基本的Select 操作

SELECT [ALL | DISTINCT] select_expr, select_expr, ...

FROM table_reference

[WHERE where_condition]

[GROUP BY col_list [HAVING condition]]

[ CLUSTER BY col_list

| [DISTRIBUTE BY col_list] [SORT BY| ORDER BY col_list]

]

[LIMIT number]

使用ALL和DISTINCT选项区分对重复记录的处理。默认是ALL，表示查询所有记录。DISTINCT表示去掉重复的记录

Where 条件

类似我们传统SQL的where 条件

目前支持 AND,OR ,0.9版本支持between

IN, NOT IN

不支持EXIST ,NOT EXIST

ORDER BY与SORT BY的不同

ORDER BY 全局排序，只有一个Reduce任务

SORT BY 只在本机做排序

Limit

Limit 可以限制查询的记录数

SELECT * FROM t1 LIMIT 5

实现Top k 查询

下面的查询语句查询销售记录最大的 5 个销售代表。

SET mapred.reduce.tasks = 1 SELECT * FROM test SORT BY amount DESC LIMIT 5

REGEX Column Specification

按先件查询

hive> SELECT a.foo FROM invites a WHERE a.ds='<DATE>';

将查询数据输出至目录：

hive> INSERT OVERWRITE DIRECTORY '/tmp/hdfs_out' SELECT a.* FROM invites a WHERE a.ds='<DATE>';

将查询结果输出至本地目录：

hive> INSERT OVERWRITE LOCAL DIRECTORY '/tmp/local_out' SELECT a.* FROM pokes a;

选择所有列到本地目录 ：

hive> INSERT OVERWRITE TABLE events SELECT a.* FROM profiles a; hive> INSERT OVERWRITE TABLE events SELECT a.* FROM profiles a WHERE a.key < 100; hive> INSERT OVERWRITE LOCAL DIRECTORY '/tmp/reg_3' SELECT a.* FROM events a; hive> INSERT OVERWRITE DIRECTORY '/tmp/reg_4' select a.invites, a.pokes FROM profiles a; hive> INSERT OVERWRITE DIRECTORY '/tmp/reg_5' SELECT COUNT(1) FROM invites a WHERE a.ds='<DATE>'; hive> INSERT OVERWRITE DIRECTORY '/tmp/reg_5' SELECT a.foo, a.bar FROM invites a; hive> INSERT OVERWRITE LOCAL DIRECTORY '/tmp/sum' SELECT SUM(a.pc) FROM pc1 a;

将一个表的统计结果插入另一个表中：

hive> FROM invites a INSERT OVERWRITE TABLE events SELECT a.bar, count(1) WHERE a.foo > 0 GROUP BY a.bar; hive> INSERT OVERWRITE TABLE events SELECT a.bar, count(1) FROM invites a WHERE a.foo > 0 GROUP BY a.bar; JOIN hive> FROM pokes t1 JOIN invites t2 ON (t1.bar = t2.bar) INSERT OVERWRITE TABLE events SELECT t1.bar, t1.foo, t2.foo;

将多表数据插入到同一表中：

FROM src INSERT OVERWRITE TABLE dest1 SELECT src.* WHERE src.key < 100 INSERT OVERWRITE TABLE dest2 SELECT src.key, src.value WHERE src.key >= 100 and src.key < 200 INSERT OVERWRITE TABLE dest3 PARTITION(ds='2008-04-08', hr='12') SELECT src.key WHERE src.key >= 200 and src.key < 300 INSERT OVERWRITE LOCAL DIRECTORY '/tmp/dest4.out' SELECT src.value WHERE src.key >= 300;

将文件流直接插入文件：

hive> FROM invites a INSERT OVERWRITE TABLE events SELECT TRANSFORM(a.foo, a.bar) AS (oof, rab) USING '/bin/cat' WHERE a.ds > '2008-08-09'; This streams the data in the map phase through the script /bin/cat (like hadoop streaming). Similarly - streaming can be used on the reduce side (please see the Hive Tutorial or examples)

Join

Syntax

join_table: table_reference JOIN table_factor [join_condition] | table_reference {LEFT|RIGHT|FULL} [OUTER] JOIN table_reference join_condition | table_reference LEFT SEMI JOIN table_reference join_condition

table_reference: table_factor | join_table

table_factor: tbl_name [alias] | table_subquery alias | ( table_references )

join_condition: ON equality_expression ( AND equality_expression )*

equality_expression: expression = expression

Hive 只支持等值连接（equality joins）、外连接（outer joins）和（left semi joins）。Hive 不支持所有非等值的连接，因为非等值连接非常难转化到 map/reduce 任务

LEFT，RIGHT和FULL OUTER关键字用于处理join中空记录的情况

LEFT SEMI JOIN 是 IN/EXISTS 子查询的一种更高效的实现

join 时，每次 map/reduce 任务的逻辑是这样的：reduce 会缓存 join 序列中除了最后一个表的所有表的记录，再通过最后一个表将结果序列化到文件系统

实践中，应该把最大的那个表写在最后

join 查询时，需要注意几个关键点

只支持等值join

SELECT a.* FROM a JOIN b ON (a.id = b.id)

SELECT a.* FROM a JOIN b ON (a.id = b.id AND a.department = b.department)

可以 join 多于 2 个表，例如

SELECT a.val, b.val, c.val FROM a JOIN b ON (a.key = b.key1) JOIN c ON (c.key = b.key2)

如果join中多个表的 join key 是同一个，则 join 会被转化为单个 map/reduce 任务

*LEFT*，*RIGHT*和*FULL OUTER*

例子

SELECT a.val, b.val FROM a LEFT OUTER JOIN b ON (a.key=b.key)

如果你想限制 join 的输出，应该在 WHERE 子句中写过滤条件——或是在 join 子句中写

容易混淆的问题是表分区的情况

SELECT c.val, d.val FROM c LEFT OUTER JOIN d ON (c.key=d.key) WHERE a.ds='2010-07-07' AND b.ds='2010-07-07‘

如果 d 表中找不到对应 c 表的记录，d 表的所有列都会列出 NULL，包括 ds 列。也就是说，join 会过滤 d 表中不能找到匹配 c 表 join key 的所有记录。这样的话，LEFT OUTER 就使得查询结果与 WHERE 子句无关

解决办法

SELECT c.val, d.val FROM c LEFT OUTER JOIN d ON (c.key=d.key AND d.ds='2009-07-07' AND c.ds='2009-07-07')

LEFT SEMI JOIN

LEFT SEMI JOIN 的限制是， JOIN 子句中右边的表只能在 ON 子句中设置过滤条件，在 WHERE 子句、SELECT 子句或其他地方过滤都不行

SELECT a.key, a.value FROM a WHERE a.key in (SELECT b.key FROM B);

可以被重写为：

SELECT a.key, a.val FROM a LEFT SEMI JOIN b on (a.key = b.key)

UNION ALL

用来合并多个select的查询结果，需要保证select中字段须一致

select_statement UNION ALL select_statement UNION ALL select_statement ...