Hive高级应用（三）

ゝ一纸荒年。 2022-12-17 07:10 256阅读 0赞

### 目录 ###

*   *  十五、Hive中数据备份
     *  十六、Hive常用的查询
     *  十七、Hive中join表的关联
     *  十八、Hive几种By的使用详解
     *   *  （1）order by
         *  （2）sort by
         *  （3）distribute by
         *  （4）cluster by
     *  十九、Hive中UDF编程详解
     *  二十、通过hiverserver2/beeline连接Hive
     *  二十一、Hive与JDBC的集成

## 十五、Hive中数据备份 ##

1.export导出数据 :

EXPORT TABLE db_hive.order TO '/user/caizhengjie/datas/export/order'

2.import导入数据：

import table order_imp from '/user/caizhengjie/datas/export/order';

IMPORT  TABLE order_imp_1 FROM '/user/caizhengjie/datas/export/order' LOCATION '/user/kfk/datas/imp/order';    --（location）指定数据表目录

## 十六、Hive常用的查询 ##

准备数据：

/opt/datas/hive/order.txt

0001,cenry,2018-10-09,product-1,350,guangzhou
    0002,beny,2018-09-09,product-2,180,beijing
    0003,ben,2018-09-09,product-3,580,beijing
    0004,cherry,2018-10-09,product-4,450,shenzheng
    0005,jone,2018-10-09,product-5,30,nanjing
    0006,lili,2018-09-09,product-6,50,hangzhou
    0007,chen,2018-10-09,product-7,90,wuhan
    0008,wiwi,2018-09-09,product-8,150,chengdu

创建表：

create table order(userid string,username string,order_date string,product_name string,price int,city string) row format delimited fields terminated by ',' stored as textfile;

1.字段查询

select userid,username from order;

userid  username
    0001    cenry
    0002    beny
    0003    ben
    0004    cherry
    0005    jone
    0006    lili
    0007    chen
    0008    wiwi

2.where查询

select * from order where price > 200;

order.userid    order.username  order.order_date        order.product_name      order.price     order.city
    0001    cenry   2018-10-09      product-1       350     guangzhou
    0003    ben     2018-09-09      product-3       580     beijing
    0004    cherry  2018-10-09      product-4       450     shenzheng

3.limit查询

select * from order limit 3;

4.distinct去重

select distinct city from order;

city
    beijing
    chengdu
    guangzhou
    hangzhou
    nanjing
    shenzheng
    wuhan

5.max/min/count/sum

select max(price) from order;

_c0
    580

6.group by / having 分组查询

select sum(price) price,city from order group by city having price > 200;

price   city
    760     beijing
    350     guangzhou
    450     shenzheng

## 十七、Hive中join表的关联 ##

准备数据order1.txt

1,0001,2018-09-09,product-1,180
    2,0001,2018-09-09,product-2,240
    3,0001,2018-09-09,product-3,350
    4,0002,2018-09-09,product-4,110
    5,0002,2018-09-09,product-5,230
    6,0003,2018-09-09,product-6,245
    7,0004,2018-09-09,product-7,965
    8,0004,2018-09-09,product-8,741
    9,0008,2018-09-09,product-8,690
    10,0009,2018-09-09,product-9,120
    11,0009,2018-09-09,product-9,120

customer.txt

0001,jack,17425845325,guangzhou
    0002,beny,15451225965,shenzhen
    0003,jone,13754859326,nanjing
    0004,cherry,13785415255,suzhou
    0005,alex,15745869325,beijing
    0006,aili,13245632581,xiamen

创建order1表：

create table if not exists db_hive.order1(orderid string,userid string,order_date string,product_name string,price string) row format delimited fields terminated by ',' stored as textfile;

创建customer表：

create table if not exists db_hive.customer(userid string,username string,telephone string,city string) row format delimited fields terminated by ',' stored as textfile;

加载数据：

load data local inpath '/opt/datas/hive/order1.txt' into table order1;
    
    load data local inpath '/opt/datas/hive/customer.txt' into table customer;

**1.等值连接**

select * from customer t1,order1 t2 where t1.userid = t2.userid;

t1.userid       t1.username     t1.telephone    t1.city t2.orderid      t2.userid       t2.order_date   t2.product_name t2.price
    0001    jack    17425845325     guangzhou       1       0001    2018-09-09      product-1       180
    0001    jack    17425845325     guangzhou       2       0001    2018-09-09      product-2       240
    0001    jack    17425845325     guangzhou       3       0001    2018-09-09      product-3       350
    0002    beny    15451225965     shenzhen        4       0002    2018-09-09      product-4       110
    0002    beny    15451225965     shenzhen        5       0002    2018-09-09      product-5       230
    0003    jone    13754859326     nanjing 6       0003    2018-09-09      product-6       245
    0004    cherry  13785415255     suzhou  7       0004    2018-09-09      product-7       965
    0004    cherry  13785415255     suzhou  8       0004    2018-09-09      product-8       741

**2.左连接**

select  t1.username,t2.product_name  from customer t1 left join order1 t2 on t1.userid = t2.userid;

t1.username     t2.product_name
    jack    product-1
    jack    product-2
    jack    product-3
    beny    product-4
    beny    product-5
    jone    product-6
    cherry  product-7
    cherry  product-8
    alex    NULL
    aili    NULL

select  t2.username,t1.product_name  from order1 t1 left join customer t2 on t1.userid = t2.userid;

t2.username     t1.product_name
    jack    product-1
    jack    product-2
    jack    product-3
    beny    product-4
    beny    product-5
    jone    product-6
    cherry  product-7
    cherry  product-8
    NULL    product-8
    NULL    product-9
    NULL    product-9

**3.右连接**

select  t1.username,t2.product_name  from customer t1 right join order1 t2 on t1.userid = t2.userid;

t1.username     t2.product_name
    jack    product-1
    jack    product-2
    jack    product-3
    beny    product-4
    beny    product-5
    jone    product-6
    cherry  product-7
    cherry  product-8
    NULL    product-8
    NULL    product-9
    NULL    product-9

select  t2.username,t1.product_name  from order1 t1 right join customer t2 on t1.userid = t2.userid;

t2.username     t1.product_name
    jack    product-1
    jack    product-2
    jack    product-3
    beny    product-4
    beny    product-5
    jone    product-6
    cherry  product-7
    cherry  product-8
    alex    NULL
    aili    NULL

**4.全连接**

select  t2.username,t1.product_name  from order1 t1 full join customer t2 on t1.userid = t2.userid;

t2.username     t1.product_name
    jack    product-3
    jack    product-2
    jack    product-1
    beny    product-5
    beny    product-4
    jone    product-6
    cherry  product-8
    cherry  product-7
    alex    NULL
    aili    NULL
    NULL    product-8
    NULL    product-9
    NULL    product-9

## 十八、Hive几种By的使用详解 ##

order /sort / cluster / distribute by  
准备数据：

order.userid    order.username  order.order_date        order.product_name      order.price     order.city
    0001    cenry   2018-10-09      product-1       350     guangzhou
    0002    beny    2018-09-09      product-2       180     beijing
    0003    ben     2018-09-09      product-3       580     beijing
    0004    cherry  2018-10-09      product-4       450     shenzheng
    0005    jone    2018-10-09      product-5       30      nanjing
    0006    lili    2018-09-09      product-6       50      hangzhou
    0007    chen    2018-10-09      product-7       90      wuhan
    0008    wiwi    2018-09-09      product-8       150     chengdu

### （1）order by ###

**order by (只有一个reduce ,全局排序)**

select * from order order by price desc;
    
    select * from order order by price asc;

order.userid    order.username  order.order_date        order.product_name      order.price     order.city
    0003    ben     2018-09-09      product-3       580     beijing
    0004    cherry  2018-10-09      product-4       450     shenzheng
    0001    cenry   2018-10-09      product-1       350     guangzhou
    0002    beny    2018-09-09      product-2       180     beijing
    0008    wiwi    2018-09-09      product-8       150     chengdu
    0007    chen    2018-10-09      product-7       90      wuhan
    0006    lili    2018-09-09      product-6       50      hangzhou
    0005    jone    2018-10-09      product-5       30      nanjing

设置reduce个数，这里设置为3个

set mapreduce.job.reduces=3;

但是不起作用，运行的时候还是一个。

### （2）sort by ###

每个reduce端都会进行排序，也就是局部有序，可以指定多个reduce。同时，如果想测试一下执行的效果，建议将输出结果保存到本地，并调整reduce的数量。（我设置成3个）

**sort by (对每一个reduce内部的数据进行排序，最后的全局结果集不排序)**  
设置reduce个数，这里设置为3个

set mapreduce.job.reduces=3;

这里的写法是将数据写入到本地

insert overwrite local directory '/opt/datas/hive/sort' row format delimited fields terminated by ',' select * from order sort by price desc;

[caizhengjie@bigdata-pro-m01 sort]$ ls
    000000_0  000001_0  000002_0
    [caizhengjie@bigdata-pro-m01 sort]$ cat 000000_0
    0008,wiwi,2018-09-09,product-8,150,chengdu
    0007,chen,2018-10-09,product-7,90,wuhan
    0006,lili,2018-09-09,product-6,50,hangzhou
    0005,jone,2018-10-09,product-5,30,nanjing

[caizhengjie@bigdata-pro-m01 sort]$ cat 000001_0
    0003,ben,2018-09-09,product-3,580,beijing
    0004,cherry,2018-10-09,product-4,450,shenzheng
    0002,beny,2018-09-09,product-2,180,beijing

[caizhengjie@bigdata-pro-m01 sort]$ cat 000002_0
    0001,cenry,2018-10-09,product-1,350,guangzhou

### （3）distribute by ###

指定分区原则。通常和sort by一起用，distribute by必须要写在sort by之前。理解成：按照XX字段分区，再按照XX字段排序。

**distribute by (作用类似于partition,一般与sort by 一起使用)**  
设置reduce个数，这里设置为3个

set mapreduce.job.reduces=3;

insert overwrite local directory '/opt/datas/hive/sort' row format delimited fields terminated by ',' select * from order distribute by city sort by price desc;

[caizhengjie@bigdata-pro-m01 sort]$ ls
    000000_0  000001_0  000002_0
    [caizhengjie@bigdata-pro-m01 sort]$ cat 000000_0
    0004,cherry,2018-10-09,product-4,450,shenzheng
    0001,cenry,2018-10-09,product-1,350,guangzhou

[caizhengjie@bigdata-pro-m01 sort]$ cat 000001_0
    0003,ben,2018-09-09,product-3,580,beijing
    0002,beny,2018-09-09,product-2,180,beijing
    0007,chen,2018-10-09,product-7,90,wuhan
    0006,lili,2018-09-09,product-6,50,hangzhou
    0005,jone,2018-10-09,product-5,30,nanjing

[caizhengjie@bigdata-pro-m01 sort]$ cat 000002_0
    0008,wiwi,2018-09-09,product-8,150,chengdu

### （4）cluster by ###

当distribute by 和 sort by 所指定的字段相同时，即可以使用cluster by。  
注意:cluster by指定的列只能是升序，不能指定asc和desc。

设置reduce个数，这里设置为3个

set mapreduce.job.reduces=3;

**cluster by (distribute by 和sort by字段相同时，使用cluster by 代替)**

insert overwrite local directory '/opt/datas/hive/sort' row format delimited fields terminated by ',' select * from order distribute by username sort by username;

[caizhengjie@bigdata-pro-m01 sort]$ cat 000000_0
    0003,ben,2018-09-09,product-3,580,beijing
    0007,chen,2018-10-09,product-7,90,wuhan
    0004,cherry,2018-10-09,product-4,450,shenzheng
    0006,lili,2018-09-09,product-6,50,hangzhou

等价于：

insert overwrite local directory '/opt/datas/hive/sort' row format delimited fields terminated by ',' select * from order cluster by username;

## 十九、Hive中UDF编程详解 ##

**1.自定义UDF函数**  
加载pom.xml文件内容：

<dependency>
            <groupId>org.apache.hive</groupId>
            <artifactId>hive-exec</artifactId>
            <version>${hive.version}</version>
    </dependency>

编写udf代码

package com.kfk.hive;
    
    import org.apache.hadoop.hive.ql.exec.UDF;
    import org.apache.hadoop.io.Text;
    
    /** * @author : 蔡政洁 * @email :caizhengjie888@icloud.com * @date : 2020/10/23 * @time : 8:31 下午 */
    public class KfkUDF extends UDF { 
        public Text evaluate(final Text s) { 
            if (s == null) {  return null; }
            return new Text(s.toString().toUpperCase());
        }
    
        public static void main(String[] args) { 
            System.out.println(new KfkUDF().evaluate(new Text("spark")));
        }
    }

**2.打jar包**

**3.添加jar包**

hive (db_hive)> add jar /opt/jars/bigdata_study_udf.jar;
    hive (db_hive)> list jar;

**4.Create Function**

create temporary function kfk_udf  as 'com.kfk.hive.KfkUDF';

5.使用自定义函数

select kfk_udf(city) city from order;

city
    GUANGZHOU
    BEIJING
    BEIJING
    SHENZHENG
    NANJING
    HANGZHOU
    WUHAN
    CHENGDU

有关hive的UDF操作可以见官网：  
[https://cwiki.apache.org/confluence/display/Hive/HivePlugins][https_cwiki.apache.org_confluence_display_Hive_HivePlugins]

## 二十、通过hiverserver2/beeline连接Hive ##

1.启动metastore：

bin/hive --service metastore

2.启动hiverserver2：

bin/hiveserver2

3.启动beeline客户端：

bin/beeline

4.连接hive：

% bin/beeline
    beeline> !connect jdbc:hive2://<host>:<port>/<db>;auth=noSasl hiveuser pass

!connect jdbc:hive2://bigdata-pro-m01:10000 caizhengjie 199911

5.查看数据

1: jdbc:hive2://bigdata-pro-m01:10000> select * from order;
    +---------------+-----------------+-------------------+---------------------+--------------+-------------+--+
    | order.userid  | order.username  | order.order_date  | order.product_name  | order.price  | order.city  |
    +---------------+-----------------+-------------------+---------------------+--------------+-------------+--+
    | 0001          | cenry           | 2018-10-09        | product-1           | 350          | guangzhou   |
    | 0002          | beny            | 2018-09-09        | product-2           | 180          | beijing     |
    | 0003          | ben             | 2018-09-09        | product-3           | 580          | beijing     |
    | 0004          | cherry          | 2018-10-09        | product-4           | 450          | shenzheng   |
    | 0005          | jone            | 2018-10-09        | product-5           | 30           | nanjing     |
    | 0006          | lili            | 2018-09-09        | product-6           | 50           | hangzhou    |
    | 0007          | chen            | 2018-10-09        | product-7           | 90           | wuhan       |
    | 0008          | wiwi            | 2018-09-09        | product-8           | 150          | chengdu     |
    +---------------+-----------------+-------------------+---------------------+--------------+-------------+--+

可以看出来，通过beeline客户端连接的hive比hive本身的命令显示出来的数据格式更加直观。  
具体beeline操作可以详见官网：  
[https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=82903124\#HiveServer2Clients-table][https_cwiki.apache.org_confluence_pages_viewpage.action_pageId_82903124_HiveServer2Clients-table]

## 二十一、Hive与JDBC的集成 ##

1.添加pom.xml配置文件

<dependency>
        <groupId>org.apache.hive</groupId>
        <artifactId>hive-jdbc</artifactId>
        <version>${hive.version}</version>
    </dependency>

2.编写JDBC代码

package com.kfk.hive;
    
    /** * @author : 蔡政洁 * @email :caizhengjie888@icloud.com * @date : 2020/10/23 * @time : 9:53 下午 */
    import java.sql.SQLException;
    import java.sql.Connection;
    import java.sql.ResultSet;
    import java.sql.Statement;
    import java.sql.DriverManager;
    
    public class HiveJDBC { 
        private static String driverName = "org.apache.hive.jdbc.HiveDriver";
    
        /** * @param args * @throws SQLException */
        public static void main(String[] args) throws SQLException { 
            try { 
                Class.forName(driverName);
            } catch (ClassNotFoundException e) { 
                // TODO Auto-generated catch block
                e.printStackTrace();
                System.exit(1);
            }
            //replace "hive" here with the name of the user the queries should run as
            Connection con = DriverManager.getConnection("jdbc:hive2://bigdata-pro-m01:10000/db_hive", "caizhengjie", "199911");
            Statement stmt = con.createStatement();
            String tableName = "order";
    
            ResultSet res = null;
    
            // select * query
            String sql = "select * from " + tableName;
            System.out.println("Running: " + sql);
            res = stmt.executeQuery(sql);
            while (res.next()) { 
                System.out.println(String.valueOf(res.getString(1)) + "\t" + res.getString(2));
            }
        }
    }

运行结果：

Running: select * from order
    0001	cenry
    0002	beny
    0003	ben
    0004	cherry
    0005	jone
    0006	lili
    0007	chen
    0008	wiwi

有关Hive与JDBC的集成操作见官网：  
[https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=82903124\#HiveServer2Clients-UsingJDBC][https_cwiki.apache.org_confluence_pages_viewpage.action_pageId_82903124_HiveServer2Clients-UsingJDBC]

--------------------

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[https_cwiki.apache.org_confluence_display_Hive_HivePlugins]: https://cwiki.apache.org/confluence/display/Hive/HivePlugins
[https_cwiki.apache.org_confluence_pages_viewpage.action_pageId_82903124_HiveServer2Clients-table]: https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=82903124#HiveServer2Clients-table
[https_cwiki.apache.org_confluence_pages_viewpage.action_pageId_82903124_HiveServer2Clients-UsingJDBC]: https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=82903124#HiveServer2Clients-UsingJDBC