Apache Access Log analysis with Apache Pig

Posted on February 1, 2017 by Pandian Ramaiah

So far I have documented some of the key functions in Apache Pig. Today, Let’s write a simple Pig Latin script to parse and analyse Apache’s access log. Here you go.

$ cat accessLogETL.pig
-- demo script javashine.wordpress.com
-- extract user IPs
access = load '/user/cloudera/pig/access.log' using PigStorage (' ') as (hostIp:chararray, clientId:chararray, userId:chararray, reqTime:chararray, reqTimeZone:chararray, reqMethod:chararray, reqLine:chararray, reqProt:chararray, statusCode:chararray, respLength:int, referrer:chararray, userAgentMozilla:chararray, userAgentPf1:chararray, userAgentPf2:chararray, userAgentPf3:chararray, userAgentRender:chararray, userAgentBrowser:chararray);
hostIpList = FOREACH access GENERATE hostIp;
hostIpList = DISTINCT hostIpList;
STORE hostIpList INTO '/user/cloudera/pig/hostIpList' USING PigStorage('\t');

hostIpUrlList = FOREACH access GENERATE (hostIp,reqTime,reqTimeZone,reqLine);
hostIpUrlList = DISTINCT hostIpUrlList;
STORE hostIpUrlList INTO '/user/cloudera/pig/hostIpUrlList' USING PigStorage('\t');

hostIpBandwidthList = FOREACH access GENERATE (hostIp), respLength;
groupByIp = GROUP hostIpBandwidthList BY hostIp;
bandwidthByIp = FOREACH groupByIp GENERATE hostIpBandwidthList.hostIp, SUM(hostIpBandwidthList.respLength);
STORE bandwidthByIp INTO '/user/cloudera/pig/bandwidthByIp' USING PigStorage('\t');

$ pig -x mapreduce accessLogETL.pig
Job Stats (time in seconds):
JobId   Maps    Reduces MaxMapTime      MinMapTIme      AvgMapTime      MedianMapTime   MaxReduceTime   MinReduceTime   AvgReduceTime   MedianReducetime        Alias   Feature Outputs
job_1485688219066_0027  1       1       12      12      12      12      8       8       8       8       access,hostIpList,hostIpUrlList DISTINCT,MULTI_QUERY    /user/cloudera/pig/hostIpList,/user/cloudera/pig/hostIpUrlList,
job_1485688219066_0028  1       1       7       7       7       7       9       9       9       9       bandwidthByIp,groupByIp,hostIpBandwidthList     GROUP_BY        /user/cloudera/pig/bandwidthByIp,

Input(s):
Successfully read 470749 records (59020755 bytes) from: "/user/cloudera/pig/access.log"

Output(s):
Successfully stored 877 records (12342 bytes) in: "/user/cloudera/pig/hostIpList"
Successfully stored 449192 records (31075055 bytes) in: "/user/cloudera/pig/hostIpUrlList"
Successfully stored 877 records (6729928 bytes) in: "/user/cloudera/pig/bandwidthByIp"

Counters:
Total records written : 450946
Total bytes written : 37817325
Spillable Memory Manager spill count : 7
Total bags proactively spilled: 3
Total records proactively spilled: 213378

Let’s see our results now.

The above script will yield us three output.
First is the list of user IPs accessed the web server.

$ hadoop fs -ls /user/cloudera/pig/hostIpList
Found 2 items
-rw-r--r--   1 cloudera cloudera          0 2017-01-31 12:43 /user/cloudera/pig/hostIpList/_SUCCESS
-rw-r--r--   1 cloudera cloudera      12342 2017-01-31 12:43 /user/cloudera/pig/hostIpList/part-r-00000
[cloudera@quickstart pig]$ hadoop fs -cat /user/cloudera/pig/hostIpList/part-r-00000
::1
10.1.1.5
107.21.1.8
14.134.7.6
37.48.94.6
46.4.90.68
46.4.90.86

The second output will give us the list of user IPs, their access time and accessed URL

$ hadoop fs -ls /user/cloudera/pig/hostIpUrlList
Found 2 items
-rw-r--r--   1 cloudera cloudera          0 2017-01-31 12:43 /user/cloudera/pig/hostIpUrlList/_SUCCESS
-rw-r--r--   1 cloudera cloudera   31075055 2017-01-31 12:43 /user/cloudera/pig/hostIpUrlList/part-r-00000
[cloudera@quickstart pig]$ hadoop fs -cat /user/cloudera/pig/hostIpUrlList/part-r-00000
(10.1.1.5,[22/Jan/2017:17:51:34,+0000],/egcrm)
(10.1.1.5,[22/Jan/2017:17:51:34,+0000],/egcrm2/)
(10.1.1.5,[22/Jan/2017:17:51:34,+0000],/egcrm/helloWorld.action)

And, finally the bandwidth spent for each user IP.

$ hadoop fs -ls /user/cloudera/pig/bandwidthByIp
Found 2 items
-rw-r--r--   1 cloudera cloudera          0 2017-01-31 12:44 /user/cloudera/pig/bandwidthByIp/_SUCCESS
-rw-r--r--   1 cloudera cloudera    6729928 2017-01-31 12:44 /user/cloudera/pig/bandwidthByIp/part-r-00000
$ hadoop fs -cat /user/cloudera/pig/bandwidthByIp/part-r-00000
{(193.138.219.245)}     1313
{(193.138.219.250),(193.138.219.250),(193.138.219.250)} 3939
{(195.154.181.113)}     496
{(195.154.181.168)}     1026

Apache Pig – Operators and Functions with Examples

Posted on January 28, 2017 by Pandian Ramaiah

Here you go! Click the below given page for the examples

Apache Pig with Examples

Lab 27: Filtering operators in Pig

Posted on October 18, 2016 by Pandian Ramaiah

Hi Hadoopers,

This blog post would demonstrate the filtering capabilities of Pig.

Filter by

This is similar to WHERE clause in SQL. We have a table for employee as below.

grunt> fs -cat 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

Relation for employee is defined as below.

grunt> employee = load 'employee.csv' using PigStorage (',') as (eid:int,ename:chararray,eage:int,edes:chararray);

Let’s get the employee whose id is 1.

e1 = filter employee by eid==1;
dump e1;
(1, Dharma,45, Sr Manager)

Let’s get the employee whose name is Nakula.

e2 = filter employee by ename == ' Nakula';
dump e2;
(4, Nakula,35, Jr Instructor)

Distinct

As the name implies, let’s select the distinct records. Following is our input file.


grunt> fs -cat employee3.csv
1, Dharma, 45, Sr Manager
2, Bheema, 43, Cook
3, Arjuna, 41, Instructor
4, Nakula, 35, Jr Instructor
5, Sahadeva, 33, Jr Instructor
1, Dharma, 45, Sr Manager
2, Bheema, 43, Cook
3, Arjuna, 41, Instructor
4, Nakula, 35, Jr Instructor
5, Sahadeva, 33, Jr Instructor

Lets create a relation for the employee.

grunt> employee = load 'employee3.csv' using PigStorage (',') as (eid:int,ename:chararray,eage:int,edes:chararray);

We are creating another relation for the distinct records. Here you go.


grunt> edist = distinct employee;

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

For-each

And finally, for – each.

Let’s iterate through the edist relation I have shown above.


grunt> foreachdata = foreach edist generate eid, ename;

(1, Dharma)
(2, Bheema)
(3, Arjuna)
(4, Nakula)
(5, Sahadeva)

Lab 26: Achieving Partitioner with Apache Pig – Split

Posted on October 17, 2016 by Pandian Ramaiah

Hi Hadoopers,

Partitioner is a powerful concept in MapReduce. I have given an example in Lab 06 – A simple hadoop partitioner

Let’s see how to implement same concept using Pig – It’s simple – using split!

Let’s take a simple employee table as below


grunt> fs  -cat 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

Let’s create the employee relation as given below.


grunt> employee = load 'employee.csv' using PigStorage (',') as (eid:int,ename:chararray,eage:int,edes:chararray);

Now I’ll split employees based on age. Above 40, Below 40.


grunt> split employee into employee1 if eage>=40, employee2 if (eage<40);

grunt> dump employee1
(1, Dharma,45, Sr Manager)
(2, Bheema,43, Cook)
(3, Arjuna,41, Instructor)

grunt> dump employee2
(4, Nakula,35, Jr Instructor)
(5, Sahadeva,33, Jr Instructor)

Note – You need to specify the else column – it is must.

Lab 25: Combiners using Pig Union

Posted on October 16, 2016 by Pandian Ramaiah

Hi Friends,

I archive the blog entries and news entries on daily basis using one of my projects jatomrss. It saves the news articles as tab limited text

$ hadoop fs -ls /user/hadoop/feed/*.jatomrss.log
-rw-r--r--   3 hadoop supergroup    5933545 2016-10-01 21:44 /user/hadoop/feed/2016-10-01.jatomrss.log
-rw-r--r--   3 hadoop supergroup    6313692 2016-10-02 17:03 /user/hadoop/feed/2016-10-02.jatomrss.log
-rw-r--r--   3 hadoop supergroup     596174 2016-10-03 06:37 /user/hadoop/feed/2016-10-03.jatomrss.log
-rw-r--r--   3 hadoop supergroup     827974 2016-10-04 06:53 /user/hadoop/feed/2016-10-04.jatomrss.log
-rw-r--r--   3 hadoop supergroup    1367507 2016-10-05 07:41 /user/hadoop/feed/2016-10-05.jatomrss.log
-rw-r--r--   3 hadoop supergroup      10927 2016-10-06 07:29 /user/hadoop/feed/2016-10-06.jatomrss.log
-rw-r--r--   3 hadoop supergroup    1536870 2016-10-07 06:24 /user/hadoop/feed/2016-10-07.jatomrss.log
-rw-r--r--   3 hadoop supergroup    1719126 2016-10-08 07:13 /user/hadoop/feed/2016-10-08.jatomrss.log
-rw-r--r--   3 hadoop supergroup    1870073 2016-10-09 09:36 /user/hadoop/feed/2016-10-09.jatomrss.log
-rw-r--r--   3 hadoop supergroup    1376982 2016-10-11 05:11 /user/hadoop/feed/2016-10-11.jatomrss.log

Lets use Union operators to combine the feeds on Oct 1st and 2nd.

The relation for October 1st is given as below.


feedEntryRecord20161001 = load '/user/hadoop/feed/2016-10-01.jatomrss.log' using PigStorage ('\t') as (generator:chararray, feedTitle:chararray, feedAuthor:chararray, feedUrl:chararray, feed_time:chararray, entrySubject:chararray, entryAuthor:chararray, entryUrl:chararray, entryDate:chararray, categorySet:chararray, descriptionFile:chararray, uniqueId:chararray);

The relation for October 2nd is given as below.


feedEntryRecord20161002 = load '/user/hadoop/feed/2016-10-02.jatomrss.log' using PigStorage ('\t') as (generator:chararray, feedTitle:chararray, feedAuthor:chararray, feedUrl:chararray, feed_time:chararray, entrySubject:chararray, entryAuthor:chararray, entryUrl:chararray, entryDate:chararray, categorySet:chararray, descriptionFile:chararray, uniqueId:chararray);

Let’s describe them.


grunt> <span style="color: #ff0000;">describe feedEntryRecord20161001;</span>
feedEntryRecord20161001: {generator: chararray,feedTitle: chararray,feedAuthor: chararray,feedUrl: chararray,feed_time: chararray,entrySubject: chararray,entryAuthor: chararray,entryUrl: chararray,entryDate: chararray,categorySet: chararray,descriptionFile: chararray,uniqueId: chararray}
grunt> <span style="color: #ff0000;">describe feedEntryRecord20161002</span>
feedEntryRecord20161002: {generator: chararray,feedTitle: chararray,feedAuthor: chararray,feedUrl: chararray,feed_time: chararray,entrySubject: chararray,entryAuthor: chararray,entryUrl: chararray,entryDate: chararray,categorySet: chararray,descriptionFile: chararray,uniqueId: chararray}

Here is the combiner! To make the combiner (union) to work, we need to identical columns in both data. Yes. we do have.


union_data = union feedEntryRecord20161001, feedEntryRecord20161002;

grunt> describe union_data;
union_data: {generator: chararray,feedTitle: chararray,feedAuthor: chararray,feedUrl: chararray,feed_time: chararray,entrySubject: chararray,entryAuthor: chararray,entryUrl: chararray,entryDate: chararray,categorySet: chararray,descriptionFile: chararray,uniqueId: chararray}

Let's store it to check if the combiner works as expected.

store union_data into '/user/hadoop/lab25/union_data' using PigStorage ('\t');

Input(s):
Successfully read 10612 records from: "/user/hadoop/feed/2016-10-02.jatomrss.log"
Successfully read 10295 records from: "/user/hadoop/feed/2016-10-01.jatomrss.log"

Output(s):
Successfully stored 20907 records (206484670 bytes) in: "/user/hadoop/lab25/union_data"

Counters:
Total records written : 20907
Total bytes written : 206484670
Spillable Memory Manager spill count : 0
Total bags proactively spilled: 0
Total records proactively spilled: 0

Job DAG:
job_local1564505665_0014

Lab 24: Joins using Pig

Posted on October 15, 2016 by Pandian Ramaiah

Let’s make some joins today, dear hadoopers.

Join

We have a table for employee.

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)

We have another table salary as given below.

grunt> dump salary;

(1,2000)
 (2,1500)
 (3,1000)
 (4,500)
 (5,500)

Let’s join salary table with employee table on emp_id field.

grunt> salary_table = JOIN employee by emp_id, salary by emp_id;

grunt> dump salary_table;
 Input(s):
 Successfully read 5 records from: "hdfs://gandhari:9000/user/hadoop/lab23/salary.csv"
 Successfully read 5 records from: "hdfs://gandhari:9000/user/hadoop/lab23/employee.csv"

Output(s):
 Successfully stored 5 records (16134806 bytes) in: "hdfs://gandhari:9000/tmp/temp-2101978857/tmp596113376"

Counters:
 Total records written : 5
 Total bytes written : 16134806
 Spillable Memory Manager spill count : 0
 Total bags proactively spilled: 0
 Total records proactively spilled: 0

(1, Dharma,45, Sr Manager,1,2000)
 (2, Bheema,43, Cook,2,1500)
 (3, Arjuna,41, Instructor,3,1000)
 (4, Nakula,35, Jr Instructor,4,500)
 (5, Sahadeva,33, Jr Instructor,5,500)

Inner Join

The employee table looks like this.

(1, dharma,45, Sr Manager)
 (2, Bheema,43, Cook)
 (3, Arjuna,41, Instructor)
 (4, Nakula,35, Jr Instructor)
 (5, Sahadeva,33, Jr Instructor)

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

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

There is a change in salary table. No salary for Arjuna this month 🙂 Id 3 is missing in salary table.

grunt> dump salary;
 (1,2000)
 (2,1500)
 (4,500)
 (5,500)

grunt> salary = load 'salary.csv' using PigStorage (',') as (emp_id:int,emp_salary:int);

grunt> describe salary;
 salary: {emp_id: int,emp_salary: int}

Lets do an inner join now.

grunt> employee_salary = join employee by emp_id, salary by emp_id;

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

grunt> illustrate employee_salary;

--------------------------------------------------------------------------------------------------
 | employee     | emp_id:int    | emp_name:chararray    | emp_age:int    | emp_desig:chararray    |
 --------------------------------------------------------------------------------------------------
 |              | 5             |  Sahadeva             | 33             |  Jr Instructor         |
 |              | 5             |  Sahadeva             | 33             |  Jr Instructor         |
 --------------------------------------------------------------------------------------------------
 --------------------------------------------------
 | salary     | emp_id:int    | emp_salary:int    |
 --------------------------------------------------
 |            | 5             | 500               |
 |            | 5             | 500               |
 --------------------------------------------------
 -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 | employee_salary     | employee::emp_id:int    | employee::emp_name:chararray    | employee::emp_age:int    | employee::emp_desig:chararray    | salary::emp_id:int    | salary::emp_salary:int    |
 -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 |                     | 5                       |  Sahadeva                       | 33                       |  Jr Instructor                   | 5                     | 500                       |
 |                     | 5                       |  Sahadeva                       | 33                       |  Jr Instructor                   | 5                     | 500                       |
 |                     | 5                       |  Sahadeva                       | 33                       |  Jr Instructor                   | 5                     | 500                       |
 |                     | 5                       |  Sahadeva                       | 33                       |  Jr Instructor                   | 5                     | 500                       |
 -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

grunt> dump employee_salary;

(1, dharma,45, Sr Manager,1,2000)
 (2, Bheema,43, Cook,2,1500)
 (4, Nakula,35, Jr Instructor,4,500)
 (5, Sahadeva,33, Jr Instructor,5,500)

Left Outer Join

grunt> left_outer = join employee by emp_id left outer, salary by emp_id;

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

grunt> dump left_outer;

(1, dharma,45, Sr Manager,1,2000)
 (2, Bheema,43, Cook,2,1500)
 (3, Arjuna,41, Instructor,,)
 (4, Nakula,35, Jr Instructor,4,500)
 (5, Sahadeva,33, Jr Instructor,5,500)

We get all the records from employee table with null value for those value is not given in salary table.

Right Outer Join

Lets do a right outer join now

grunt> right_outer = join salary by emp_id right, employee by emp_id;

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

grunt> dump right_outer;

(1,2000,1, dharma,45, Sr Manager)
 (2,1500,2, Bheema,43, Cook)
 (,,3, Arjuna,41, Instructor)
 (4,500,4, Nakula,35, Jr Instructor)
 (5,500,5, Sahadeva,33, Jr Instructor)

We could see all the records from right table even if there is no match in the left table.

Full outer join

Last topic of this blog post – Lets do a full outer join, to record from each table when there is at least a single match.

grunt> full_outer = join employee by emp_id full outer, salary by emp_id;
 grunt> describe full_outer;
 full_outer: {employee::emp_id: int,employee::emp_name: chararray,employee::emp_age: int,employee::emp_desig: chararray,salary::emp_id: int,salary::emp_salary: int}

grunt> dump full_outer;

(1, dharma,45, Sr Manager,1,2000)
 (2, Bheema,43, Cook,2,1500)
 (3, Arjuna,41, Instructor,,)
 (4, Nakula,35, Jr Instructor,4,500)
 (5, Sahadeva,33, Jr Instructor,5,500)

Can we join by two keys? yes. join employee by (emp_id, emp_name)!

Cross

Lets do a cross to bring the X relationship between tables. To make it simpler, I have taken less data in our employee table.

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

grunt> dump employee;

(1, Dharma,45, Sr Manager)
(2, Bheema,43, Cook)

grunt> leave = load 'leave.csv' using PigStorage (',') as (emp_id:int,emp_leave_date:chararray);

grunt> dump leave;

(1,2016-10-11)
(2,2016-10-12)

grunt> cross_data = cross employee, leave;

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

grunt> dump cross_data;

(2, Bheema,43, Cook,2,2016-10-12)
(2, Bheema,43, Cook,1,2016-10-11)
(1, Dharma,45, Sr Manager,2,2016-10-12)
(1, Dharma,45, Sr Manager,1,2016-10-11)

See you in another interesting post.

Lab 23: Apache Pig Basics

Posted on October 14, 2016 by Pandian Ramaiah

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> 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

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