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  • Hive基本操作

     
    HIVE基本操作:
     
    本地运行
    set hive.exec.mode.local.auto=true;
     
    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;
     
    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 '/hive/hadoop/input/hive/partitions/file1' into table logs partition (dt='2011-01-01',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 documentation
     
    显示表的结构信息
    hive> DESCRIBE invites;
    DESC TABLE_NAME
    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 '/hive/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);
     
    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;不只是复制表结构,数据也来了
     
    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;
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  • 原文地址:https://www.cnblogs.com/dasiji/p/11356942.html
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