Pig in Hadoop

Pig Hadoop is basically a high-level programming language that is helpful for the analysis of huge datasets. Pig Hadoop was developed by Yahoo! and is generally used with Hadoop to perform a lot of data administration operations.

For writing data analysis programs, Pig renders a high-level programming language called Pig Latin. Several operators are provided by Pig Latin using which personalized functions for writing, reading, and processing of data can be developed by programmers.

For analyzing data through Apache Pig, we need to write scripts using Pig Latin. Then, these scripts need to be transformed into MapReduce tasks. This is achieved with the help of Pig Engine.

Why Apache Pig?

By now, we know that Apache Pig is used with Hadoop, and Hadoop is based on the Java programming language. Now, the question that arises in our minds is ‘Why Pig?’ The need for Apache Pig came up when many programmers weren’t comfortable with Java and were facing a lot of struggle working with Hadoop, especially, when MapReduce tasks had to be performed. Apache Pig came into the Hadoop world as a boon for all such programmers.

• After the introduction of Pig Latin, now, programmers are able to work on MapReduce tasks without the use of complicated codes as in Java.
• To reduce the length of codes, the multi-query approach is used by Apache Pig, which results in reduced development time by 16 folds.
• Since Pig Latin is very similar to SQL, it is comparatively easy to learn Apache Pig if we have little knowledge of SQL.

Features of Pig Hadoop

There are several features of Apache Pig:

1. In-built operators: Apache Pig provides a very good set of operators for performing several data operations like sort, join, filter, etc.
2. Ease of programming: Since Pig Latin has similarities with SQL, it is very easy to write a Pig script.
3. Automatic optimization: The tasks in Apache Pig are automatically optimized. This makes the programmers concentrate only on the semantics of the language.
4. Handles all kinds of data: Apache Pig can analyze both structured and unstructured data and store the results in HDFS.

Apache Pig Architecture

The main reason why programmers have started using Hadoop Pig is that it converts the scripts into a series of MapReduce tasks making their job easy.

Pig Hadoop framework has four main components:

1. Parser: When a Pig Latin script is sent to Hadoop Pig, it is first handled by the parser. The parser is responsible for checking the syntax of the script, along with other miscellaneous checks. Parser gives an output in the form of a Directed Acyclic Graph (DAG) that contains Pig Latin statements, together with other logical operators represented as nodes.
2. Optimizer: After the output from the parser is retrieved, a logical plan for DAG is passed to a logical optimizer. The optimizer is responsible for carrying out the logical optimizations.
3. Compiler: The role of the compiler comes in when the output from the optimizer is received. The compiler compiles the logical plan sent by the optimize The logical plan is then converted into a series of MapReduce tasks or jobs.
4. Execution Engine: After the logical plan is converted to MapReduce jobs, these jobs are sent to Hadoop in a properly sorted order, and these jobs are executed on Hadoop for yielding the desired result.

Downloading and Installing Pig Hadoop

Follow the below steps for the Apache Pig installation. These steps are for Linux/CentOS/Windows (using VM/Ubuntu/Cloudera). In this tutorial section on ‘Pig Hadoop’, we are using CentOS.

Step 1: Download the Pig.tar file by writing the following command on your Terminal:

wget http://www-us.apache.org/dist/pig/pig-0.16.0/pig-0.16.0.tar.gz

Step 2: Extract the tar file (you downloaded in the previous step) using the following command:

 tar -xzf pig-0.16.0.tar.gz

Your tar file gets extracted automatically from this command. To check whether your file is extracted, write the command ls for displaying the contents of the file. If you see the below output, the Pig file has been successfully extracted.

Step 3: Edit the .bashrc file for updating the Apache Pig environment variables. It is required to set this up in order to access Pig from a directory instead of going to the Pig directory for executing Pig commands. Other applications can also access Pig using this path of Apache Pig from this file

A new window will open up wherein you need to add a few commands.

When the above window pops up, write down the following commands at the end of the file:

# Set PIG_HOME

export PIG_HOME=/home/training/pig-0.16.0

export PATH=$PATH:/home/training/pig-0.16.0/bin

export PIG_CLASSPATH=$HADOOP_CONF_DIR

You then need to save this file in:

File > Save

You have to close this window and then, on your terminal, enter the following command for getting the changes updated:

source .bashrc

Step 4: Check the Pig Version. To check whether Pig is successfully installed, you can run the following command:

pig -version

Step 5: Start the Grunt Shell (used to run Pig Latin scripts) by running the command: Pig

By default, Pig Hadoop chooses to run MapReduce jobs in which access is required to the Hadoop cluster and the HDFS installation. But there is another mode, i.e., a local mode in which all the files are installed and run using a localhost, along with the file system. You can run the localhost mode using the command: pig -x local
I hope you were able to successfully install Apache Pig. In this section on Apache Pig, we learned ‘What is Apache Pig?’, why we need Pig, its features, architecture, and finally the installation of Apache Pig.

Apache Pig:

It is a high-level platform for creating programs that runs on Hadoop, the language is known as Pig Latin. Pig can execute its Hadoop jobs in MapReduce

Data types:

A particular kind of data defined by the values it can take

• Simple data types:
  • Int – It is a signed 32 bit integer
  • Long- It is a signed 64 bit integer
  • Float- 32 bit floating point
  • Double- 64 bit floating point
  • Chararray- Character array in UTF 8 format
  • Bytearray- byte array (blob)
  • Boolean: True or False
• Complex data types:
  • Tuple: It is an ordered set of fields
  • Bag: It is a collection of tuples
  • Map: A set of key value pairs

Apache Pig Components:

• Parser: Parser is used to check the syntax of the scripts.
• Optimizer: It is used for the logical optimizations such as projection and push down
• Compiler: Compiler is used to compile the optimized logical plan into a series of MapReduce jobs
• Execution engine: The MapReduced jobs are executed on Hadoop, and the desired results are obtained

Pig execution modes:

• Grunt mode: This is a very interactive and useful mode in testing syntax checking and ad hoc data exploration
• Script mode: It is used to run set of instructions from a file
• Embedded mode: It is useful to execute pig programs from a java program
• Local mode: In this mode the entire pig job runs as a single JVM process
• MapReduce Mode: In this mode, pig runs the jobs as a series of map reduce jobs
• Tez: In this mode, pig jobs run as a series of tez jobs

Apache Pig Architecture

Pig commands equivalent to the SQL functions:

Pig Operators:

Basic Operators:

Relational Operators:

Diagnostic Operators:

Differentiation between Operational vs. Analytical Systems

Traditional Enterprise Approach

This approach of enterprise will use a computer to store and process big data. For storage purpose is available of their choice of database vendors such as Oracle, IBM, etc. The user interacts with the application, which executes data storage and analysis.

Limitation

This approach are good for those applications which require low storage, processing and database capabilities, but when it comes to dealing with large  amounts of scalable data, it imposes a bottleneck.

Solution

Google solved this problem using an algorithm based on MapReduce. This algorithm divides the task into small parts or units and assigns them to multiple computers, and intermediate results together integrated results in the desired results. Intellipaat’s Big Data Hadoop training will really help you get a better understanding the concepts of Big Data Solutions in Open Data Platform!

Pig built-in functions:

Eval functions:

• AVG(col): computes the average of the numerical values in a single column of a bag
• CONCAT(string expression1, string expression2) : Concatenates two expressions of identical type
• COUNT(DataBag bag): Computes the number of elements in a bag excluding null values
• COUNT STAR (DataBag bag1, DataBag bag 2): Computes the number of elements in a bag including null values.
• DIFF(DataBag bag1, DataBag bag2): It is used to compare two bags, if any element in one bag is not present in the other bag are returned in a bag
• IsEmpty(DataBag bag), IsEmpty(Map map): It is used to check if the bag or map is empty
• Max(col): Computes the maximum of the numeric values or character in a single column bag
• MIN(col): Computes the minimum of the numeric values or character in a single column bag
• DEFINE pluck pluckTuple(expression1): It allows the user to specify a string prefix, and filters the columns which begins with that prefix
• SIZE(expression): Computes the number of elements based on any pig data
• SUBSTRACT(DataBag bag1, DataBag bag2): It returns the bag which does not contain bag1 element in bag2
• SUM: Computes the sum of the values in a single-column bag
• TOKENIZE(String expression[,‘field delimiter’): It splits the string and outputs a bag of words

Load or Store Functions:

• PigStorage ():

Syntax: PigStorage(field_delimiter)
A = LOAD ‘Employee’ USING PigStorage(‘\t’) AS (name: chararray, age:int, gpa: float);
Loads and stores data as structured text file

• TextLoader():

Syntax: A = LOAD ‘data’ USING TextLoader();
Loads unstructured data in UTF 8 format

• BinStorage():

Syntax: A = LOAD ‘data’ USING BinStorage();
Loads and stores data in machine readable format

• Handling compression:

It loads and stores compressed data in Pig

• JsonLoader, JsonStorage:

Syntax: A = load ‘a.json’ using JsonLoader();
It loads and stores JSON data

• Pig dump:

Syntax: STORE X INTO ‘output’ USING PigDump ();
Stores data in UTF 8 format

Math functions:

• ABS:

Syntax: ABS(expression)
It returns the absolute value of an expression

• COS:

Syntax: COS(expression)
It Returns the trigonometric cosine of an expression.

• SIN:

Syntax: SIN (expression)
It returns the sine of an expression.

• CEIL:

Syntax: CEIL(expression)
It is used to return the value of an expression rounded up to the nearest integer

• TAN:

Syntax: TAN(expression)
It is used to return the trigonometric tangent of an angle.

• ROUND:

Syntax: ROUND(expression)
It returns the value of an expression rounded to an integer (if the result type is float) or long (if the result type is double)

• RANDOM:

Synatx: RANDOM ()
It returns a pseudo random number (type double) greater than or equal to 0.0 and less than 1.0

• Floor:

Syntax: FLOOR(expression)
Returns the value of an expression rounded down to the nearest integer.

• CBRT:

Synatx: CBRT(expression)
It returns the cube root of an expression

• EXP:

Syntax: EXP(expression)
Returns Euler’s number e raised to the power of x.

String Functions:

• INDEXOF:

Syntax: INDEXOF (string, ‘character’, startIndex)
It returns an index of the first occurrence of a character in a string

• LAST_INDEX:

Syntax: LAST_INDEX_OF (expression)
It returns an index of the last occurrence of a character in a string

• TRIM:

Syntax: TRIM(expression)
It returns a copy of the string with leading and trailing whitespaces removed

• SUBSTRING:

Syntax: SUBSTRING (string, startIndex, stopIndex)
It will return a substring from a given string

• UCFIRST:

Syntax: UCFIRST(expression)
It will return a string with the first character changed to the upper case

• LOWER:

Syntax: LOWER(expression)
Converts all characters in a string to lowercase

• UPPER:

Synatx: UPPER(expression)
Converts all characters in a string to the uppercase

Tuple, Bag and Map functions:

In the next section of this tutorial, we will learn about Apache Hive.