前言：本文詳細介紹了 HBase DependentColumnFilter 過濾器 Java&Shell API 的使用，並貼出了相關示例代碼以供參考。DependentColumnFilter 也稱參考列過濾器，是一種允許用戶指定一個參考列或引用列來過濾其他列的過濾器，過濾的原則是基於參考列的時間戳來進行篩選。
該過濾器嘗試找到該列所在的每一行，並返回該行具有相同時間戳的全部鍵值對;如果某行不包含這個指定的列，則什麼都不返回。參數dropDependentColumn 決定參考列被返回還是丟棄，爲true時表示參考列被返回，爲false時表示被丟棄。可以把DependentColumnFilter理解爲一個valueFilter和一個時間戳過濾器的組合。如果想要獲取同一時間線的數據可以考慮使用此過濾器。比較器細節及原理請參照之前的更文：HBase Filter 過濾器之比較器 Comparator 原理及源碼學習。
一。Java Api
頭部代碼
public class DependentColumnFilterDemo {
private static boolean isok = false;
private static String tableName = "test";
private static String[] cfs = new String[]{"f1", "f2"};
private static String[] data1 = new String[]{"row-1:f2:c3:1234abc56", "row-3:f1:c3:1234321"};
private static String[] data2 = new String[]{
"row-1:f1:c1:abcdefg", "row-1:f2:c2:abc", "row-2:f1:c1:abc123456", "row-2:f2:c2:1234abc567"
};
public static void main(String[] args) throws IOException, InterruptedException {
MyBase myBase = new MyBase();
Connection connection = myBase.createConnection();
if (isok) {
myBase.deleteTable(connection, tableName);
myBase.createTable(connection, tableName, cfs);
// 造數據
myBase.putRows(connection, tableName, data1); // 第一批數據
Thread.sleep(10);
myBase.putRows(connection, tableName, data2); // 第二批數據
}
Table table = connection.getTable(TableName.valueOf(tableName));
Scan scan = new Scan();
中部代碼
向右滑動滾動條可查看輸出結果。
// 構造方法一
DependentColumnFilter filter = new DependentColumnFilter(Bytes.toBytes("f1"), Bytes.toBytes("c1")); // [row-1:f1:c1:abcdefg, row-1:f2:c2:abc, row-2:f1:c1:abc123456, row-2:f2:c2:1234abc567]
// 構造方法二 boolean dropDependentColumn=true
DependentColumnFilter filter = new DependentColumnFilter(Bytes.toBytes("f1"), Bytes.toBytes("c1"), true); // [row-1:f2:c2:abc, row-2:f2:c2:1234abc567]
// 構造方法二 boolean dropDependentColumn=false 默認爲false
DependentColumnFilter filter = new DependentColumnFilter(Bytes.toBytes("f1"), Bytes.toBytes("c1"), false); // [row-1:f1:c1:abcdefg, row-1:f2:c2:abc, row-2:f1:c1:abc123456, row-2:f2:c2:1234abc567]
// 構造方法三 + BinaryComparator 比較器過濾數據
DependentColumnFilter filter = new DependentColumnFilter(Bytes.toBytes("f1"), Bytes.toBytes("c1"), false,
CompareFilter.CompareOp.EQUAL, new BinaryComparator(Bytes.toBytes("abcdefg"))); // [row-1:f1:c1:abcdefg, row-1:f2:c2:abc]
// 構造方法三 + BinaryPrefixComparator 比較器過濾數據
DependentColumnFilter filter = new DependentColumnFilter(Bytes.toBytes("f1"), Bytes.toBytes("c1"), false,
CompareFilter.CompareOp.EQUAL, new BinaryPrefixComparator(Bytes.toBytes("abc"))); // [row-1:f1:c1:abcdefg, row-1:f2:c2:abc, row-2:f1:c1:abc123456, row-2:f2:c2:1234abc567]
// 構造方法三 + SubstringComparator 比較器過濾數據
DependentColumnFilter filter = new DependentColumnFilter(Bytes.toBytes("f1"), Bytes.toBytes("c1"), false,
CompareFilter.CompareOp.EQUAL, new SubstringComparator("1234")); // [row-2:f1:c1:abc123456, row-2:f2:c2:1234abc567]
// 構造方法三 + RegexStringComparator 比較器過濾數據
DependentColumnFilter filter = new DependentColumnFilter(Bytes.toBytes("f1"), Bytes.toBytes("c1"), false,
CompareFilter.CompareOp.EQUAL, new RegexStringComparator("[a-z]")); // [row-1:f1:c1:abcdefg, row-1:f2:c2:abc, row-2:f1:c1:abc123456, row-2:f2:c2:1234abc567]
// 構造方法三 + RegexStringComparator 比較器過濾數據
DependentColumnFilter filter = new DependentColumnFilter(Bytes.toBytes("f1"), Bytes.toBytes("c1"), false,
CompareFilter.CompareOp.EQUAL, new RegexStringComparator("1234[a-z]")); // [] 思考題：與上例對比，想想爲什麼爲空？
該過濾器同時也支持各比較器的不同比較語法，同之前介紹的各種過濾器是一樣的，這裏不再一一舉例了。
尾部代碼
scan.setFilter(filter);
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
HBase test 表數據一覽：
hbase(main):009:0> scan 'test'
ROW COLUMN+CELL
row-1 column=f1:c1, timestamp=1589794115268, value=abcdefg
row-1 column=f2:c2, timestamp=1589794115268, value=abc
row-1 column=f2:c3, timestamp=1589794115241, value=1234abc56
row-2 column=f1:c1, timestamp=1589794115268, value=abc123456
row-2 column=f2:c2, timestamp=1589794115268, value=1234abc567
row-3 column=f1:c3, timestamp=1589794115241, value=1234321
3 row(s) in 0.0280 seconds
0. 簡單構造方法
hbase(main):006:0> scan 'test',{FILTER=>"DependentColumnFilter('f1','c1')"}
ROW COLUMN+CELL
row-1 column=f1:c1, timestamp=1589794115268, value=abcdefg
row-1 column=f2:c2, timestamp=1589794115268, value=abc
row-2 column=f1:c1, timestamp=1589794115268, value=abc123456
row-2 column=f2:c2, timestamp=1589794115268, value=1234abc567
2 row(s) in 0.0450 seconds
hbase(main):008:0> scan 'test',{FILTER=>"DependentColumnFilter('f1','c1',false)"}
ROW COLUMN+CELL
row-1 column=f1:c1, timestamp=1589794115268, value=abcdefg
row-1 column=f2:c2, timestamp=1589794115268, value=abc
row-2 column=f1:c1, timestamp=1589794115268, value=abc123456
row-2 column=f2:c2, timestamp=1589794115268, value=1234abc567
2 row(s) in 0.0310 seconds
hbase(main):007:0> scan 'test',{FILTER=>"DependentColumnFilter('f1','c1',true)"}
ROW COLUMN+CELL
row-1 column=f2:c2, timestamp=1589794115268, value=abc
row-2 column=f2:c2, timestamp=1589794115268, value=1234abc567
2 row(s) in 0.0250 seconds
1. BinaryComparator 構造過濾器
方式一：
hbase(main):004:0> scan 'test',{FILTER=>"DependentColumnFilter('f1','c1',false,=,'binary:abcdefg')"}
ROW COLUMN+CELL
row-1 column=f1:c1, timestamp=1589794115268, value=abcdefg
row-1 column=f2:c2, timestamp=1589794115268, value=abc
1 row(s) in 0.0330 seconds
hbase(main):005:0> scan 'test',{FILTER=>"DependentColumnFilter('f1','c1',true,=,'binary:abcdefg')"}
ROW COLUMN+CELL
row-1 column=f2:c2, timestamp=1589794115268, value=abc
1 row(s) in 0.0120 seconds
支持的比較運算符：= != > >= < <=，不再一一舉例。
方式二：
import org.apache.hadoop.hbase.filter.CompareFilter
import org.apache.hadoop.hbase.filter.BinaryComparator
import org.apache.hadoop.hbase.filter.DependentColumnFilter
hbase(main):016:0> scan 'test',{FILTER => DependentColumnFilter.new(Bytes.toBytes("f1"), Bytes.toBytes("c1"), false,CompareFilter::CompareOp.valueOf('EQUAL'), BinaryComparator.new(Bytes.toBytes('abcdefg')))}
ROW COLUMN+CELL
row-1 column=f1:c1, timestamp=1589794115268, value=abcdefg
row-1 column=f2:c2, timestamp=1589794115268, value=abc
1 row(s) in 0.0170 seconds
hbase(main):017:0> scan 'test',{FILTER => DependentColumnFilter.new(Bytes.toBytes("f1"), Bytes.toBytes("c1"), true,CompareFilter::CompareOp.valueOf('EQUAL'), BinaryComparator.new(Bytes.toBytes('abcdefg')))}
ROW COLUMN+CELL
row-1 column=f2:c2, timestamp=1589794115268, value=abc
1 row(s) in 0.0140 seconds
支持的比較運算符：LESS、LESS_OR_EQUAL、EQUAL、NOT_EQUAL、GREATER、GREATER_OR_EQUAL，不再一一舉例。
推薦使用方式一，更簡潔方便。
2. BinaryPrefixComparator 構造過濾器
方式一：
hbase(main):019:0> scan 'test',{FILTER=>"DependentColumnFilter('f1','c1',false,=,'binaryprefix:abc')"}
ROW COLUMN+CELL
row-1 column=f1:c1, timestamp=1589794115268, value=abcdefg
row-1 column=f2:c2, timestamp=1589794115268, value=abc
row-2 column=f1:c1, timestamp=1589794115268, value=abc123456
row-2 column=f2:c2, timestamp=1589794115268, value=1234abc567
2 row(s) in 0.0330 seconds
hbase(main):020:0> scan 'test',{FILTER=>"DependentColumnFilter('f1','c1',true,=,'binaryprefix:abc')"}
ROW COLUMN+CELL
row-1 column=f2:c2, timestamp=1589794115268, value=abc
row-2 column=f2:c2, timestamp=1589794115268, value=1234abc567
2 row(s) in 0.0600 seconds
方式二：
import org.apache.hadoop.hbase.filter.CompareFilter
import org.apache.hadoop.hbase.filter.BinaryPrefixComparator
import org.apache.hadoop.hbase.filter.DependentColumnFilter
hbase(main):023:0> scan 'test',{FILTER => DependentColumnFilter.new(Bytes.toBytes("f1"), Bytes.toBytes("c1"), false,CompareFilter::CompareOp.valueOf('EQUAL'), BinaryPrefixComparator.new(Bytes.toBytes('abc')))}
ROW COLUMN+CELL
row-1 column=f1:c1, timestamp=1589794115268, value=abcdefg
row-1 column=f2:c2, timestamp=1589794115268, value=abc
row-2 column=f1:c1, timestamp=1589794115268, value=abc123456
row-2 column=f2:c2, timestamp=1589794115268, value=1234abc567
2 row(s) in 0.0180 seconds
hbase(main):022:0> scan 'test',{FILTER => DependentColumnFilter.new(Bytes.toBytes("f1"), Bytes.toBytes("c1"), true,CompareFilter::CompareOp.valueOf('EQUAL'), BinaryPrefixComparator.new(Bytes.toBytes('abc')))}
ROW COLUMN+CELL
row-1 column=f2:c2, timestamp=1589794115268, value=abc
row-2 column=f2:c2, timestamp=1589794115268, value=1234abc567
2 row(s) in 0.0190 seconds
其它同上。
3. SubstringComparator 構造過濾器
方式一：
hbase(main):025:0> scan 'test',{FILTER=>"DependentColumnFilter('f1','c1',false,=,'substring:abc')"}
ROW COLUMN+CELL
row-1 column=f1:c1, timestamp=1589794115268, value=abcdefg
row-1 column=f2:c2, timestamp=1589794115268, value=abc
row-2 column=f1:c1, timestamp=1589794115268, value=abc123456
row-2 column=f2:c2, timestamp=1589794115268, value=1234abc567
2 row(s) in 0.0340 seconds
hbase(main):024:0> scan 'test',{FILTER=>"DependentColumnFilter('f1','c1',true,=,'substring:abc')"}
ROW COLUMN+CELL
row-1 column=f2:c2, timestamp=1589794115268, value=abc
row-2 column=f2:c2, timestamp=1589794115268, value=1234abc567
2 row(s) in 0.0160 seconds
方式二：
import org.apache.hadoop.hbase.filter.CompareFilter
import org.apache.hadoop.hbase.filter.SubstringComparator
import org.apache.hadoop.hbase.filter.DependentColumnFilter
hbase(main):028:0> scan 'test',{FILTER => DependentColumnFilter.new(Bytes.toBytes("f1"), Bytes.toBytes("c1"), false,CompareFilter::CompareOp.valueOf('EQUAL'), SubstringComparator.new('abc'))}
ROW COLUMN+CELL
row-1 column=f1:c1, timestamp=1589794115268, value=abcdefg
row-1 column=f2:c2, timestamp=1589794115268, value=abc
row-2 column=f1:c1, timestamp=1589794115268, value=abc123456
row-2 column=f2:c2, timestamp=1589794115268, value=1234abc567
2 row(s) in 0.0150 seconds
hbase(main):029:0> scan 'test',{FILTER => DependentColumnFilter.new(Bytes.toBytes("f1"), Bytes.toBytes("c1"), true,CompareFilter::CompareOp.valueOf('EQUAL'), SubstringComparator.new('abc'))}
ROW COLUMN+CELL
row-1 column=f2:c2, timestamp=1589794115268, value=abc
row-2 column=f2:c2, timestamp=1589794115268, value=1234abc567
2 row(s) in 0.0170 seconds
區別於上的是這裏直接傳入字符串進行比較，且只支持EQUAL和NOT_EQUAL兩種比較符。
4. RegexStringComparator 構造過濾器
import org.apache.hadoop.hbase.filter.CompareFilter
import org.apache.hadoop.hbase.filter.RegexStringComparator
import org.apache.hadoop.hbase.filter.DependentColumnFilter
hbase(main):035:0> scan 'test',{FILTER => DependentColumnFilter.new(Bytes.toBytes("f1"), Bytes.toBytes("c1"), false,CompareFilter::CompareOp.valueOf('EQUAL'), RegexStringComparator.new('[a-z]'))}
ROW COLUMN+CELL
row-1 column=f1:c1, timestamp=1589794115268, value=abcdefg
row-1 column=f2:c2, timestamp=1589794115268, value=abc
row-2 column=f1:c1, timestamp=1589794115268, value=abc123456
row-2 column=f2:c2, timestamp=1589794115268, value=1234abc567
2 row(s) in 0.0170 seconds
hbase(main):034:0* scan 'test',{FILTER => DependentColumnFilter.new(Bytes.toBytes("f1"), Bytes.toBytes("c1"), true,CompareFilter::CompareOp.valueOf('EQUAL'), RegexStringComparator.new('[a-z]'))}
ROW COLUMN+CELL
row-1 column=f2:c2, timestamp=1589794115268, value=abc
row-2 column=f2:c2, timestamp=1589794115268, value=1234abc567
2 row(s) in 0.0150 seconds
該比較器直接傳入字符串進行比較，且只支持EQUAL和NOT_EQUAL兩種比較符。若想使用第一種方式可以傳入regexstring試一下，我的版本有點低暫時不支持，不再演示了。
注意這裏的正則匹配指包含關係，對應底層find()方法。
DependentColumnFilter不支持使用LongComparator比較器，且BitComparator、NullComparator比較器用之甚少，也不再介紹。
到此爲止，所有的比較過濾器就總結完畢了。
查看文章全部源代碼請訪以下GitHub地址：
https://github.com/zhoupengbo/demos-bigdata/blob/master/hbase/hbase-filters-demos/src/main/java/com/zpb/demos/DependentColumnFilterDemo.java
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