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Hive笔记（三）

2021-09-29 19:31:25 作者：互联网

文章目录

一、Hive 常用函数

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()
// like、rlike、regexp用法

数值计算

取整函数(四舍五入)：round
向上取整：ceil
向下取整：floor

条件函数

if： if(表达式,如果表达式成立的返回值,如果表达式不成立的返回值)

select if(1>0,1,0); 
select if(1>0,if(-1>0,-1,1),0);

COALESCE

select COALESCE(null,'1','2'); // 1 从左往右 一次匹配 直到非空为止
select COALESCE('1',null,'2'); // 1

case when

select  score
        ,case when score>120 then '优秀'
              when score>100 then '良好'
              when score>90 then '及格'
        else '不及格'
        end as pingfen
from default.score limit 20;

select  name
        ,case name when "施笑槐" then "槐ge"
                  when "吕金鹏" then "鹏ge"
                  when "单乐蕊" then "蕊jie"
        else "算了不叫了"
        end as nickname
from default.students limit 10;

注意条件的顺序

日期函数

select from_unixtime(1610611142,'YYYY/MM/dd HH:mm:ss');

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

// '2021年01月14日' -> '2021-01-14'
select from_unixtime(unix_timestamp('2021年01月14日','yyyy年MM月dd日'),'yyyy-MM-dd');
// "04202116" -> "2021/04/16"
select from_unixtime(unix_timestamp("04李2021倩6","MMyyyydd"),"yyyy/MM/dd");

字符串函数

concat('123','456'); // 123456
concat('123','456',null); // NULL

select concat_ws('#','a','b','c'); // a#b#c
select concat_ws('#','a','b','c',NULL); // a#b#c 可以指定分隔符，并且会自动忽略NULL
select concat_ws("|",cast(id as string),name,cast(age as string),gender,clazz) from students limit 10;

select substring("abcdefg",1); // abcdefg HQL中涉及到位置的时候 是从1开始计数
// '2021/01/14' -> '2021-01-14'
select concat_ws("-",substring('2021/01/14',1,4),substring('2021/01/14',6,2),substring('2021/01/14',9,2));

select split("abcde,fgh",","); // ["abcde","fgh"]
select split("a,b,c,d,e,f",",")[2]; // c

select explode(split("abcde,fgh",",")); // abcde
										//  fgh

// 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"); // 100

二、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;

// 结果
hadoop	4
hbase	2
hdfs	2
hello	2
hive	4
java	2
python	1
scala	1

三、Hive 开窗函数 `（重点）`

好像给每一份数据开一扇窗户所以叫开窗函数

在sql中有一类函数叫做聚合函数,例如sum()、avg()、max()等等,这类函数可以将多行数据按照规则聚集为一行,一般来讲聚集后的行数是要少于聚集前的行数的.但是有时我们想要既显示聚集前的数据,又要显示聚集后的数据,这时我们便引入了窗口函数.

测试数据

111,69,class1,department1
112,80,class1,department1
113,74,class1,department1
114,94,class1,department1
115,93,class1,department1
121,74,class2,department1
122,86,class2,department1
123,78,class2,department1
124,70,class2,department1
211,93,class1,department2
212,83,class1,department2
213,94,class1,department2
214,94,class1,department2
215,82,class1,department2
216,74,class1,department2
221,99,class2,department2
222,78,class2,department2
223,74,class2,department2
224,80,class2,department2
225,85,class2,department2

建表语句

create table new_score(
    id  int
    ,score int
    ,clazz string
    ,department string
) row format delimited fields terminated by ",";

row_number：无并列排名

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

rank：有并列排名，不依次递增

percent_rank：(rank的结果-1)/(分区内数据的个数-1)

cume_dist：计算某个窗口或分区中某个值的累积分布。

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

窗口帧：用于从分区中选择指定的多条记录，供窗口函数处理

Hive 提供了两种定义窗口帧的形式：ROWS 和 RANGE。两种类型都需要配置上界和下界。例如，ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW 表示选择分区起始记录到当前记录的所有行；SUM(close) RANGE BETWEEN 100 PRECEDING AND 200 FOLLOWING 则通过 字段差值 来进行选择。如当前行的 close 字段值是 200，那么这个窗口帧的定义就会选择分区中 close 字段值落在 100 至 400 区间的记录。以下是所有可能的窗口帧定义组合。如果没有定义窗口帧，则默认为 RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW。

只能运用在max、min、avg、count、sum、FIRST_VALUE、LAST_VALUE这几个窗口函数上
(ROWS | RANGE) BETWEEN (UNBOUNDED | [num]) PRECEDING AND ([num] PRECEDING | CURRENT ROW | (UNBOUNDED | [num]) FOLLOWING)
(ROWS | RANGE) BETWEEN CURRENT ROW AND (CURRENT ROW | (UNBOUNDED | [num]) FOLLOWING)
(ROWS | RANGE) BETWEEN [num] FOLLOWING AND (UNBOUNDED | [num]) FOLLOWING
range between 3 PRECEDING and 11 FOLLOWING
SELECT id
        ,score
        ,clazz
        ,SUM(score) OVER w as sum_w
        ,round(avg(score) OVER w,3) as avg_w
        ,count(score) OVER w as cnt_w
FROM new_score
WINDOW w AS (PARTITION BY clazz ORDER BY score rows between 2 PRECEDING and 2 FOLLOWING);
111	69	class1	217	72.333	3
113	74	class1	297	74.25	4
216	74	class1	379	75.8	5
112	80	class1	393	78.6	5
215	82	class1	412	82.4	5
212	83	class1	431	86.2	5
211	93	class1	445	89.0	5
115	93	class1	457	91.4	5
213	94	class1	468	93.6	5
114	94	class1	375	93.75	4
214	94	class1	282	94.0	3
124	70	class2	218	72.667	3
121	74	class2	296	74.0	4
223	74	class2	374	74.8	5
222	78	class2	384	76.8	5
123	78	class2	395	79.0	5
224	80	class2	407	81.4	5
225	85	class2	428	85.6	5
122	86	class2	350	87.5	4
221	99	class2	270	90.0	3

select  id
        ,score
        ,clazz
        ,department
        ,row_number() over (partition by clazz order by score desc) as rn_rk
        ,dense_rank() over (partition by clazz order by score desc) as dense_rk
        ,rank() over (partition by clazz order by score desc) as rk
        ,percent_rank() over (partition by clazz order by score desc) as percent_rk
        ,round(cume_dist() over (partition by clazz order by score desc),3) as cume_rk
        ,NTILE(3) over (partition by clazz order by score desc) as ntile_num
        ,max(score) over (partition by clazz order by score desc range between 3 PRECEDING and 11 FOLLOWING) as max_p
from new_score;


id  score   clazz   department  rn_rk  ds_rk  rk  percent_rk  cume_rk ntile_num max_p
114	 94	    class1	department1	  1	     1	   1	  0.0	    0.273	   1	94
214	 94	    class1	department2	  2	     1	   1	  0.0	    0.273	   1	94
213	 94	    class1	department2	  3	     1	   1	  0.0	    0.273	   1	94
211	 93	    class1	department2	  4	     2	   4	  0.3	    0.455	   1	94
115	 93	    class1	department1	  5	     2	   4	  0.3	    0.455	   2	94
212	 83	    class1	department2	  6	     3	   6	  0.5	    0.545	   2	94
215	 82	    class1	department2	  7	     4	   7	  0.6	    0.636	   2	94
112	 80	    class1	department1	  8	     5	   8	  0.7	    0.727	   2	94
113	 74	    class1	department1	  9	     6	   9	  0.8	    0.909	   3	94
216	 74	    class1	department2	  10	 6	   9	  0.8	    0.909	   3	94
111	 69	    class1	department1	  11	 7	   11	  1.0	    1.0        3    94
221	 99	    class2	department2	  1	     1	   1	  0.0	    0.111	   1	99
122	 86	    class2	department1	  2	     2	   2	  0.125	    0.222	   1	99
225	 85	    class2	department2	  3	     3	   3	  0.25	    0.333	   1	99
224	 80	    class2	department2	  4	     4	   4	  0.375	    0.444	   2	99
123	 78	    class2	department1	  5	     5	   5	  0.5	    0.667	   2	99
222	 78	    class2	department2	  6	     5	   5	  0.5	    0.667	   2	99
121	 74	    class2	department1	  7	     6	   7	  0.75	    0.889	   3	99
223	 74	    class2	department2	  8	     6	   7	  0.75	    0.889	   3	99
124	 70	    class2	department1	  9	     7	   9	  1.0	    1.0        3    99

LAG(col,n)：往前第n行数据

LEAD(col,n)：往后第n行数据

FIRST_VALUE：取分组内排序后，截止到当前行，第一个值

LAST_VALUE：取分组内排序后，截止到当前行，最后一个值，对于并列的排名，取最后一个

select  id
        ,score
        ,clazz
        ,department
        ,lag(id,2) over (partition by clazz order by score desc) as lag_num
        ,LEAD(id,2) over (partition by clazz order by score desc) as lead_num
        ,FIRST_VALUE(id) over (partition by clazz order by score desc) as first_v_num
        ,LAST_VALUE(id) over (partition by clazz order by score desc) as last_v_num
        ,NTILE(3) over (partition by clazz order by score desc) as ntile_num
from new_score;


id  score   clazz   department  lag_num lead_num  first_v_num last_v_num  ntile_num
114	 94	    class1	department1	  NULL	   213	    114	          213	      1
214	 94	    class1	department2	  NULL	   211	    114	          213	      1
213	 94	    class1	department2	  114	   115	    114	          213	      1
211	 93	    class1	department2	  214	   212	    114	          115	      1
115	 93	    class1	department1	  213	   215	    114	          115	      2
212	 83	    class1	department2	  211	   112	    114	          212	      2
215	 82	    class1	department2	  115	   113	    114	          215	      2
112	 80	    class1	department1	  212	   216	    114	          112	      2
113	 74	    class1	department1	  215	   111	    114	          216	      3
216	 74	    class1	department2	  112	   NULL	    114	          216	      3
111	 69	    class1	department1	  113	   NULL	    114	          111	      3
221	 99	    class2	department2	  NULL	   225	    221	          221	      1
122	 86	    class2	department1	  NULL	   224	    221	          122	      1
225	 85	    class2	department2	  221	   123	    221	          225	      1
224	 80	    class2	department2	  122	   222	    221	          224	      2
123	 78	    class2	department1	  225	   121	    221	          222	      2
222	 78	    class2	department2	  224	   223	    221	          222	      2
121	 74	    class2	department1	  123	   124	    221	          223	      3
223	 74	    class2	department2	  222	   NULL	    221	          223	      3
124	 70	    class2	department1	  121	   NULL	    221	          124	      3

https://blog.csdn.net/qq_26937525/article/details/54925827

Hive 行转列

lateral view explode

create table testArray2(
    name string,
    weight array<string>
)row format delimited 
fields terminated by '\t'
COLLECTION ITEMS terminated by ',';

志	"150","170","180"
单	"150","180","190"



select name,col1  from testarray2 lateral view explode(weight) t1 as col1;

志	150
志	170
志	180
单	150
单	180
单	190

select key from (select explode(map('key1',1,'key2',2,'key3',3)) as (key,value)) t;

key1
key2
key3

select name,col1,col2  from testarray2 lateral view explode(map('key1',1,'key2',2,'key3',3)) t1 as col1,col2;
志	key1	1
志	key2	2
志	key3	3
单	key1	1
单	key2	2
单	key3	3


select name,pos,col1  from testarray2 lateral view posexplode(weight) t1 as pos,col1;

志	0	150
志	1	170
志	2	180
单	0	150
单	1	180
单	2	190

Hive 列转行

// testLieToLine
name col1
志	150
志	170
志	180
单	150
单	180
单	190

create table testLieToLine(
    name string,
    col1 int
)row format delimited 
fields terminated by '\t';


select name,collect_list(col1) from testLieToLine group by name;

// 结果
单	["150","180","190"]
志	["150","170","180"]

select  t1.name
        ,collect_list(t1.col1) 
from (
    select  name
            ,col1 
    from testarray2 
    lateral view explode(weight) t1 as col1
) t1 group by t1.name;

四、Hive自定义函数UserDefineFunction

UDF：一进一出

创建maven项目，并加入依赖

        <dependency>
            <groupId>org.apache.hive</groupId>
            <artifactId>hive-exec</artifactId>
            <version>2.3.8</version>
        </dependency>

编写代码，继承org.apache.hadoop.hive.ql.exec.UDF，实现evaluate方法，在evaluate方法中实现自己的逻辑

import org.apache.hadoop.hive.ql.exec.UDF;

public class HiveUDF extends UDF {
    // hadoop => #hadoop$
    public String evaluate(String col1) {
    // 给传进来的数据 左边加上 # 号 右边加上 $
        String result = "#" + col1 + "$";
        return result;
    }
}

打成jar包并上传至Linux虚拟机
在hive shell中，使用 add jar 路径将jar包作为资源添加到hive环境中

add jar /usr/local/soft/jars/HiveUDF2-1.0.jar;

使用jar包资源注册一个临时函数，fxxx1是你的函数名，'MyUDF’是主类名

create temporary function fxxx1 as 'MyUDF';

使用函数名处理数据

select fxx1(name) as fxx_name from students limit 10;

#施笑槐$
#吕金鹏$
#单乐蕊$
#葛德曜$
#宣谷芹$
#边昂雄$
#尚孤风$
#符半双$
#沈德昌$
#羿彦昌$

UDTF：一进多出

“key1:value1,key2:value2,key3:value3”

key1 value1

key2 value2

key3 value3

方法一：使用 explode+split

select split(t.col1,":")[0],split(t.col1,":")[1] 
from (select explode(split("key1:value1,key2:value2,key3:value3",",")) as col1) t;

方法二：自定UDTF

代码

import org.apache.hadoop.hive.ql.exec.UDFArgumentException;
import org.apache.hadoop.hive.ql.metadata.HiveException;
import org.apache.hadoop.hive.ql.udf.generic.GenericUDTF;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory;
import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory;

import java.util.ArrayList;

public class HiveUDTF extends GenericUDTF {
    // 指定输出的列名 及 类型
    @Override
    public StructObjectInspector initialize(StructObjectInspector argOIs) throws UDFArgumentException {
        ArrayList<String> filedNames = new ArrayList<String>();
        ArrayList<ObjectInspector> filedObj = new ArrayList<ObjectInspector>();
        filedNames.add("col1");
        filedObj.add(PrimitiveObjectInspectorFactory.javaStringObjectInspector);
        filedNames.add("col2");
        filedObj.add(PrimitiveObjectInspectorFactory.javaStringObjectInspector);
        return ObjectInspectorFactory.getStandardStructObjectInspector(filedNames, filedObj);
    }

    // 处理逻辑 my_udtf(col1,col2,col3)
    // "key1:value1,key2:value2,key3:value3"
    // my_udtf("key1:value1,key2:value2,key3:value3")
    public void process(Object[] objects) throws HiveException {
        // objects 表示传入的N列
        String col = objects[0].toString();
        // key1:value1  key2:value2  key3:value3
        String[] splits = col.split(",");
        for (String str : splits) {
            String[] cols = str.split(":");
            // 将数据输出
            forward(cols);
        }

    }

    // 在UDTF结束时调用
    public void close() throws HiveException {

    }
}

select my_udtf("key1:value1,key2:value2,key3:value3");

字段：id,col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11,col12 共13列

数据：

a,1,2,3,4,5,6,7,8,9,10,11,12

b,11,12,13,14,15,16,17,18,19,20,21,22

c,21,22,23,24,25,26,27,28,29,30,31,32

转成3列：id,hours,value

例如：

a,1,2,3,4,5,6,7,8,9,10,11,12

a,0时,1

a,2时,2

a,4时,3

a,6时,4

…

create table udtfData(
    id string
    ,col1 string
    ,col2 string
    ,col3 string
    ,col4 string
    ,col5 string
    ,col6 string
    ,col7 string
    ,col8 string
    ,col9 string
    ,col10 string
    ,col11 string
    ,col12 string
)row format delimited fields terminated by ',';

代码：

import org.apache.hadoop.hive.ql.exec.UDFArgumentException;
import org.apache.hadoop.hive.ql.metadata.HiveException;
import org.apache.hadoop.hive.ql.udf.generic.GenericUDTF;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory;
import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory;

import java.util.ArrayList;

public class HiveUDTF2 extends GenericUDTF {
    @Override
    public StructObjectInspector initialize(StructObjectInspector argOIs) throws UDFArgumentException {
        ArrayList<String> filedNames = new ArrayList<String>();
        ArrayList<ObjectInspector> fieldObj = new ArrayList<ObjectInspector>();
        filedNames.add("col1");
        fieldObj.add(PrimitiveObjectInspectorFactory.javaStringObjectInspector);
        filedNames.add("col2");
        fieldObj.add(PrimitiveObjectInspectorFactory.javaStringObjectInspector);
        return ObjectInspectorFactory.getStandardStructObjectInspector(filedNames, fieldObj);
    }

    public void process(Object[] objects) throws HiveException {
        int hours = 0;
        for (Object obj : objects) {
            hours = hours + 1;
            String col = obj.toString();
            ArrayList<String> cols = new ArrayList<String>();
            cols.add(hours + "时");
            cols.add(col);
            forward(cols);
        }
    }

    public void close() throws HiveException {

    }
}

添加jar资源:

add jar /usr/local/soft/HiveUDF2-1.0.jar;

注册udtf函数：

create temporary function my_udtf as 'MyUDTF';

SQL:

select id,hours,value from udtfData lateral view my_udtf(col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11,col12) t as hours,value ;

UDAF：多进一出

五、Hive Shell

第一种：

hive -e "select * from test1.students limit 10"

第二种：

hive -f hql文件路径

将HQL写在一个文件里，再使用 -f 参数指定该文件

连续登陆问题

在电商、物流和银行可能经常会遇到这样的需求：统计用户连续交易的总额、连续登陆天数、连续登陆开始和结束时间、间隔天数等

数据：

注意：每个用户每天可能会有多条记录

id	datestr	  amount
1,2019-02-08,6214.23 
1,2019-02-08,6247.32 
1,2019-02-09,85.63 
1,2019-02-09,967.36 
1,2019-02-10,85.69 
1,2019-02-12,769.85 
1,2019-02-13,943.86 
1,2019-02-14,538.42
1,2019-02-15,369.76
1,2019-02-16,369.76
1,2019-02-18,795.15
1,2019-02-19,715.65
1,2019-02-21,537.71
2,2019-02-08,6214.23 
2,2019-02-08,6247.32 
2,2019-02-09,85.63 
2,2019-02-09,967.36 
2,2019-02-10,85.69 
2,2019-02-12,769.85 
2,2019-02-13,943.86 
2,2019-02-14,943.18
2,2019-02-15,369.76
2,2019-02-18,795.15
2,2019-02-19,715.65
2,2019-02-21,537.71
3,2019-02-08,6214.23 
3,2019-02-08,6247.32 
3,2019-02-09,85.63 
3,2019-02-09,967.36 
3,2019-02-10,85.69 
3,2019-02-12,769.85 
3,2019-02-13,943.86 
3,2019-02-14,276.81
3,2019-02-15,369.76
3,2019-02-16,369.76
3,2019-02-18,795.15
3,2019-02-19,715.65
3,2019-02-21,537.71

建表语句

create table deal_tb(
    id string
    ,datestr string
    ,amount string
)row format delimited fields terminated by ',';

计算逻辑

先按用户和日期分组求和，使每个用户每天只有一条数据

 select  id
         ,datestr
         ,sum(amount) as sum_amount
 from deal_tb
 group by id,datestr

根据用户ID分组按日期排序，将日期和分组序号相减得到连续登陆的开始日期，如果开始日期相同说明连续登陆

 select  tt1.id
         ,tt1.datestr
         ,tt1.sum_amount
         ,date_sub(tt1.datestr,rn) as grp
 from(
     select  t1.id
             ,t1.datestr
             ,t1.sum_amount
             ,row_number() over(partition by id order by datestr) as rn
     from(
     	select  id
                 ,datestr
                 ,sum(amount) as sum_amount
        from deal_tb
        group by id,datestr
     ) t1
 ) tt1

统计用户连续交易的总额、连续登陆天数、连续登陆开始和结束时间、间隔天数

select  ttt1.id
        ,ttt1.grp
        ,round(sum(ttt1.sum_amount),2) as sc_sum_amount
        ,count(1) as sc_days
        ,min(ttt1.datestr) as sc_start_date
        ,max(ttt1.datestr) as sc_end_date
        ,datediff(ttt1.grp,lag(ttt1.grp,1) over(partition by ttt1.id order by ttt1.grp)) as iv_days
from(
    select  tt1.id
            ,tt1.datestr
            ,tt1.sum_amount
            ,date_sub(tt1.datestr,rn) as grp
    from(
        select  t1.id
                ,t1.datestr
                ,t1.sum_amount
                ,row_number() over(partition by id order by datestr) as rn
        from(
            select  id
                    ,datestr
                    ,sum(amount) as sum_amount
            from deal_tb
            group by id,datestr
        ) t1
    ) tt1
) ttt1
group by ttt1.id,ttt1.grp;

结果

1	2019-02-07	13600.23	3	2019-02-08	2019-02-10 NULL
1	2019-02-08	2991.650	5	2019-02-12	2019-02-16	1
1	2019-02-09	1510.8		2	2019-02-18	2019-02-19	1
1	2019-02-10	537.71		1	2019-02-21	2019-02-21	1
2	2019-02-07	13600.23	3	2019-02-08	2019-02-10 NULL
2	2019-02-08	3026.649	4	2019-02-12	2019-02-15	1
2	2019-02-10	1510.8		2	2019-02-18	2019-02-19	2
2	2019-02-11	537.71		1	2019-02-21	2019-02-21	1
3	2019-02-07	13600.23	3	2019-02-08	2019-02-10 NULL
3	2019-02-08	2730.04		5	2019-02-12	2019-02-16	1
3	2019-02-09	1510.8		2	2019-02-18	2019-02-19	1
3	2019-02-10	537.71		1	2019-02-21	2019-02-21	1

标签：02,笔记,score,2019,Hive,class2,select,class1
来源： https://blog.csdn.net/qq_43278189/article/details/120554575

Hive笔记（三）

文章目录

一、Hive 常用函数

关系运算

数值计算

条件函数

日期函数

字符串函数

二、Hive 中的wordCount

三、Hive 开窗函数 （重点）

测试数据

建表语句

row_number：无并列排名

dense_rank：有并列排名，并且依次递增

rank：有并列排名，不依次递增

percent_rank：(rank的结果-1)/(分区内数据的个数-1)

cume_dist：计算某个窗口或分区中某个值的累积分布。

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

窗口帧：用于从分区中选择指定的多条记录，供窗口函数处理

LAG(col,n)：往前第n行数据

LEAD(col,n)：往后第n行数据

FIRST_VALUE：取分组内排序后，截止到当前行，第一个值

LAST_VALUE：取分组内排序后，截止到当前行，最后一个值，对于并列的排名，取最后一个

Hive 行转列

Hive 列转行

四、Hive自定义函数UserDefineFunction

UDF：一进一出

UDTF：一进多出

方法一：使用 explode+split

方法二：自定UDTF

UDAF：多进一出

五、Hive Shell

第一种：

第二种：

连续登陆问题

数据：

建表语句

计算逻辑

三、Hive 开窗函数 `（重点）`