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Table of contents
1.
Introduction
2.
Need of Pig and Pig Latin
3.
Features of Pig and Pig Latin
4.
Applications of Pig and Pig Latin
5.
Pig Data Types
6.
Types of Data Models in Pig
7.
Ways to run pig programs
8.
Operations Supported by pig
9.
Frequently Asked Questions
9.1.
Who developed Pig and when?
9.2.
Is Pig script case sensitive?
9.3.
What is the difference between Pig Latin and Pig Engine?
9.4.
What is pig storage?
9.5.
What are the Pig execution environment modes?
10.
Conclusion
Last Updated: Mar 27, 2024
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Pig and Pig Latin

Author SAURABH ANAND
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Data structures & algorithms (Beginner to Intermediate)
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Introduction

Pig is a high-level platform or tool for processing massive data sets. It provides a high level of abstraction for MapReduce computation. It includes a high-level scripting language called Pig Latin, used to create data analysis codes. The programmers will build scripts in the Pig Latin Language to process the data stored in the Hadoop distributed file system(HDFS). 

 

Internally, Pig Engine (an Apache Pig component) converted all of these scripts into a single map and reduced the process. However, to give a high level of abstraction, these are not visible to programmers. The Apache Pig tool's two primary components are Pig Latin and Pig Engine. Pig's output is always saved in HDFS.

 

Pig and Pig Latin are very much related to each other. Pig provides a high-level language known as Pig Latin for writing data analysis applications. This language has several operators that programmers can use to create their own functions for reading, writing, and processing data.


To analyze data using Apache Pig, programmers must create scripts in the Pig Latin language. Internally, all of these scripts are turned to Map and Reduce jobs. The Pig Engine component of Apache Pig accepts Pig Latin scripts as input and turns them into MapReduce jobs.

Need of Pig and Pig Latin

Programmers that are not fluent in Java typically struggle when working with Hadoop, particularly when doing MapReduce jobs. Apache Pig is a godsend for all of these programmers.

  • Programmers can readily perform MapReduce tasks using Pig and Pig Latin without entering sophisticated Java code.
  • Apache Pig employs a multi-query method, which reduces code length. For example, in Apache Pig, an operation that would require 200 lines of code (LoC) in Java can be completed in as little as 10 LoC. Finally, Apache Pig cuts development time by nearly 16 times.
  • Pig Latin is a SQL-like language that is simple to learn if familiar with SQL.
  • Apache Pig includes many built-in operators to help with data operations such as joins, filters, sorting, etc. Furthermore, it adds nested data types such as tuples, bags, and maps that MapReduce lacks.
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Features of Pig and Pig Latin

Apache Pig and Pig Latin come with the following features.

  • Rich set of operators: It has a variety of operators for performing operations like join, sort, filter, etc.
     
  • Handles all kinds of data: Apache Pig examines various data types, organized and unstructured. The findings are saved in HDFS.
     
  •  User-Defined Functions(UDFs): Pig allows you to write user-defined functions in other programming languages, such as Java, and then invoke or embed them in Pig Scripts.
     
  • Extensibility: Users can create their functions to read, process, and write data using current operators.
     
  • Ease of programming: Pig Latin is comparable to SQL, and writing a Pig script is simple if you know SQL.
     
  • Optimization opportunities: The jobs in Apache Pig optimize their execution automatically, so programmers need to focus on language semantics.

Applications of Pig and Pig Latin

Few applications of Pig and Pig Latin are mentioned below.

  • Pig scripts are used for exploring massive databases.
     
  • Pig and Pig Latin provide support for ad-hoc queries across huge data sets.
     
  • Pig scripts aid in the development of massive data set processing methods.
     
  • Pig is required for the processing of time-sensitive data loads.
     
  • Pig scripts are used to collect massive volumes of data in search logs and web crawls.

Pig Data Types

Apache Pig supports a wide range of data formats. A collection of Apache Pig data types, together with descriptions and examples, is provided below.

Type

Description

Example

Int

Signed 32 bit integer

2

Long

Signed 64 bit integer

15L or 15l

Float

32 bit floating point

2.5f or 2.5F

Double

32 bit floating point

1.5 or 1.5e2 or 1.5E2

charArray

Character array

hello codingNinjas

byteArray

BLOB(Byte array)

 

tuple

Ordered set of fields

(12,43)

bag

Collection f tuples

{(12,43),(54,28)}

map

collection of tuples

[open#apache]

Types of Data Models in Pig

It consists of the four types of data models as follows:  

  • Atom: It is an atomic data value used to store as a string. The primary use of this model is that it can be used as a number and a string.
  • Tuple: It is an ordered set of fields.
  • Bag: It is a collection of tuples.
  • Map: It is a set of key/value pairs.

Ways to run pig programs

Pig scripts can be run in three different ways, all of them compatible with local and Hadoop modes:

  • Script: A file containing Pig Latin commands are identified by the pig suffix (for example, file_x.pig). These commands are interpreted by Pig and executed in sequential order.
     
  • Grunt: Grunt is a command parser. If you type Pig Latin on the grunt command line, Grunt will execute the command for you. This is quite helpful for prototyping and "what if" scenarios.
     
  • Embedded: Pig programs can be executed as part of a Java program.

Read about Batch Operating System here.

Operations Supported by pig

Pig Latin has a very rich syntax. It supports operators for the following

operations:

  • Loading and storing of data
  • Streaming data
  • Filtering data
  • Grouping and joining data
  • Sorting data
  • Combining and splitting data

 

Start practicing these top 100 SQL problems before getting your hands on Pig and Pig Latin.

Must Read Apache Server

Frequently Asked Questions

Who developed Pig and when?

Apache Pig was developed in 2006 as a research project at Yahoo to construct and run MapReduce tasks on any dataset.

Is Pig script case sensitive?

Pig script is case sensitive as well as case insensitive. For example, in user-written functions, the field name and relations are case sensitive, i.e., CODINGNINJAS is not the same as codingninjas, and M=load 'test' is not the same m=load 'test'. In addition, Pig script keywords are case insensitive, so LOAD is the same as a load.

What is the difference between Pig Latin and Pig Engine?

Pig Latin is a scripting language similar to Perl used to search large data sets. It is composed of a sequence of transformations and operations that are applied to the input data to create data.
The Pig engine is the environment in which Pig Latin programs are executed. It translates Pig Latin operators into MapReduce jobs.

What is pig storage?

Pig has a built-in load function called pig storage. In addition, whenever we wish to import data from a file system into the Pig, we can use Pig storage.

What are the Pig execution environment modes?

Apache Pig scripts can be executed in three ways: interactive mode, batch mode, and embedded mode.

Conclusion

In this article, we have extensively discussed the concepts of Pig and Pig Latin. We started with introducing Pig and Pig Latin, the need for Pig and Pig Latin, features of Pig and Pig Latin, and finally concluded with the Types of Data Models in Pig.

We hope that this blog has helped you enhance your knowledge regarding Pig and Pig Latin and if you would like to learn more, check out our articles on managing big data. Do upvote our blog to help other ninjas grow. Happy Coding!

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