1. 创建仓库
hive> create database shizhan03;
OK
Time taken: 0.379 seconds
相当于在hdfs里面创建了一个目录,路径如下:/user/hive/warehouse/shizhan03.db
2. 使用仓库
hive> use shizhan03;
OK
Time taken: 0.042 seconds
3. 建表
hive> create table t_sz01(id int,name string);
OK
Time taken: 0.232 seconds
相当于在数据库路径下面创建了一个目录,
路径如下:/user/hive/warehouse/shizhan03.db/t_sz01
4. 放入数据到路径里面
hadoop fs -put data.txt /user/hive/warehouse/shizhan03.db/t_sz01
内容如下:
1,zhangsan
2,lisi
3,wangwu
4,zhaoliu
5,zhouqi
5. 创建表
hive> create table t_sz01(id int,name string)
> row format delimited
> fields terminated by ',';
OK
Time taken: 0.5 seconds
6. 查看数据
hive> select * from t_sz01;
OK
1 zhangsan
2 lisi
3 wangwu
4 zhaoliu
5 zhouqi
Time taken: 0.552 seconds, Fetched: 5 row(s)
7. 统计第一列有多少行
hive> select count(1) from t_sz01;
Query ID = hadoop_20180904102400_d78a0a22-a4a2-4a96-984e-d0fbcf452264
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Job running in-process (local Hadoop)
2018-09-04 10:24:03,695 Stage-1 map = 100%, reduce = 100%
Ended Job = job_local396966903_0001
MapReduce Jobs Launched:
Stage-Stage-1: HDFS Read: 184 HDFS Write: 0 SUCCESS
Total MapReduce CPU Time Spent: 0 msec
OK
5
Time taken: 2.98 seconds, Fetched: 1 row(s)
注意:这里执行了mapreduce程序
8. 其他查询
select id,name from t_sz01 order by name;