Hive| ETL清洗& 查询练习

ETL清洗数据 

导Jar包

<dependencies>
        <dependency>
            <groupId>log4j</groupId>
            <artifactId>log4j</artifactId>
            <version>RELEASE</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.7.2</version>
        </dependency>

    </dependencies>

ETLUtil.java

public class ETLUtil {
    public static String etl(String original){
        StringBuilder stringBuilder = new StringBuilder();
        String[] fields = original.split("	");
        if (fields.length < 9){
            return null;
        }
        //日志合规
        //替换空格
        fields[3] = fields[3].replace(" ", "");
        for (int i = 0; i < fields.length - 1; i++){
            if (i == fields.length - 1){
                stringBuilder.append(fields[i]);

            }else if (i < 9){
                stringBuilder.append(fields[i]).append("	");
            }else {
                stringBuilder.append(fields[i]).append("&");
            }
        }
        return stringBuilder.toString();
    }
}

ETLMapper.java

public class ETLMapper extends Mapper<LongWritable, Text, Text, NullWritable> {
    Text k = new Text();
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String original = value.toString();
        String etlString = ETLUtil.etl(original);
        if (StringUtils.isNotEmpty(etlString)){
            k.set(etlString);
            context.write(k, NullWritable.get());
            context.getCounter("ETL", "True").increment(1);

        }else {
            context.getCounter("ETL", "False").increment(1);
        }
    }
}

ETLDriver.java

public class ETLDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Job job = Job.getInstance(new Configuration());

        job.setJarByClass(ETLDriver.class);
        job.setMapperClass(ETLMapper.class);
        job.setNumReduceTasks(0);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(NullWritable.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(NullWritable.class);

        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        boolean b = job.waitForCompletion(true);
        System.exit(b ? 0 : 1);
    }
}

[kris@hadoop102 hadoop-2.7.2]$ hadoop fs -mkdir -p /guli/user
[kris@hadoop102 hadoop-2.7.2]$ hadoop fs -mkdir /guli/video
[kris@hadoop102 hadoop-2.7.2]$ hadoop fs -mkdir /guli/etl
[kris@hadoop102 datas]$ hadoop fs -moveFromLocal user.txt /guli/user
[kris@hadoop102 datas]$ hadoop fs -moveFromLocal *.txt /guli/video
[kris@hadoop102 hadoop-2.7.2]$ hadoop jar ETLVideo.jar com.atguigu.etl.ETLDriver /guli/video /guli/video_etl
 ETL
                False=5792
                True=743569

创建表：
create external table gulivideo_ori(
    videoId string, 
    uploader string, 
    age int, 
    category array<string>, 
    length int, 
    views int, 
    rate float, 
    ratings int, 
    comments int,
    relatedId array<string>)
row format delimited 
fields terminated by "	"
collection items terminated by "&"
stored as textfile
location '/guli/video_etl';

create external table gulivideo_user_ori(
    uploader string,
    videos int,
    friends int)
row format delimited 
fields terminated by "	" 
stored as textfile
location '/guli/user';


create table gulivideo_orc(
    videoId string, 
    uploader string, 
    age int, 
    category array<string>, 
    length int, 
    views int, 
    rate float, 
    ratings int, 
    comments int,
    relatedId array<string>)
row format delimited fields terminated by "	" 
collection items terminated by "&" 
stored as orc;

create table gulivideo_user_orc(
    uploader string,
    videos int,
    friends int)
row format delimited 
fields terminated by "	" 
stored as orc;

0: jdbc:hive2://hadoop101:10000> insert into table gulivideo_orc select * from gulivideo_ori;
0: jdbc:hive2://hadoop101:10000> insert into table gulivideo_user_orc select * from gulivideo_user_ori;
                

1.--统计视频观看数Top10
select videoid, uploader, views from gulivideo_orc
order by views desc limit 10;
+--------------+------------------+-----------+--+
| videoid | uploader | views |
+--------------+------------------+-----------+--+
| dMH0bHeiRNg | judsonlaipply | 42513417 |
| 0XxI-hvPRRA | smosh | 20282464 |
| 1dmVU08zVpA | NBC | 16087899 |
| RB-wUgnyGv0 | ChrisInScotland | 15712924 |
| QjA5faZF1A8 | guitar90 | 15256922 |
| -_CSo1gOd48 | tasha | 13199833 |
| 49IDp76kjPw | TexMachina | 11970018 |
| tYnn51C3X_w | CowSayingMoo | 11823701 |
| pv5zWaTEVkI | OkGo | 11672017 |
| D2kJZOfq7zk | mrWoot | 11184051 |
+--------------+------------------+-----------+--+
10 rows selected (22.612 seconds)
使用group by的两个要素:
(1) 出现在select后面的字段 要么是是聚合函数中的,要么就是group by 中的.
(2) 要筛选结果 可以先使用where 再用group by 或者先用group by 再用having



--2.统计视频类别热度Top10 (类别的videoid--视频的唯一id越多就代表热度高, 类别排序的多少排序；不能分组分组是在组内排序)
①统计视频类别：
select videoid, categories from gulivideo_orc lateral view explode(category) tbl as categories
②按类别的热度排名
select t1.videoid, t1.categories, count(videoid) num from (select videoid, categories from gulivideo_orc lateral view explode(category) tbl as categories) t1 
group by t1.categories order by num desc limit 10;
--->拼一块：t1.videoid不能出现在select后边，
select t1.categories, count(videoid) num from (select videoid, categories from gulivideo_orc lateral view explode(category) tbl as categories) t1 
group by t1.categories order by num desc limit 10;

+----------------+---------+--+
| t1.categories | num |
+----------------+---------+--+
| Music | 179049 |
| Entertainment | 127674 |
| Comedy | 87818 |
| Animation | 73293 |
| Film | 73293 |
| Sports | 67329 |
| Gadgets | 59817 |
| Games | 59817 |
| Blogs | 48890 |
| People | 48890 |
+----------------+---------+--+
10 rows selected (70.01 seconds)

3.--统计出视频观看数最高的20个视频的所属类别以及类别包含Top20视频的个数  //所有类别中包含Top20视频的个数

//Expression not in GROUP BY key 'videoid'
not in GROUP BY key 'views'，后边有views，select后必须加views
############
①观看数最高的20个视频:
select videoid, category, views from gulivideo_orc order by views desc limit 20
②把类别category炸开--所属类别
select videoid, categories, views from t1 lateral view explode(category) tbl categories 
--->前两句合起：
select t1.videoid, categories, t1.views from (select videoid, category, views from gulivideo_orc order by views desc limit 20
) t1 lateral view explode(category) tbl as categories;
+--------------+----------------+-----------+--+
|  t1.videoid  |   categories   | t1.views  |
+--------------+----------------+-----------+--+
| dMH0bHeiRNg  | Comedy         | 42513417  |
| 0XxI-hvPRRA  | Comedy         | 20282464  |
| 1dmVU08zVpA  | Entertainment  | 16087899  |
| RB-wUgnyGv0  | Entertainment  | 15712924  |
| QjA5faZF1A8  | Music          | 15256922  |
| -_CSo1gOd48  | People         | 13199833  |
| -_CSo1gOd48  | Blogs          | 13199833  |
| 49IDp76kjPw  | Comedy         | 11970018  |
| tYnn51C3X_w  | Music          | 11823701  |
| pv5zWaTEVkI  | Music          | 11672017  |
| D2kJZOfq7zk  | People         | 11184051  |
| D2kJZOfq7zk  | Blogs          | 11184051  |
| vr3x_RRJdd4  | Entertainment  | 10786529  |
| lsO6D1rwrKc  | Entertainment  | 10334975  |
| 5P6UU6m3cqk  | Comedy         | 10107491  |
| 8bbTtPL1jRs  | Music          | 9579911   |
| _BuRwH59oAo  | Comedy         | 9566609   |
| aRNzWyD7C9o  | UNA            | 8825788   |
| UMf40daefsI  | Music          | 7533070   |
| ixsZy2425eY  | Entertainment  | 7456875   |
| MNxwAU_xAMk  | Comedy         | 7066676   |
| RUCZJVJ_M8o  | Entertainment  | 6952767   |
+--------------+----------------+-----------+--+
③类别中包含top20的视频的个数：在上条基础上加上按类别分组，计数组内videoid计数
--->
select categories, count(videoid) from (select videoid, category, views from gulivideo_orc order by views desc limit 20
) t1 lateral view explode(category) tbl as categories group by categories 
+----------------+------+--+
|   categories   | _c1  |
+----------------+------+--+
| Blogs          | 2    |
| Comedy         | 6    |
| Entertainment  | 6    |
| Music          | 5    |
| People         | 2    |
| UNA            | 1    |
+----------------+------

-- over里边不能使用limit, 怎么获取分区排序前几个呢？需要使用一个子查询;分区是数据存储上的分子文件，查询时还是在一张表
select t1.videoid, t1.views, t1.ran, t1.categories from(
select videoid, views, categories, rank() over(partition by categories order by views desc) ran
from gulivideo_orc lateral view explode(category) tbl as categories) t1
where t1.ran <= 5;
+--------------+-----------+---------+----------------+--+
|  t1.videoid  | t1.views  | t1.ran  | t1.categories  |
+--------------+-----------+---------+----------------+--+
| 2GWPOPSXGYI  | 3660009   | 1       | Animals        |
| xmsV9R8FsDA  | 3164582   | 2       | Animals        |
| 12PsUW-8ge4  | 3133523   | 3       | Animals        |
| OeNggIGSKH8  | 2457750   | 4       | Animals        |
| WofFb_eOxxA  | 2075728   | 5       | Animals        |
| sdUUx5FdySs  | 5840839   | 1       | Animation      |
| 6B26asyGKDo  | 5147533   | 2       | Animation      |
| H20dhY01Xjk  | 3772116   | 3       | Animation      |
| 55YYaJIrmzo  | 3356163   | 4       | Animation      |
| JzqumbhfxRo  | 3230774   | 5       | Animation      |
| RjrEQaG5jPM  | 2803140   | 1       | Autos    
......

4.--统计视频观看数Top50所关联视频的所属类别排序
Top50---relatedid---种类---; 炸开之后直接join，因它是张虚拟表，hive是不支持的
select videoid, views, relatedid from gulivideo_orc order by views desc limit 50
炸开单独写一个sql： t1 select distinct(tbl.relatedids) rid from t1 lateral view explode(relatedid) tbl as relatedids
自己join自己下： t2 select g.videoid, g.category from t2 left join gulivideo_orc g on t2.vid=g.videoid
把category炸开并排序：select cateegories, count(videoid) hot from t3 lateral view explode(category) tb12 as catogories group by categores order by hot desc;


select categories, count(videoid) hot
from(select g.videoid, g.category
from(select distinct(tbl.relatedids) rid
from(select videoid, views, relatedid
from gulivideo_orc order by views desc limit 50) t1 lateral view explode(relatedid) tbl as relatedids) t2
join gulivideo_orc g on t2.rid=g.videoid) t3 lateral view explode(category) tbl2 as categories
group by categories order by hot desc;
+----------------+------+--+
|   categories   | hot  |
+----------------+------+--+
| Comedy         | 217  |
| Entertainment  | 207  |
| Music          | 186  |
| Blogs          | 49   |
| People         | 49   |
| Film           | 46   |
| Animation      | 46   |
| News           | 21   |
| Politics       | 21   |
| Games          | 19   |
| Gadgets        | 19   |
| Sports         | 17   |
| Places         | 12   |
| UNA            | 12   |
| Travel         | 12   |
| Howto          | 12   |
| DIY            | 12   |
| Animals        | 11   |
| Pets           | 11   |
| Autos          | 3    |
| Vehicles       | 3    |
+----------------+------+--+
21 rows selected (115.239 seconds)

5.--统计每个类别中的视频热度Top10,以Music为例
创建类别表：
create table gulivideo_category(
videoid string, uploader string, age int, categoryid string, length int, views int, rate float,
ratings int, comments int, relatedid array<string>)
row format delimited fields terminated by "	"
collection items terminated by "&"
stored as orc;
插入数据：
insert into table gulivideo_category
select videoid, uploader, age, categoryid, length, views, rate, ratings, comments, relatedid
from gulivideo_orc lateral view explode(category) category as categoryid;
--->把一张表全查出来：
select categoryid, videoid, paiming from (
select categoryid, videoid, rank() over(partition by categoryid order by views desc) paiming from gulivideo_category) t1
where t1.paiming <= 10;


select categoryid, videoid, views
from gulivideo_category
where categoryid="music"
order by views desc limit 10;



6.--统计每个类别中视频流量Top10,以Music为例
select videoid, ratings
from gulivideo_category
where categoryid="music"
order by ratings desc limit 10;


7.--统计上传视频最多的用户Top10以及他们上传的观看次数在前20的视频

①上传视频最多的用户Top10:
select videos,uploader
from gulivideo_user_orc
order by videos desc limit 10;
②找出这10个人上传的视频
select g.videoid, rank() over(partition by g.uploader order by g.views desc) hot from t1 join gulivideo_orc g on t1.uploader = g.uploader
③找出前20
select t2.uploader, t2.videoid from t2 where t2.hot <= 20;
select t2.uploader, t2.videoid from(
select g.uploader, g.videoid, g.views, rank() over(partition by g.uploader order by g.views desc) hot from 
(select uploader,videos
from gulivideo_user_orc
order by videos desc limit 10) t1
left join gulivideo_orc g on t1.uploader=g.uploader) t2
where t2.hot <= 20;
 +----------------+--------------+--+
|  t2.uploader   |  t2.videoid  |
+----------------+--------------+--+
| NULL           | NULL         |
| NULL           | NULL         |
| NULL           | NULL         |
| NULL           | NULL         |
| Ruchaneewan    | xbYyjUdhtJw  |
| Ruchaneewan    | 4dkKeIUkN7E  |
| Ruchaneewan    | qCfuQA6N4K0  |
| Ruchaneewan    | TmYbGQaRcNM  |
| Ruchaneewan    | dOlfPsFSjw0  |
| expertvillage  | -IxHBW0YpZw  |
| expertvillage  | BU-fT5XI_8I  |
| expertvillage  | ADOcaBYbMl0  |
...

8.--统计每个类别视频观看数Top10

select t.categoryid, t.videoid, t.ranking
from(
select categoryid, videoid, rank() over(partition by categoryid order by views desc) ranking
from gulivideo_category) t
where t.ranking <= 10;


+----------------+--------------+------------+--+
|  t.categoryid  |  t.videoid   | t.ranking  |
+----------------+--------------+------------+--+
| Animals        | 2GWPOPSXGYI  | 1          |
| Animals        | xmsV9R8FsDA  | 2          |
| Animals        | 12PsUW-8ge4  | 3          |
| Animals        | OeNggIGSKH8  | 4          |
| Animals        | WofFb_eOxxA  | 5          |
| Animals        | AgEmZ39EtFk  | 6          |
| Animals        | a-gW3RbJd8U  | 7          |
| Animals        | 8CL2hetqpfg  | 8          |
| Animals        | QmroaYVD_so  | 9          |
| Animals        | Sg9x5mUjbH8  | 10         |
| Animation      | sdUUx5FdySs  | 1          |
| Animation      | 6B26asyGKDo  | 2          |
| Animation      | H20dhY01Xjk  | 3          |
| Animation      | 55YYaJIrmzo  | 4          |
| Animation      | JzqumbhfxRo  | 5          |
| Animation      | eAhfZUZiwSE  | 6          |
| Animation      | h7svw0m-wO0  | 7          |
| Animation      | tAq3hWBlalU  | 8          |
| Animation      | AJzU3NjDikY  | 9          |
| Animation      | ElrldD02if0  | 10         |
| Autos          | RjrEQaG5jPM  | 1          |
......
210 rows selected (24.379 seconds)

1.分组TOPN选出今年每个学校,每个年级,每个科目分数前三.

: 时间,学校,年级,姓名,科目,成绩

建表

create external table score_test(school string, grade string, name string, subject string, score int)
partitioned by (year string)
row format delimited fields terminated by ','
stored as textfile
location '/hive_data';
stored as textfile  ##把它放后边报错

select t1.name, t1.subject, t1.ran from(select name, subject, row_number() over(partition by school, grade, subject order by score desc) ran
from score_test where year="2013") t1 where t1.ran <= 3;

2. 今年 清华 1年级 总成绩大于200分的学生 以及学生数 ||多个字段的group by,还要按name分

select school, grade, name, sum(score) sum_score, count(1) over() num from score_test
where year = "2013" and school="清华" and grade="1"
group by school, grade, name having sum_score > 200;

3. 
CREATE TABLE transaction_details (cust_id INT, amount FLOAT, month STRING, country STRING) 
ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘,’ ; 
建按月份的分区表:
create table transaction_details(cust_id int, amount float, month string, country string)
partitioned by(month string)
row format delimited fields terminated by ',';

每个月的总收入:
select cust_id, sum(amount) over(partition by month) as total from transaction_details;

4. 将内部表a，转换成外部表：
alter tale a set tblproperties ('external'='true');

5.订单详情表ord_det(order_id订单号，sku_id商品编号，sale_qtty销售数量，dt日期分区)
任务计算2016年1月1日商品销量的Top100，并按销量降级排序
select order_id, sale_qtty from ord_det 
where dt = "20160101" order by sale_qtty desc limit 100; 

STG.ORDER，有如下字段:Date，Order_id，User_id，amount。请给出sql进行统计:
数据样例:2017-01-01,10029028,1000003251,33.57
1) 给出2017年每个月的订单数、用户数、总成交金额
一个分区中的数据肯定很大，不要用distinct，用group by user_id做一个子查询再count(user_id)
select count(user_id) from (select user_id from stg.order group by user_id);
select count(order_id) order_count, count(distinct(user_id)) user_count, sum(amount) all, substring(date, 1, 7) month from stg.order 
where substring(date, 1, 4)='2017' 
group by month;

2) 给出2017年11月的新客数(指在11月才有第一笔订单)。
select count(1) from
(select order_id, lag(date, 1) over(partition by user_id order by date) fistOrder from stg.order) t1
where substring(date, 1, 7) = '2017-11' and fistOrder is null;

蚂蚁森林植物申领统计
===================================================================
表1:user_low_carbon表记录了用户每天的蚂蚁森林低碳生活领取的记录流水
user_id（int）   data_dt（string）     low_carbon
用户             日期                  减少碳排放（g）

数据样例：
user_id  |   date_dt   |  low_carbon
————————————————————
u_001    |   2017/1/1  |  10
u_001    |   2017/1/2  |  150
u_001    |   2017/1/2  |  110
u_001    |   2017/1/2  |  10
u_001    |   2017/1/4  |  50
u_001    |   2017/1/4  |  10
u_001    |   2017/1/6  |  45
u_001    |   2017/1/6  |  90
u_002    |   2017/1/1  |  10
u_002    |   2017/1/2  |  150
u_002    |   2017/1/2  |  70
u_002    |   2017/1/3  |  30
u_002    |   2017/1/3  |  80
u_002    |   2017/1/4  |  150
u_002    |   2017/1/5  |  101
u_002    |   2017/1/6  |  68

================================================================
表2:plant_carbon表，用于记录申领环保植物所需要减少的碳排放量
  
plant_id（int）    plant_name    low_carbon
植物编号            植物名        换购植物所需要的碳

数据样例：
plant_id  |  plant_name  |  plant_carbon
————————————————————
p001      |  梭梭树      |  17
p002      |  沙柳        |  19
p003      |  樟子树      |  146
p004      |  胡杨        |  215
================================================================
题目一
蚂蚁森林植物申领统计
问题：假设2017年1月1日开始记录低碳数据（user_low_carbon），假设2017年10月1日之前满足申领条件的用户都申领了一颗“p004-胡杨”，
剩余的能量全部用来领取“p002-沙柳”。
统计在10月1日累计申领“p002-沙柳” 排名前10的用户信息；以及他比后一名多领了几颗沙柳。
得到的统计结果如下表样式：
user_id  plant_count less_count(比后一名多领了几颗沙柳)
u_101    1000         100
u_088    900          400
u_103    500          …

1.累计能量排名前10的用户信息,取日期在10月1日之前的|按用户id分组|总能量排序|取-过滤前11：
t1
select user_id, sum(low_carbon) sum_carbon from user_low_carbon 
where datediff(regexp_replace("2017/10/1", "/", "-"), regexp_replace(date_dt, "/", "-")) > 0
group by user_id order by sum_carbon desc limit 11;
2.胡杨的能量
t2
select plant_carbon huyang from plant_carbon where plant_name = "胡杨";
3.杨柳的能量
t3
select plant_carbon yangliu from plant_carbon where plant_name = "杨柳";
4.能领取的杨柳个数num
t4
select floor((sum_carbon-huyang)/yangliu) plant_count from t1, t2, t3;

5.他比后一名的人多领取的数，用lead往后第n行数据,把它们做比较的放在同1行;
t5
select lead(sum_carbon, 1, 0) over(sort by sum_carbon desc) plant_count2 from t4;

6.做比较
select user_id,plant_count, (plant_count - plant_count2) less_count from t5 limit10;


=================================================================
题目二
蚂蚁森林低碳用户排名分析
问题：查询user_low_carbon表中每日流水记录，条件为：
用户在2017年，连续三天（或以上）的天数里，
每天减少碳排放（low_carbon）都超过100g的用户低碳流水。
需要查询返回满足以上条件的user_low_carbon表中的记录流水。
例如用户u_002符合条件的记录如下，因为2017/1/2~2017/1/5连续四天的碳排放量之和都大于等于100g：
seq（key） user_id data_dt  low_carbon
xxxxx10    u_002  2017/1/2  150
xxxxx11    u_002  2017/1/2  70
xxxxx12    u_002  2017/1/3  30
xxxxx13    u_002  2017/1/3  80
xxxxx14    u_002  2017/1/4  150
xxxxx14    u_002  2017/1/5  101


1.过滤用户在2007年中，碳排放量超过100g能量的用户;
按在2007年的| 用户id、日期(因为不同行有可能是一个日期)进行分组| 选择每天的能量>100的;
t1
select user_id, date_dt, sum(low_carbon) sum_day from user_low_carbon where substring(data_dt, 1, 4) year = "2017"
group by user_id, data_dt order by user_id, data_dt having sum_day > 100

2.每条数据的日期以及前两条和后两条数据的日期
t2
select user_id,
data_dt, 
lag(data_dt, 2, "2000/1/1") over(partition by user_id) lag_2,
lag(data_dt, 1, "2000/1/1") over(partition by user_id) lag_1,
lead(data_dt, 2, "2000/1/1") over(partition by user_id) lead_2,
lead(data_dt, 1, "2000/1/1") over(partition by user_id) lead_1
from t1;
3.计算当前日期与前后两条数据的日期差
t3:
select user_id, data_dt
datediff(regexp_replace("data_dt", "/", "-"), regexp_replace("lag_2", "/", "-")) lag2,
datediff(regexp_replace("data_dt", "/", "-"), regexp_replace("lag_1", "/", "-")) lag1,
datediff(regexp_replace("data_dt", "/", "-"), regexp_replace("lead_2"), "/", "-") lead2,
datediff(regexp_replace("data_dt", "/", "-"), regexp_replace("lead_1", "/", "-")) lead1
from t2;
 
4.连续3天有三种情况：
    ①当前日期和前一天日期差为1，当前日期和前两天的日期差为2;
    ②当前日期和前一天日期差为1，当前日期和后一天的日期差为-1;
    ③当前日期和后一天的日期差为-1，当前日期和后两天的日期差为-2;
t4:    
select user_id, data_dt from t3
where (lag1=1 and lag2=2) or (lag1=1 and lead1=-1) or (lead1=-1 and lead2=-2);

5.最后的结果
select t5.user_id, t5.data_dt, t5.low_carbon from user_low_carbon t5 
inner join t4 on t4.user_id = t5.user_id 
where t4.user_id = t5.user_id and t4.data_dt = t5.date_dt;


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注：
涉及到的hive函数
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1:regexp_replace（arg1,arg2,arg3）
    arg1:被替换字符串的正则表达式
    arg2:被替换的字符
    arg3:被换成的字符
e.g. ：regexp_replace("2017/1/4","/","-")=2017-1-4
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2:datediff(arg1,arg2)
    arg1:日期1
    arg2:日期2
e.g.：datediff("2017-1-6","2017-1-5")=1