bytes.toBytes("course"));
ResultScanner scanner = table.getScanner(scan);
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
}
/*
hbase(main):031:0> scan 'scores',{FILTER=>org.apache.hadoop.hbase.filter.ColumnPaginationFilter.new(2,1)}
或者
hbase(main):030:0> scan 'scores',{FILTER=> "ColumnPaginationFilter(2,1)"}
ROW COLUMN+CELL
lisi01 column=course:math, timestamp=1498003561726, value=89
lisi01 column=grade:, timestamp=1498003561726, value=201
zhangsan01 column=course:math, timestamp=1498003561726, value=99
zhangsan01 column=grade:, timestamp=1498003593575, value=101
zhangsan02 column=course:math, timestamp=1498003561726, value=66
zhangsan02 column=grade:, timestamp=1498003601365, value=102
3 row(s) in 0.0100 seconds
*/
/**
9，ColumnRangeFilter
构造函数：
ColumnRangeFilter(byte[] minColumn, boolean minColumnInclusive, byte[] maxColumn, boolean maxColumnInclusive)
*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为bbbb到dddd的范围
*该过滤器可以进行高效的列名内部扫描。（为何是高效呢？？？因为列名是已经按字典排序好的）HBase-0.9.2 版本引入该功能。
*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
*/
public void ColumnRangeFilter(String tableName) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
Filter filter = new ColumnRangeFilter(Bytes.toBytes("a"),true, Bytes.toBytes("n"),true);
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
}
/*
hbase(main):032:0> scan 'scores',{FILTER=> "ColumnRangeFilter('a',true,'n',true)"}
ROW COLUMN+CELL
lisi01 column=course:art, timestamp=1498003655021, value=89
lisi01 column=course:math, timestamp=1498003561726, value=89
zhangsan01 column=course:art, timestamp=1498003561726, value=90
zhangsan01 column=course:math, timestamp=1498003561726, value=99
zhangsan02 column=course:art, timestamp=1498003601365, value=90
zhangsan02 column=course:math, timestamp=1498003561726, value=66
3 row(s) in 0.0140 seconds
*/
/**
10, DependentColumnFilter （该过滤器有两个参数：family和Qualifier,尝试找到该列所在的每一行，
并返回该行具有相同时间戳的全部键值对。如果某一行不包含指定的列，则该行的任何键值对都不返回，
该过滤器还可以有一个可选的布尔参数-如果为true,从属的列不返回；
该过滤器还可以有两个可选的参数--一个比较操作符和一个值比较器，用于family和Qualifier
的进一步检查，如果从属的列找到，其值还必须通过值检查，然后就是时间戳必须考虑）
*/
public void DependentColumnFilter(String tableName) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
// Filter filter = new DependentColumnFilter(Bytes.toBytes("course"), Bytes.toBytes("art"),false);
// Filter filter = new DependentColumnFilter(Bytes.toBytes("course"), Bytes.toBytes("art"),true);
Filter filter = new DependentColumnFilter(Bytes.toBytes("course"), Bytes.toBytes("art"),false,CompareOp.EQUAL,new BinaryComparator(Bytes.toBytes("90")));
// Filter filter = new DependentColumnFilter(Bytes.toBytes("course"), Bytes.toBytes("art"),true,CompareOp.EQUAL,new BinaryComparator(Bytes.toBytes("90")));
//上面这四种情况输出的for循环中的内容也不一样，要做相应的修改，否则会报java.lang.NullPointerException
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
}
/*
hbase(main):036:0> scan 'scores',{FILTER=> "DependentColumnFilter('course','art',false,=,'binary:90')"}
ROW COLUMN+CELL
zhangsan01 column=course:art, timestamp=1498003561726, value=90
zhangsan01 column=course:math, timestamp=1498003561726, value=99
zhangsan02 column=course:art, timestamp=1498003601365, value=90
zhangsan02 column=grade:, timestamp=1498003601365, value=102
2 row(s) in 0.0160 seconds
*/
/**
11，FirstKeyOnlyFilter
如名字所示，结果只返回每行的第一个值对
*/
public void FirstKeyOnlyFilter(String tableName) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
Filter filter = new FirstKeyOnlyFilter();
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
}
/*
hbase(main):037:0> scan 'scores',{FILTER=> "FirstKeyOnlyFilter()"}
ROW COLUMN+CELL
lisi01 column=course:art, timestamp=1498003655021, value=89
zhangsan01 column=course:art, timestamp=1498003561726, value=90
zhangsan02 column=course:art, timestamp=1498003601365, value=90
3 row(s) in 0.0160 seconds
*/
/**
12，FuzzyRowFilter
模糊row查询
pair中第一个参数为模糊查询的string
第二个参数为byte[]其中装与string位数相同的数值0或1,0表示该位必须与string中值相同，1表示可以不同
*/
public void FuzzyRowFilter(String tableName) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
Filter filter = new FuzzyRowFilter( Arrays.asList(new Pair<byte[], byte[]>(Bytes.toBytes("zhangsan01"),
new byte[] {0, 0, 0, 0 , 0, 0, 0, 0, 0, 1})));
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
}
/*
。。。。。
*/
/**
13，InclusiveStopFilter
指定stopRow，程序在scan时从头扫描全部返回，直到stopRow停止（stopRow这行也会返回，然后scan停止）
*/
public void InclusiveStopFilter(String tableName) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
Filter filter = new InclusiveStopFilter(Bytes.toBytes("zhangsan01"));
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
}
/*
hbase(main):012:0> scan 'scores', {FILTER => "InclusiveStopFilter('zhangsan01')"}
ROW COLUMN+CELL
lisi01 column=course:art, timestamp=1498003655021, value=89
lisi01 column=course:math, timestamp=1498003561726, value=89
lisi01 column=grade:, timestamp=1498003561726, value=201
zhangsan01 column=course:art, timestamp=1498003561726, value=90
zhangsan01 column=course:math, timestamp=1498003561726, value=99
zhangsan01 column=grade:, timestamp=1498003593575, value=101
2 row(s) in 0.0170 seconds
*/
/**
14，KeyOnlyFilter
只取key值，size正常，说明value不是没取而是在取的时候被重写为空（能打印，不是null）
lenAsVal这个值没大搞明白，当设为false时打印为空，如果设为true时打印的将会是“口口口口”
*/
public void KeyOnlyFilter(String tableName) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
Filter filter = new KeyOnlyFilter(true);
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
}
/*
hbase(main):016:0> scan 'scores', {FILTER => "KeyOnlyFilter(true)"}
ROW COLUMN+CELL
lisi01 column=course:art, timestamp=1498003655021, value=\x00\x00\x00\x02
lisi01 column=course:math, timestamp=1498003561726, value=\x00\x00\x00\x02
lisi01 column=grade:, timestamp=1498003561726, value=\x00\x00\x00\x03
zhangsan01 column=course:art, timestamp=1498003561726, value=\x00\x00\x00\x02
zhangsan01 column=course:math, timestamp=1498003561726, value=\x00\x00\x00\x02
zhangsan01 column=grade:, timestamp=1498003593575, value=\x00\x00\x00\x03
zhangsan02 column=course:art, timestamp=1498003601365, value=\x00\x00\x00\x02
zhangsan02 column=course:math, timestamp=1498003561726, value=\x00\x00\x00\x02
zhangsan02 column=grade:, timestamp=1498003601365, value=\x00\x00\x00\x03
3 row(s) in 0.0320 seconds
hbase(main):015:0> scan 'scores', {FILTER => "KeyOnlyFilter(false)"}
ROW COLUMN+CELL
lisi01 column=course:art, timestamp=1498003655021, value=
lisi01 column=course:math, timestamp=1498003561726, value=
lisi01 column=grade:, timestamp=1498003561726, value=
zhangsan01 column=course:art, timestamp=1498003561726, value=
zhangsan01 column=course:math, timestamp=1498003561726, value=
zhangsan01 column=grade:, timestamp=1498003593575, value=
zhangsan02 column=course:art, timestamp=1498003601365, value=
zhangsan02 column=course:math, timestamp=1498003561726, value=
zhangsan02 column=grade:, timestamp=1498003601365, value=
3 row(s) in 0.0190 seconds
*/
/**
15，PageFilter
取回XX条数据
*/
public void PageFilter(String tableName) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
Filter filter = new PageFilter(2);
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
}
/*
hbase(main):017:0> scan 'scores', {FILTER => "PageFilter(2)"}
ROW COLUMN+CELL
lisi01 column=course:art, timestamp=1498003655021, value=89
lisi01 column=course:math, timestamp=1498003561726, value=89
lisi01 column=grade:, timestamp=1498003561726, value=201
zhangsan01 column=course:art, timestamp=1498003561726, value=90
zhangsan01 column=course:math, timestamp=1498003561726, value=99
zhangsan01 column=grade:, timestamp=1498003593575, value=101
2 row(s) in 0.0130 seconds
*/
/**
16，RandomRowFilter
参数小于0时一条查不出大于1值会返回所有，而想取随机行的话有效区间为0~1，值代表取到的几率
*/
public void RandomRowFilter(String tableName) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
Filter filter = new RandomRowFilter((float)0.5);
//即使是0.5有时候也一条查不出来，有时候却全出来了，是几率并不是一定，那我就不知道这个具体有什么实际运用了。。。根据rowkey随机而不是根据列随机
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
}
/*
。。。。。
*/
/**
17，SingleColumnValueFilter和SingleColumnValueExcludeFilter
用来查找并返回指定条件的列的数据
a，如果查找时没有该列，两种filter都会把该行所有数据返回
b，如果查找时有该列，但是不符合条件，则该行所有列都不返回
c，如果找到该列，并且符合条件，前者返回所有列，后者返回除该列以外的所有列
*/
public void SingleColumnValueFilter(String tableName) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
//完整匹配字节数组
// Filter filter = new SingleColumnValueFilter(Bytes.toBytes("course"), Bytes.toBytes("art"),CompareOp.EQUAL,new BinaryComparator(Bytes.toBytes("90")));
//匹配正则表达式
// Filter filter = new SingleColumnValueFilter(Bytes.toBytes("course"), Bytes.toBytes("art"),CompareOp.EQUAL,new RegexStringComparator("8"));
//匹配是否包含子串,大小写不敏感
// Filter filter = new SingleColumnValueFilter(Bytes.toBytes("course"), Bytes.toBytes("art"),CompareOp.EQUAL,new SubstringComparator("9"));
Filter filter = new SingleColumnValueExcludeFilter(Bytes.toBytes("course"), Bytes.toBytes("art"), CompareOp.EQUAL,new SubstringComparator("9"));
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
}
/*
hbase(main):032:0> scan 'scores', {FILTER => "SingleColumnValueExcludeFilter('course','art',=,'substring:9')"}
ROW COLUMN+CELL
lisi01 column=course:math, timestamp=1498003561726, value=89
lisi01 column=grade:, timestamp=1498003561726, value=201
zhangsan01 column=course:math, timestamp=1498003561726, value=99
zhangsan01 column=grade:, timestamp=1498003593575, value=101
zhangsan02 column=course:math, timestamp=1498003561726, value=66
zhangsan02 column=grade:, timestamp=1498003601365, value=102
3 row(s) in 0.0150 seconds
*/
/**
18，ValueFilter
按value全数据库搜索（全部列的value均会被检索）
*/
public void ValueFilter(String tableName) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
Filter filter = new ValueFilter(CompareOp.NOT_EQUAL,new BinaryComparator(Bytes.toBytes("102")));
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
}
/*
NOT_EQUAL不知道该咋么表示。。。。。
*/
/**
19，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))));
*/
public void SkipFilter(String tableName) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
Filter filter = new SkipFilter(new ValueFilter(CompareOp.NOT_EQUAL,new BinaryComparator(Bytes.toBytes("102"))));
// Filter filter = new SkipFilter(new DependentColumnFilter(Bytes.toBytes("course"), Bytes.toBytes("art"),false,CompareOp.NOT_EQUAL,new BinaryComparator(Bytes.toBytes("90"))));
//该过滤器需要配合其他过滤器来使用
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
}
/*
不知道咋么把两个过滤器嵌套使用。。。。。
*/
/**
20，TimestampsFilter
a，按时间戳搜索数据库
b，需设定List<Long> 存放所有需要检索的时间戳，
*/
public void TimestampsFilter(String tableName) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
//ls中存放所有需要查找匹配的时间戳
List<Long> ls = new ArrayList<Long>();
ls.add((long)1498003561726L);
ls.add((long)1498003601365L);
//java语言的整型常量默认为int型，声明long型常量可以后加”l“或”L“
Filter filter = new TimestampsFilter(ls);
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
// for (Result result : scanner) {
// if(new String(result.getRow()).equals("zhangsan01")){
// System.out.println(new String(result.getRow())+" "
// +"course:art"+"-->"+new String(result.getValue(Bytes.toBytes("course"), Bytes.toBytes("art")))+" "
// +"course:math"+"-->"+new String(result.getValue(Bytes.toBytes("course"), Bytes.toBytes("math"))));
// }else if(new String(result.getRow()).equals("zhangsan02")){
// System.out.println(new String(result.getRow())+" "
// +"course:art"+"-->"+new String(result.getValue(Bytes.toBytes("course"), Bytes.toBytes("art")))+" "
// +"course:math"+"-->"+new String(result.getValue(Bytes.toBytes("course"), Bytes.toBytes("math")))+" "
// +"grade:"+"-->"+new String(result.getValue(Bytes.toBytes("grade"), Bytes.toBytes(""))));
// }else{
// System.out.println(new String(result.getRow())+" "
// +"course:math"+"-->"+new String(result.getValue(Bytes.toBytes("course"), Bytes.toBytes("math")))+" "
// +"grade:"+"-->"+new String(result.getValue(Bytes.toBytes("grade"), Bytes.toBytes(""))));
// }
// }
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
}
/*
hbase(main):039:0> scan 'scores', {FILTER => "TimestampsFilter(1498003561726,1498003601365)"}
ROW COLUMN+CELL
lisi01 column=course:math, timestamp=1498003561726, value=89
lisi01 column=grade:, timestamp=1498003561726, value=201
zhangsan01 column=course:art, timestamp=1498003561726, value=90
zhangsan01 column=course:math, timestamp=1498003561726, value=99
zhangsan02 column=course:art, timestamp=1498003601365, value=90
zhangsan02 column=course:math, timestamp=1498003561726, value=66
zhangsan02 column=grade:, timestamp=1498003601365, value=102
3 row(s) in 0.0160 seconds
*/
/**
21，WhileMatchFilter
相当于while执行，直到不match就break了返回了。
*/
public void WhileMatchFilter(String tableName) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
Filter filter = new WhileMatchFilter(new ValueFilter(CompareOp.NOT_EQUAL,new BinaryComparator(Bytes.toBytes("101"))));
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
}
/*
。。。。。
*/
/**
22，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：
*/
public void FilterList(String tableName) throws Exception {
Table table = conn.getTable(TableName.valueOf(tableName));
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(Bytes.toBytes("course"), Bytes.toBytes("math"),CompareOp.EQUAL,new BinaryComparator(Bytes.toBytes("89")));
list.addFilter(filter1);
SingleColumnValueFilter filter2 = new SingleColumnValueFilter(Bytes.toBytes("course"), Bytes.toBytes("math"),CompareOp.EQUAL,new BinaryComparator(Bytes.toBytes("66")));
list.addFilter(filter2);
Scan scan = new Scan();
scan.setFilter(list);
ResultScanner scanner = table.getScanner(scan);
for (Result r : scanner) {
for (Cell cell : r.rawCells()) {
System.out.println(
"Rowkey-->"+Bytes.toString(r.getRow())+" "+
"Familiy:Quilifier-->"+Bytes.toString(CellUtil.cloneQualifier(cell))+" "+
"Value-->"+Bytes.toString(CellUtil.cloneValue(cell)));
}
}
}
/*
hbase(main):009:0> scan 'scores', {FILTER => "PrefixFilter('zhang') OR QualifierFilter(>=,'binary:b')"}
ROW COLUMN+CELL
lisi01 column=course:math, timestamp=1489747666861, value=89
lisi01 column=grade:, timestamp=1489747677402, value=201
zhangsan01 column=course:art, timestamp=1489747593360, value=90
zhangsan01 column=course:math, timestamp=1489747589255, value=99
zhangsan01 column=grade:, timestamp=1489747598001, value=101
zhangsan02 column=course:art, timestamp=1489747607561, value=60
zhangsan02 column=course:math, timestamp=1489747602883, value=66
zhangsan02 column=grade:, timestamp=1489747614601, value=102
3 row(s) in 0.0180 seconds
*/
}参考：
http://blog.csdn.net/blue__yeah/article/details/41040399
http://blog.csdn.net/liangtingac/article/details/40078637
http://blog.csdn.net/u010967382/article/details/37653177
http://blog.csdn.net/sparkexpert/article/details/51942354
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