> Apache Pig中文手册 > Apache Pig Join运算符

JOIN 运算符用于组合来自两个或多个关系的记录。在执行连接操作时,我们从每个关系中声明一个(或一组)元组作为key。 当这些key匹配时,两个特定的元组匹配,否则记录将被丢弃。连接可以是以下类型:

  • Self-join
  • Inner-join
  • Outer-join − left join, right join, and full join

本章介绍了如何在Pig Latin中使用join运算符的示例。假设在HDFS的 /pig_data/ 目录中有两个文件,即 customers.txt orders.txt ,如下所示。

customers.txt

1,Ramesh,32,Ahmedabad,2000.00
2,Khilan,25,Delhi,1500.00
3,kaushik,23,Kota,2000.00
4,Chaitali,25,Mumbai,6500.00 
5,Hardik,27,Bhopal,8500.00
6,Komal,22,MP,4500.00
7,Muffy,24,Indore,10000.00

orders.txt

102,2009-10-08 00:00:00,3,3000
100,2009-10-08 00:00:00,3,1500
101,2009-11-20 00:00:00,2,1560
103,2008-05-20 00:00:00,4,2060

我们将这两个文件 customers 和 orders 关系一起加载到Pig中,如下所示。

grunt> customers = LOAD 'hdfs://localhost:9000/pig_data/customers.txt' USING PigStorage(',')
   as (id:int, name:chararray, age:int, address:chararray, salary:int);
  
grunt> orders = LOAD 'hdfs://localhost:9000/pig_data/orders.txt' USING PigStorage(',')
   as (oid:int, date:chararray, customer_id:int, amount:int);

现在让我们对这两个关系执行各种连接操作。

Self-join(自连接)

Self-join 用于将表与其自身连接,就像表是两个关系一样,临时重命名至少一个关系。通常,在Apache Pig中,为了执行self-join,我们将在不同的别名(名称)下多次加载相同的数据。那么,将文件 customers.txt 的内容加载为两个表,如下所示。

grunt> customers1 = LOAD 'hdfs://localhost:9000/pig_data/customers.txt' USING PigStorage(',')
   as (id:int, name:chararray, age:int, address:chararray, salary:int);
  
grunt> customers2 = LOAD 'hdfs://localhost:9000/pig_data/customers.txt' USING PigStorage(',')
   as (id:int, name:chararray, age:int, address:chararray, salary:int); 

语法

下面给出使用 JOIN 运算符执行self-join操作的语法。

grunt> Relation3_name = JOIN Relation1_name BY key, Relation2_name BY key ;

通过如图所示加入两个关系 customers1 customers2 ,对关系 customers 执行self-join 操作

grunt> customers3 = JOIN customers1 BY id, customers2 BY id;

验证

使用 DUMP 运算符验证关系 customers3 ,如下所示。

grunt> Dump customers3;

输出

将产生以下输出,显示关系 customers 的内容。

(1,Ramesh,32,Ahmedabad,2000,1,Ramesh,32,Ahmedabad,2000)
(2,Khilan,25,Delhi,1500,2,Khilan,25,Delhi,1500)
(3,kaushik,23,Kota,2000,3,kaushik,23,Kota,2000)
(4,Chaitali,25,Mumbai,6500,4,Chaitali,25,Mumbai,6500)
(5,Hardik,27,Bhopal,8500,5,Hardik,27,Bhopal,8500)
(6,Komal,22,MP,4500,6,Komal,22,MP,4500)
(7,Muffy,24,Indore,10000,7,Muffy,24,Indore,10000)

Inner Join(内部连接)

Inner Join使用较为频繁;它也被称为等值连接当两个表中都存在匹配时,内部连接将返回行。基于连接谓词(join-predicate),通过组合两个关系(例如A和B)的列值来创建新关系。查询将A的每一行与B的每一行进行比较,以查找满足连接谓词的所有行对。当连接谓词被满足时,A和B的每个匹配的行对的列值被组合成结果行。

语法

以下是使用 JOIN 运算符执行inner join操作的语法。

grunt> result = JOIN relation1 BY columnname, relation2 BY columnname;

让我们对customersorders执行inner join操作,如下所示。

grunt> coustomer_orders = JOIN customers BY id, orders BY customer_id;

验证

使用 DUMP 运算符验证 coustomer_orders 关系,如下所示。

grunt> Dump coustomer_orders;

输出

将获得以下输出,是名为 coustomer_orders 的关系的内容。

(2,Khilan,25,Delhi,1500,101,2009-11-20 00:00:00,2,1560)
(3,kaushik,23,Kota,2000,100,2009-10-08 00:00:00,3,1500)
(3,kaushik,23,Kota,2000,102,2009-10-08 00:00:00,3,3000)
(4,Chaitali,25,Mumbai,6500,103,2008-05-20 00:00:00,4,2060)

注意

Outer Join:与inner join不同,outer join返回至少一个关系中的所有行。outer join操作以三种方式执行:

  • Left outer join
  • Right outer join
  • Full outer join

Left Outer Join(左外连接)

left outer join操作返回左表中的所有行,即使右边的关系中没有匹配项

语法

下面给出使用 JOIN 运算符执行left outer join操作的语法。

grunt> Relation3_name = JOIN Relation1_name BY id LEFT OUTER, Relation2_name BY customer_id;

让我们对customers和orders的两个关系执行left outer join操作,如下所示。

grunt> outer_left = JOIN customers BY id LEFT OUTER, orders BY customer_id;

验证

使用 DUMP 运算符验证关系 outer_left ,如下所示。

grunt> Dump outer_left;

输出

它将产生以下输出,显示关系 outer_left 的内容。

(1,Ramesh,32,Ahmedabad,2000,,,,)
(2,Khilan,25,Delhi,1500,101,2009-11-20 00:00:00,2,1560)
(3,kaushik,23,Kota,2000,100,2009-10-08 00:00:00,3,1500)
(3,kaushik,23,Kota,2000,102,2009-10-08 00:00:00,3,3000)
(4,Chaitali,25,Mumbai,6500,103,2008-05-20 00:00:00,4,2060)
(5,Hardik,27,Bhopal,8500,,,,)
(6,Komal,22,MP,4500,,,,)
(7,Muffy,24,Indore,10000,,,,) 

Right Outer Join(右外连接)

right outer join操作将返回右表中的所有行,即使左表中没有匹配项。

语法

下面给出使用 JOIN 运算符执行right outer join操作的语法。

grunt> outer_right = JOIN customers BY id RIGHT, orders BY customer_id;

让我们对customersorders执行right outer join操作,如下所示。

grunt> outer_right = JOIN customers BY id RIGHT, orders BY customer_id;

验证

使用 DUMP 运算符验证关系 outer_right ,如下所示。

grunt> Dump outer_right

输出

它将产生以下输出,显示关系 outer_right 的内容。

(2,Khilan,25,Delhi,1500,101,2009-11-20 00:00:00,2,1560)
(3,kaushik,23,Kota,2000,100,2009-10-08 00:00:00,3,1500)
(3,kaushik,23,Kota,2000,102,2009-10-08 00:00:00,3,3000)
(4,Chaitali,25,Mumbai,6500,103,2008-05-20 00:00:00,4,2060)

Full Outer Join(全外连接)

当一个关系中存在匹配时,full outer join操作将返回行。

语法

下面给出使用 JOIN 运算符执行full outer join的语法。

grunt> outer_full = JOIN customers BY id FULL OUTER, orders BY customer_id;

让我们对customersorders执行full outer join操作,如下所示。

grunt> outer_full = JOIN customers BY id FULL OUTER, orders BY customer_id;

验证

使用 DUMP 运算符验证关系 outer_full ,如下所示。

grun> Dump outer_full; 

输出

它将产生以下输出,显示关系 outer_full 的内容。

(1,Ramesh,32,Ahmedabad,2000,,,,)
(2,Khilan,25,Delhi,1500,101,2009-11-20 00:00:00,2,1560)
(3,kaushik,23,Kota,2000,100,2009-10-08 00:00:00,3,1500)
(3,kaushik,23,Kota,2000,102,2009-10-08 00:00:00,3,3000)
(4,Chaitali,25,Mumbai,6500,103,2008-05-20 00:00:00,4,2060)
(5,Hardik,27,Bhopal,8500,,,,)
(6,Komal,22,MP,4500,,,,)
(7,Muffy,24,Indore,10000,,,,)

使用多个Key

我们可以使用多个key执行JOIN操作。

语法

下面是如何使用多个key对两个表执行JOIN操作。

grunt> Relation3_name = JOIN Relation2_name BY (key1, key2), Relation3_name BY (key1, key2);

假设在HDFS的 /pig_data/ 目录中有两个文件,即 employee.txt employee_contact.txt ,如下所示。

employee.txt

001,Rajiv,Reddy,21,programmer,003
002,siddarth,Battacharya,22,programmer,003
003,Rajesh,Khanna,22,programmer,003
004,Preethi,Agarwal,21,programmer,003
005,Trupthi,MohAnthy,23,programmer,003
006,Archana,Mishra,23,programmer,003
007,Komal,Nayak,24,teamlead,002
008,Bharathi,Nambiayar,24,manager,001

employee_contact.txt

001,9848022337,Rajiv@gmail.com,Hyderabad,003
002,9848022338,siddarth@gmail.com,Kolkata,003
003,9848022339,Rajesh@gmail.com,Delhi,003
004,9848022330,Preethi@gmail.com,Pune,003
005,9848022336,Trupthi@gmail.com,Bhuwaneshwar,003
006,9848022335,Archana@gmail.com,Chennai,003
007,9848022334,Komal@gmail.com,trivendram,002
008,9848022333,Bharathi@gmail.com,Chennai,001

将这两个文件加载到Pig中,通过关系 employee employee_contact ,如下所示。

grunt> employee = LOAD 'hdfs://localhost:9000/pig_data/employee.txt' USING PigStorage(',')
   as (id:int, firstname:chararray, lastname:chararray, age:int, designation:chararray, jobid:int);
  
grunt> employee_contact = LOAD 'hdfs://localhost:9000/pig_data/employee_contact.txt' USING PigStorage(',') 
   as (id:int, phone:chararray, email:chararray, city:chararray, jobid:int);

现在,让我们使用 JOIN 运算符连接这两个关系的内容,如下所示。

grunt> emp = JOIN employee BY (id,jobid), employee_contact BY (id,jobid);

验证

使用 DUMP 运算符验证关系 emp ,如下所示。

grunt> Dump emp; 

输出

它将产生以下输出,显示名为 emp 的关系的内容,如下所示。

(1,Rajiv,Reddy,21,programmer,113,1,9848022337,Rajiv@gmail.com,Hyderabad,113)
(2,siddarth,Battacharya,22,programmer,113,2,9848022338,siddarth@gmail.com,Kolka ta,113)  
(3,Rajesh,Khanna,22,programmer,113,3,9848022339,Rajesh@gmail.com,Delhi,113)  
(4,Preethi,Agarwal,21,programmer,113,4,9848022330,Preethi@gmail.com,Pune,113)  
(5,Trupthi,Mohanthy,23,programmer,113,5,9848022336,Trupthi@gmail.com,Bhuwaneshw ar,113)  
(6,Archana,Mishra,23,programmer,113,6,9848022335,Archana@gmail.com,Chennai,113)  
(7,Komal,Nayak,24,teamlead,112,7,9848022334,Komal@gmail.com,trivendram,112)  
(8,Bharathi,Nambiayar,24,manager,111,8,9848022333,Bharathi@gmail.com,Chennai,111)