The document provides a comprehensive overview of using Pig, a high-level platform for creating programs that run on Apache Hadoop. It covers various join operations (inner, left outer, right outer), the split operator, filter and distinct operators, the foreach operator for data transformations, and the assert operator for data validation. Additionally, it discusses the creation of reusable scripts with macros in Pig Latin and outlines topics for the next training session.

1. Hadoop Developer
Training
Session 04 - PIG

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Agenda
PIG
• Loads in Pig Continued
• Verification
• Filters
• Macros in Pig

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Load in Pig
Inner Join is used quite frequently; it is also referred to as equijoin. An inner
join returns rows when there is a match in both tables.
It creates a new relation by combining column values of two relations (say A
and B) based upon the join-predicate. The query compares each row of A
with each row of B to find all pairs of rows which satisfy the join-predicate.
When the join-predicate is satisfied, the column values for each matched pair
of rows of A and B are combined into a result row.
Syntax
Here is the syntax of performing inner join operation using the JOIN operator.
grunt> result = JOIN relation1 BY columnname, relation2 BY columnname;
Example
Let us perform inner join operation on the two relations customers and
orders as shown below.
grunt> coustomer_orders = JOIN customers BY id, orders BY customer_id

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Verification
Verify the relation coustomer_orders using the DUMP operator as shown
below.
grunt> Dump coustomer_orders; Output
You will get the following output that will the contents of the relation
named coustomer_orders.
(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)
Outer Join: Unlike inner join, outer join returns all the rows from at least one
of the relations. An outer join operation is carried out in three ways −
Left outer join
Right outer join
Full outer join
Left Outer Join

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Verification
The left outer Join operation returns all rows from the left table, even if
there are no matches in the right relation.
Syntax
Given below is the syntax of performing left outer join operation using the
JOIN operator.
grunt> Relation3_name = JOIN Relation1_name BY id LEFT OUTER,
Relation2_name BY customer_id;
Example
Let us perform left outer join operation on the two relations customers and
orders as shown below.
grunt> outer_left = JOIN customers BY id LEFT OUTER, orders BY
customer_id;

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Verification
Verify the relation outer_left using the DUMP operator as shown below.
grunt> Dump outer_left; Output
It will produce the following output, displaying the contents of the relation
outer_left.
(1,Peter,32,Salt Lake City,2000,,,,)
(2,Aaron,25,Salt Lake City,1500,101,2009-11-20 00:00:00,2,1560)
(3,Danny,23,Salt Lake City,2000,100,2009-10-08 00:00:00,3,1500)
(3,Danny,23,Salt Lake City,2000,102,2009-10-08 00:00:00,3,3000)
(4,Angela,25,Salt Lake City,6500,103,2008-05-20 00:00:00,4,2060)
(5,Peggy,27,Bhopal,8500,,,,)
(6,King,22,MP,4500,,,,)
(7,Carolyn,24,Indore,10000,,,,)

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Verification
Right Outer Join
The right outer join operation returns all rows from the right table, even if
there are no matches in the left table.
Syntax
Given below is the syntax of performing right outer join operation using the
JOIN operator.
grunt> outer_right = JOIN customers BY id RIGHT, orders BY customer_id;
Example
Let us perform right outer join operation on the two relations customers and
orders as shown below.
grunt> outer_right = JOIN customers BY id RIGHT, orders BY customer_id;
Verification
Verify the relation outer_right using the DUMP operator as shown below.
grunt> Dump outer_right Output

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Verification
It will produce the following output, displaying the contents of the relation
outer_right.
(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
The SPLIT operator is used to split a relation into two or more relations.
Syntax
Given below is the syntax of the SPLIT operator.
SPLIT student_details into student_details1 if age<23, student_details2 if
(22<age and age>25);
Dump student_details1;
grunt> Dump student_details2;

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Verification
Output
It will produce the following output, displaying the contents of the relations
student_details1 and student_details2 respectively.
grunt> Dump student_details1;
(1, Peter, Burke, 4353521729, Salt Lake City)
(2, Aaron, Kimberlake, 8013528191, Salt Lake City)
(3, Danny, Jacob, 2958295582, Salt Lake City)
(4, Angela, Kouth, 2938811911, Salt Lake City)
grunt> Dump student_details2;
(5, Peggy, Karter, 3202289119, Salt Lake City)
(6, King, Salmon, 2398329282, Salt Lake City)
(7, Carolyn, Fisher, 2293322829, Salt Lake City)
(8, John, Hopkins, 2102392020, Salt Lake City)

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Verification
The FILTER operator is used to select the required tuples from a relation
based on a condition.
Syntax
Given below is the syntax of the FILTER operator.
grunt> Relation2_name = FILTER Relation1_name BY (condition);
Example
Assume that we have a file named student_details.txt in the HDFS directory
/pig_data/ as shown below.
student_details.txt
1, Peter, Burke, 4353521729, Salt Lake City
2, Aaron, Kimberlake, 8013528191, Salt Lake City
3, Danny, Jacob, 2958295582, Salt Lake City
4, Angela, Kouth, 2938811911, Salt Lake City

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Verification
5, Peggy, Karter, 3202289119, Salt Lake City
6, King, Salmon, 2398329282, Salt Lake City
7, Carolyn, Fisher, 2293322829, Salt Lake City
8, John, Hopkins, 2102392020, Salt Lake City
And we have loaded this file into Pig with the relation name student_details
as shown below.
grunt> student_details = LOAD '/pig_data/student_details.txt' USING
PigStorage(',') as (id:int, firstname:chararray, lastname:chararray, age:int,
phone:chararray, city:chararray);
cx = FILTER student_details BY city == 'Chennai’;
Verification
Verify the relation filter_data using the DUMP operator as shown below.
grunt> Dump filter_data;

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Verification
Output
It will produce the following output, displaying the contents of the relation
filter_data as follows.
(6, King, Salmon, 2398329282, Salt Lake City)
(8, John, Hopkins, 2102392020, Salt Lake City)
The DISTINCT operator is used to remove redundant (duplicate) tuples from
a relation.
distinct_data = DISTINCT student_details;
grunt> Dump distinct_data;
The FOREACH operator is used to generate specified data transformations
based on the column data
grunt> foreach_data = FOREACH student_details GENERATE id,age,city;
grunt> Dump foreach_data; Output
It will produce the following output, displaying the contents of the relation
foreach_data.

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Verification
(1,21,Salt Lake City)
(2,22, Salt Lake City)
(3,22, Salt Lake City)
(4,21, Salt Lake City)
(5,23, Salt Lake City)
(6,23, Salt Lake City)
(7,24, Salt Lake City)
(8,24, Salt Lake City)
Assert operator is used for data validation. The script will fail if it doesn't
meets the specified condition in assert
paste the data on desktop as
12,23
23,34
-21,22

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Verification
a = load '/home/mishra/Desktop/exp' USING PigStorage(',') AS (id:int,roll:int);
now apply the assert operator
grunt> assert a by id >0,'a cant be neg'
dump a;
an error is generated as one of the values in id is negative
check the details at the generated log file of the pig..at the end of the file you
will find
Assertion violated: a cant be neg
now assume another example witth same data
grunt> b = load '/home/mishra/Desktop/exp' USING PigStorage(',') AS
(id:int,roll:int);
grunt> assert b by id > 13,'value is below 13';
Dump b;
an error is generated as few values in id is less than 13
check the details at the generated log file of the pig..at the end of the file you
will find
Assertion violated: value is below 13

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Macros in Pig
We can develop more reusable scripts in Pig Latin Using Macros also.Macro is
a kind of function written in Pig Latin.
DEFINE keyword is used to make macros
You can define a function by writing a macro and then reuse that macro
paste the undergiven data of student with fields as id,name,fees,rollno
respectively:::::::::::::
10,Peter,10000,1
15,Aaron,20000,25
30,Danny,30000,1
40,Angela,40000,35
move the data to hdfs by:::::
hadoop fs -put /home/ands/Desktop/xyz.txt /pigy
make a file on Desktop with .pig extention(say macro.pig) and paste the lines
below:::::::::::::::
DEFINE myfilter(relvar,colvar) returns x{
$x = filter $relvar by $colvar==1;
};

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Macros in Pig
stu = load '/pigy'using PigStorage(',') as (id,name,fees,rollno);
studrollno1 =myfilter( stu,rollno);
dump studrollno1;
Above macro takes two values as input,one is relation variable (relvar) and
second is column variable (colvar)
macro checks if colvar equals to 1 or not
now change directory to desktop and run:::::
pig -f macro.pig

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Topics to be covered in next session
• Sqoop
• Sqoop Installation
• Exporting the data
• Exporting from Hadoop to SQL

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Thank you!