Hive语法及其进阶(二)

1、使用JDBC连接Hive

 1 import java.sql.Connection;  2 import java.sql.DriverManager;  3 import java.sql.PreparedStatement;  4 import java.sql.ResultSet;  5 
 6 public class HiveDemo {  7     public static void main(String[] args) throws Exception {  8         Class.forName("org.apache.hive.jdbc.HiveDriver");  9         //"jdbc:hive2://master:10000/test3"
10         Connection connection = DriverManager.getConnection("jdbc:hive2://master:10000/myhive"); 11         String sql="select * from students"; 12         PreparedStatement ps = connection.prepareStatement(sql); 13         ResultSet rs = ps.executeQuery(); 14         while (rs.next()){ 15             int id = rs.getInt(1); 16             String name = rs.getString(2); 17             int age = rs.getInt(3); 18             String gender = rs.getString(4); 19             String clazz = rs.getString(5); 20             System.out.println(id + "," + name + "," + age + "," + gender + "," + clazz); 21  } 22  rs.close(); 23  ps.close(); 24  connection.close(); 25  } 26 }

2、Hive常用函数

1.关系运算

// 等值比较 = == <=>
// 不等值比较 != <>
// 区间比较： select * from default.students where id between 1500100001 and 1500100010;
// 空值/非空值判断：is null、is not null、nvl()、isnull()

2 .数值计算

取整函数(四舍五入)：round
向上取整：ceil
向下取整：floor

 like、rlike、

（1）查找名字以A开头的员工信息

hive (default)> select * from emp where ename LIKE 'A%';

（2）查找名字中第二个字母为A的员工信息

hive (default)> select * from emp where ename LIKE '_A%';

（3）查找名字中带有A的员工信息

hive (default)> select * from emp where ename  RLIKE '[A]';

.

 3.日期函数

1 select from_unixtime(1610611142,'YYYY/MM/dd HH:mm:ss'); 2unix_timestamp(),获取当前时间的时间戳

3 select from_unixtime(unix_timestamp(),'YYYY/MM/dd HH:mm:ss'); 

4 // '2021年01月14日' -> '2021-01-14' 
6 select from_unixtime(unix_timestamp('2021年01月14日','yyyy年MM月dd日'),'yyyy-MM-dd');
8 select from_unixtime(unix_timestamp("04-2021-16","MM-yyyy-dd"),"yyyy/MM/dd");

4.字符串函数

1）cancat（）字符串拼接 当有空值则为NULL

2）cancat_ws()指定可以指定分隔符，并且会自动忽略NULL

 3)substring字符串的截取

 4)split字符串的切分

 5）explode列转行

 解析json格式的数据

select get_json_object
('{"name":"zhangsan",
"age":18,
"score":[{"course_name":"math","score":100},{"course_name":"english","score":60}]}',
"$.score[0].score");

 6) Hive中的wordcount

create table words( words string )row format delimited fields terminated by '|'; // 数据 hello,java,hello,java,scala,python hbase,hadoop,hadoop,hdfs,hive,hive hbase,hadoop,hadoop,hdfs,hive,hive select word,count(*) from (select explode(split(words,',')) word from words) a group by a.word;

4.开窗函数

##### row_number：无并列排名

分组求TOPN

select * from (select  *, row_number() over(partition by clazz order by score desc)as s from new_score)tt where tt.s<=3;

用法： select xxxx, row_number() over(partition by 分组字段 order by 排序字段 desc) as rn from tb group by xxxx

##### dense_rank：有并列排名，并且依次递增

hive> select  *, row_number() over(partition by clazz order by score desc)as s, > dense_rank()  over(partition by clazz order by score desc)as s from new_score;

##### rank：有并列排名，不依次递增

hive> select  *, row_number() over(partition by clazz order by score desc)as s, > dense_rank()  over(partition by clazz order by score desc), > rank() over(partition by clazz order by score desc)from new_score;

##### percent_rank：(rank的结果-1)/(分区内数据的个数-1)

select *, row_number() over(partition by clazz order by score desc)as s,
> rank() over(partition by clazz order by score desc),
> percent_rank() over(partition by clazz order by score desc)from new_score;

##### cume_dist：计算某个窗口或分区中某个值的累积分布。

select *, row_number() over(partition by clazz order by score desc)as s, > rank() over(partition by clazz order by score desc), > percent_rank() over(partition by clazz order by score desc), > cume_dist() over(partition by clazz order by score desc) from new_score;

> 假定升序排序，则使用以下公式确定累积分布： 小于等于当前值x的行数 / 窗口或partition分区内的总行数。其中，x 等于 order by 子句中指定的列的当前行中的值。

##### NTILE(n)：对分区内数据再分成n组，然后打上组号

##### max、min、avg、count、sum：基于每个partition分区内的数据做对应的计算

5.窗口帧格式
　　格式1:按照行的记录取值

　　　　　　ROWS BETWEEN (UNBOUNDED | [num]) PRECEDING AND ([num] PRECEDING | CURRENT ROW | (UNBOUNDED | [num]) FOLLOWING)

　　格式2:当前所指定值的范围取值

　　　　　　RANGE BETWEEN (UNBOUNDED | [num]) PRECEDING AND ([num] PRECEDING | CURRENT ROW | (UNBOUNDED | [num]) FOLLOWING)
注意:
　　　　UNBOUNDED:无界限

　　　　CURRENT ROW:当前行

rows格式1:前2行+当前行+后两行

　　　　sum(score) over (partition by clazz order by score desc rows between 2 PRECEDING and 2 FOLLOWING)

rows格式2:前记录到最末尾的总和

　　　　sum(score) over (partition by clazz order by score desc rows between CURRENT ROW and UNBOUNDED FOLLOWING)

range格式1: 如果当前值在80,取值就会落在范围在80-2=78和80+2=82组件之内的行

　　　　max(score) over (partition by clazz order by score desc range between 2 PRECEDING and 2 FOLLOWING)