load data local inpath 『filepath' [overwrite] into table tablename [partition (partcol1=val1,...)]
注意:
(1)local代表本地
(2)overwrite代表覆蓋,如果不加,就是追加
(3)分割槽表載入,特殊性 partition (partcol1=val1,...)
例項1:
load data local inpath '/opt/datas/emp.txt' into table emp_part2 partition(`datetime`='20171209',hour='01');
例項2:
(1)準備資料
[root@localhost data]# more data1.txt
1,tom,m
3,mike,m
[root@localhost data]# more data2.txt
2,mary,f
(2)準備分割槽表
hive> desc patition_table;
oksid int
sname string
gender string
# partition information
# col_name data_type comment
gender string
time taken: 0.328 seconds, fetched: 8 row(s)
(3)匯入資料到分割槽表
hive> load data local inpath '/root/data/data1.txt' into table patition_table partition (gender='m');
copying data from file:/root/data/data1.txt
copying file: file:/root/data/data1.txt
loading data to table default.patition_table partition (gender=m)
partition default.patition_table stats: [numfiles=1, numrows=0, totalsize=17, rawdatasize=0]
oktime taken: 2.772 seconds
hive> load data local inpath '/root/data/data2.txt' into table patition_table partition (gender='f');
copying data from file:/root/data/data2.txt
copying file: file:/root/data/data2.txt
loading data to table default.patition_table partition (gender=f)
partition default.patition_table stats: [numfiles=1, numrows=0, totalsize=9, rawdatasize=0]
oktime taken: 1.615 seconds
沒有local
load data inpath 『filepath' [overwrite] into table tablename [partition (partcol1=val1,...)]
insert into table tablename select * from tablenamesource
資料本身已經存在hdfs上,建表的時候指定hdfs上的路徑
create table emp_inner(
empno int,
ename string,
job string,
mgr int,
hiredate string,
sal double,
comm double,
deptno int
)row format delimited fields terminated by '\t'
location '/user/hive/warehouse/hadoop.db/emp';
特殊:如果分割槽表,要記得alter table emp_part add(增加)/drop(刪除) partition(`datetime`='20171209',hour='03').例如
alter table emp_part2 add partition(`datetime`='20171209',hour='03');
create table track_log.result as
select `date`,hour,count(url) pv,count(distinct guid) uv from track_log.yhd_part group by `date`,hour;
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