大数据-HiveDDL和DML操作
DDL和DML操作
一、DDL操作
（1）查看数据库show databases;
（2）创建数据库
create databases [if not exists] databases_name [location path];
（3）查看数据库结构
desc databases [extended] databases_name;
（4）添加额外的描述信息
alter databases databases_name set dbproperties ('');
（5）删除数据库
drop database [if exists] databases_name [cascade];
（6）创建表
create [external] table [if not exists] table_name(field) [partitioned by (field)] [clustered by (field)] [sorted by (field)] row format delimited fields terminated by 'split';
# external 删除外部表，却不能删除HDFS数据
（7）查看表类型
desc formatted table_name;
（8）不登录Hive客户端直接命令操作Hive
hive -e SQL
（9）通过文件操作Hive
hive -f filePath
（10）查看HDFS文件
dfs ls path;
dfs cat filePath;
（11）查看历史操作
cat ./hivehistory
（12）单分区查询
select field from table_name where field = value;
（13）联合查询
select field from table_name where field = value union select field from table_name where field = value;
（14）添加单个分区
alter table table_name add partition();
（15）查看分区
show partition table_name;
（16）删除分区
alter table table_name drop partition();
（17）分区表在HDFS中分目录文件夹导入数据修复
msck repair table table_name;
二、DML操作
（1）导入数据load data [local] inpath path into table;
#local 本地Linux数据，无local则是HDFS数据
（2）插入数据
insert into table table_name [partition(field = value)] values();
insert overwrite table table_name partition(field = value) select * from table_name;
create table [if not exists] table_name as select * from table_name;
create table table_name(field) row format delimited fields terminated by split location hdfs_file;
（3）结果导出到Linux
insert overwrite local directory linux_path select * from table_name;
（4）表导出到HDFS
export table table_name to path;
（5）HDFS导入到Hive
import table table_name form path;
（6）清空表数据
truncate table table_name;
（7）求总数
select count(field) from table_name;
（8）求最大/最小值
select max/min(field) from table_name;
（9）求最大/最小值
select max/min(field) from table_name;
（10）求总和/平均
select sum/avg(field) from table_name;
（11）where语句
select * from table_name where field;
（12）is null与is not null
select * from table_name where field is not null;
（13）like语句
select * from table_name where field like string;
'field%' 以field开头
'_field%' 第二位是field
‘%field%’ 含有field
（14）and和or语句
select * from table_name where field1 and [or] field2;
（15）not语句
select * from table_name where field1 not in (value1,value2);
（16）having语句
select * from table_name having count (field) > 1;
（16）having语句
select * from table_name having count (field) > 1;
（17）join语句
select a.field,b.field from table_name a [left] join table_name b on a.field = b.field; #保留左表，右表没有join，就显示null
select a.field,b.field from table_name a right join table_name b on a.field = b.field; #保留右表，左表没有join，就显示null
（18）group by语句
select field from table_name group by field;
（19）order by语句
select * from table_name order by field;
（20）sort by语句（内部排序）
select * from table_name sort by field;
设置reduce个数:set mapreduce.job.reduces = value;
（20）distribute by语句（分区排序）
select * from table_name distribute by field distribute by field;
（20）cluster by语句（分区内部排序）
select * from table_name distribute by field cluster by field;
设置分桶:set hive.enforce.bucketing = true;
设置reduce个数:set mapreduce.job.reduces = -1;
create table table_name(field) clustered by(field) into value buckets row format delimited fields terminated by split;
select * from table_name(bucket 1 out of 2 on id)
三、UDF自定义函数
（1）继承UDF
（2）打包放到lib目录下
（3）create temporary function function as "package"（暂时的）
hive.aux.jars.path
file:///root/hd/hive/lib/jar包的名字