Apache Pig 简明教程
Apache Pig - Group Operator
GROUP 运算符用于对一个或多个关系中的数据进行分组。它收集具有相同键的数据。
Example
假设我们有一个名为 student_details.txt 的文件位于 HDFS 目录 /pig_data/ 中,如下所示。
student_details.txt
001,Rajiv,Reddy,21,9848022337,Hyderabad
002,siddarth,Battacharya,22,9848022338,Kolkata
003,Rajesh,Khanna,22,9848022339,Delhi
004,Preethi,Agarwal,21,9848022330,Pune
005,Trupthi,Mohanthy,23,9848022336,Bhuwaneshwar
006,Archana,Mishra,23,9848022335,Chennai
007,Komal,Nayak,24,9848022334,trivendram
008,Bharathi,Nambiayar,24,9848022333,Chennai
我们将此文件加载到 Apache Pig 中,关系名称为 student_details ,如下所示。
grunt> student_details = LOAD 'hdfs://localhost:9000/pig_data/student_details.txt' USING PigStorage(',')
as (id:int, firstname:chararray, lastname:chararray, age:int, phone:chararray, city:chararray);
现在,让我们按年龄对关系中的记录/元组进行分组,如下所示。
grunt> group_data = GROUP student_details by age;
Output
然后,您将得到输出,显示关系 group_data 的内容,如下所示。在这里,您可以看到结果模式有两列−
-
一个是 age ,我们按它对关系进行分组。
-
另一个是 bag ,其中包含元组组、具有相应年龄的学生记录。
(21,{(4,Preethi,Agarwal,21,9848022330,Pune),(1,Rajiv,Reddy,21,9848022337,Hydera bad)})
(22,{(3,Rajesh,Khanna,22,9848022339,Delhi),(2,siddarth,Battacharya,22,984802233 8,Kolkata)})
(23,{(6,Archana,Mishra,23,9848022335,Chennai),(5,Trupthi,Mohanthy,23,9848022336 ,Bhuwaneshwar)})
(24,{(8,Bharathi,Nambiayar,24,9848022333,Chennai),(7,Komal,Nayak,24,9848022334, trivendram)})
您可以使用 describe 命令查看分组数据后的表模式,如下所示。
grunt> Describe group_data;
group_data: {group: int,student_details: {(id: int,firstname: chararray,
lastname: chararray,age: int,phone: chararray,city: chararray)}}
同样,您可以使用 illustrate 命令获取模式的示例说明,如下所示。
$ Illustrate group_data;
它将生成如下输出:
-------------------------------------------------------------------------------------------------
|group_data| group:int | student_details:bag{:tuple(id:int,firstname:chararray,lastname:chararray,age:int,phone:chararray,city:chararray)}|
-------------------------------------------------------------------------------------------------
| | 21 | { 4, Preethi, Agarwal, 21, 9848022330, Pune), (1, Rajiv, Reddy, 21, 9848022337, Hyderabad)}|
| | 2 | {(2,siddarth,Battacharya,22,9848022338,Kolkata),(003,Rajesh,Khanna,22,9848022339,Delhi)}|
-------------------------------------------------------------------------------------------------
Grouping by Multiple Columns
让我们按年龄和城市对关系进行分组,如下所示。
grunt> group_multiple = GROUP student_details by (age, city);
您可以使用 Dump 运算符验证关系 group_multiple 的内容,如下所示。
grunt> Dump group_multiple;
((21,Pune),{(4,Preethi,Agarwal,21,9848022330,Pune)})
((21,Hyderabad),{(1,Rajiv,Reddy,21,9848022337,Hyderabad)})
((22,Delhi),{(3,Rajesh,Khanna,22,9848022339,Delhi)})
((22,Kolkata),{(2,siddarth,Battacharya,22,9848022338,Kolkata)})
((23,Chennai),{(6,Archana,Mishra,23,9848022335,Chennai)})
((23,Bhuwaneshwar),{(5,Trupthi,Mohanthy,23,9848022336,Bhuwaneshwar)})
((24,Chennai),{(8,Bharathi,Nambiayar,24,9848022333,Chennai)})
(24,trivendram),{(7,Komal,Nayak,24,9848022334,trivendram)})
Group All
您可以按所有列对关系进行分组,如下所示。
grunt> group_all = GROUP student_details All;
现在,验证关系 group_all 的内容,如下所示。
grunt> Dump group_all;
(all,{(8,Bharathi,Nambiayar,24,9848022333,Chennai),(7,Komal,Nayak,24,9848022334 ,trivendram),
(6,Archana,Mishra,23,9848022335,Chennai),(5,Trupthi,Mohanthy,23,9848022336,Bhuw aneshwar),
(4,Preethi,Agarwal,21,9848022330,Pune),(3,Rajesh,Khanna,22,9848022339,Delhi),
(2,siddarth,Battacharya,22,9848022338,Kolkata),(1,Rajiv,Reddy,21,9848022337,Hyd erabad)})