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Date: Fri, 12 Apr 2019 11:30:22 +0800
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README.md | 6 +-
...理器.md => Hbase协处理器详解.md} | 0
notes/Hbase过滤器详解.md | 450 ++++++++++++++++++
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pictures/hbase-compareFilter.png | Bin 0 -> 9250 bytes
pictures/hbase-filterbase-subclass.png | Bin 0 -> 123771 bytes
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rename notes/{Hbase协处理器.md => Hbase协处理器详解.md} (100%)
create mode 100644 notes/Hbase过滤器详解.md
create mode 100644 pictures/hbase-bytearraycomparable.png
create mode 100644 pictures/hbase-compareFilter.png
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diff --git a/README.md b/README.md
index 7603b46..c9f36b1 100644
--- a/README.md
+++ b/README.md
@@ -92,7 +92,7 @@ TODO
## 八、Azkaban
-1. Azkaban项目简介
+1. [Azkaban简介](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Azkaban简介.md)
2. [Azkaban3.x 编译及部署](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Azkaban%203.x%20%E7%BC%96%E8%AF%91%E5%8F%8A%E9%83%A8%E7%BD%B2.md)
3. [Azkaban Flow 1.0 的使用](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Azkaban%20Flow%201.0%20%E7%9A%84%E4%BD%BF%E7%94%A8.md)
4. [Azkaban Flow 2.0 的使用](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Azkaban%20Flow%202.0%20%E7%9A%84%E4%BD%BF%E7%94%A8.md)
@@ -104,8 +104,8 @@ TODO
3. [HBase基本环境搭建（Standalone /pseudo-distributed mode）](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Hbase%E5%9F%BA%E6%9C%AC%E7%8E%AF%E5%A2%83%E6%90%AD%E5%BB%BA.md)
4. [HBase常用Shell命令](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase%20Shell.md)
5. [HBase Java API](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase%20Java%20API.md)
-6. Hbase 过滤器
-7. [HBase 协处理器](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase协处理器.md)
+6. [Hbase 过滤器详解](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase过滤器详解.md)
+7. [HBase 协处理器详解](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase协处理器详解.md)
8. [HBase 容灾与备份](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase%E5%AE%B9%E7%81%BE%E4%B8%8E%E5%A4%87%E4%BB%BD.md)
9. [HBase的SQL中间层——Phoenix](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hbase%E7%9A%84SQL%E5%B1%82%E2%80%94%E2%80%94Phoenix.md)
10. [Spring/Spring Boot 整合 Mybatis + Phoenix](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Spring%2BMybtais%2BPhoenix%E6%95%B4%E5%90%88.md)
diff --git a/notes/Hbase协处理器.md b/notes/Hbase协处理器详解.md
similarity index 100%
rename from notes/Hbase协处理器.md
rename to notes/Hbase协处理器详解.md
diff --git a/notes/Hbase过滤器详解.md b/notes/Hbase过滤器详解.md
new file mode 100644
index 0000000..8ecb9c2
--- /dev/null
+++ b/notes/Hbase过滤器详解.md
@@ -0,0 +1,450 @@
+# Hbase 过滤器详解
+
+
+
+
+
+## 一、HBase过滤器简介
+
+Hbase提供了种类丰富的过滤器（filter）来提高数据处理的效率，用户可以通过内置或自定义的过滤器来对数据进行过滤，所有的过滤器都在服务端生效，即谓词下推（predicate push down）。这样可以保证过滤掉的数据不会被传送到客户端，减轻网络传输和客户端处理的压力。
+
+ 
+
+
+
+## 二、过滤器基础
+
+#### 2.1 Filter接口和FilterBase抽象类
+
+Filter接口中定义了过滤器的基本方法，FilterBase抽象类实现了Filter接口。所有内置的过滤器则直接或者间接继承自FilterBase抽象类。用户只需要将定义好的过滤器通过`setFilter`方法传递给`Scan`或`put`的实例即可。
+
+```java
+setFilter(Filter filter)
+```
+
+```java
+ // Scan 中定义的setFilter
+ @Override
+ public Scan setFilter(Filter filter) {
+ super.setFilter(filter);
+ return this;
+ }
+```
+
+```java
+ // Get 中定义的setFilter
+ @Override
+ public Get setFilter(Filter filter) {
+ super.setFilter(filter);
+ return this;
+ }
+```
+
+FilterBase的所有子类过滤器如下： 
+
+> 说明：上图基于当前时间点（2019.4）最新的Hbase-2.1.4 ，下文所有说明均基于此版本。
+
+
+
+#### 2.2 过滤器分类
+
+HBase 内置过滤器可以分为三类：分别是比较过滤器，专用过滤器和包装过滤器。分别在下面的三个小节中做详细的介绍。
+
+
+
+## 三、比较过滤器
+
+所有比较过滤器均继承自`CompareFilter`。创建一个比较过滤器需要两个参数，分别是**比较运算符**和**比较器实例**。
+
+```java
+ public CompareFilter(final CompareOp compareOp,final ByteArrayComparable comparator) {
+ this.compareOp = compareOp;
+ this.comparator = comparator;
+ }
+```
+
+#### 3.1 比较运算符
+
+- LESS (<)
+- LESS_OR_EQUAL (<=)
+- EQUAL (=)
+- NOT_EQUAL (!=)
+- GREATER_OR_EQUAL (>=)
+- GREATER (>)
+- NO_OP (排除所有符合条件的值)
+
+比较运算符均定义在枚举类`CompareOperator`中
+
+```java
+@InterfaceAudience.Public
+public enum CompareOperator {
+ LESS,
+ LESS_OR_EQUAL,
+ EQUAL,
+ NOT_EQUAL,
+ GREATER_OR_EQUAL,
+ GREATER,
+ NO_OP,
+}
+```
+
+> 注意：在1.x 版本的HBase中比较运算符定义在`CompareFilter.CompareOp`枚举类中，但在2.0之后这个类就被标识为 @deprecated ，并会在3.0移除。
+>
+> 所以1.x 版本的比较运算符需要使用`CompareFilter.CompareOp`枚举类， 2.0 版本HBase 则需要使用 `CompareOperator`枚举类。
+
+#### 3.2 比较器
+
+所有比较器均继承自`ByteArrayComparable`抽象类
+
+ 
+
+常用的有以下几种：
+
+- BinaryComparator : 使用`Bytes.compareTo(byte []，byte [])`按字典序比较指定的字节数组
+- BinaryPrefixComparator : 按字典序与指定的字节数组进行比较，但只比较到这个字节数组的长度。
+- RegexStringComparator : 使用给定的正则表达式与指定的字节数组进行比较。仅支持 EQUAL 和 NOT_EQUAL 操作
+- SubStringComparator : 测试给定的子字符串是否出现在指定的字节数组中，比较不区分大小写。仅支持 EQUAL 和NOT_EQUAL 操作
+- NullComparator ：判断给定的值是否为空
+- BitComparator ：按位进行比较
+
+BinaryPrefixComparator 和 BinaryComparator的区别不是很好表述，这里举例说明一下：
+
+在进行`EQUAL`的比较时，如果比较器传入的是`abcd`的字节数组，但是待比较数据是`abcdefgh`：
+
++ 如果使用的是`BinaryPrefixComparator `比较器，则比较以`abcd`字节数组的长度为准，即`efgh`不会参与比较，这时候认为`abcd`与`abcdefgh` 是满足`EQUAL`条件的；
++ 如果使用的是`BinaryComparator`比较器，则认为其是不相等的。
+
+#### 3.3 比较过滤器种类
+
+比较过滤器共有五个（Hbase 1.x 版本和2.x 版本相同），见下图：
+
+ 
+
++ RowFilter ：基于行键来过滤数据；
++ FamilyFilterr ：基于列族来过滤数据；
++ QualifierFilterr ：基于列限定符（列名）来过滤数据；
++ ValueFilterr ：基于单元格(cell) 的值来过滤数据；
++ DependentColumnFilter ：指定一个参考列来过滤其他列的过滤器，过滤的原则是基于参考列的时间戳来进行筛选 。
+
+前四种过滤器的使用方法相同，均只要传递比较运算符和运算器实例即可构建，然后通过`setFilter`方法传递给`scan`：
+
+```java
+ Filter filter = new RowFilter(CompareOperator.LESS_OR_EQUAL,
+ new BinaryComparator(Bytes.toBytes("xxx")));
+ scan.setFilter(filter);
+```
+
+DependentColumnFilter 的使用稍微复杂一点，这里单独做如下说明。
+
+#### 3.4 DependentColumnFilter
+
+可以把DependentColumnFilter理解为**一个valueFilter和一个时间戳过滤器的组合**。DependentColumnFilter 有三个带参构造器，这里选择一个参数最全的进行说明：
+
+```java
+DependentColumnFilter(final byte [] family, final byte[] qualifier,
+ final boolean dropDependentColumn, final CompareOperator op,
+ final ByteArrayComparable valueComparator)
+```
+
++ family ：列族
++ qualifier ：列限定符（列名）
++ boolean dropDependentColumn ：决定参考列是否被包含在返回结果内，为true时表示参考列被返回，为false时表示被丢弃
+
++ CompareOperator op ：比较运算符
+
++ ByteArrayComparable valueComparator ：比较器
+
+这里举例进行说明：
+
+```java
+DependentColumnFilter dependentColumnFilter = new DependentColumnFilter(
+ Bytes.toBytes("student"),
+ Bytes.toBytes("name"),
+ false,
+ CompareOperator.EQUAL,
+ new BinaryPrefixComparator(Bytes.toBytes("xiaolan")));
+```
+
+(1) 这里会先去查找`student:name`中值以`xiaolan`开头的所有数据获得`参考数据集`，这一步的查找等同于valueFilter过滤器；
+
+(2) 其次再用参考数据集中所有数据的时间戳去检索其他列，获得时间戳相同的其他列的数据作为`结果数据集`，这一步等同于时间戳过滤器；
+
+(3) 最后如果`dropDependentColumn `为true，则返回`参考数据集`+`结果数据集`，若为false，则抛弃参考数据集，只返回结果数据集。
+
+
+
+## 四、专用过滤器
+
+专用过滤器通常直接继承自`FilterBase`，适用于范围更小的筛选规则。
+
+#### 4.1 单列列值过滤器 (SingleColumnValueFilter)
+
+基于某列（参考列）的值决定某行数据是否被过滤。其实例有以下方法：
+
++ setFilterIfMissing(boolean filterIfMissing) ：默认值为false，即如果该行数据不包含参考列，其依然被包含在最后的结果中；设置为true时，则不包含；
++ setLatestVersionOnly(boolean latestVersionOnly) ：默认为true，即只检索参考列的最新版本数据；设置为false，则检索所有版本数据。
+
+```shell
+SingleColumnValueFilter singleColumnValueFilter = new SingleColumnValueFilter(
+ "student".getBytes(),
+ "name".getBytes(),
+ CompareOperator.EQUAL,
+ new SubstringComparator("xiaolan"));
+singleColumnValueFilter.setFilterIfMissing(true);
+scan.setFilter(singleColumnValueFilter);
+```
+
+#### 4.2 单列列值排除器 (SingleColumnValueExcludeFilter)
+
+`SingleColumnValueExcludeFilter`继承自上面的`SingleColumnValueFilter`，过滤行为与其相反。
+
+#### 4.3 行键前缀过滤器 (PrefixFilter)
+
+基于RowKey值决定某行数据是否被过滤。
+
+```java
+PrefixFilter prefixFilter = new PrefixFilter(Bytes.toBytes("xxx"));
+scan.setFilter(prefixFilter);
+```
+
+#### 4.4 列名前缀过滤器 (ColumnPrefixFilter)
+
+基于列限定符（列名）决定某行数据是否被过滤。
+
+```java
+ColumnPrefixFilter columnPrefixFilter = new ColumnPrefixFilter(Bytes.toBytes("xxx"));
+ scan.setFilter(columnPrefixFilter);
+```
+
+#### 4.5 分页过滤器 (PageFilter)
+
+可以使用这个过滤器实现对结果按行进行分页，创建PageFilter实例的时候需要传入每页的行数。
+
+```java
+public PageFilter(final long pageSize) {
+ Preconditions.checkArgument(pageSize >= 0, "must be positive %s", pageSize);
+ this.pageSize = pageSize;
+ }
+```
+
+下面的代码体现了客户端实现分页查询的主要逻辑，这里对其进行一下解释说明：
+
+客户端进行分页查询，需要传递`startRow`(起始RowKey)，知道起始`startRow`后，就可以返回对应的pageSize行数据。这里唯一的问题就是，对于第一次查询，显然`startRow`就是表格的第一行数据，但是之后第二次、第三次查询我们并不知道`startRow`，只能知道上一次查询的最后一条数据的RowKey（简单称之为`lastRow`）。
+
+我们不能将`lastRow`作为新一次查询的`startRow`传入，因为scan的查询区间是[startRow，endRow) ，即前开后闭区间，这样`startRow`在新的查询也会被返回，这条数据就重复了。
+
+同时在不使用第三方数据库存储RowKey的情况下，我们是无法通过知道`lastRow`的下一个RowKey的，因为RowKey的设计可能是连续的也有可能是不连续的。
+
+由于Hbase的RowKey是按照字典序进行排序的。这种情况下，就可以在`lastRow`后面加上`0` ，作为`startRow`传入，因为按照字典序的规则，某个值加上`0` 后的新值，在字典序上一定是这个值的下一个值，对于HBase来说下一个RowKey在字典序上一定也是等于或者大于这个新值的。
+
+所以最后传入`lastRow`+`0`，如果等于这个值的RowKey存在就从这个值开始scan,否则从字典序的下一个RowKey开始scan。
+
+> 25个字母以及数字字符，字典排序如下:
+>
+> `'0' < '1' < '2' < ... < '9' < 'a' < 'b' < ... < 'z'`
+
+分页查询主要实现逻辑：
+
+```java
+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) {
+ // 如果不是首行 则lastRow + 0
+ byte[] startRow = Bytes.add(lastRow, POSTFIX);
+ System.out.println("start row: " +
+ Bytes.toStringBinary(startRow));
+ scan.withStartRow(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();
+ //最后一页，查询结束
+ if (localRows == 0) break;
+}
+System.out.println("total rows: " + totalRows);
+```
+
+>需要注意的是在多台Regin Services上执行分页过滤的时候，由于并行执行的过滤器不能共享它们的状态和边界，所以有可能每个过滤器都会在完成扫描前获取了PageCount行的结果，这种情况下会返回比分页条数更多的数据，分页过滤器就有失效的可能。
+
+
+
+#### 4.6 时间戳过滤器 (TimestampsFilter)
+
+```java
+List list = new ArrayList<>();
+list.add(1554975573000L);
+TimestampsFilter timestampsFilter = new TimestampsFilter(list);
+scan.setFilter(timestampsFilter);
+```
+
+#### 4.7 首次行键过滤器 (FirstKeyOnlyFilter)
+
+FirstKeyOnlyFilter只扫描每行的第一列，扫描完第一列后就结束对当前行的扫描，并跳转到下一行。相比于全表扫描，其性能更好，通常用于行数统计的场景，因为如果某一行存在，则行中必然至少有一列。
+
+```java
+FirstKeyOnlyFilter firstKeyOnlyFilter = new FirstKeyOnlyFilter();
+scan.set(firstKeyOnlyFilter);
+```
+
+## 五、包装过滤器
+
+包装过滤器就是通过包装其他过滤器以实现某些拓展的功能。
+
+#### 5.1 SkipFilter过滤器
+
+SkipFilter包装一个过滤器，当被包装的过滤器遇到一个需要过滤的KeyValue实例时，则拓展过滤整行数据。下面是一个使用示例：
+
+```java
+// 定义ValueFilter过滤器
+Filter filter1 = new ValueFilter(CompareOperator.NOT_EQUAL,
+ new BinaryComparator(Bytes.toBytes("xxx")));
+// 使用SkipFilter进行包装
+Filter filter2 = new SkipFilter(filter1);
+```
+
+
+
+#### 5.2 WhileMatchFilter过滤器
+
+WhileMatchFilter包装一个过滤器，当被包装的过滤器遇到一个需要过滤的KeyValue实例时，WhileMatchFilter则结束本次扫描，返回已经扫描到的结果。下面是其使用示例：
+
+```java
+Filter filter1 = new RowFilter(CompareOperator.NOT_EQUAL,
+ new BinaryComparator(Bytes.toBytes("rowKey4")));
+
+Scan scan = new Scan();
+scan.setFilter(filter1);
+ResultScanner scanner1 = table.getScanner(scan);
+for (Result result : scanner1) {
+ for (Cell cell : result.listCells()) {
+ System.out.println(cell);
+ }
+}
+scanner1.close();
+
+System.out.println("--------------------");
+
+// 使用WhileMatchFilter进行包装
+Filter filter2 = new WhileMatchFilter(filter1);
+
+scan.setFilter(filter2);
+ResultScanner scanner2 = table.getScanner(scan);
+for (Result result : scanner1) {
+ for (Cell cell : result.listCells()) {
+ System.out.println(cell);
+ }
+}
+scanner2.close();
+```
+
+```properties
+rowKey0/student:name/1555035006994/Put/vlen=8/seqid=0
+rowKey1/student:name/1555035007019/Put/vlen=8/seqid=0
+rowKey2/student:name/1555035007025/Put/vlen=8/seqid=0
+rowKey3/student:name/1555035007037/Put/vlen=8/seqid=0
+rowKey5/student:name/1555035007051/Put/vlen=8/seqid=0
+rowKey6/student:name/1555035007057/Put/vlen=8/seqid=0
+rowKey7/student:name/1555035007062/Put/vlen=8/seqid=0
+rowKey8/student:name/1555035007068/Put/vlen=8/seqid=0
+rowKey9/student:name/1555035007073/Put/vlen=8/seqid=0
+--------------------
+rowKey0/student:name/1555035006994/Put/vlen=8/seqid=0
+rowKey1/student:name/1555035007019/Put/vlen=8/seqid=0
+rowKey2/student:name/1555035007025/Put/vlen=8/seqid=0
+rowKey3/student:name/1555035007037/Put/vlen=8/seqid=0
+```
+
+可以看到被包装后，只返回了`rowKey4`之前的数据。
+
+## 六、FilterList
+
+以上都是讲解单个过滤器的作用，当需要多个过滤器共同作用于一次查询的时候，就需要使用FilterList。FilterList支持通过构造器或者`addFilter`方法传入多个过滤器。
+
+```java
+// 构造器传入
+public FilterList(final Operator operator, final List filters)
+public FilterList(final List filters)
+public FilterList(final Filter... filters)
+
+// 方法传入
+ public void addFilter(List filters)
+ public void addFilter(Filter filter)
+```
+
+多个过滤器组合的结果由`operator`参数定义 ，其可选参数定义在`Operator`枚举类中。只有`MUST_PASS_ALL`和`MUST_PASS_ONE`两个可选的值：
+
++ MUST_PASS_ALL ：相当于AND，必须所有的过滤器都通过才认为通过；
++ MUST_PASS_ONE ：相当于OR，只有要一个过滤器通过则认为通过。
+
+```java
+@InterfaceAudience.Public
+ public enum Operator {
+ /** !AND */
+ MUST_PASS_ALL,
+ /** !OR */
+ MUST_PASS_ONE
+ }
+```
+
+
+
+```java
+List filters = new ArrayList();
+
+Filter filter1 = new RowFilter(CompareOperator.GREATER_OR_EQUAL,
+ new BinaryComparator(Bytes.toBytes("XXX")));
+filters.add(filter1);
+
+Filter filter2 = new RowFilter(CompareOperator.LESS_OR_EQUAL,
+ new BinaryComparator(Bytes.toBytes("YYY")));
+filters.add(filter2);
+
+Filter filter3 = new QualifierFilter(CompareOperator.EQUAL,
+ new RegexStringComparator("ZZZ"));
+filters.add(filter3);
+
+FilterList filterList = new FilterList(filters);
+
+Scan scan = new Scan();
+scan.setFilter(filterList);
+```
+
+
+
+## 参考资料
+
+[HBase: The Definitive Guide _> Chapter 4. Client API: Advanced Features](https://www.oreilly.com/library/view/hbase-the-definitive/9781449314682/ch04.html)
\ No newline at end of file
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HcmV?d00001
diff --git a/pictures/hbase-filterbase-subclass.png b/pictures/hbase-filterbase-subclass.png
new file mode 100644
index 0000000000000000000000000000000000000000..66f25a97ff15581db6f7628a55ec6b922e36fb06
GIT binary patch
literal 123771
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