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README.md | 13 +-
notes/Hive常用DML操作.md | 29 ++-
notes/Hive数据查询详解.md | 407 ++++++++++++++++++++++++++++++++
pictures/hive-order-by.png | Bin 0 -> 7336 bytes
pictures/hive-right-join.png | Bin 0 -> 39956 bytes
pictures/sql-join.jpg | Bin 0 -> 50515 bytes
resources/dept.txt | 4 +
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create mode 100644 notes/Hive数据查询详解.md
create mode 100644 pictures/hive-order-by.png
create mode 100644 pictures/hive-right-join.png
create mode 100644 pictures/sql-join.jpg
create mode 100644 resources/dept.txt
diff --git a/README.md b/README.md
index c383d6b..ec82e1f 100644
--- a/README.md
+++ b/README.md
@@ -60,13 +60,14 @@
1. [数据仓库Hive简介](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hive.md)
2. [Linux环境下Hive的安装部署](https://github.com/heibaiying/BigData-Notes/blob/master/notes/installation/Linux%E7%8E%AF%E5%A2%83%E4%B8%8BHive%E7%9A%84%E5%AE%89%E8%A3%85%E9%83%A8%E7%BD%B2.md)
-3. [Hive Shell基本使用](https://github.com/heibaiying/BigData-Notes/blob/master/notes/HiveShell基本使用.md)
-4. [Hive 核心概念讲解](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hive核心概念讲解.md)
-5. [Hive 分区表和分桶表](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hive分区表和分桶表.md)
+3. 连接Hive的三种方式
+4. [Hive Shell基本使用](https://github.com/heibaiying/BigData-Notes/blob/master/notes/HiveShell基本使用.md)
+5. [Hive 核心概念讲解](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hive核心概念讲解.md)
6. [Hive 常用DDL操作](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hive常用DDL操作.md)
-7. Hive 数据查询
-8. Hive 视图和索引
-9. Hive 模式设计
+7. [Hive 分区表和分桶表](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hive分区表和分桶表.md)
+8. [Hive 视图和索引](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hive数据查询详解.md)
+9. [Hive常用DML操作](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hive常用DML操作.md)
+10. [Hive 数据查询详解](https://github.com/heibaiying/BigData-Notes/blob/master/notes/Hive数据查询详解.md)
## 三、Spark
diff --git a/notes/Hive常用DML操作.md b/notes/Hive常用DML操作.md
index 35d789a..7f6d039 100644
--- a/notes/Hive常用DML操作.md
+++ b/notes/Hive常用DML操作.md
@@ -16,7 +16,8 @@
将文件数据加载到表时，Hive不会进行任何转换，加载操作是纯复制/移动操作，它将数据文件移动到Hive表定义的存储位置。
```shell
-LOAD DATA [LOCAL] INPATH 'filepath' [OVERWRITE] INTO TABLE tablename [PARTITION (partcol1=val1, partcol2=val2 ...)]
+LOAD DATA [LOCAL] INPATH 'filepath' [OVERWRITE]
+INTO TABLE tablename [PARTITION (partcol1=val1, partcol2=val2 ...)]
```
- Load 关键字代表从本地文件系统加载文件，省略则代表从HDFS上加载文件：
@@ -74,8 +75,11 @@ LOAD DATA INPATH "hdfs://hadoop001:8020/mydir/emp.txt" OVERWRITE INTO TABLE emp
### 2.1 语法
```sql
-INSERT OVERWRITE TABLE tablename1 [PARTITION (partcol1=val1, partcol2=val2 ...) [IF NOT EXISTS]] select_statement1 FROM from_statement;
-INSERT INTO TABLE tablename1 [PARTITION (partcol1=val1, partcol2=val2 ...)] select_statement1 FROM from_statement;
+INSERT OVERWRITE TABLE tablename1 [PARTITION (partcol1=val1, partcol2=val2 ...)
+ [IF NOT EXISTS]] select_statement1 FROM from_statement;
+
+INSERT INTO TABLE tablename1 [PARTITION (partcol1=val1, partcol2=val2 ...)]
+ select_statement1 FROM from_statement;
```
+ Hive 0.13.0开始，建表时可以通过使用TBLPROPERTIES（“immutable”=“true”）来创建不可变表(immutable table) ，如果不可以变表中存在数据，则INSERT INTO失败。（注：INSERT OVERWRITE的语句不受`immutable`属性的影响）;
@@ -90,7 +94,8 @@ INSERT INTO TABLE tablename1 [PARTITION (partcol1=val1, partcol2=val2 ...)] sele
```sql
FROM from_statement
- INSERT OVERWRITE TABLE tablename1 [PARTITION (partcol1=val1, partcol2=val2 ...) [IF NOT EXISTS]] select_statement1
+ INSERT OVERWRITE TABLE tablename1
+ [PARTITION (partcol1=val1, partcol2=val2 ...) [IF NOT EXISTS]] select_statement1
[INSERT OVERWRITE TABLE tablename2 [PARTITION ... [IF NOT EXISTS]] select_statement2]
[INSERT INTO TABLE tablename2 [PARTITION ...] select_statement2] ...;
```
@@ -98,8 +103,11 @@ INSERT INTO TABLE tablename1 [PARTITION (partcol1=val1, partcol2=val2 ...)] sele
### 2.2 动态插入分区
```sql
-INSERT OVERWRITE TABLE tablename PARTITION (partcol1[=val1], partcol2[=val2] ...) select_statement FROM from_statement;
-INSERT INTO TABLE tablename PARTITION (partcol1[=val1], partcol2[=val2] ...) select_statement FROM from_statement;
+INSERT OVERWRITE TABLE tablename PARTITION (partcol1[=val1], partcol2[=val2] ...)
+select_statement FROM from_statement;
+
+INSERT INTO TABLE tablename PARTITION (partcol1[=val1], partcol2[=val2] ...)
+select_statement FROM from_statement;
```
在向分区表插入数据时候，分区列名是必须的，但是列值是可选的。如果给出了分区列值，我们将其称为静态分区，否则它是动态分区。动态分区列必须在SELECT语句的列中最后指定，并且与它们在PARTITION()子句中出现的顺序相同。
@@ -146,7 +154,8 @@ TRUNCATE TABLE emp_ptn;
3. 静态分区演示：从`emp`表中查询部门编号为20的员工数据，并插入`emp_ptn`表中，语句如下：
```sql
-INSERT OVERWRITE TABLE emp_ptn PARTITION (deptno=20) SELECT empno,ename,job,mgr,hiredate,sal,comm FROM emp WHERE deptno=20;
+INSERT OVERWRITE TABLE emp_ptn PARTITION (deptno=20)
+SELECT empno,ename,job,mgr,hiredate,sal,comm FROM emp WHERE deptno=20;
```
完成后`emp_ptn`表中数据如下：
@@ -160,7 +169,8 @@ INSERT OVERWRITE TABLE emp_ptn PARTITION (deptno=20) SELECT empno,ename,job,mgr,
set hive.exec.dynamic.partition.mode=nonstrict;
-- 动态分区 此时查询语句的最后一列为动态分区列，即deptno
-INSERT OVERWRITE TABLE emp_ptn PARTITION (deptno) SELECT empno,ename,job,mgr,hiredate,sal,comm,deptno FROM emp WHERE deptno=30;
+INSERT OVERWRITE TABLE emp_ptn PARTITION (deptno)
+SELECT empno,ename,job,mgr,hiredate,sal,comm,deptno FROM emp WHERE deptno=30;
```
完成后`emp_ptn`表中数据如下：
@@ -172,7 +182,8 @@ INSERT OVERWRITE TABLE emp_ptn PARTITION (deptno) SELECT empno,ename,job,mgr,hir
## 三、使用SQL语句插入值
```sql
-INSERT INTO TABLE tablename [PARTITION (partcol1[=val1], partcol2[=val2] ...)] VALUES ( value [, value ...] )
+INSERT INTO TABLE tablename [PARTITION (partcol1[=val1], partcol2[=val2] ...)]
+VALUES ( value [, value ...] )
```
+ 使用时必须为表中的每个列都提供值。不支持只向部分列插入值（可以为缺省值的列提供空值来消除这个弊端）；
diff --git a/notes/Hive数据查询详解.md b/notes/Hive数据查询详解.md
new file mode 100644
index 0000000..25b7ce5
--- /dev/null
+++ b/notes/Hive数据查询详解.md
@@ -0,0 +1,407 @@
+# Hive数据查询详解
+
+
+
+
+
+## 一、数据准备
+
+为了演示查询操作，这里需要预先创建两张表，并加载数据。
+
+> 这里的表结构主要参考自Oracle内置的练习表——emp表和dept表，涉及到数据文件emp.txt和dept.txt可以在本仓库的sources目录下下载。
+
+### 1.1 员工表
+
+```sql
+ -- 建表语句
+ CREATE TABLE emp(
+ empno INT, -- 员工表编号
+ ename STRING, -- 员工姓名
+ job STRING, -- 职位类型
+ mgr INT,
+ hiredate TIMESTAMP, --雇佣日期
+ sal DECIMAL(7,2), --工资
+ comm DECIMAL(7,2),
+ deptno INT) --部门编号
+ ROW FORMAT DELIMITED FIELDS TERMINATED BY "\t";
+
+ --加载数据
+LOAD DATA LOCAL INPATH "/usr/file/emp.txt" OVERWRITE INTO TABLE emp;
+```
+
+### 1.2 部门表
+
+```sql
+ -- 建表语句
+ CREATE TABLE dept(
+ deptno INT, --部门编号
+ dname STRING, --部门名称
+ loc STRING --部门所在的城市
+ )
+ ROW FORMAT DELIMITED FIELDS TERMINATED BY "\t";
+
+ --加载数据
+ LOAD DATA LOCAL INPATH "/usr/file/dept.txt" OVERWRITE INTO TABLE dept;
+```
+
+### 1.3 分区表
+
+这里需要额外创建一张分区表，主要是为了演示分区查询：
+
+```sql
+CREATE EXTERNAL TABLE emp_ptn(
+ empno INT,
+ ename STRING,
+ job STRING,
+ mgr INT,
+ hiredate TIMESTAMP,
+ sal DECIMAL(7,2),
+ comm DECIMAL(7,2)
+ )
+ PARTITIONED BY (deptno INT) -- 按照部门编号进行分区
+ ROW FORMAT DELIMITED FIELDS TERMINATED BY "\t";
+
+
+--加载数据
+LOAD DATA LOCAL INPATH "/usr/file/emp.txt" OVERWRITE INTO TABLE emp_ptn PARTITION (deptno=20)
+LOAD DATA LOCAL INPATH "/usr/file/emp.txt" OVERWRITE INTO TABLE emp_ptn PARTITION (deptno=30)
+LOAD DATA LOCAL INPATH "/usr/file/emp.txt" OVERWRITE INTO TABLE emp_ptn PARTITION (deptno=40)
+LOAD DATA LOCAL INPATH "/usr/file/emp.txt" OVERWRITE INTO TABLE emp_ptn PARTITION (deptno=50)
+```
+
+
+
+## 二、单表查询
+
+### 2.1 SELECT
+
+```sql
+-- 查询表中全部数据
+SELECT * FROM emp;
+```
+
+
+
+### 2.2 WHERE
+
+```sql
+-- 查询10号部门中员工编号大于 7782 的员工信息
+SELECT * FROM emp WHERE empno > 7782 AND deptno = 10;
+```
+
+
+
+### 2.3 DISTINCT
+
+Hive支持使用DISTINCT关键字去重。
+
+```sql
+-- 查询所有工作类型
+SELECT DISTINCT job FROM emp;
+```
+
+
+
+### 2.4 分区查询
+
+分区查询(Partition Based Queries)，可以指定某个分区或者分区范围。
+
+```sql
+-- 查询分区表中部门编号在[20,40]之间的员工
+SELECT emp_ptn.* FROM emp_ptn
+WHERE emp_ptn.deptno >= 20 AND emp_ptn.deptno <= 40;
+```
+
+
+
+### 2.5 LIMIT
+
+```sql
+-- 查询薪资最高的5名员工
+SELECT * FROM emp ORDER BY sal DESC LIMIT 5;
+```
+
+
+
+### 2.6 GROUP BY
+
+Hive支持使用GROUP BY进行分组聚合操作。
+
+```sql
+set hive.map.aggr=true;
+
+-- 查询各个部门薪酬综合
+SELECT deptno,SUM(sal) FROM emp GROUP BY deptno;
+```
+
+`hive.map.aggr`控制程序如何进行聚合。默认值为false。如果设置为true，Hive会在map任务中就执行一次聚合。这可以提高聚合效率，但需要消耗更多内存。
+
+
+
+### 2.7 ORDER AND SORT
+
+可以使用ORDER BY或者Sort BY对查询结果进行排序，排序字段可以是整型也可以是字符串：如果是整型，则按照大小排序；如果是字符串，则按照字典序排序。ORDER BY 和 Sort BY 的区别如下：
+
++ 使用ORDER BY时会有一个Reducer对全部查询结果进行排序，能保证数据的全局有序性；
++ 使用Sort BY时只会在每个Reducer中进行排序，这可以保证每个Reducer的输出数据时有序的，但是并不能保证全局有序。
+
+由于ORDER BY的操作时间可能过长，如果你设置了严格模式(hive.mapred.mode = strict)，则其后面必须再跟一个`limit`子句。
+
+> 注 ：hive.mapred.mode默认值是nonstrict ，也就是非严格模式。
+
+```sql
+-- 查询员工工资，结果按照部门升序，按照工资降序排列
+SELECT empno, deptno, sal FROM emp ORDER BY deptno ASC, sal DESC;
+```
+
+
+
+### 2.8 HAVING
+
+可以使用HAVING对分组数据进行过滤。
+
+```sql
+-- 查询工资总和大于9000的所有部门
+SELECT deptno,SUM(sal) FROM emp GROUP BY deptno HAVING SUM(sal)>9000;
+```
+
+
+
+### 2.9 DISTRIBUTE BY
+
+默认情况下，MapReduce程序会对Map输出结果的Key值进行散列，并均匀分发到所有Reducer上。如果想要把具有相同Key值的数据分发到同一个Reducer进行处理，这就需要使用DISTRIBUTE BY字句。
+
+需要注意的是，DISTRIBUTE BY虽然能保证具有相同Key值的数据分发到同一个Reducer，但是不能保证数据在Reducer上是有序的。情况如下：
+
+把以下5个数据发送到两个Reducer上进行处理：
+
+```properties
+k1
+k2
+k4
+k3
+k1
+```
+
+Reducer1得到如下乱序数据：
+
+```properties
+k1
+k2
+k1
+```
+
+
+Reducer2得到数据如下：
+
+```properties
+k4
+k3
+```
+
+如果想让Reducer上的数据时有序的，可以结合`SORT BY`使用(示例如下)，或者使用下面我们将要介绍的CLUSTER BY。
+
+```sql
+-- 将数据按照部门分发到对应的Reducer上处理
+SELECT empno, deptno, sal FROM emp DISTRIBUTE BY deptno SORT BY deptno ASC;
+```
+
+
+
+### 2.10 CLUSTER BY
+
+如果`SORT BY`和`DISTRIBUTE BY`指定的是相同字段，且SORT BY排序规则是ASC，此时可以使用`CLUSTER BY`进行替换，同时`CLUSTER BY`可以保证数据在全局是有序的。
+
+```sql
+SELECT empno, deptno, sal FROM emp CLUSTER BY deptno ;
+```
+
+
+
+## 三、多表联结查询
+
+Hive支持内连接，外连接，左外连接，右外连接，笛卡尔连接，这和传统数据库中的概念一致。关于以上概念，可以参见下图。
+
+需要特别强调：JOIN语句的关联条件必须用ON指定，不能用WHERE指定，否则就会先做笛卡尔积，再过滤，这会导致你得不到预期的结果(下面的演示会有说明)。
+
+ 
+
+
+
+### 3.1 INNER JOIN
+
+```sql
+-- 查询员工编号为7369的员工的详细信息
+SELECT e.*,d.* FROM
+emp e JOIN dept d
+ON e.deptno = d.deptno
+WHERE empno=7369;
+
+--如果是三表或者更多表连接，语法如下
+SELECT a.val, b.val, c.val FROM a JOIN b ON (a.key = b.key1) JOIN c ON (c.key = b.key1)
+```
+
+### 3.2 LEFT OUTER JOIN
+
+LEFT OUTER JOIN 和 LEFT JOIN是等价的。
+
+```sql
+-- 左连接
+SELECT e.*,d.*
+FROM emp e LEFT OUTER JOIN dept d
+ON e.deptno = d.deptno;
+```
+
+### 3.3 RIGHT OUTER JOIN
+
+```sql
+--右连接
+SELECT e.*,d.*
+FROM emp e RIGHT OUTER JOIN dept d
+ON e.deptno = d.deptno;
+```
+
+执行右连接后，由于40号部门下没有任何员工，所以此时员工信息为NULL。这个查询可以很好的复述上面提到的——JOIN语句的关联条件必须用ON指定，不能用WHERE指定。你可以把ON改成WHERE，你会发现无论如何都查不出40号部门这条数据，因为笛卡尔运算不会有(NULL, 40)这种情况。
+
+ 
+
+### 3.4 FULL OUTER JOIN
+
+```sql
+SELECT e.*,d.*
+FROM emp e FULL OUTER JOIN dept d
+ON e.deptno = d.deptno;
+```
+
+### 3.5 LEFT SEMI JOIN
+
+LEFT SEMI JOIN （左半连接）是 IN/EXISTS 子查询的一种更高效的实现。
+
++ JOIN 子句中右边的表只能在 ON 子句中设置过滤条件;
++ 查询结果只包含左边表的数据，所以只能SELECT左表中的列。
+
+```sql
+-- 查询在纽约办公的所有员工信息
+SELECT emp.*
+FROM emp LEFT SEMI JOIN dept
+ON emp.deptno = dept.deptno AND dept.loc="NEW YORK";
+
+--上面的语句就等价于
+SELECT emp.* FROM emp
+WHERE emp.deptno IN (SELECT deptno FROM dept WHERE loc="NEW YORK");
+```
+
+### 3.6 JOIN
+
+笛卡尔积连接，这个连接日常的开发中可能很少遇到，且性能消耗会比较大，基于这个原因，如果在严格模式下(hive.mapred.mode = strict)，Hive会阻止用户执行此操作。
+
+```sql
+SELECT * FROM emp JOIN dept;
+```
+
+
+
+## 四、JOIN优化
+
+### 4.1 STREAMTABLE
+
+在多表进行联结的时候，如果每个ON字句都使用到共同的列（如下面的`b.key1`），此时Hive会进行优化，将多表join在同一个map / reduce作业上进行。同时假定查询的最后一个表（如下面的 c 表）是最大的一个表，在对每行记录进行join操作时，它将尝试将其他的表缓存起来，然后扫描最后那个表进行计算。因为用户需要保证连续查询的表的大小从左到右是依次增加的。
+
+```sql
+`SELECT a.val, b.val, c.val FROM a JOIN b ON (a.key = b.key1) JOIN c ON (c.key = b.key2)`
+```
+
+然后，用户并非需要总是把最大的表放在查询语句的最后面，Hive提供了`/*+ STREAMTABLE() */`标志，用于标识最大的表，示例如下：
+
+```sql
+SELECT /*+ STREAMTABLE(d) */ e.*,d.*
+FROM emp e JOIN dept d
+ON e.deptno = d.deptno
+WHERE job='CLERK';
+```
+
+
+
+### 4.2 MAPJOIN
+
+如果所有表中只有一张表是小表，那么完全可以把这张小表加载到内存中。这时候程序会在map阶段直接拿另外一个表的数据和内存中表数据做匹配，由于在map就进行了join操作，从而可以省略reduce过程，这样效率可以提升很多。Hive中提供了`/*+ MAPJOIN() */`来标记小表，示例如下：
+
+```sql
+SELECT /*+ MAPJOIN(d) */ e.*,d.*
+FROM emp e JOIN dept d
+ON e.deptno = d.deptno
+WHERE job='CLERK';
+```
+
+
+
+## 五、SELECT的其他用途
+
+查看当前数据库：
+
+```sql
+SELECT current_database()
+```
+
+
+
+## 六、本地模式
+
+在上面演示的语句中，大多数都会触发MapReduce, 少部分不会触发，比如`select * from emp limit 5`就不会触发MR，此时Hive只是简单的读取数据文件中的内容，然后格式化后进行输出。在需要执行MapReduce的查询中，你会发现执行时间可能会很长，这时候你可以选择开启本地模式。
+
+```sql
+--本地模式默认关闭，需要手动开启此功能
+SET hive.exec.mode.local.auto=true;
+```
+
+启用后，Hive将分析查询中每个map-reduce作业的大小，如果满足以下条件，则可以在本地运行它：
+
+- 作业的总输入大小低于：hive.exec.mode.local.auto.inputbytes.max（默认为128MB）；
+- map-tasks的总数小于：hive.exec.mode.local.auto.tasks.max（默认为4）；
+- 所需的reduce任务总数为1或0。
+
+因为我们测试的数据集很小，所以你再次去执行上面涉及MR操作的查询，你会发现速度会有显著的提升。
+
+
+
+
+
+## 参考资料
+
+1. [LanguageManual Select](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Select)
+
+2. [LanguageManual Joins](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Joins)
+3. [LanguageManual GroupBy](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+GroupBy)
+4. [LanguageManual SortBy](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+SortBy)
+
+
+
+
+
+
+
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