1.3 專用過濾器 (Dedicated Filters)
----
HBase 提供的第二類過濾器直接基於 FilterBase 並實現了更特定的應用場景。其中的很多過濾器事實上只適用於執行掃描操作時，因爲它們過濾整個行。
對於 get() 調用，這些過濾器的限制過於苛刻：包括所有行，後者什麼也不包括。
■ 前綴過濾器 (PrefixFilter)
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實例化過濾器時給定一個行的前綴，所有行鍵匹配這個前綴的行返回給客戶端：
PrefixFilter(final byte[] prefix)
示例： Example using the prefix based filter
Filter filter = new PrefixFilter(Bytes.toBytes("row-1"));
Scan scan = new Scan();
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result result : scanner) {
for (Cell cell : result.rawCells()) {
System.out.println("Cell: " + cell + ", Value: " +
Bytes.toString(cell.getValueArray(), cell.getValueOffset(),
cell.getValueLength()));
}
}
scanner.close();
Get get = new Get(Bytes.toBytes("row-5"));
get.setFilter(filter);
Result result = table.get(get);
for (Cell cell : result.rawCells()) {
System.out.println("Cell: " + cell + ", Value: " +
Bytes.toString(cell.getValueArray(), cell.getValueOffset(),
cell.getValueLength()));
}
輸出：
Results of scan:
Cell: row-1/colfam1:col-1/1427280142327/Put/vlen=7/seqid=0, Value: val-1.1
Cell: row-1/colfam1:col-10/1427280142379/Put/vlen=8/seqid=0, Value: val-1.10
...
Cell: row-1/colfam2:col-8/1427280142375/Put/vlen=7/seqid=0, Value: val-1.8
Cell: row-1/colfam2:col-9/1427280142377/Put/vlen=7/seqid=0, Value: val-1.9
Cell: row-10/colfam1:col-1/1427280142530/Put/vlen=8/seqid=0, Value: val-10.1
Cell: row-10/colfam1:col-10/1427280142546/Put/vlen=9/seqid=0, Value: val-10.10
...
Cell: row-10/colfam2:col-8/1427280142542/Put/vlen=8/seqid=0, Value: val-10.8
Cell: row-10/colfam2:col-9/1427280142544/Put/vlen=8/seqid=0, Value: val-10.9
Result of get:
需要注意的是，get() 方法並沒有返回任何結果，因爲它請求的行與過濾器的前綴不匹配。這個過濾器在使用 get() 方法時作用不大，但在掃描操作中
非常有用。
當掃描器遇到一個比前綴大的行鍵時掃描結束。與 start row 配合使用，此過濾器會提升掃描的整體性能，因爲它知道什麼時候忽略掉所有剩下的行。
■ 分頁過濾器 (PageFilter)
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可以使用這個過濾器對結果按行分頁。創建該過濾器實例時，要指定 pageSize 參數，控制返回每頁包含的行數。
PageFilter(final long pageSize)
NOTE:
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在物理上分離的服務器上進行過濾有一個根本性的問題。過濾器併發地運行在不同的分區服務器上，並且不能跨越這種邊界保持它們的當前狀態。因
此，每一個過濾器要求掃描到至少到 pageCount 行纔會結束掃描。
客戶端代碼需要記住返回的最後的行，然後，在另一次迭代要開始時，以此來設定掃描的起始行(start row)，保持相同的過濾器屬性。
示例： Example using a filter to paginate through rows
private static final byte[] POSTFIX = new byte[] { 0x00 };
Filter filter = new PageFilter(15);
int totalRows = 0;
byte[] lastRow = null;
while (true) {
Scan scan = new Scan();
scan.setFilter(filter);
if (lastRow != null) {
byte[] startRow = Bytes.add(lastRow, POSTFIX);
System.out.println("start row: " + Bytes.toStringBinary(startRow));
scan.setStartRow(startRow);
}
ResultScanner scanner = table.getScanner(scan);
int localRows = 0;
Result result;
while ((result = scanner.next()) != null) {
System.out.println(localRows++ + ": " + result);
totalRows++;
lastRow = result.getRow();
}
scanner.close();
if (localRows == 0) break;
}
System.out.println("total rows: " + totalRows);
輸出：
Adding rows to table...
0: keyvalues={row-1/colfam1:col-1/1427280402935/Put/vlen=7/ seqid=0, ...}
1: keyvalues={row-10/colfam1:col-1/1427280403125/Put/vlen=8/seqid=0, ...}
...
14: keyvalues={row-110/colfam1:col-1/1427280404601/Put/vlen=9/seqid=0, ...}
start row: row-110\x00
0: keyvalues={row-111/colfam1:col-1/1427280404615/Put/vlen=9/seqid=0, ...}
1: keyvalues={row-112/colfam1:col-1/1427280404628/Put/vlen=9/seqid=0, ...}
...
14: keyvalues={row-124/colfam1:col-1/1427280404786/Put/vlen=9/seqid=0, ...}
start row: row-124\x00
0: keyvalues={row-125/colfam1:col-1/1427280404799/Put/vlen=9/seqid=0, ...}
...
start row: row-999\x00
total rows: 1000
HBase 中的行鍵是按字典序排序的，因此返回的結果也是如此排序，並且起始行是包括在結果中的。需要拼接一個 0 字節到前一個 row key.
這確保最後看到的行鍵被忽略，並且下一個，按排序次序，能被找到。內容爲 0 的字節只有很少的增加量，因此可安全地用於重置掃描邊界。
■ 鍵過濾器 (KeyOnlyFilter)
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有些應用只需要訪問每個 cell 的 key, 而忽略其實際的值。KeyOnlyFilter 提供這種功能。構造器：
KeyOnlyFilter()
KeyOnlyFilter(boolean lenAsVal)
可選的 lenAsVal 參數，用於處理內部轉換調用 as-is, 控制對每一個 Cell 實例的值部分的處理。默認值爲 false, 簡單地設置爲 0 長度的值，而
相反爲 true 則設置值爲原始值的長度表示。
示例： Only returns the first found cell from each row
int rowCount = 0;
for (Result result : scanner) {
for (Cell cell : result.rawCells()) {
System.out.println("Cell: " + cell + ", Value: " + (
cell.getValueLength() > 0 ?
Bytes.toInt(cell.getValueArray(), cell.getValueOffset(),
cell.getValueLength()) : "n/a" ));
}
rowCount++;
}
System.out.println("Total num of rows: " + rowCount);
scanner.close();
}
public static void main(String[] args) throws IOException {
Configuration conf = HBaseConfiguration.create();
HBaseHelper helper = HBaseHelper.getHelper(conf);
helper.dropTable("testtable");
helper.createTable("testtable", "colfam1");
System.out.println("Adding rows to table...");
helper.fillTableRandom("testtable", /* row */ 1, 5, 0,
/* col */ 1, 30, 0, /* val */ 0, 10000, 0, true, "colfam1");
Connection connection = ConnectionFactory.createConnection(conf);
table = connection.getTable(TableName.valueOf("testtable"));
System.out.println("Scan #1");
Filter filter1 = new KeyOnlyFilter();
scan(filter1);
Filter filter2 = new KeyOnlyFilter(true);
scan(filter2);
輸出：
Results of scan:
Cell: row-0/colfam1:col-17/6/Put/vlen=0/seqid=0, Value: n/a
Cell: row-0/colfam1:col-27/3/Put/vlen=0/seqid=0, Value: n/a
...
Cell: row-4/colfam1:col-3/2/Put/vlen=0/seqid=0, Value: n/a
Cell: row-4/colfam1:col-5/16/Put/vlen=0/seqid=0, Value: n/a
Total num of rows: 5
Scan #2
Results of scan:
Cell: row-0/colfam1:col-17/6/Put/vlen=4/seqid=0, Value: 8
Cell: row-0/colfam1:col-27/3/Put/vlen=4/seqid=0, Value: 6
...
Cell: row-4/colfam1:col-3/2/Put/vlen=4/seqid=0, Value: 7
Cell: row-4/colfam1:col-5/16/Put/vlen=4/seqid=0, Value: 8
Total num of rows: 5
■ 首行鍵過濾器 (FirstKeyOnlyFilter)
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雖然這個名稱隱含着 KeyValue, 或 key only, 這兩者都是用詞不當。這個過濾器返回它找到的行中的第一個 cell, 及其所有的細節，包括值，或許它
應命名爲 FirstCellFilter 之類的名稱。
如果需要訪問每一行的第一個列(因爲 HBase 隱式排序的), 這個過濾器可以提供這類功能。通常，這個過濾器用於行計數(row counter) 類型的應用中，
因爲它只需要檢查一個行是否存在。在面向列的數據庫中，一個行實際上是由列組成的，並且如果沒有列，行業就不存在。
另一可能使用的場景依賴於列是按詞彙表排序的，並且設置 column qualifier 爲紀元值。這會使排序的列最早的時間戳名最新獲取到。配合使用這個
過濾器，一次掃描就可以從每一行上獲取最早的列。更有趣的是，如果反轉列限定符的時間戳設置，那麼久可以在一次掃描中獲取每一行中最新的項目。
這個類使用了過濾器框架提供的另一個優化特性：它在檢查完第一個列之後會通知 region server 結束對當前行的掃描，並跳到下一行，與全表掃描相比，
其性能得到了提升。
示例： Only returns the first found cell from each row
Filter filter = new FirstKeyOnlyFilter();
Scan scan = new Scan();
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
int rowCount = 0;
for (Result result : scanner) {
for (Cell cell : result.rawCells()) {
System.out.println("Cell: " + cell + ", Value: " +
Bytes.toString(cell.getValueArray(), cell.getValueOffset(), cell.getValueLength()));
}
rowCount++;
}
System.out.println("Total num of rows: " + rowCount);
scanner.close();
輸出：
Adding rows to table...
Results of scan:
Cell: row-0/colfam1:col-10/19/Put/vlen=6/seqid=0, Value: val-76
Cell: row-1/colfam1:col-0/0/Put/vlen=6/seqid=0, Value: val-19
...
Cell: row-8/colfam1:col-10/4/Put/vlen=6/seqid=0, Value: val-35
Cell: row-9/colfam1:col-1/5/Put/vlen=5/seqid=0, Value: val-0
Total num of rows: 30
顯示出每行只有一個 cell 返回
■ FirstKeyValueMatchingQualifiersFilter
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這個過濾器是 FirstKeyOnlyFilter 的擴展，但不是返回找到的第一個 cell, 而是返回一個行中所有的列，直到一個給定的列限定符(column qualifier)
如果行中沒有這個限定符，返回所有的列。這個過濾器主要用於 rowcounter shell command。
構造器：
FirstKeyValueMatchingQualifiersFilter(Set<byte[]> qualifiers)
示例： Returns all columns, or up to the first found reference qualifier, for each row
Set<byte[]> quals = new HashSet<byte[]>();
quals.add(Bytes.toBytes("col-2"));
quals.add(Bytes.toBytes("col-4"));
quals.add(Bytes.toBytes("col-6"));
quals.add(Bytes.toBytes("col-8"));
Filter filter = new FirstKeyValueMatchingQualifiersFilter(quals);
Scan scan = new Scan();
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
int rowCount = 0;
for (Result result : scanner) {
for (Cell cell : result.rawCells()) {
System.out.println("Cell: " + cell + ", Value: " +
Bytes.toString(cell.getValueArray(), cell.getValueOffset(),
cell.getValueLength()));
}
rowCount++;
}
System.out.println("Total num of rows: " + rowCount);
scanner.close();
輸出：
Adding rows to table...
Results of scan:
Cell: row-0/colfam1:col-0/1/Put/vlen=6/seqid=0, Value: val-48
Cell: row-0/colfam1:col-1/4/Put/vlen=6/seqid=0, Value: val-78
Cell: row-0/colfam1:col-5/1/Put/vlen=6/seqid=0, Value: val-62
Cell: row-0/colfam1:col-6/6/Put/vlen=5/seqid=0, Value: val-6
Cell: row-10/colfam1:col-1/3/Put/vlen=6/seqid=0, Value: val-73
Cell: row-10/colfam1:col-6/5/Put/vlen=6/seqid=0, Value: val-11
...
Cell: row-6/colfam1:col-1/0/Put/vlen=6/seqid=0, Value: val-39
Cell: row-7/colfam1:col-9/6/Put/vlen=6/seqid=0, Value: val-57
Cell: row-8/colfam1:col-0/2/Put/vlen=6/seqid=0, Value: val-90
Cell: row-8/colfam1:col-1/4/Put/vlen=6/seqid=0, Value: val-92
Cell: row-8/colfam1:col-6/4/Put/vlen=6/seqid=0, Value: val-12
Cell: row-9/colfam1:col-1/5/Put/vlen=6/seqid=0, Value: val-35
Cell: row-9/colfam1:col-2/2/Put/vlen=6/seqid=0, Value: val-22
Total num of rows: 47
■ 包含結束的過濾器 (InclusiveStopFilter)
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一個掃描的行邊界是包含起始行(inclusive for the start row), 而不包含結束行(exclusive for the stop row). 可以使用這個過濾器改變結束行的
語義，即包含給定的結束行。
示例： Example using a filter to include a stop row
Filter filter = new InclusiveStopFilter(Bytes.toBytes("row-5"));
Scan scan = new Scan();
scan.setStartRow(Bytes.toBytes("row-3"));
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result result : scanner) {
System.out.println(result);
}
scanner.close();
輸出：
Adding rows to table...
Results of scan:
keyvalues={row-3/colfam1:col-1/1427282689001/Put/vlen=7/seqid=0}
keyvalues={row-30/colfam1:col-1/1427282689069/Put/vlen=8/seqid=0}
...
keyvalues={row-48/colfam1:col-1/1427282689100/Put/vlen=8/seqid=0}
keyvalues={row-49/colfam1:col-1/1427282689102/Put/vlen=8/seqid=0}
keyvalues={row-5/colfam1:col-1/1427282689004/Put/vlen=7/seqid=0}
■ FuzzyRowFilter
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這個過濾器作用於行鍵，但是以一種模糊的形式(in a fuzzy manner)。它需要一個應返回的行鍵的列表，加上一個相應的 byte[] 數組用於表示行鍵中
每一個字節的重要性。構造器如下：
FuzzyRowFilter(List<Pair<byte[], byte[]>> fuzzyKeysData)
fuzzyKeysData 指定一個行鍵字節的重要性，接受兩個值之一：
0 ：指示在行鍵中同一位置的字節必須匹配 as-is
1 ：意思是不關心對應的行鍵字節，並且總是接受
示例： Example filtering by column prefix
List<Pair<byte[], byte[]>> keys = new ArrayList<Pair<byte[], byte[]>>();
keys.add(new Pair<byte[], byte[]>(Bytes.toBytes("row-?5"), new byte[] { 0, 0, 0, 0, 1, 0 }));
Filter filter = new FuzzyRowFilter(keys);
Scan scan = new Scan()
.addColumn(Bytes.toBytes("colfam1"), Bytes.toBytes("col-5"))
.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result result : scanner) {
System.out.println(result);
}
scanner.close();
輸出：
Adding rows to table...
Results of scan:
keyvalues={row-05/colfam1:col-01/1/Put/vlen=9/seqid=0,
row-05/colfam1:col-02/2/Put/vlen=9/seqid=0,
...
row-05/colfam1:col-09/9/Put/vlen=9/seqid=0,
row-05/colfam1:col-10/10/Put/vlen=9/seqid=0}
keyvalues={row-15/colfam1:col-01/1/Put/vlen=9/seqid=0,
row-15/colfam1:col-02/2/Put/vlen=9/seqid=0,
...
row-15/colfam1:col-09/9/Put/vlen=9/seqid=0,
row-15/colfam1:col-10/10/Put/vlen=9/seqid=0}
測試代碼寫入 20 行到表中，名稱爲 row-01 到 row-20. 我們要獲取匹配模式爲 row-?5 的所有的行。
■ ColumnCountGetFilter
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可以使用這個過濾器只獲取一個每行有指定的最大數量列的數據。
ColumnCountGetFilter(final int n)
一旦找到某一行匹配最大數量的列數整個掃描就停止，因此對於 scan 操作沒什麼用處，它只是用於在 get() 調用中測試過濾器。
■ ColumnPrefixFilter
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類似於 PrefixFilter, 這個過濾器通過對列名稱進行前綴匹配過濾。需要指定一個前綴來創建過濾器：
ColumnPrefixFilter(final byte[] prefix)
所有與設定的前綴匹配的列都包含在結果中。
示例： Example filtering by column prefix, selects all columns starting with col-1.
Filter filter = new ColumnPrefixFilter(Bytes.toBytes("col-1"));
Scan scan = new Scan();
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result result : scanner) {
System.out.println(result);
}
scanner.close();
輸出：
Adding rows to table...
Results of scan:
keyvalues={row-1/colfam1:col-1/1/Put/vlen=7/seqid=0,
row-1/colfam1:col-10/10/Put/vlen=8/seqid=0,
...
row-1/colfam1:col-19/19/Put/vlen=8/seqid=0}
...
■ MultipleColumnPrefixFilter
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這個過濾器是 ColumnPrefixFilter 的直接擴展，運行應用請求一個列限定符的前綴列表，而不僅僅是一個前綴。
MultipleColumnPrefixFilter(final byte[][] prefixes)
示例： Example filtering by column prefix, adds two column prefixes, and also a row prefix to limit the output.
Filter filter = new MultipleColumnPrefixFilter(new byte[][] {
Bytes.toBytes("col-1"), Bytes.toBytes("col-2")
});
Scan scan = new Scan()
//Limit to rows starting with a specific prefix
.setRowPrefixFilter(Bytes.toBytes("row-1"))
.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result result : scanner) {
System.out.print(Bytes.toString(result.getRow()) + ": ");
for (Cell cell : result.rawCells()) {
System.out.print(Bytes.toString(cell.getQualifierArray(),
cell.getQualifierOffset(), cell.getQualifierLength()) + ",
");
}
System.out.println();
}
scanner.close();
輸出：
Adding rows to table...
Results of scan:
row-1: col-1, col-10, col-11, col-12, col-13, col-14, col-15,
col-16,
col-17, col-18, col-19, col-2, col-20, col-21, col-22, col-23,
col-24,
col-25, col-26, col-27, col-28, col-29,
row-10: col-1, col-10, col-11, col-12, col-13, col-14, col-15,
col-16,
col-17, col-18, col-19, col-2, col-20, col-21, col-22, col-23,
col-24,
col-25, col-26, col-27, col-28, col-29,
row-18: col-1, col-10, col-11, col-12, col-13, col-14, col-15,
col-16,
col-17, col-18, col-19, col-2, col-20, col-21, col-22, col-23,
col-24,
col-25, col-26, col-27, col-28, col-29,
row-19: col-1, col-10, col-11, col-12, col-13, col-14, col-15,
col-16,
col-17, col-18, col-19, col-2, col-20, col-21, col-22, col-23,
col-24,
col-25, col-26, col-27, col-28, col-29,
■ ColumnRangeFilter
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這個過濾器類似於兩個 QualifierFilter 實例一起工作，一個用於檢查低邊界，另一個用於檢查高邊界。兩個實例通過提供的BinaryPrefixComparator
分別爲 LESS_OR_EQUAL 和 GREATER_OR_EQUAL 兩個比較操作符：
ColumnRangeFilter(final byte[] minColumn, boolean minColumnInclusive,
final byte[] maxColumn, boolean maxColumnInclusive)
應提供可選的 minColumn 和 maxColumn 以及 boolean 值 minColumnInclusive 和 maxColumnInclusive 用於標誌排除或者包含。如果沒有指定
minColumn 則從 table 的開始掃描，如果沒有指定 maxColumn，則掃描到 table 的末尾。
示例： Example filtering by columns within a given range
Filter filter = new ColumnRangeFilter(Bytes.toBytes("col-05"), true, Bytes.toBytes("col-11"), false);
Scan scan = new Scan()
.setStartRow(Bytes.toBytes("row-03"))
.setStopRow(Bytes.toBytes("row-05"))
.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result result : scanner) {
System.out.println(result);
}
scanner.close();
輸出：
Adding rows to table...
Results of scan:
keyvalues={row-03/colfam1:col-05/5/Put/vlen=9/seqid=0,
row-03/colfam1:col-06/6/Put/vlen=9/seqid=0,
row-03/colfam1:col-07/7/Put/vlen=9/seqid=0,
row-03/colfam1:col-08/8/Put/vlen=9/seqid=0,
row-03/colfam1:col-09/9/Put/vlen=9/seqid=0,
row-03/colfam1:col-10/10/Put/vlen=9/seqid=0}
keyvalues={row-04/colfam1:col-05/5/Put/vlen=9/seqid=0,
row-04/colfam1:col-06/6/Put/vlen=9/seqid=0,
row-04/colfam1:col-07/7/Put/vlen=9/seqid=0,
row-04/colfam1:col-08/8/Put/vlen=9/seqid=0,
row-04/colfam1:col-09/9/Put/vlen=9/seqid=0,
row-04/colfam1:col-10/10/Put/vlen=9/seqid=0}
■ 單列值過濾器 (SingleColumnValueFilter)
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當用一個列的值精確匹配來決定整個行是否返回到結果中時，使用這個過濾器。需要首先指定要跟蹤的列，然後指定要檢查的值。
SingleColumnValueFilter(final byte[] family, final byte[] qualifier, final CompareOp compareOp, final byte[] value)
SingleColumnValueFilter(final byte[] family, final byte[] qualifier, final CompareOp compareOp, final ByteArrayComparable comparator)
protected SingleColumnValueFilter(final byte[] family, final byte[] qualifier, final CompareOp compareOp,
ByteArrayComparable comparator, final boolean filterIfMissing, final boolean latestVersionOnly)
第一個構造器很簡單，在內部創建 BinaryComparator 實例。第二個構造器與一直使用的基於 CompareFilter 的參數相同。雖然 SingleColumnValueFilter
不是直接繼承自 CompareFilter, 它們還是具有相同的構造器參數類型。第三個構造器，增添了兩個額外的 boolean 標誌，這兩個標誌也可以在過濾器實例
構造之後，通過 getter 和 setter 方法設置：
boolean getFilterIfMissing()
void setFilterIfMissing(boolean filterIfMissing)
boolean getLatestVersionOnly()
void setLatestVersionOnly(boolean latestVersionOnly)
前面的方法用於控制如果行中根本沒有指定的列如何處理。默認是包含在結果中，但可以通過 setFilterIfMissing(true) 來反轉這種行爲，那樣，所有沒有
引用列的行都從結果中移除。
通過 setLatestVersionOnly(false), 默認爲 true, 可以改變過濾器的默認行爲，也就是隻檢查引用列的最新版本，改變默認行爲只會，也會在檢查中包含
以前的版本。
示例： Example using a filter to return only rows with a given value in a given column
SingleColumnValueFilter filter = new SingleColumnValueFilter(
Bytes.toBytes("colfam1"),
Bytes.toBytes("col-5"),
CompareFilter.CompareOp.NOT_EQUAL,
new SubstringComparator("val-5"));
filter.setFilterIfMissing(true);
Scan scan = new Scan();
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result result : scanner) {
for (Cell cell : result.rawCells()) {
System.out.println("Cell: " + cell + ", Value: " +
Bytes.toString(cell.getValueArray(), cell.getValueOffset(),
cell.getValueLength()));
}
}
scanner.close();
Get get = new Get(Bytes.toBytes("row-6"));
get.setFilter(filter);
Result result = table.get(get);
System.out.println("Result of get: ");
for (Cell cell : result.rawCells()) {
System.out.println("Cell: " + cell + ", Value: " +
Bytes.toString(cell.getValueArray(), cell.getValueOffset(),
cell.getValueLength()));
}
輸出：
Adding rows to table...
Results of scan:
Cell: row-1/colfam1:col-1/1427279447557/Put/vlen=7/seqid=0, Value: val-1.1
Cell: row-1/colfam1:col-10/1427279447613/Put/vlen=8/seqid=0, Value: val-1.10
...
Cell: row-4/colfam2:col-8/1427279447667/Put/vlen=7/seqid=0, Value: val-4.8
Cell: row-4/colfam2:col-9/1427279447669/Put/vlen=7/seqid=0, Value: val-4.9
Cell: row-6/colfam1:col-1/1427279447692/Put/vlen=7/seqid=0, Value: val-6.1
Cell: row-6/colfam1:col-10/1427279447709/Put/vlen=8/seqid=0, Value: val-6.10
...
Cell: row-9/colfam2:col-8/1427279447759/Put/vlen=7/seqid=0, Value: val-9.8
Cell: row-9/colfam2:col-9/1427279447761/Put/vlen=7/seqid=0, Value: val-9.9
Result of get:
Cell: row-6/colfam1:col-1/1427279447692/Put/vlen=7/seqid=0, Value: val-6.1
Cell: row-6/colfam1:col-10/1427279447709/Put/vlen=8/seqid=0, Value: val-6.10
...
Cell: row-6/colfam2:col-8/1427279447705/Put/vlen=7/seqid=0, Value: val-6.8
Cell: row-6/colfam2:col-9/1427279447707/Put/vlen=7/seqid=0, Value: val-6.9
■ 單列排除過濾器 (SingleColumnValueExcludeFilter)
-------------------------------------------------------------------------------------------------------------------------------------
SingleColumnValueExcludeFilter 繼承自 SingleColumnValueFilter，經擴展後提供一種稍微不同的語義：參考列從結果中排除。換句話說，用戶可以
使用與 SingleColumnValueFilter 相同的構造器、方法以及特性來控制此過濾器的工作。唯一不同的是，客戶端的 Result 實例中，永遠不會獲得作爲
檢查目標的參考列，因爲它是在結果中被排除的。
■ 時間戳過濾器 (TimestampsFilter)
-------------------------------------------------------------------------------------------------------------------------------------
當需要在掃描結果中對版本進行細粒度的控制時，這個過濾器可以滿足需求。需要傳入一個裝載了時間戳的 List 實例：
TimestampsFilter(List<Long> timestamps)
如前所述，一個版本(version) 是指一個列在一個特定時間的值，因此用一個時間戳(timestamp) 來表示。當過濾器請求一系列的時間戳時，它會找到與
其中時間戳精確匹配的列版本。
示例： Example filtering data by timestamps, sets up a filter with three timestamps and adds a time range to the second scan
List<Long> ts = new ArrayList<Long>();
//Add timestamps to the list.
ts.add(new Long(5));
ts.add(new Long(10));
ts.add(new Long(15));
Filter filter = new TimestampsFilter(ts);
Scan scan1 = new Scan();
//Add the filter to an otherwise default Scan instance.
scan1.setFilter(filter);
ResultScanner scanner1 = table.getScanner(scan1);
for (Result result : scanner1) {
System.out.println(result);
}
scanner1.close();
Scan scan2 = new Scan();
scan2.setFilter(filter);
//Also add a time range to verify how it affects the filter
scan2.setTimeRange(8, 12);
ResultScanner scanner2 = table.getScanner(scan2);
for (Result result : scanner2) {
System.out.println(result);
}
scanner2.close();
輸出：
Adding rows to table...
Results of scan #1:
keyvalues={row-1/colfam1:col-10/10/Put/vlen=8/seqid=0,
row-1/colfam1:col-15/15/Put/vlen=8/seqid=0,
row-1/colfam1:col-5/5/Put/vlen=7/seqid=0}
keyvalues={row-100/colfam1:col-10/10/Put/vlen=10/seqid=0,
row-100/colfam1:col-15/15/Put/vlen=10/seqid=0,
row-100/colfam1:col-5/5/Put/vlen=9/seqid=0}
...
keyvalues={row-99/colfam1:col-10/10/Put/vlen=9/seqid=0,
row-99/colfam1:col-15/15/Put/vlen=9/seqid=0,
row-99/colfam1:col-5/5/Put/vlen=8/seqid=0}
Results of scan #2:
keyvalues={row-1/colfam1:col-10/10/Put/vlen=8/seqid=0}
keyvalues={row-10/colfam1:col-10/10/Put/vlen=9/seqid=0}
...
keyvalues={row-98/colfam1:col-10/10/Put/vlen=9/seqid=0}
keyvalues={row-99/colfam1:col-10/10/Put/vlen=9/seqid=0}
■ 隨機行過濾器 (RandomRowFilter)
-------------------------------------------------------------------------------------------------------------------------------------
最後，有一種過濾器可以讓結果中包含隨機行。構造器需要傳入參數 chance, 取值在 0.0 到 1.0 之間。
RandomRowFilter(float chance)
在過濾器內部會使用 Java 的 Random.nextFloat() 調用來決定一行是否被過濾，使用這個方法的結果會與設定的 chance 進行比較。如果 chance 值
爲負值會導致所有結果被過濾掉，相反，如果 chance 爲大於 1.0 則結果集中包含所有行。
示例： Example filtering rows randomly
Filter filter = new RandomRowFilter(0.5f);
for (int loop = 1; loop <= 3; loop++) {
Scan scan = new Scan();
scan.setFilter(filter);
ResultScanner scanner = table.getScanner(scan);
for (Result result : scanner) {
System.out.println(Bytes.toString(result.getRow()));
}
scanner.close();
}
輸出：
Adding rows to table...
Results of scan for loop: 1
row-1
row-10
row-3
row-9
Results of scan for loop: 2
row-10
row-2
row-3
row-5
row-6
row-8
Results of scan for loop: 3
row-1
row-3
row-4
row-8
row-9
1.4 裝飾過濾器 (Decorating Filters)
-----------------------------------------------------------------------------------------------------------------------------------------
HBase 提供的過濾器已十分強大，有時需要對這些過濾器進行修改、或者擴展，以對過濾器的行爲進行更多地控制其返回結果非常有用。這些額外的控制不
依賴於過濾器本身，而是應用到過濾器上。這就是裝飾過濾器(decorating filter)這組類的作用。
裝飾過濾器和其它單一目的的過濾器類似，實現 Filter 接口。因此，它們可以用於這些過濾器的替代，配合它們所封裝的過濾器實例的行爲。
■ SkipFilter
-------------------------------------------------------------------------------------------------------------------------------------
這個過濾器封裝了一個給定的過濾器並擴展它，當封裝的過濾器建議某個 Cell 被忽略時，排除一整行。換句話說，只要一個過濾器指出某個列在其行中
被忽略，則整個行就被忽略。
NOTE:
---------------------------------------------------------------------------------------------------------------------------------
被封裝的過濾器必須實現 filterKeyValue() 方法，否則 SkipFilter 不會如期工作。這是因爲 SkipFilter 只檢查該方法的返回結果以決定如何
處理當前的行。
下面的示例配合使用帶有一個 ValueFilter 的 SkipFilter, 首先選擇所有非零值的列(column), 之後丟棄所有其它不含有某個匹配值的行。
示例： Example of using a filter to skip entire rows based on another filter’s results
Filter filter1 = new ValueFilter(CompareFilter.CompareOp.NOT_EQUAL, new BinaryComparator(Bytes.toBytes("val-0")));
Scan scan = new Scan();
//Only add the ValueFilter to the first scan.
scan.setFilter(filter1);
ResultScanner scanner1 = table.getScanner(scan);
for (Result result : scanner1) {
for (Cell cell : result.rawCells()) {
System.out.println("Cell: " + cell + ", Value: " +
Bytes.toString(cell.getValueArray(), cell.getValueOffset(),
cell.getValueLength()));
}
}
scanner1.close();
//Add the decorating skip filter for the second scan.
Filter filter2 = new SkipFilter(filter1);
scan.setFilter(filter2);
ResultScanner scanner2 = table.getScanner(scan);
for (Result result : scanner2) {
for (Cell cell : result.rawCells()) {
System.out.println("Cell: " + cell + ", Value: " +
Bytes.toString(cell.getValueArray(), cell.getValueOffset(),
cell.getValueLength()));
}
}
scanner2.close();
輸出：
Adding rows to table...
Results of scan #1:
Cell: row-01/colfam1:col-01/1/Put/vlen=5/seqid=0, Value: val-4
Cell: row-01/colfam1:col-02/2/Put/vlen=5/seqid=0, Value: val-4
Cell: row-01/colfam1:col-03/3/Put/vlen=5/seqid=0, Value: val-1
Cell: row-01/colfam1:col-04/4/Put/vlen=5/seqid=0, Value: val-3
Cell: row-01/colfam1:col-05/5/Put/vlen=5/seqid=0, Value: val-1
Cell: row-02/colfam1:col-01/1/Put/vlen=5/seqid=0, Value: val-1
Cell: row-02/colfam1:col-03/3/Put/vlen=5/seqid=0, Value: val-2
Cell: row-02/colfam1:col-04/4/Put/vlen=5/seqid=0, Value: val-4
Cell: row-02/colfam1:col-05/5/Put/vlen=5/seqid=0, Value: val-2
...
Cell: row-30/colfam1:col-01/1/Put/vlen=5/seqid=0, Value: val-2
Cell: row-30/colfam1:col-02/2/Put/vlen=5/seqid=0, Value: val-4
Cell: row-30/colfam1:col-03/3/Put/vlen=5/seqid=0, Value: val-4
Cell: row-30/colfam1:col-05/5/Put/vlen=5/seqid=0, Value: val-4
Total cell count for scan #1: 124
Results of scan #2:
Cell: row-01/colfam1:col-01/1/Put/vlen=5/seqid=0, Value: val-4
Cell: row-01/colfam1:col-02/2/Put/vlen=5/seqid=0, Value: val-4
Cell: row-01/colfam1:col-03/3/Put/vlen=5/seqid=0, Value: val-1
Cell: row-01/colfam1:col-04/4/Put/vlen=5/seqid=0, Value: val-3
Cell: row-01/colfam1:col-05/5/Put/vlen=5/seqid=0, Value: val-1
Cell: row-06/colfam1:col-01/1/Put/vlen=5/seqid=0, Value: val-4
Cell: row-06/colfam1:col-02/2/Put/vlen=5/seqid=0, Value: val-4
Cell: row-06/colfam1:col-03/3/Put/vlen=5/seqid=0, Value: val-4
Cell: row-06/colfam1:col-04/4/Put/vlen=5/seqid=0, Value: val-3
Cell: row-06/colfam1:col-05/5/Put/vlen=5/seqid=0, Value: val-2
...
Cell: row-28/colfam1:col-01/1/Put/vlen=5/seqid=0, Value: val-2
Cell: row-28/colfam1:col-02/2/Put/vlen=5/seqid=0, Value: val-1
Cell: row-28/colfam1:col-03/3/Put/vlen=5/seqid=0, Value: val-2
Cell: row-28/colfam1:col-04/4/Put/vlen=5/seqid=0, Value: val-4
Cell: row-28/colfam1:col-05/5/Put/vlen=5/seqid=0, Value: val-2
Total cell count for scan #2: 55
■ WhileMatchFilter
-------------------------------------------------------------------------------------------------------------------------------------
這個過濾器與上一個類似，不過當一條數據被過濾掉時，它會直接放棄本次掃描操作。它使用其封裝的過濾器來檢查它是否通過其 row key 來忽略一行,
或者某個 Cell 的檢查忽略某個行的列。
示例： Example of using a filter to skip entire rows based on another filter’s results
Filter filter1 = new RowFilter(CompareFilter.CompareOp.NOT_EQUAL,
new BinaryComparator(Bytes.toBytes("row-05")));
Scan scan = new Scan();
scan.setFilter(filter1);
ResultScanner scanner1 = table.getScanner(scan);
for (Result result : scanner1) {
for (Cell cell : result.rawCells()) {
System.out.println("Cell: " + cell + ", Value: " +
Bytes.toString(cell.getValueArray(), cell.getValueOffset(),
cell.getValueLength()));
}
}
scanner1.close();
Filter filter2 = new WhileMatchFilter(filter1);
scan.setFilter(filter2);
ResultScanner scanner2 = table.getScanner(scan);
for (Result result : scanner2) {
for (Cell cell : result.rawCells()) {
System.out.println("Cell: " + cell + ", Value: " +
Bytes.toString(cell.getValueArray(), cell.getValueOffset(),
cell.getValueLength()));
}
}
scanner2.close();
輸出：
Adding rows to table...
Results of scan #1:
Cell: row-01/colfam1:col-01/1/Put/vlen=9/seqid=0, Value: val-01.01
Cell: row-02/colfam1:col-01/1/Put/vlen=9/seqid=0, Value: val-02.01
Cell: row-03/colfam1:col-01/1/Put/vlen=9/seqid=0, Value: val-03.01
Cell: row-04/colfam1:col-01/1/Put/vlen=9/seqid=0, Value: val-04.01
Cell: row-06/colfam1:col-01/1/Put/vlen=9/seqid=0, Value: val-06.01
Cell: row-07/colfam1:col-01/1/Put/vlen=9/seqid=0, Value: val-07.01
Cell: row-08/colfam1:col-01/1/Put/vlen=9/seqid=0, Value: val-08.01
Cell: row-09/colfam1:col-01/1/Put/vlen=9/seqid=0, Value: val-09.01
Cell: row-10/colfam1:col-01/1/Put/vlen=9/seqid=0, Value: val-10.01
Total cell count for scan #1: 9
Results of scan #2:
Cell: row-01/colfam1:col-01/1/Put/vlen=9/seqid=0, Value: val-01.01
Cell: row-02/colfam1:col-01/1/Put/vlen=9/seqid=0, Value: val-02.01
Cell: row-03/colfam1:col-01/1/Put/vlen=9/seqid=0, Value: val-03.01
Cell: row-04/colfam1:col-01/1/Put/vlen=9/seqid=0, Value: val-04.01
Total cell count for scan #2: 4
1.5 FilterList
-----------------------------------------------------------------------------------------------------------------------------------------
到目前爲止，已經看到了過濾器如何在一個表的各種維度上進行過濾工作，從行，到列，再到一個列的各種版本的值。在實際應用中，可能需要多個過濾器
共同限制返回到客戶端的結果，FilterList 提供了這項功能。構造器：
FilterList(final List<Filter> rowFilters)
FilterList(final Filter... rowFilters)
FilterList(final Operator operator)
FilterList(final Operator operator, final List<Filter> rowFilters)
FilterList(final Operator operator, final Filter... rowFilters)
rowFilters 參數指定過濾器列表，用於一起訪問，使用 operator 來聯合它們的結果。可能的值如下表：
Possible values for the FilterList.Operator enumeration
+---------------+--------------------------------------------------------------------------------------------------
| Operator | Description
+---------------+--------------------------------------------------------------------------------------------------
| MUST_PASS_ALL | A value is only included in the result when all filters agree to do so
+---------------+--------------------------------------------------------------------------------------------------
| MUST_PASS_ONE | As soon as a value was allowed to pass one of the filters, it is included in the overall result.
+---------------+--------------------------------------------------------------------------------------------------
默認值爲 MUST_PASS_ALL。
在 FilterList 實例已經創建之後向其添加過濾器，可以通過如下方法：
void addFilter(Filter filter)
可以更進一步控制列表中所包含的過濾器執行的順序，選擇一個合適的 List 實現，例如，ArrayList 會保證過濾器的執行順序與它們加入列表的順序相同。
示例： Example of using a filter list to combine single purpose filters
List<Filter> filters = new ArrayList<Filter>();
Filter filter1 = new RowFilter(CompareFilter.CompareOp.GREATER_OR_EQUAL, new BinaryComparator(Bytes.toBytes("row-03")));
filters.add(filter1);
Filter filter2 = new RowFilter(CompareFilter.CompareOp.LESS_OR_EQUAL, new BinaryComparator(Bytes.toBytes("row-06")));
filters.add(filter2);
Filter filter3 = new QualifierFilter(CompareFilter.CompareOp. EQUAL, new RegexStringComparator("col-0[03]"));
filters.add(filter3);
FilterList filterList1 = new FilterList(filters);
Scan scan = new Scan();
scan.setFilter(filterList1);
ResultScanner scanner1 = table.getScanner(scan);
for (Result result : scanner1) {
for (Cell cell : result.rawCells()) {
System.out.println("Cell: " + cell + ", Value: " +
Bytes.toString(cell.getValueArray(), cell.getValueOffset(),
cell.getValueLength()));
}
}
scanner1.close();
FilterList filterList2 = new FilterList(FilterList.Operator.MUST_PASS_ONE, filters);
scan.setFilter(filterList2);
ResultScanner scanner2 = table.getScanner(scan);
for (Result result : scanner2) {
for (Cell cell : result.rawCells()) {
System.out.println("Cell: " + cell + ", Value: " +
Bytes.toString(cell.getValueArray(), cell.getValueOffset(),
cell.getValueLength()));
}
}
scanner2.close();
輸出：
Adding rows to table...
Results of scan #1 - MUST_PASS_ALL:
Cell: row-03/colfam1:col-03/3/Put/vlen=9/seqid=0, Value: val-03.03
258 Chapter 4: Client API: Advanced Features
Cell: row-04/colfam1:col-03/3/Put/vlen=9/seqid=0, Value: val-04.03
Cell: row-05/colfam1:col-03/3/Put/vlen=9/seqid=0, Value: val-05.03
Cell: row-06/colfam1:col-03/3/Put/vlen=9/seqid=0, Value: val-06.03
Total cell count for scan #1: 4
Results of scan #2 - MUST_PASS_ONE:
Cell: row-01/colfam1:col-01/1/Put/vlen=9/seqid=0, Value: val-01.01
Cell: row-01/colfam1:col-02/2/Put/vlen=9/seqid=0, Value: val-01.02
...
Cell: row-10/colfam1:col-04/4/Put/vlen=9/seqid=0, Value: val-10.04
Cell: row-10/colfam1:col-05/5/Put/vlen=9/seqid=0, Value: val-10.05
Total cell count for scan #2: 50
1.6 自定義過濾器 (Custom Filters)
-----------------------------------------------------------------------------------------------------------------------------------------
用戶可能需要按各自的需求實現自定義過濾器，可以實現 Filter 接口，或者直接繼承 FilterBase 類，該類已經爲接口中所有成員方法提供了默認實現。
Filter 接口有如下結構：
public abstract class Filter {
public enum ReturnCode {
INCLUDE, INCLUDE_AND_NEXT_COL, SKIP, NEXT_COL, NEXT_ROW,
SEEK_NEXT_USING_HINT
}
public void reset() throws IOException
public boolean filterRowKey(byte[] buffer, int offset, int length) throws IOException
public boolean filterAllRemaining() throws IOException
public ReturnCode filterKeyValue(final Cell v) throws IOException
public Cell transformCell(final Cell v) throws IOException
public void filterRowCells(List<Cell> kvs) throws IOException
public boolean hasFilterRow()
public boolean filterRow() throws IOException
public Cell getNextCellHint(final Cell currentKV) throws IOException
public boolean isFamilyEssential(byte[] name) throws IOException
public void setReversed(boolean reversed)
public boolean isReversed()
public byte[] toByteArray() throws IOException
public static Filter parseFrom(final byte[] pbBytes)
throws DeserializationException
}
接口提供了一個公共的枚舉類型 ReturnCode, 用於 filterKeyValue() 方法的返回值，指明執行框架下一步應進行的操作。過濾器可以選擇跳過一個值，
一個列的剩餘部分或一行的剩餘部分，而不是遍歷所有的數據。因此獲取數據的效率會大大提升。
Possible values for the Filter.ReturnCode enumeration
+-----------------------+--------------------------------------------------------------------------------------------------------------
| Return code | Description
+-----------------------+--------------------------------------------------------------------------------------------------------------
| INCLUDE | Include the given Cell instance in the result
+-----------------------+--------------------------------------------------------------------------------------------------------------
| INCLUDE_AND_NEXT_COL | Include current cell and move to next column, i.e. skip all further versions of the current.
+-----------------------+--------------------------------------------------------------------------------------------------------------
| SKIP | Skip the current cell and proceed to the next.
+-----------------------+--------------------------------------------------------------------------------------------------------------
| NEXT_COL | Skip the remainder of the current column, proceeding to the next. This is used by the TimestampsFilter
+-----------------------+--------------------------------------------------------------------------------------------------------------
| NEXT_ROW | Similar to the previous, but skips the remainder of the current row, moving to the next.
| | The RowFilter makes use of this return code, for example.
+-----------------------+--------------------------------------------------------------------------------------------------------------
| SEEK_NEXT_USING_HINT | Some filters want to skip a variable number of cells and use this return code to indicate that the framework
| | should use the getNextCellHint() method to determine where to skip to. The ColumnPrefixFilter, for example,
| | uses this feature.
+-----------------------+--------------------------------------------------------------------------------------------------------------
示例： Implements a filter that lets certain rows pass
public class CustomFilter extends FilterBase {
private byte[] value = null;
private boolean filterRow = true;
public CustomFilter() {
super();
}
public CustomFilter(byte[] value) {
this.value = value;
}
@Override
public void reset() {
this.filterRow = true;
}
@Override
public ReturnCode filterKeyValue(Cell cell) {
if (CellUtil.matchingValue(cell, value)) {
filterRow = false;
}
return ReturnCode.INCLUDE;
}
@Override
public boolean filterRow() {
return filterRow;
}
@Override
public byte [] toByteArray() {
FilterProtos.CustomFilter.Builder builder =
FilterProtos.CustomFilter.newBuilder();
if (value != null) builder.setValue(ByteStringer.wrap(value));
return builder.build().toByteArray();
}
//@Override
public static Filter parseFrom(final byte[] pbBytes) throws DeserializationException {
FilterProtos.CustomFilter proto;
try {
proto = FilterProtos.CustomFilter.parseFrom(pbBytes);
} catch (InvalidProtocolBufferException e) {
throw new DeserializationException(e);
}
return new CustomFilter(proto.getValue().toByteArray());
}
}
//Example using a custom filter
List<Filter> filters = new ArrayList<Filter>();
Filter filter1 = new CustomFilter(Bytes.toBytes("val-05.05"));
filters.add(filter1);
Filter filter2 = new CustomFilter(Bytes.toBytes("val-02.07"));
filters.add(filter2);
Filter filter3 = new CustomFilter(Bytes.toBytes("val-09.01"));
filters.add(filter3);
FilterList filterList = new FilterList(
FilterList.Operator.MUST_PASS_ONE, filters);
Scan scan = new Scan();
scan.setFilter(filterList);
ResultScanner scanner = table.getScanner(scan);
for (Result result : scanner) {
for (Cell cell : result.rawCells()) {
System.out.println("Cell: " + cell + ", Value: " +
Bytes.toString(cell.getValueArray(), cell.getValueOffset(),
cell.getValueLength()));
}
}
scanner.close();
輸出：
Adding rows to table...
Results of scan:
Cell: row-02/colfam1:col-01/1/Put/vlen=9/seqid=0, Value: val-02.01
Cell: row-02/colfam1:col-02/2/Put/vlen=9/seqid=0, Value: val-02.02
...
Cell: row-02/colfam1:col-06/6/Put/vlen=9/seqid=0, Value: val-02.06
Cell: row-02/colfam1:col-07/7/Put/vlen=9/seqid=0, Value: val-02.07
Cell: row-02/colfam1:col-08/8/Put/vlen=9/seqid=0, Value: val-02.08
...
Cell: row-05/colfam1:col-04/4/Put/vlen=9/seqid=0, Value: val-05.04
Cell: row-05/colfam1:col-05/5/Put/vlen=9/seqid=0, Value: val-05.05
Cell: row-05/colfam1:col-06/6/Put/vlen=9/seqid=0, Value: val-05.06
...
Cell: row-05/colfam1:col-10/10/Put/vlen=9/seqid=0, Value: val-05.10
Cell: row-09/colfam1:col-01/1/Put/vlen=9/seqid=0, Value: val-09.01
Cell: row-09/colfam1:col-02/2/Put/vlen=9/seqid=0, Value: val-09.02
...
Cell: row-09/colfam1:col-09/9/Put/vlen=9/seqid=0, Value: val-09.09
Cell: row-09/colfam1:col-10/10/Put/vlen=9/seqid=0, Value: val-09.10
自定義過濾器載入 (Custom Filter Loading)
-------------------------------------------------------------------------------------------------------------------------------------
完成過濾器的編寫之後，需要將其部署到 HBase 上。首先需要編譯好過濾器類，然後打包成 JAR, 並保證可以被 region 服務器調用。
可以使用編譯系統來準備配置用的 JAR 文件，同時使用配置管理系統把文件分發到每個 region 服務器中。文件分發完成後，有兩種選擇載入 JAR 文件
靜態配置 (Static Configuration):
這種情況下，需要將 JAR 文件配置到 hbase-env.sh 文件中，如：
# Extra Java CLASSPATH elements. Optional.
# export HBASE_CLASSPATH=
export HBASE_CLASSPATH="/hbase-book/ch04/target/hbase-bookch04-2.0.jar
動態載入 (Dynamic Loading):
利用集羣範圍的，在 HDFS 中共享 JAR 文件目錄來載入 JAR 文件。hbase-default.xml 文件中有如下屬性設置：
<property>
<name>hbase.dynamic.jars.dir</name>
<value>${hbase.rootdir}/lib</value>
</property>
默認指向 ${hbase.rootdir}/lib，通常在 HDFS 中解析爲 /hbase/lib/ . 完整路徑類似爲：hdfs://master.foobar.com:9000/hbase/lib , 如果該
目錄存在，並且含有以 .jar 結尾的文件，則服務器會載入這些文件，並使其中包含的類可用。
1.7 過濾器解析工具 (Filter Parser Utility)
-----------------------------------------------------------------------------------------------------------------------------------------
客戶端過濾器包是另一個輔助類，名爲 ParseFilter. 它用於所有過濾器需要描述爲文本的地方，最終轉換爲一個 Java class。一般出現在網關服務器上(
gateway servers), 如 REST or Thrift. HBase Shell 也使用這個類允許 shell 用戶則命令行上指定一個過濾器，然後作爲後續掃描或者 get 操作的一部分
執行過濾器，例如：
hbase(main):001:0> scan 'testtable', { FILTER => "PrefixFilter('row-2') AND QualifierFilter(<=,'binary:col-2')" }
輸出：
ROW COLUMN+CELL
row-20 column=colfam1:col-0, timestamp=7, value=val-46
row-21 column=colfam1:col-0, timestamp=7, value=val-87
row-21 column=colfam1:col-2, timestamp=5, value=val-26
...
row-28 column=colfam1:col-2, timestamp=3, value=val-74
row-29 column=colfam1:col-1, timestamp=0, value=val-86
row-29 column=colfam1:col-2, timestamp=3, value=val-21
10 row(s) in 0.0170 seconds
"binary:col-2" 參數。冒號分隔的第二部分是過濾器處理的值。第一部分是過濾器解析器類允許爲過濾器指定的一個基於 CompareFilter 的比較器。支持的
比較器前綴如下表：
String representation of Comparator types
+---------------+----------------------------
| String | Type
+---------------+----------------------------
| binary | BinaryComparator
+---------------+----------------------------
| binaryprefix | BinaryPrefixComparator
+---------------+----------------------------
| regexstring | RegexStringComparator
+---------------+----------------------------
| substring | SubstringComparator
+---------------+----------------------------
由於比較過濾器也需要一個比較操作，因此有字符串格式的表示方法。例如上例子中的 "<=" 表示小於或等於。下表列出可用的字符串表示的操作符類型：
String representation of compare operation
+---------------+----------------------------
| String | Type
+---------------+----------------------------
| < | CompareOp.LESS
+---------------+----------------------------
| <= | CompareOp.LESS_OR_EQUAL
+---------------+----------------------------
| > | CompareOp.GREATER
+---------------+----------------------------
| >= | CompareOp.GREATER_OR_EQUAL
+---------------+----------------------------
| = | CompareOp.EQUAL
+---------------+----------------------------
| != | CompareOp.NOT_EQUAL
+---------------+----------------------------
過濾器解析器支持一些基於 token 的文本翻譯成過濾器類。可以配合過濾器使用 AND 和 OR 關鍵字，會被之後翻譯爲 FilterList 實例，或者設置爲
MUST_PASS_ALL, 或者 MUST_PASS_ONE
示例：
hbase(main):001:0> scan 'testtable',{ FILTER => "(PrefixFilter('row-2') AND (QualifierFilter(>=, 'binary:col-2'))) AND (TimestampsFilter(1,
5))" }
輸出：
ROW COLUMN+CELL
row-2 column=colfam1:col-9, timestamp=5, value=val-31
row-21 column=colfam1:col-2, timestamp=5, value=val-26
row-23 column=colfam1:col-5, timestamp=5, value=val-55
row-28 column=colfam1:col-5, timestamp=1, value=val-54
4 row(s) in 0.3190 seconds
最後，可以使用 SKIP 和 WHILE 關鍵字表示 SkipFilter 和 WhileMatchFilter.
示例：
hbase(main):001:0> scan 'testtable',{ FILTER => "SKIP ValueFilter(>=, 'binary:val-5') " }
輸出：
ROW COLUMN+CELL
row-11 column=colfam1:col-0, timestamp=8, value=val-82
row-48 column=colfam1:col-3, timestamp=6, value=val-55
row-48 column=colfam1:col-7, timestamp=3, value=val-80
row-48 column=colfam1:col-8, timestamp=2, value=val-65
row-7 column=colfam1:col-9, timestamp=6, value=val-57
3 row(s) in 0.0150 seconds
Precedence of string keywords
+---------------+-------------------------------------------------------------------------------------------
| Keyword | Description
+---------------+-------------------------------------------------------------------------------------------
| SKIP/WHILE | Wrap filter into SkipFilter, or WhileMatchFilter instance.
+---------------+-------------------------------------------------------------------------------------------
| AND | Add both filters left and right of keyword to FilterList instance using MUST_PASS_ALL.
+---------------+-------------------------------------------------------------------------------------------
| OR | Add both filters left and right of keyword to FilterList instance using MUST_PASS_ONE.
+---------------+-------------------------------------------------------------------------------------------
代碼中，可以調用如下方法來解析字符串爲類實例：
Filter parseFilterString(String filterString) throws CharacterCodingException
Filter parseFilterString (byte[] filterStringAsByteArray) throws CharacterCodingException
Filter parseSimpleFilterExpression(byte[] filterStringAsByteArray) throws CharacterCodingException
ParseFilter 默認情況下只支持 HBase 自帶過濾器的解析，不支持 FirstKeyValueMatchingQualifiersFilter, FuzzyRowFilter, and RandomRowFilter
在自己的代碼中，可以註冊自定義過濾器，並獲取支持的過濾器列表，使用如下方法：
static Map<String, String> getAllFilters()
Set<String> getSupportedFilters()
static void registerFilter(String name, String filterClass)
本篇完
參考：