HBase Filter 过滤器之 ValueFilter 详解

前言：本文详细介绍了 HBase ValueFilter 过滤器 Java&shell api 的使用，并贴出了相关示例代码以供参考。ValueFilter 基于列值进行过滤，在工作中涉及到需要通过HBase 列值进行数据过滤时可以考虑使用它。比较器细节及原理请参照之前的更文：HBase Filter 过滤器之比较器 Comparator 原理及源码学习

一。Java Api

头部代码

public class ValueFilterDemo {
    private static boolean isok = false;
    private static String tableName = "test";
    private static String[] cfs = new String[]{"f1","f2"};
    private static String[] data = new String[]{
            "row-1:f1:c1:abcdefg", 
			"row-2:f1:c2:abc", 
			"row-3:f2:c3:abc123456", 
			"row-4:f2:c4:1234abc567"
    };
    public static void main(String[] args) throws ioException {

        MyBase myBase = new MyBase();
        Connection connection = myBase.createConnection();
        if (isok) {
            myBase.deleteTable(connection, tableName);
            myBase.createTable(connection, tableName, cfs);
            // 造数据
            myBase.putRows(connection, tableName, data);
        }
        Table table = connection.getTable(TableName.valueOf(tableName));
        Scan scan = new Scan();

中部代码

向右滑动滚动条可查看输出结果。

1. BinaryComparator 构造过滤器

        ValueFilter valueFilter = new ValueFilter(CompareFilter.CompareOp.EQUAL, new BinaryComparator(Bytes.toBytes("abc"))); // [row-2:f1:c2:abc]
        ValueFilter valueFilter = new ValueFilter(CompareFilter.CompareOp.NOT_EQUAL, new BinaryComparator(Bytes.toBytes("abc"))); // [row-1:f1:c1:abcdefg, row-3:f2:c3:abc123456, row-4:f2:c4:1234abc567]
        ValueFilter valueFilter = new ValueFilter(CompareFilter.CompareOp.GREATER, new BinaryComparator(Bytes.toBytes("abc"))); // [row-1:f1:c1:abcdefg, row-3:f2:c3:abc123456]
        ValueFilter valueFilter = new ValueFilter(CompareFilter.CompareOp.GREATER_OR_EQUAL, new BinaryComparator(Bytes.toBytes("abc1"))); // [row-1:f1:c1:abcdefg, row-3:f2:c3:abc123456]
        ValueFilter valueFilter = new ValueFilter(CompareFilter.CompareOp.LESS, new BinaryComparator(Bytes.toBytes("abc"))); // [row-4:f2:c4:1234abc567]
        ValueFilter valueFilter = new ValueFilter(CompareFilter.CompareOp.LESS_OR_EQUAL, new BinaryComparator(Bytes.toBytes("abc"))); // [row-2:f1:c2:abc, row-4:f2:c4:1234abc567]

2. BinaryPrefixComparator 构造过滤器

        ValueFilter valueFilter = new ValueFilter(CompareFilter.CompareOp.EQUAL, new BinaryPrefixComparator(Bytes.toBytes("123"))); // [row-4:f2:c4:1234abc567]
        ValueFilter valueFilter = new ValueFilter(CompareFilter.CompareOp.NOT_EQUAL, new BinaryPrefixComparator(Bytes.toBytes("ab"))); // [row-4:f2:c4:1234abc567]
        ValueFilter valueFilter = new ValueFilter(CompareFilter.CompareOp.GREATER, new BinaryPrefixComparator(Bytes.toBytes("ab"))); // [] 只比较prefix长度的字节
        ValueFilter valueFilter = new ValueFilter(CompareFilter.CompareOp.GREATER_OR_EQUAL, new BinaryPrefixComparator(Bytes.toBytes("ab"))); // [row-1:f1:c1:abcdefg, row-2:f1:c2:abc, row-3:f2:c3:abc123456]
        ValueFilter valueFilter = new ValueFilter(CompareFilter.CompareOp.LESS, new BinaryPrefixComparator(Bytes.toBytes("abc"))); // [row-4:f2:c4:1234abc567]
        ValueFilter valueFilter = new ValueFilter(CompareFilter.CompareOp.LESS_OR_EQUAL, new BinaryPrefixComparator(Bytes.toBytes("abc"))); // [row-1:f1:c1:abcdefg, row-2:f1:c2:abc, row-3:f2:c3:abc123456, row-4:f2:c4:1234abc567]

3. SubstrinGComparator 构造过滤器

        ValueFilter valueFilter = new ValueFilter(CompareFilter.CompareOp.EQUAL, new SubstringComparator("123")); // [row-3:f2:c3:abc123456, row-4:f2:c4:1234abc567]
        ValueFilter valueFilter = new ValueFilter(CompareFilter.CompareOp.NOT_EQUAL, new SubstringComparator("def")); // [row-2:f1:c2:abc, row-3:f2:c3:abc123456, row-4:f2:c4:1234abc567]```

4. RegexStringComparator 构造过滤器

        ValueFilter valueFilter = new ValueFilter(CompareFilter.CompareOp.NOT_EQUAL, new RegexStringComparator("4[a-z]")); // [row-1:f1:c1:abcdefg, row-2:f1:c2:abc, row-3:f2:c3:abc123456]
        ValueFilter valueFilter = new ValueFilter(CompareFilter.CompareOp.EQUAL, new RegexStringComparator("4[a-z]")); // [row-4:f2:c4:1234abc567]
        ValueFilter valueFilter = new ValueFilter(CompareFilter.CompareOp.EQUAL, new RegexStringComparator("abc")); // [row-1:f1:c1:abcdefg, row-2:f1:c2:abc, row-3:f2:c3:abc123456, row-4:f2:c4:1234abc567]

尾部代码

		scan.setFilter(valueFilter);
        ResultScanner scanner = table.getScanner(scan);
        Iterator iterator = scanner.iterator();
        LinkedList keys = new LinkedList<>();
        while (iterator.hasNext()) {
            String key = "";
            Result result = iterator.next();
            for (Cell cell : result.rawCells()) {
                byte[] rowkey = CellUtil.cloneRow(cell);
                byte[] family = CellUtil.cloneFamily(cell);
                byte[] column = CellUtil.cloneQualifier(cell);
                byte[] value = CellUtil.cloneValue(cell);
                key = Bytes.toString(rowkey) + ":" + Bytes.toString(family) + ":" + Bytes.toString(column) + ":" + Bytes.toString(value);
                keys.add(key);
            }
        }
        System.out.println(keys);
        scanner.close();
        table.close();
        connection.close();
    }
}

二。Shell Api

1. BinaryComparator 构造过滤器

方式一：

hbase(main):006:0> scan "test",{FILTER=>"ValueFilter(=,"binary:abc")"}
ROW                                              COLUMN+CELL                                                                                                                                   
 row-2                                           column=f1:c2, timestamp=1589453592471, value=abc                                                                                              
1 row(s) in 0.0240 seconds

支持的比较运算符：= != > >= < <=，不再一一举例。

方式二：

import org.apache.hadoop.hbase.filter.CompareFilter
import org.apache.hadoop.hbase.filter.BinaryComparator
import org.apache.hadoop.hbase.filter.ValueFilter

hbase(main):010:0> scan "test",{FILTER => ValueFilter.new(CompareFilter::CompareOp.valueOf("EQUAL"), BinaryComparator.new(Bytes.toBytes("abc")))}
ROW                                              COLUMN+CELL                                                                                                                                   
 row-2                                           column=f1:c2, timestamp=1589453592471, value=abc                                                                                              
1 row(s) in 0.0230 seconds

支持的比较运算符：LESS、LESS_OR_EQUAL、EQUAL、NOT_EQUAL、GREATER、GREATER_OR_EQUAL，不再一一举例。

推荐使用方式一，更简洁方便。

2. BinaryPrefixComparator 构造过滤器

方式一：

hbase(main):011:0> scan "test",{FILTER=>"ValueFilter(=,"binaryprefix:ab")"}
ROW                                              COLUMN+CELL                                                                                                                                   
 row-1                                           column=f1:c1, timestamp=1589453592471, value=abcdefg                                                                                          
 row-2                                           column=f1:c2, timestamp=1589453592471, value=abc                                                                                              
 row-3                                           column=f2:c3, timestamp=1589453592471, value=abc123456                                                                                        
3 row(s) in 0.0430 seconds

方式二：

import org.apache.hadoop.hbase.filter.CompareFilter
import org.apache.hadoop.hbase.filter.BinaryPrefixComparator
import org.apache.hadoop.hbase.filter.ValueFilter

hbase(main):013:0> scan "test",{FILTER => ValueFilter.new(CompareFilter::CompareOp.valueOf("EQUAL"), BinaryPrefixComparator.new(Bytes.toBytes("ab")))}
ROW                                              COLUMN+CELL                                                                                                                                   
 row-1                                           column=f1:c1, timestamp=1589453592471, value=abcdefg                                                                                          
 row-2                                           column=f1:c2, timestamp=1589453592471, value=abc                                                                                              
 row-3                                           column=f2:c3, timestamp=1589453592471, value=abc123456                                                                                        
3 row(s) in 0.0440 seconds

其它同上。

3. SubstringComparator 构造过滤器

方式一：

hbase(main):014:0> scan "test",{FILTER=>"ValueFilter(=,"substring:123")"}
ROW                                              COLUMN+CELL                                                                                                                                   
 row-3                                           column=f2:c3, timestamp=1589453592471, value=abc123456                                                                                        
 row-4                                           column=f2:c4, timestamp=1589453592471, value=1234abc567                                                                                       
2 row(s) in 0.0340 seconds

方式二：

import org.apache.hadoop.hbase.filter.CompareFilter
import org.apache.hadoop.hbase.filter.SubstringComparator
import org.apache.hadoop.hbase.filter.ValueFilter

hbase(main):016:0> scan "test",{FILTER => ValueFilter.new(CompareFilter::CompareOp.valueOf("EQUAL"), SubstringComparator.new("123"))}
ROW                                              COLUMN+CELL                                                                                                                                   
 row-3                                           column=f2:c3, timestamp=1589453592471, value=abc123456                                                                                        
 row-4                                           column=f2:c4, timestamp=1589453592471, value=1234abc567                                                                                       
2 row(s) in 0.0240 seconds

区别于上的是这里直接传入字符串进行比较，且只支持EQUAL和NOT_EQUAL两种比较符。

4. RegexStringComparator 构造过滤器

import org.apache.hadoop.hbase.filter.CompareFilter
import org.apache.hadoop.hbase.filter.RegexStringComparator
import org.apache.hadoop.hbase.filter.ValueFilter

hbase(main):018:0> scan "test",{FILTER => ValueFilter.new(CompareFilter::CompareOp.valueOf("EQUAL"), RegexStringComparator.new("4[a-z]"))}
ROW                                              COLUMN+CELL                                                                                                                                   
 row-4                                           column=f2:c4, timestamp=1589453592471, value=1234abc567                                                                                       
1 row(s) in 0.0290 seconds

该比较器直接传入字符串进行比较，且只支持EQUAL和NOT_EQUAL两种比较符。若想使用第一种方式可以传入regexstring试一下，我的版本有点低暂时不支持，不再演示了。

注意这里的正则匹配指包含关系，对应底层find()方法。

ValueFilter 不支持使用 LongComparator 比较器，且 BitComparator、NullComparator 比较器用之甚少，也不再介绍。

查看文章全部源代码请访以下GitHub地址：

https://GitHub.com/zhoupengbo/demos-bigdata/blob/master/hbase/hbase-filters-demos/src/main/java/com/zpb/demos/ValueFilterDemo.java

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