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

/**
 * 用于列值过滤。
 */
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<Result> iterator = scanner.iterator();
        LinkedList<String> 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: LESSLESS_OR_EQUALEQUALNOT_EQUALGREATERGREATER_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 EQUALNOT_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 EQUALNOT_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 BitComparatorNullComparator 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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