While writing a script in a file, we can include comments in it as shown below.
Multi-line comments
We will begin the multi-line comments with ‘/*’, end them with ‘*/’.
/* These are the multi-line comments
In the pig script */
Single –line comments
We will begin the single-line comments with ‘–‘.
–we can write single line comments like this.

Executing Pig Script in Batch mode

While executing Apache Pig statements in batch mode, follow the steps given below.
Step 1
Write all the required Pig Latin statements in a single file. We can write all the Pig Latin statements and commands in a single file and save it as .pig file.
Step 2
Execute the Apache Pig script. You can execute the Pig script from the shell (Linux) as shown below.

Local mode	MapReduce mode
$ pig -x local Sample_script.pig	$ pig -x mapreduce Sample_script.pig

You can execute it from the Grunt shell as well using the exec command as shown below.
grunt> exec /sample_script.pig
Executing a Pig Script from HDFS
We can also execute a Pig script that resides in the HDFS. Suppose there is a Pig script with the name Sample_script.pig in the HDFS directory named /pig_data/. We can execute it as shown below.
$ pig -x mapreduce hdfs://localhost:9000/pig_data/Sample_script.pig
Example
Assume we have a file student_details.txt in HDFS with the following content.
student_details.txt
001,Rajiv,Reddy,21,9848022337,Hyderabad
002,siddarth,Battacharya,22,9848022338,Kolkata
003,Rajesh,Khanna,22,9848022339,Delhi
004,Preethi,Agarwal,21,9848022330,Pune
005,Trupthi,Mohanthy,23,9848022336,Bhuwaneshwar
006,Archana,Mishra,23,9848022335,Chennai
007,Komal,Nayak,24,9848022334,trivendram
008,Bharathi,Nambiayar,24,9848022333,Chennai
We also have a sample script with the name sample_script.pig, in the same HDFS directory. This file contains statements performing operations and transformations on the student relation, as shown below.
student = LOAD ‘hdfs://localhost:9000/pig_data/student_details.txt’ USING PigStorage(‘,’)
as (id:int, firstname:chararray, lastname:chararray, phone:chararray, city:chararray);

student_order = ORDER student BY age DESC;

student_limit = LIMIT student_order 4;

Dump student_limit;
• The first statement of the script will load the data in the file named student_details.txt as a relation named student.
• The second statement of the script will arrange the tuples of the relation in descending order, based on age, and store it as student_order.
• The third statement of the script will store the first 4 tuples of student_order as student_limit.
• Finally the fourth statement will dump the content of the relation student_limit.
Let us now execute the sample_script.pig as shown below.
$./pig -x mapreduce hdfs://localhost:9000/pig_data/sample_script.pig
Apache Pig gets executed and gives you the output with the following content.
(7,Komal,Nayak,24,9848022334,trivendram)
(8,Bharathi,Nambiayar,24,9848022333,Chennai)
(5,Trupthi,Mohanthy,23,9848022336,Bhuwaneshwar)
(6,Archana,Mishra,23,9848022335,Chennai)
2015-10-19 10:31:27,446 [main] INFO org.apache.pig.Main – Pig script completed in 12
minutes, 32 seconds and 751 milliseconds (752751 ms)

So, this brings us to the end of blog. This Tecklearn ‘How to run Apache Pig Scripts in Batch Mode’ helps you with commonly asked questions if you are looking out for a job in Apache Pig and Big Data Domain.
If you wish to learn Apache Pig and build a career in Apache Pig or Big Data domain, then check out our interactive, Big Data Hadoop Analyst Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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Hadoop Fundamentals
• The Motivation for Hadoop
• Hadoop Overview
• Data Storage: HDFS
• Distributed Data Processing: YARN, MapReduce, and Spark
• Data Processing and Analysis: Pig, Hive, and Impala
• Data Integration: Sqoop
• Other Hadoop Data Tools
• Exercise Scenarios Explanation
Introduction to Pig
• What Is Pig?
• Pig’s Features
• Pig Use Cases
• Interacting with Pig
Basic Data Analysis with Pig
• Pig Latin Syntax
• Loading Data
• Simple Data Types
• Field Definitions
• Data Output
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• Storage Formats
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• Techniques for Combining Data Sets
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• Splitting Data Sets
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• Troubleshooting Pig
• Logging
• Using Hadoop’s Web UI
• Data Sampling and Debugging
• Performance Overview
• Understanding the Execution Plan
• Tips for Improving the Performance of Your Pig Jobs
Introduction to Hive and Impala
• What Is Hive?
• What Is Impala?
• Schema and Data Storage
• Comparing Hive to Traditional Databases
• Hive Use Cases
Querying with Hive and Impala
• Databases and Tables
• Basic Hive and Impala Query Language Syntax
• Data Types
• Differences Between Hive and Impala Query Syntax
• Using Hue to Execute Queries
• Using the Impala Shell
Data Management
• Data Storage
• Creating Databases and Tables
• Loading Data
• Altering Databases and Tables
• Simplifying Queries with Views
• Storing Query Results
Data Storage and Performance
• Partitioning Tables
• Choosing a File Format
• Managing Metadata
• Controlling Access to Data
Relational Data Analysis with Hive and Impala
• Joining Datasets
• Common Built-In Functions
• Aggregation and Windowing
Working with Impala
• How Impala Executes Queries
• Extending Impala with User-Defined Functions
• Improving Impala Performance
Analyzing Text and Complex Data with Hive
• Complex Values in Hive
• Using Regular Expressions in Hive
• Sentiment Analysis and N-Grams
• Conclusion
Hive Optimization
• Understanding Query Performance
• Controlling Job Execution Plan
• Bucketing
• Indexing Data
Extending Hive
• SerDes
• Data Transformation with Custom Scripts
• User-Defined Functions
• Parameterized Queries
Choosing the Best Tool for the Job
• Comparing MapReduce, Pig, Hive, Impala, and Relational Databases

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