Hive基本命令整理

創建表：
hive> CREATE TABLE pokes (foo INT, bar STRING); 
        Creates a table called pokes withtwo columns, the first being an integer and the other a string

創建一個新表，結構與其他一樣
hive> create table new_table like records;

創建分區表：
hive> create table logs(ts bigint,line string) partitioned by (dt String,countryString);

加載分區表數據：
hive> load data local inpath '/home/hadoop/input/hive/partitions/file1' intotable logs partition (dt='2001-01-01',country='GB');

展示表中有多少分區：
hive> show partitions logs;

展示所有表：
hive> SHOW TABLES;
        lists all the tables
hive> SHOW TABLES '.*s';

listsall 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

顯示錶的結構信息
hive> DESCRIBE invites;
        shows the list of columns

更新表的名稱：
hive> ALTER TABLE source RENAME TO target;

添加新一列
hive> ALTER TABLE invites ADD COLUMNS (new_col2 INT COMMENT 'a comment');
 
刪除表：
hive> DROP TABLE records;
刪除表中數據，但要保持表的結構定義
hive> dfs -rmr /user/hive/warehouse/records;

從本地文件加載數據：
hive> LOAD DATA LOCAL INPATH '/home/hadoop/input/ncdc/micro-tab/sample.txt'OVERWRITE INTO TABLE records;

顯示所有函數：
hive> show functions;

查看函數用法：
hive> describe function substr;

查看數組、map、結構
hive> select col1[0],col2['b'],col3.c from complex;

內連接：
hive> SELECT sales.*, things.* FROM sales JOIN things ON (sales.id =things.id);

查看hive爲某個查詢使用多少個MapReduce作業
hive> Explain SELECT sales.*, things.* FROM sales JOIN things ON (sales.id =things.id);

外連接：
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查詢：Hive不支持，但可以使用LEFTSEMI JOIN
hive> SELECT * FROM things LEFT SEMI JOIN sales ON (sales.id = things.id);

Map連接：Hive可以把較小的表放入每個Mapper的內存來執行連接操作
hive> SELECT /*+ MAPJOIN(things) */ sales.*, things.* FROM sales JOIN thingsON (sales.id = things.id);

INSERTOVERWRITE TABLE ..SELECT：新表預先存在
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 SELECTyear, COUNT(1) WHERE temperature != 9999 AND (quality = 0 OR quality = 1 ORquality = 4 OR quality = 5 OR quality = 9) GROUP BY year;  

CREATETABLE ... AS SELECT：新表表預先不存在
hive>CREATE TABLE target AS SELECT col1,col2 FROM source;

創建視圖：
hive> CREATE VIEW valid_records AS SELECT * FROM records2 WHERE temperature!=9999;

查看視圖詳細信息：
hive> DESCRIBE EXTENDED valid_records;