Apache Pig: Introduction, Description, Installation, Pig Latin Commands, Use, Examples, Usefulness are demonstrated in this presentation. Tushar B. Kute Researcher, http://tusharkute.com

1. PIG: A Big Data Processor
Tushar B. Kute,
http://tusharkute.com

2. What is Pig?
• Apache Pig is an abstraction over MapReduce. It is a
tool/platform which is used to analyze larger sets of
data representing them as data flows.
• Pig is generally used with Hadoop; we can perform all
the data manipulation operations in Hadoop using
Apache Pig.
• To write data analysis programs, Pig provides a high-
level language known as Pig Latin.
• This language provides various operators using which
programmers can develop their own functions for
reading, writing, and processing data.

3. Apache Pig
• To analyze data using Apache Pig, programmers
need to write scripts using Pig Latin language.
• All these scripts are internally converted to Map
and Reduce tasks.
• Apache Pig has a component known as Pig
Engine that accepts the Pig Latin scripts as
input and converts those scripts into
MapReduce jobs.

4. Why do we need Apache Pig?
• Using Pig Latin, programmers can perform MapReduce tasks
easily without having to type complex codes in Java.
• Apache Pig uses multi-query approach, thereby reducing the
length of codes. For example, an operation that would require you
to type 200 lines of code (LoC) in Java can be easily done by typing
as less as just 10 LoC in Apache Pig. Ultimately, Apache Pig
reduces the development time by almost 16 times.
• Pig Latin is SQL-like language and it is easy to learn Apache Pig
when you are familiar with SQL.
• Apache Pig provides many built-in operators to support data
operations like joins, filters, ordering, etc. In addition, it also
provides nested data types like tuples, bags, and maps that are
missing from MapReduce.

5. Features of Pig
• Rich set of operators: It provides many operators to perform
operations like join, sort, filer, etc.
• Ease of programming: Pig Latin is similar to SQL and it is easy to write
a Pig script if you are good at SQL.
• Optimization opportunities: The tasks in Apache Pig optimize their
execution automatically, so the programmers need to focus only on
semantics of the language.
• Extensibility: Using the existing operators, users can develop their
own functions to read, process, and write data.
• UDF’s: Pig provides the facility to create User-defined Functions in
other programming languages such as Java and invoke or embed them
in Pig Scripts.
• Handles all kinds of data: Apache Pig analyzes all kinds of data, both
structured as well as unstructured. It stores the results in HDFS.

6. Pig vs. MapReduce

7. Pig vs. SQL

8. Pig vs. Hive

9. Applications of Apache Pig
• To process huge data sources such as web logs.
• To perform data processing for search
platforms.
• To process time sensitive data loads.

10. Apache Pig – History
• In 2006, Apache Pig was developed as a
research project at Yahoo, especially to create
and execute MapReduce jobs on every dataset.
• In 2007, Apache Pig was open sourced via
Apache incubator.
• In 2008, the first release of Apache Pig came
out. In 2010, Apache Pig graduated as an
Apache top-level project.

11. Pig Architecture

12. Apache Pig – Components
• Parser: Initially the Pig Scripts are handled by the Parser. It
checks the syntax of the script, does type checking, and other
miscellaneous checks. The output of the parser will be a DAG
(directed acyclic graph), which represents the Pig Latin
statements and logical operators.
• Optimizer: The logical plan (DAG) is passed to the logical
optimizer, which carries out the logical optimizations such as
projection and pushdown.
• Compiler: The compiler compiles the optimized logical plan
into a series of MapReduce jobs.
• Execution engine: Finally the MapReduce jobs are submitted
to Hadoop in a sorted order. Finally, these MapReduce jobs are
executed on Hadoop producing the desired results.

13. Apache Pig – Data Model

14. Apache Pig – Elements
• Atom
– Any single value in Pig Latin, irrespective of their
data, type is known as an Atom.
– It is stored as string and can be used as string
and number. int, long, float, double, chararray,
and bytearray are the atomic values of Pig.
– A piece of data or a simple atomic value is known
as a field.
– Example: ‘raja’ or ‘30’

15. Apache Pig – Elements
• Tuple
– A record that is formed by an ordered set of
fields is known as a tuple, the fields can be of any
type. A tuple is similar to a row in a table of
RDBMS.
– Example: (Raja, 30)

16. Apache Pig – Elements
• Bag
– A bag is an unordered set of tuples. In other words, a
collection of tuples (non-unique) is known as a bag. Each
tuple can have any number of fields (flexible schema). A
bag is represented by ‘{}’. It is similar to a table in RDBMS,
but unlike a table in RDBMS, it is not necessary that every
tuple contain the same number of fields or that the fields
in th same position (column) have the same type.
– Example: {(Raja, 30), (Mohammad, 45)}
– A bag can be a field in a relation; in that context, it is
known as inner bag.
– Example: {Raja, 30, {9848022338, raja@gmail.com,}}

17. Apache Pig – Elements
• Relation
– A relation is a bag of tuples. The relations in Pig
Latin are unordered (there is no guarantee that
tuples are processed in any particular order).
• Map
– A map (or data map) is a set of key-value pairs.
The key needs to be of type chararray and should
be unique. The value might be of any type. It is
represented by ‘[]’
– Example: [name#Raja, age#30]

18. Installation of PIG

19. Download
• Download the tar.gz file of Apache Pig from
here:
http://mirror.fibergrid.in/apache/pig/pig-0.15.0/
pig-0.15.0.tar.gz

20. Extract and copy
• Extract this file using right-click -> 'Extract here'
option or by tar -xzvf command.
• Rename the created folder 'pig-0.15.0' to 'pig'
• Now, move this folder to /usr/lib using following
command:
$ sudo mv pig/ /usr/lib

21. Edit the bashrc file
• Open the bashrc file:
sudo gedit ~/.bashrc
• Go to end of the file and add following lines.
export PIG_HOME=/usr/lib/pig
export PATH=$PATH:$PIG_HOME/bin
• Type following command to make it in effect:
source ~/.bashrc

22. Start the Pig
• Start the pig in local mode:
pig -x local
• Start the pig in mapreduce mode (needs hadoop
datanode started):
pig -x mapreduce

23. Grunt shell

24. Data Processing with PIG

25. Example: movies_data.csv
1,Dhadakebaz,1986,3.2,7560
2,Dhumdhadaka,1985,3.8,6300
3,Ashi hi banva banvi,1988,4.1,7802
4,Zapatlela,1993,3.7,6022
5,Ayatya Gharat Gharoba,1991,3.4,5420
6,Navra Maza Navsacha,2004,3.9,4904
7,De danadan,1987,3.4,5623
8,Gammat Jammat,1987,3.4,7563
9,Eka peksha ek,1990,3.2,6244
10,Pachhadlela,2004,3.1,6956

26. Load data
• $ pig -x local
• grunt> movies = LOAD
'movies_data.csv' USING
PigStorage(',') as
(id,name,year,rating,duration)
• grunt> dump movies;
it displays the contents

27. Filter data
• grunt> movies_greater_than_35 =
FILTER movies BY (float)rating > 3.5;
• grunt> dump movies_greater_than_35;

28. Store the results data
• grunt> store movies_greater_than_35
into 'my_movies';
• It stores the result in local file system directory
named 'my_movies'.

29. Display the result
• Now display the result from local file system.
cat my_movies/part-m-00000

30. Load command
• The load command specified only the column
names. We can modify the statement as follows
to include the data type of the columns:
• grunt> movies = LOAD
'movies_data.csv' USING
PigStorage(',') as (id:int,
name:chararray, year:int,
rating:double, duration:int);

31. Check the filters
• List the movies that were released between 1950 and
1960
grunt> movies_between_90_95 = FILTER
movies by year > 1990 and year < 1995;
• List the movies that start with the Alpahbet D
grunt> movies_starting_with_D = FILTER
movies by name matches 'D.*';
• List the movies that have duration greater that 2 hours
grunt> movies_duration_2_hrs = FILTER
movies by duration > 7200;

32. Output
Movies between
1990 to 1995
Movies starts
W
ith 'D'
Movies greater
Than 2 hours

33. Describe
• DESCRIBE The schema of a relation/alias can be
viewed using the DESCRIBE command:
grunt> DESCRIBE movies;
movies: {id: int, name: chararray,
year: int, rating: double, duration:
int}

34. Foreach
• FOREACH gives a simple way to apply
transformations based on columns. Let’s understand
this with an example.
• List the movie names its duration in minutes
grunt> movie_duration = FOREACH movies
GENERATE name, (double)(duration/60);
• The above statement generates a new alias that has
the list of movies and it duration in minutes.
• You can check the results using the DUMP command.

35. Output

36. Group
• The GROUP keyword is used to group fields in a
relation.
• List the years and the number of movies released
each year.
grunt> grouped_by_year = group movies
by year;
grunt> count_by_year = FOREACH
grouped_by_year GENERATE group,
COUNT(movies);

37. Output

38. Order by
• Let us question the data to illustrate the ORDER BY
operation.
• List all the movies in the ascending order of year.
grunt> desc_movies_by_year = ORDER
movies BY year ASC;
grunt> DUMP desc_movies_by_year;
• List all the movies in the descending order of year.
grunt> asc_movies_by_year = ORDER movies
by year DESC;
grunt> DUMP asc_movies_by_year;

39. Output- Ascending by year
From
1985
To
2004

40. Limit
• Use the LIMIT keyword to get only a limited number
for results from relation.
grunt> top_5_movies = LIMIT movies 5;
grunt> DUMP top_10_movies;

41. Pig: Modes of Execution
• Pig programs can be run in three methods which
work in both local and MapReduce mode. They are
– Script Mode
– Grunt Mode
– Embedded Mode

42. Script mode
• Script Mode or Batch Mode: In script mode, pig runs
the commands specified in a script file. The following
example shows how to run a pig programs from a
script file:
$ vim scriptfile.pig
A = LOAD 'script_file';
DUMP A;
$ pig ­x local scriptfile.pig

43. Grunt mode
• Grunt Mode or Interactive Mode: The grunt mode can also
be called as interactive mode. Grunt is pig's interactive shell.
It is started when no file is specified for pig to run.
$ pig ­x local
grunt> A = LOAD 'grunt_file';
grunt> DUMP A;
• You can also run pig scripts from grunt using run and exec
commands.
grunt> run scriptfile.pig
grunt> exec scriptfile.pig

44. Embedded mode
• You can embed pig programs in Java, python and
ruby and can run from the same.

45. Example: Wordcount program
• Q) How to find the number of occurrences of the
words in a file using the pig script?
• You can find the famous word count example written
in map reduce programs in apache website. Here we
will write a simple pig script for the word count
problem.
• The pig script given in next slide finds the number of
times a word repeated in a file:

46. Example: text file- shivneri.txt

47. Example: Wordcount program
lines = LOAD 'shivneri.txt' AS
(line:chararray);
words = FOREACH lines GENERATE
FLATTEN(TOKENIZE(line)) as word;
grouped = GROUP words BY word;
w_count = FOREACH grouped GENERATE group,
COUNT(words);
DUMP w_count;
forts.pig

48. Output snapshot
$ pig -x local forts.pig

49. References
• “Programming Pig” by Alan Gates, O'Reilly
Publishers.
• “Pig Design Patterns” by Pradeep Pasupuleti,
PACKT Publishing
• Tutorials Point
• http://github.com/rohitdens
• http://pig.apache.org

50. tushar@tusharkute.com
Thank you
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