创建表：
hive> CREATE TABLE pokes (foo INT, bar STRING);
Creates a table called pokes with two columns, the first being an integer and the other a string
创建一个新表，结构与其他一样
hive> create table new_table like records;
直接将select的结果存成表：create table XX as select
INSERT OVERWRITE TABLE ..SELECT：新表预先存在
hive> FROM records2
> INSERT OVERWRITE TABLE stations_by_year SELECT year, COUNT(DISTINCT station) GROUP BY year
> INSERT OVERWRITE TABLE records_by_year SELECT year, COUNT(1) GROUP BY year
> INSERT OVERWRITE TABLE good_records_by_year SELECT year, COUNT(1) WHERE temperature != 9999 AND (quality = 0 OR quality = 1 OR quality = 4 OR quality = 5 OR quality = 9) GROUP BY year;
CREATE TABLE ... AS SELECT：新表表预先不存在
hive>CREATE TABLE target AS SELECT col1,col2 FROM source;
创建视图：
hive> CREATE VIEW valid_records AS SELECT * FROM records2 WHERE temperature !=9999;
查看视图详细信息：
hive> DESCRIBE EXTENDED valid_records;
创建分区表：
hive> create table logs(ts bigint,line string) partitioned by (dt String,country String);
加载分区表数据：
hive> load data local inpath '/home/hadoop/input/hive/partitions/file1' into table logs partition (dt='2001-01-01',country='GB');
展示表中有多少分区：
hive> show partitions logs;
展示所有表：
hive> SHOW TABLES;
lists all the tables
hive> SHOW TABLES '.*s';
lists all the table that end with 's'. The pattern matching follows Java regular
expressions. Check out this link for documentationhttp://java.sun.com/javase/6/docs/api/java/util/regex/Pattern.html
显示表的结构信息
hive> DESCRIBE invites;
shows the list of columns
更新表的名称：
hive> ALTER TABLE source RENAME TO target;
添加新一列
hive> ALTER TABLE invites ADD COLUMNS (new_col2 INT COMMENT 'a comment');
删除表：
hive> DROP TABLE records;
删除表中数据，但要保持表的结构定义
hive> dfs -rmr /user/hive/warehouse/records;
从本地文件加载数据：
hive> LOAD DATA LOCAL INPATH '/home/hadoop/input/ncdc/micro-tab/sample.txt' OVERWRITE INTO TABLE records;
显示所有函数：
hive> show functions;
查看函数用法：
hive> describe function substr;
查看数组、map、结构
hive> select col1[0],col2['b'],col3.c from complex;
内连接：
hive> SELECT sales.*, things.* FROM sales JOIN things ON (sales.id = things.id);
查看hive为某个查询使用多少个MapReduce作业
hive> Explain SELECT sales.*, things.* FROM sales JOIN things ON (sales.id = things.id);
外连接：
hive> SELECT sales.*, things.* FROM sales LEFT OUTER JOIN things ON (sales.id = things.id);
hive> SELECT sales.*, things.* FROM sales RIGHT OUTER JOIN things ON (sales.id = things.id);
hive> SELECT sales.*, things.* FROM sales FULL OUTER JOIN things ON (sales.id = things.id);
in查询：Hive不支持，但可以使用LEFT SEMI JOIN
hive> SELECT * FROM things LEFT SEMI JOIN sales ON (sales.id = things.id);
Map连接：Hive可以把较小的表放入每个Mapper的内存来执行连接操作
hive> SELECT /*+ MAPJOIN(things) */ sales.*, things.* FROM sales JOIN things ON (sales.id = things.id);