Lab 23: Apache Pig Basics

Hi ETL  enthusiast,

This post will talk about important concepts in Pig.

PIG Conventions
Convention	Description	Example
( )	tuple data type	(John,18,4.0F)
{ }	bag data type	(1,{(1,2,3)}) (4,{(4,2,1),(4,3,3)}) (8,{(8,3,4)})
[ ]	map data type	[name#John,phone#5551212]

Relations, Bags, Tuples, Fields

Field is a piece of data. Eg, Jessica

Tuple is an ordered set of fields (Jessica, F, 35, NY)

Bag is a collection of Tuples {(Jessica, F, 35, NY),(Nathan, M, 35, NJ)}

Data Types

Data Types
Simple and Complex
Simple Types	Description	Example
int	Signed 32-bit integer	10
long	Signed 64-bit integer	Data:     10L or 10l Display: 10L
float	32-bit floating point	Data:     10.5F or 10.5f or 10.5e2f or 10.5E2F Display: 10.5F or 1050.0F
double	64-bit floating point	Data:     10.5 or 10.5e2 or 10.5E2 Display: 10.5 or 1050.0
chararray	Character array (string) in Unicode UTF-8 format	hello world
bytearray	Byte array (blob)
boolean	boolean	true/false (case insensitive)
datetime	datetime	1970-01-01T00:00:00.000+00:00
Complex Types
tuple	An ordered set of fields.	(19,2)
bag	An collection of tuples.	{(19,2), (18,1)}
map	A set of key value pairs.	[open#apache]

Nulls, Operators, and Functions

Operator	Interaction
Comparison operators: ==, != >, < >=, <=	If either subexpression is null, the result is null.
Comparison operator: matches	If either the string being matched against or the string defining the match is null, the result is null.
Arithmetic operators: + , -, *, / % modulo ? : bincond	If either subexpression is null, the resulting expression is null.
Null operator: is null	If the tested value is null, returns true; otherwise, returns false (see Null Operators).
Null operator: is not null	If the tested value is not null, returns true; otherwise, returns false (see Null Operators).
Dereference operators: tuple (.) or map (#)	If the de-referenced tuple or map is null, returns null.
Operators: COGROUP, GROUP, JOIN	These operators handle nulls differently (see examples below).
Function: COUNT_STAR	This function counts all values, including nulls.
Cast operator	Casting a null from one type to another type results in a null.
Functions: AVG, MIN, MAX, SUM, COUNT	These functions ignore nulls.
Function: CONCAT	If either subexpression is null, the resulting expression is null.
Function: SIZE	If the tested object is null, returns null.

Operators

Following are the operators available in Pig. The items marked in red colour are used in this post.

Operator	Description
Loading and Storing
LOAD	To Load the data from the file system (local/HDFS) into a relation.
STORE	To save a relation to the file system (local/HDFS).
Filtering
FILTER	To remove unwanted rows from a relation.
DISTINCT	To remove duplicate rows from a relation.
FOREACH, GENERATE	To generate data transformations based on columns of data.
STREAM	To transform a relation using an external program.
Grouping and Joining
JOIN	To join two or more relations.
COGROUP	To group the data in two or more relations.
GROUP	To group the data in a single relation.
CROSS	To create the cross product of two or more relations.
Sorting
ORDER	To arrange a relation in a sorted order based on one or more fields (ascending or descending).
LIMIT	To get a limited number of tuples from a relation.
Combining and Splitting
UNION	To combine two or more relations into a single relation.
SPLIT	To split a single relation into two or more relations.
Diagnostic Operators
DUMP	To print the contents of a relation on the console.
DESCRIBE	To describe the schema of a relation.
EXPLAIN	To view the logical, physical, or MapReduce execution plans to compute a relation.
ILLUSTRATE	To view the step-by-step execution of a series of statements.

Pig Latin statements

Let’s use the following as a sample table

emp id	name	age	Desig
1	Dharma	45	Sr Manager
2	Bheema	43	Cook
3	Arjuna	41	Instructor
4	Nakula	35	Jr Instructor
5	Sahadeva	33	Jr Instructor

grunt> fs -cat lab23/employee.csv
1, Dharma, 45, Sr Manager
2, Bheema, 43, Cook
3, Arjuna, 41, Instructor
4, Nakula, 35, Jr Instructor
5, Sahadeva, 33, Jr Instructor

Load, Describe, illustrate, dump

grunt> employee = load ‘lab23/employee.csv’ using PigStorage(‘,’) as (emp_id:int,emp_name:chararray,emp_age:int,emp_desig:chararray);

Relation name: employee
Input file path: lab23/employee.csv (I have used relative path)
Storage function: We have used the PigStorage() function. It loads and stores data as structured text files. Default delimiter is \t. We use comma here.

grunt> describe employee;
employee: {emp_id: int,emp_name: chararray,emp_age: int,emp_desig: chararray}

grunt> illustrate employee
————————————————————————————————–
| employee     | emp_id:int    | emp_name:chararray    | emp_age:int    | emp_desig:chararray    |
————————————————————————————————–
|              | 5             |  Sahadeva             |  33            |  Jr Instructor         |
————————————————————————————————–

grunt> dump employee;
(1, Dharma,45, Sr Manager)
(2, Bheema,43, Cook)
(3, Arjuna,41, Instructor)
(4, Nakula,35, Jr Instructor)
(5, Sahadeva,33, Jr Instructor)

This will execute a MapReduce job to read data from HDFS and print the content on the screen.

grunt> explain employee;

#————————————————–
# Map Reduce Plan
#————————————————–
MapReduce node scope-119
Map Plan
employee: Store(fakefile:org.apache.pig.builtin.PigStorage) – scope-118
|
|—employee: New For Each(false,false,false,false)[bag] – scope-117
|   |
|   Cast[int] – scope-106
|   |
|   |—Project[bytearray][0] – scope-105
|   |
|   Cast[chararray] – scope-109
|   |
|   |—Project[bytearray][1] – scope-108
|   |
|   Cast[int] – scope-112
|   |
|   |—Project[bytearray][2] – scope-111
|   |
|   Cast[chararray] – scope-115
|   |
|   |—Project[bytearray][3] – scope-114
|
|—employee: Load(hdfs://gandhari:9000/user/hadoop/lab23/employee.csv:PigStorage(‘,’)) – scope-104——–
Global sort: false
—————-

Group

grunt> edesig = group employee by emp_desig;

grunt> dump edesig;
( Cook,{(2, Bheema,43, Cook)})
( Instructor,{(3, Arjuna,41, Instructor)})
( Sr Manager,{(1, Dharma,45, Sr Manager)})
( Jr Instructor,{(5, Sahadeva,33, Jr Instructor),(4, Nakula,35, Jr Instructor)})

I’m just thinking in terms of map reduce. By this time, I need to write a mapper extending Mapper, a reducer extending Reducer and a driver 🙂

Let’s do one more grouping by age

grunt> eage = group employee by emp_age;

grunt> dump eage;
(33,{(5, Sahadeva,33, Jr Instructor)})
(35,{(4, Nakula,35, Jr Instructor)})
(41,{(3, Arjuna,41, Instructor)})
(43,{(2, Bheema,43, Cook)})
(45,{(1, Dharma,45, Sr Manager)})

Let’s do a grouping based on both the columns.

grunt> e_age_desig = group employee by (emp_age, emp_desig);

grunt> dump e_age_desig;
((33, Jr Instructor),{(5, Sahadeva,33, Jr Instructor)})
((35, Jr Instructor),{(4, Nakula,35, Jr Instructor)})
((41, Instructor),{(3, Arjuna,41, Instructor)})
((43, Cook),{(2, Bheema,43, Cook)})
((45, Sr Manager),{(1, Dharma,45, Sr Manager)})

Yes. Writing MapReduce for simple tasks are time consuming. Rather we need to deploy the right tools to get the job done.

Co-group

In addition to employee, we have one other table, student as given below

grunt> fs -cat lab23/student.csv;
1, Duryodhana, 15, 11
2, Dushasana, 14, 10
3, Dushala, 13, 9
4, Dronacharya,45, 12

grunt> student = load ‘lab23/student.csv’ using PigStorage(‘,’) as (stud_id:int,stud_name:chararray,stud_age:int,stud_class:chararray);

grunt> illustrate student;
————————————————————————————————-
| student     | stud_id:int   | stud_name:chararray   | stud_age:int   | stud_class:chararray   |
————————————————————————————————-
|             | 3             |  Dushala              |  13            |  9                     |
————————————————————————————————-

grunt> cogroupdata = COGROUP student by stud_age, employee by emp_age;

(13,{(3, Dushala,13, 9)},{})
(14,{(2, Dushasana,14, 10)},{})
(15,{(1, Duryodhana,15, 11)},{})
(33,{},{(5, Sahadeva,33, Jr Instructor)})
(35,{},{(4, Nakula,35, Jr Instructor)})
(41,{},{(3, Arjuna,41, Instructor)})
(43,{},{(2, Bheema,43, Cook)})
(45,{(4, Dronacharya,45, 12)},{(1, Dharma,45, Sr Manager)})

Co group is similar to group operator. But it happens across two different relations.

 

Ref: http://pig.apache.org