Apache Pig is a high-level platform for creating programs that run on Apache Hadoop. It provides a language known as Pig Latin, which enables users to create their own operations to process and analyze large datasets stored in Hadoop clusters. This tutorial covers the basics of Apache Pig, including its architecture, syntax, and components.
Audience
This Apache Pig tutorial is intended for software professionals and students who are interested in learning the Apache Pig platform for analyzing large datasets. It can also be helpful for professionals who are looking to refresh their knowledge of Apache Pig.
Prerequisites
1. Apache Hadoop – Apache Pig requires Apache Hadoop to be installed and running on the system.
2. Java – Apache Pig requires Java to be installed on the system.
3. Environment Variables – Apache Pig requires environment variables to be set in order to run correctly.
4. Hadoop Streaming – Apache Pig requires Hadoop streaming to be installed and enabled in order to run correctly.
5. Apache Pig – Apache Pig requires itself to be installed and configured correctly in order to run correctly.
What is Apache Pig?
Apache Pig is an open-source platform for creating and executing MapReduce programs written in the Pig Latin language. It allows developers to rapidly analyze large datasets stored in Hadoop clusters using a simple programming language. Pig Latin is designed to make it easier to write programs that process large datasets in Hadoop.
What is Pig Latin?
Pig Latin is a data flow language designed to make it easier to write MapReduce programs. It is a high-level language that allows developers to write data-processing jobs in a simpler, more compact way than with Java MapReduce. It provides an abstraction layer over the underlying MapReduce framework, allowing developers to focus on the business logic of their applications instead of the details of the underlying framework.
What are the components of Apache Pig?
Apache Pig consists of several components, including the Pig Latin language, the Pig compiler, the Pig runtime environment, and the Pig Latin user interface.
Pig Latin – The Pig Latin language is the primary language used to write Pig programs. It is a high-level language that provides a simple way to write data-processing jobs.
Pig Compiler – The Pig compiler takes Pig Latin programs and compiles them into MapReduce jobs that can be executed on a Hadoop cluster.
Pig Runtime Environment – The Pig runtime environment is responsible for executing Pig Latin programs on the Hadoop cluster. It contains a set of libraries and tools that are used to manage the execution of Pig programs.
Pig Latin User Interface – The Pig Latin user interface is a web-based graphical environment that allows users to create, manage, and debug Pig Latin programs.
What are the advantages of using Apache Pig?
Apache Pig provides several advantages over using Java MapReduce. It is easier to write data-processing jobs in Pig Latin than in traditional Java MapReduce. It also provides an abstraction layer over the underlying MapReduce framework, allowing developers to focus on the business logic of their applications instead of the details of the underlying framework. Additionally, it is faster and easier to debug Pig Latin programs than Java MapReduce programs.
Apache Pig – Execution
Let us learn about execution of Apache Pig
Apache Pig Execution Modes
Apache Pig provides two modes for executing its applications:
Local mode:
This is the default mode for running Pig applications. In this mode, Pig scripts are executed on a single machine without any distribution. It is used for testing and debugging of Pig scripts.
MapReduce mode:
This mode is used for production-level execution of Pig applications. In this mode, Pig scripts are executed on a Hadoop cluster using MapReduce framework. This mode is used for large-scale data processing.
Invoking the Grunt Shell
Grunt shell can be invoked using the command “grunt” in the terminal. This will start the Grunt shell and display a prompt to enter commands. The commands available depend on the configuration of the Grunt shell.
Executing Apache Pig in Batch Mode
To execute Apache Pig in batch mode, use the following command:
pig -x local -f <script_name>.pig
This command will execute the script specified in the file <script_name>.pig locally. The output of the script will be displayed in the terminal.
Apache Pig – Grunt Shell
Apache Pig is a high-level programming language used for data analysis. It is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The language, Pig Latin, is a data flow language that enables users to write queries and transform data. The Grunt shell is an interactive shell for running Pig Latin commands. It provides an environment for users to enter Pig Latin commands and execute them. The Grunt shell can also be used to debug or modify existing Pig Latin programs.
Shell Commands
The Grunt shell of Apache Pig is a command line interpreter for Pig Latin, the language used for expressing data analysis programs. It is used to execute commands related to Pig Latin, such as loading data, running jobs, and displaying the results. It also provides an environment for developing, testing, and executing Pig Latin scripts.
The Grunt shell does not include a `sh` command, as it is a shell specifically designed to work with MongoDB. To access a system shell, such as Bash or Zsh, you need to open a terminal window outside the Grunt shell.
Grunt shell is a command-line interface for managing, configuring, and deploying applications in the cloud. It provides a set of utility commands to help developers manage their applications and resources in the cloud.
The Grunt shell provides a number of useful commands for managing applications and resources in the cloud, including:
• deploy – Deploys an application to the cloud.
• delete – Deletes an application from the cloud.
• update – Updates an application in the cloud.
• list – Lists all applications and resources in the cloud.
• start – Starts an application in the cloud.
• stop – Stops an application in the cloud.
• scale – Scales an application in the cloud.
• describe – Describes an application in the cloud.
• logs – Retrieves logs from an application in the cloud.
• help – Displays help information for the Grunt shell.
Pig Latin – Basics
Pig Latin is a language game or code where words in English are altered by adding a fabricated suffix or by moving the first letter of the word to the end and adding an ‘ay’. For example, the word “hello” becomes “ellohay” in Pig Latin.
The purpose of Pig Latin is to obscure the original English words. It can be seen as a way of having a secret language. It is also used as a way to have some fun with friends.
Pig Latin – Data Modeling
Pig Latin is a data modeling language used to process and analyze large datasets. It is a procedural language that allows users to write complex data processing algorithms. Pig Latin is based on SQL and is used to transform, filter, and aggregate data in a distributed environment. Pig Latin is used in conjunction with the Hadoop distributed file system and the MapReduce programming model. Pig Latin programs are written in a procedural language that enables users to write complex data processing algorithms. The Pig Latin language consists of a set of relational operators that are used to manipulate datasets. Pig Latin programs are written in a procedural language and can be used to process and analyze large datasets stored in a distributed file system. Pig Latin can be used to perform data transformations, data filtering, data aggregation, and other data processing tasks. Pig Latin also supports user-defined functions, which allow users to extend the language to do custom data processing tasks. Pig Latin programs can be deployed in a distributed computing environment, such as Hadoop, that can scale to process large datasets.
Pig Latin – Statemets
Pig Latin statements are primarily used to perform data operations like data filtering, data sorting and data transformation. These statements are written in a language similar to SQL. Pig Latin statements are used to transform data from one format to another or to perform calculations or aggregation on data. The data can be loaded from external sources like HDFS or from a local file system. The data can then be manipulated using Pig Latin statements and the results can be stored back to HDFS or to the local file system.
Pig Latin – Data types
In Pig Latin, the data types are similar to those in other programming languages like Java and C++. The data types in Pig Latin include: int, long, float, double, chararray, bytearray, datetime, and map. These data types are used to represent different types of data, such as numbers, strings, dates, and maps.
In Apache Pig, NULL values can be used to represent missing or unknown values. Pig provides the built-in functions ISNULL and ISNOTNULL to check for NULL values. These functions return a Boolean TRUE or FALSE value depending on whether the value is NULL or not.
For example, the following statement returns TRUE if the value of the A column is NULL:
A = ISNULL(A);
The following statement returns TRUE if the value of the A column is NOT NULL:
A = ISNOTNULL(A);
Pig Latin – Arithmetic Operators
Pig Latin supports a range of arithmetic operators, including addition (+), subtraction (-), multiplication (*), division (/), modulo (%) and exponentiation (^). It also supports numeric comparison operators like less than (<), greater than (>), less than or equal to (<=) and greater than or equal to (>=).
Pig Latin – Comparison Operators
Pig Latin supports a variety of comparison operators, including equal (==), not equal (!=), less than (<), greater than (>), less than or equal to (<=), and greater than or equal to (>=). These can be used to compare values in Pig Latin scripts, allowing for the creation of more complex queries.
Pig Latin – Type Construction Operators
Givin:
-ADD: Adds two numbers together
-SUBTRACT: Subtracts one number from another
-MULTIPLY: Multiplies two numbers together
-DIVIDE: Divides one number by another
-MODULUS: Divides one number by another and returns the remainder
Pig Latin – Relational Operations
Pig Latin is a language used to express relational operations on data stored in Apache Hadoop. Pig Latin allows the user to write code that can be used to analyze and process large amounts of data stored in a Hadoop cluster. The language is designed to be easy to use and understand, and allows the user to define data processing steps that can be executed in either map-reduce or in-memory mode. Pig Latin can also be used to create complex data pipelines that can be used to process data in different ways.
Apache Pig – Reading Data
Apache Pig is a high-level data processing language used for reading and analyzing large data sets. Pig is built on top of the Hadoop framework and uses a language called Pig Latin. It enables users to write complex data transformations without having to write code. Pig Latin is a data flow language that enables users to specify the transformations they would like to perform on the data. Pig can read data from files stored in the Hadoop Distributed File System (HDFS) or from structured data sources, such as Apache Hive.
Preparing HDFS
1. Start the HDFS Service: Start the HDFS service by running the command ‘start-dfs.sh’ from the command line.
2. Format the HDFS: Formatting the HDFS is done by running the ‘hdfs namenode -format’ command from the command line.
3. Create the HDFS directories: Create the HDFS directories by running the ‘hdfs dfs -mkdir’ command from the command line. This command will create the necessary directories for storing the data.
4. Load the HDFS data: Load the HDFS data by running the ‘hdfs dfs -put’ command from the command line. This command will load the data into the HDFS cluster.
5. Check the HDFS data: Check the HDFS data by running the ‘hdfs dfs -ls’ command from the command line. This command will list the data stored in the HDFS cluster.
LOAD operator
LOAD operator in Pig Latin is used to load the data from the filesystem into the Pig Latin program. It reads the data from the specified filesystem, parses it according to the specified data formats and stores it into the Pig Latin program.
Syntax of Load Operator
LOAD <DataSource> INTO <Target> [USING <Loader>]
Example
LOAD CSV FILE ‘C:\File.csv’ INTO TABLE Users USING CSV Loader;
Start the Pig Grunt Shell
The Pig Grunt Shell can be started by navigating to the directory containing the Pig installation and executing the command “pig -x local”.
Execute the Load Statement
The LOAD statement is used to load data from a file into a database. To execute this statement, the syntax would be LOAD DATA INFILE ‘file_name.txt’ INTO TABLE table_name;. This statement can also be used to specify specific columns or rows to load.
LOAD DATA INFILE ‘/path/to/file.csv’ INTO TABLE my_table;
Apache Pig – Storing Data
Apache Pig is a high-level scripting language used in Apache Hadoop for data analysis. It is used to process large data sets and extract meaningful information from them. It is a data flow language which allows users to write complex data transformations without having to write MapReduce jobs. Pig makes it easier to store data, as it allows you to store data in various formats such as CSV, TSV, and JSON. Pig also provides support for external data sources such as MongoDB, Cassandra, and HBase.
The Store operator in Apache Pig is used to save the output of an Apache Pig script to a file, local or HDFS. It is the last operator used in a Pig script and is used to write data to the file system.
To store data in Apache Pig using the Store operator, follow these steps:
1) Input your data into a Pig script.
2) Perform the necessary transformations on the data using Pig Latin commands.
3) Once your transformations are complete, use the Store operator to write the results to the file system.
The syntax of the Store operator is as follows:
STORE <Alias> INTO <Path to Store Results> [USING <Function>]
For example, to store the results of the data transformation in a file named ‘results.txt’ in HDFS, use the following command:
STORE result INTO ‘/results.txt’ USING PigStorage(‘,’);
This will store the output of the data transformation to the specified file location.
Verification
The best way to verify the stored data in Apache Pig using the Store operator is to use the Dump operator. This operator will output the stored data to the terminal which can be inspected by the user. It is also possible to use the Describe operator to get a summary of the data that was stored.
Apache Pig – Diagnostic Operators
Apache Pig is an open source data processing framework that enables developers to write code to process large data sets. It provides a powerful set of diagnostic operators which allow developers to debug and troubleshoot their Pig scripts. These operators are useful for isolating and exploring data within a Pig script. Some of the most commonly used diagnostic operators include DUMP, ILLUSTRATE, DESCRIBE, EXPLAIN and SPLIT.
Dump – This operator is used to display the contents of a relation. It displays the contents of a relation to the console in a tabular format.
Illustrate – This operator is used to demonstrate the execution plan for a Pig script. It is useful for understanding how data flows through the script and for debugging complex scripts.
Describe – This operator is used to display the schema of a relation. It displays the fields and data types of a relation.
Explain – This operator is used to provide information about the execution of the script. It displays the logical and physical execution plans of a Pig script.
Split – This operator is used to divide a relation into two or more relations based on a predicate. It is useful for testing and debugging complex scripts.
Apache Pig – Describe Operator
The Apache Pig Describe operator is used to describe the schema of the data in a Pig relation. This includes the field names and data types of each field in the relation. The Describe operator is often used in a Pig Latin script to see the schema of the data before performing a query or transformation. It can also be used to verify the data types of each field after a transformation. The Describe operator is used in the form of DESCRIBE <relation> where <relation> is the name of the Pig relation.
For example, consider a relation ‘grades’ which contains 3 fields – student_id, course_name and grade.
Using DESCRIBE operator, the schema of the relation ‘grades’ can be obtained as follows:
grades = LOAD ‘grades.txt’ USING PigStorage(‘,’) AS (student_id:int, course_name:chararray, grade:int);
DESCRIBE grades;
grades: {student_id: int, course_name: chararray, grade: int}
Apache Pig – Explain Operator
The EXPLAIN operator in Apache Pig is used to display the logical, physical, and MapReduce execution plans of a Pig Latin script. This operator helps in understanding how a script is going to execute and which MapReduce jobs will be created when it is executed. The EXPLAIN operator is especially useful when trying to optimize Pig Latin scripts, as it reveals which parts of the script are the most costly.
Apache Pig is a high-level procedural language for querying large datasets stored in distributed file systems such as Apache Hadoop. The Apache Pig Explain operator is used to help understand how a Pig Latin statement is being processed.
For example, let’s say we want to find the average price of a product from a list of items stored in a file. We can use the following Pig Latin statement to accomplish this:
A = LOAD ‘items.txt’ USING PigStorage(‘,’);
B = FOREACH A GENERATE $2 as Price;
C = GROUP B BY Price;
D = FOREACH C GENERATE AVG(B.Price);
We can then use the Apache Pig Explain operator to help us understand how this statement is being processed. The following is the output of the Explain operator for this statement:
#——————————————————————————-
A: LOAD ‘items.txt’ USING PigStorage(‘,’)
— Loads data from items.txt into the relation A
B: FOREACH A GENERATE $2 as Price
— Generates a relation B from relation A, with the Price field from item.txt
C: GROUP B BY Price
— Groups relation B by the Price field
D: FOREACH C GENERATE AVG(B.Price)
— Generates a relation D from relation C, with the average Price of each group in relation B.
#——————————————————————————-
Apache Pig – Illustrate Operator
The Apache Pig operator is a language used to create and execute data analysis programs written in Pig Latin. Pig Latin is a data processing language that allows users to write custom scripts to process data from a variety of sources. It can be used in conjunction with Apache Hadoop or other big data frameworks.
The Pig Latin language is a procedural language, meaning that it is composed of commands that are written in a specific order. These commands are called operators. The most common operators are the LOAD, STORE, FILTER, GROUP, JOIN, and ORDER operators.
The LOAD operator is used to read data from an input source, such as a file or a database. The STORE operator is used to write the results of a data analysis to an output destination, such as a file or a database.
The FILTER operator is used to select only the data that meets certain criteria, such as a certain set of values. The GROUP operator is used to group the data based on certain criteria, such as the values of certain columns. The JOIN operator is used to join two datasets together, based on common columns or other criteria. The ORDER operator is used to sort the data based on certain criteria.
These operators are used to create data analysis programs that can be used for a variety of purposes, such as data mining, machine learning, or data analytics.
Apache Pig is a high-level platform for creating programs that run on Apache Hadoop. It consists of a language, Pig Latin, for expressing data analysis programs, coupled with infrastructure for evaluating these programs. Pig Latin includes a set of operators that can be used to manipulate, analyze and process data.
Operator: FILTER
Example:
A = LOAD ‘/user/pig/employee.csv’ USING PigStorage(‘,’);
B = FILTER A BY salary > 2000;
DUMP B;
The FILTER statement above will filter out all rows from the employee.csv dataset that have a salary of less than 2000. The resulting dataset, B, will contain only the rows with salaries greater than 2000.
Apache Pig – Group Operator
The GROUP operator in Apache Pig is used to group a set of records together to produce more aggregate information. It is similar to the GROUP BY clause in SQL. By using the GROUP operator, users can apply various aggregate functions like COUNT, MAX, MIN, AVG, SUM, etc. on the grouped data. The GROUP operator also allows users to group by multiple columns.
Apache Pig is an open source data processing platform used to analyze large data sets. The group operator is used to group data in Pig. It groups all the data records based on a common key, and then performs an operation (such as SUM, AVG, MAX, MIN, etc.) on each group.
Example
Suppose we have a table of student data as shown below:
Name Age Score
John 19 75
Jane 20 85
Bob 19 90
If we want to find the average score of students with the same age, we can use the GROUP operator.
A = LOAD ‘students.tsv’ AS (name:chararray, age:int, score:int);
B = GROUP A BY age;
C = FOREACH B GENERATE group, AVG(A.score);
DUMP C;
The result of this query would be:
(19, 82.5)
(20, 85.0)
Apache Pig – Cogroup Operator
The Apache Pig Cogroup operator is used to join two or more datasets on one or more common fields. It is analogous to the SQL JOIN command and is used to combine data from multiple sources. It returns a relation containing one or more columns that are common among all the inputs. Cogroup is especially useful when joining datasets with different schemas. It can also be used to perform set operations such as union, intersection, difference, and more.
The Cogroup operator is a relational operator that provides a way to group together two or more relations by one or more common fields. The Cogroup operator can be used to perform joins on multiple relations, or to perform a cross-product operation.
Example:
Suppose we have two relations, A and B, with the following fields:
A (name: chararray, age: int)
B (name: chararray, address: chararray)
We can use the Cogroup operator to group the two relations by the common field name, as follows:
A = LOAD ‘data1’ AS (name:chararray, age:int);
B = LOAD ‘data2’ AS (name:chararray, address:chararray);
COGROUP A BY name, B BY name;
This will produce a relation C with the following fields:
C ((name:chararray, A: {(name: chararray, age: int)}, B: {(name: chararray, address: chararray)}))
The Cogroup operator can also be used to perform a cross-product operation on two relations, as follows:
A = LOAD ‘data1’ AS (name:chararray, age:int);
B = LOAD ‘data2’ AS (name:chararray, address:chararray);
COGROUP A ALL, B ALL;
This will produce a relation C with the following fields:
C ((A: {(name: chararray, age: int)}, B: {(name: chararray, address: chararray)}))
Apache Pig – Join Operator
Apache Pig is a data processing platform that is part of the Apache Hadoop eco-system. It is designed to provide an easy-to-use, high-level language for manipulating, analyzing and querying large datasets stored in Hadoop clusters. One of the core operators in Apache Pig is the join operator. The join operator is used to combine two or more datasets in order to produce a single, combined dataset. This is done by joining the datasets on one or more columns that they have in common. The join operator can join datasets of different types, including tables, records, maps, and bags. It can also perform inner, outer, semi, and cross joins. The join operator is a powerful tool for combining data from different sources in order to generate insights and perform analysis.
The following is an example of how to use the join operator in Apache Pig.
— Loads the data into the relations ‘students’ and ‘grades’
students = LOAD ‘students.txt’ USING PigStorage(‘,’) AS (name:chararray, age:int);
grades = LOAD ‘grades.txt’ USING PigStorage(‘,’) AS (name:chararray, grade:int);
— Joins the data from both relations based on ‘name’
student_grades = JOIN students BY name, grades BY name;
— Store the joined data
STORE student_grades INTO ‘student_grades.txt’ USING PigStorage(‘,’);
Apache Pig – Cross Operator
The Cross operator in Apache Pig is used to generate a Cartesian product of two or more input relations. It is a relational algebra operator that takes two or more relations as input and produces a new relation with all the possible combinations of records. The Cross operator is used when it is necessary to generate all the possible combinations between two or more relations.
The Cross operator in Apache Pig is used to generate the Cartesian product of two or more input datasets. This operator produces all possible combinations of the tuples from the two datasets.
For example, if two datasets A and B are given and A has two tuples (1,2) and B has three tuples (x,y,z) then the Cross operator will produce six tuples (1,x), (1,y), (1,z), (2,x), (2,y), (2,z).
Example:
A = LOAD ‘/user/data/A’ AS (id:int, name:chararray);
B = LOAD ‘/user/data/B’ AS (id:int, age:int);
C = CROSS A, B;
DUMP C;
The above code will generate the following output:
(1,x,1,10)
(1,y,1,20)
(1,z,1,30)
(2,x,2,10)
(2,y,2,20)
(2,z,2,30)
Apache Pig – Union Operator
The Apache Pig union operator is a way to combine two or more relation data sets into a single relation. This can be useful when wanting to join multiple data sets that have the same schema. The union operator is a shortcut for a join operator, as it allows you to combine the data sets without having to write the join statement. The union operator is used in both Pig Latin and the Pig programming language.
The following Pig Latin script shows an example of the Union Operator:
— Load the data
raw_data = LOAD ‘data.txt’ USING PigStorage(‘,’) AS (name:chararray, age:int, occupation:chararray);
— Filter rows where age is greater than 30
filter_data = FILTER raw_data BY age > 30;
— Load the data from another file
other_data = LOAD ‘other_data.txt’ USING PigStorage(‘,’) AS (name:chararray, age:int, occupation:chararray);
— Union the two datasets
union_data = UNION filter_data, other_data;
— Store the data in a file
STORE union_data INTO ‘union_data.txt’ USING PigStorage(‘,’);
Apache Pig – Split Operator
The Split operator in Apache Pig is used to split a single data set into multiple data sets based on a specified condition or predicate. This operator can be used to split data into multiple files, or to separate data into different datasets based on a specific field or value. For example, you could use the Split operator to separate out customer data into separate files based on the customer’s country of residence. You could also use the Split operator to split data into separate datasets based on a customer’s age group.
The Split operator in Apache Pig is used to split a relation into multiple relations based on specific conditions.
For example, if we have a relation called A that contains three columns (x, y, z) and we want to split this relation into two relations based on the value of column x, we can use the Split operator as follows:
— Split relation A into two relations, B and C, based on the value of column x
SPLIT A INTO B IF x == ‘foo’, C IF x == ‘bar’;
This will create two new relations B and C, where B contains all rows that have ‘foo’ in the x column, and C contains all rows that have ‘bar’ in the x column.
Apache Pig – Filter Operator
The Apache Pig Filter operator is used to filter data in a Pig Latin script. It takes a Boolean expression as input and returns only the tuples that satisfy the given expression. The syntax is FILTER <relation> BY <boolean_expression>. For example, the following statement will return only the tuples in the relation A where the value of column x is greater than 5:
FILTER A BY x > 5;
The following example demonstrates how the Filter operator is used in Apache Pig.
A = LOAD ‘data.txt’ USING PigStorage(‘\t’) AS (name:chararray, age:int, gender:chararray);
— Filter out records from A whose age is greater than 25
B = FILTER A BY age > 25;
— Display the output
DUMP B;
Apache Pig – Distinct Operator
Apache Pig’s DISTINCT operator is used to remove duplicate tuples from a relation. This is done by keeping track of the distinct tuples that have already been seen and discarding any incoming duplicate tuples. The DISTINCT operator returns a relation containing only the distinct tuples after removing the duplicates.
Example:
A = LOAD ‘data.txt’ USING PigStorage(‘,’) AS (name:chararray, age:int);
B = DISTINCT A;
STORE B INTO ‘distinct_data’ USING PigStorage(‘,’);
In this example, the data loaded from the file “data.txt” is stored into the relation A. The DISTINCT operator then removes duplicate tuples from the relation A and stores the resultant relation in B. Finally, the relation B is stored into the file “distinct_data” using PigStorage.
Apache Pig – Foreach Operator
The Apache Pig Foreach operator is a data processing operator in the Apache Pig language that allows developers to apply a function to each element of a data set. It is similar to the map function in other programming languages, but applies the function to each element of a data set. The Foreach operator allows developers to apply any user defined function to each element of the data set, including custom UDFs (user-defined functions). The Foreach operator also allows developers to filter data, as well as perform basic transformations such as concatenating strings, and deriving new fields from existing fields. The Foreach operator is an essential part of the Apache Pig language and is used in many data processing pipelines.
A Foreach operator in Apache Pig is used to iterate over a set of data to apply an expression or a set of expressions to each element of the data.
For example:
input_data = LOAD ‘student_data.txt’ USING PigStorage(‘\t’) AS (name:chararray,age:int,grade:int);
output_data = FOREACH input_data GENERATE name, (grade + 10);
The above example loads the student data from a file and then applies an expression to each record by adding 10 to the grade field. The output of the above script will include the name and the new grade.
Apache Pig – Order By
The ORDER BY clause in Apache Pig is used to sort the data in a relation either in ascending or descending order based on one or more columns.
Syntax:
ORDER BY relation_name [ ASC | DESC ] [ BY {column_name | column_number} [ ASC | DESC ] [, …] ]
Example:
A = LOAD ‘input’ AS (name:chararray, age:int, city:chararray); B = ORDER A BY age DESC, city ASC; DUMP B;
The above example will sort the data in the relation A by age in descending order and by city in ascending order. The output will be stored in relation B.
A = LOAD ‘data’ AS (id: int, name: chararray, price: float);
B = ORDER A BY id DESC;
DUMP B;
— Output —
(5, ‘apple’, 4.2)
(4, ‘orange’, 3.1)
(3, ‘banana’, 2.5)
(2, ‘grapes’, 1.6)
(1, ‘mango’, 2.4)
Apache Pig – Limit Operator
The Apache Pig Limit Operator is used to limit the number of tuples returned by a query. It is similar to the LIMIT clause in SQL. It takes one parameter, the maximum number of tuples to be returned. When used, it returns the first n tuples of the data set, where n is the parameter value. By default, the order of the tuples is not guaranteed, but the Limit operator can be used with the ORDER BY clause to guarantee the order of the tuples returned.
Example:
Input =
(a, 1)
(b, 2)
(c, 3)
(d, 4)
Limit 2
Output =
(a, 1)
(b, 2)
Apache Pig – Eval Functions
Apache Pig Eval Functions are used to manipulate data within Apache Pig. Eval functions can be used to perform mathematical calculations, string manipulations, type conversions, and other operations on the data. Eval functions can be used to compare values, format data, and perform other operations. Common examples of Eval functions include SUM, AVRG, COUNT, CONCAT, and UPPER. Eval functions can be used to group, filter, and join data to create more complex queries.
1. AVG: Returns the average of a set of numeric values.
2. COUNT: Returns the number of elements in a bag.
3. MAX: Returns the maximum value from a set of numeric values.
4. MIN: Returns the minimum value from a set of numeric values.
5. SUM: Returns the sum of a set of numeric values.
6. COR: Computes the correlation between two sets of numbers.
7. COV: Computes the covariance between two sets of numbers.
8. EXPR: Evaluates a boolean expression.
9. SIZE: Returns the size of a bag or a map.
10. ISEMPTY: Returns true if the expression is empty, false otherwise.
Apache Pig – Load & Store Functions
Apache Pig is a platform for analyzing large datasets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. Pig provides two main functions that can be used to load and store data.
The LOAD function is used to read data from a file into Pig. It takes the path of the file as an argument and returns a relation with the data. The LOAD function can read data from a variety of sources, including HDFS, HBase, local files, and various other file formats.
The STORE function is used to write data from a Pig relation into a file. It takes the path of the file and the relation as arguments. The STORE function can write data to HDFS, HBase, local files, and various other file formats.
Apache Pig is a data-flow language and platform for processing large datasets. It includes a set of built-in functions for loading, storing, and manipulating data.
Load Functions:
1. LOAD: Loads data from an external location, such as a local file system or HDFS, into the Pig Latin script.
2. STREAM: Similar to LOAD, but allows the user to specify how the data is loaded.
3. FOREACH: Applies a set of transformations to each record in a relation.
Store Functions:
1. STORE: Stores the output of a Pig Latin script in an external location, such as a local file system or HDFS.
2. DUMP: Prints the output of a Pig Latin script to the terminal.
3. STREAM: Writes the output of a Pig Latin script to a streaming server.
Apache Pig – Bag & Tuple Functions
Apache Pig is a data processing framework developed by Apache Software Foundation. It is a high-level platform for creating data analysis programs that run on Apache Hadoop. Pig provides two data types for representing data: bags and tuples.
Bags are unordered collections of tuples. A bag can contain multiple tuples of different sizes and shapes. Bags are useful for representing data sets where the order of the records is not important.
Tuples are ordered sequences of fields. Each field has a name and a type. Tuples are useful for representing records with a known and fixed structure, such as a database table.
Pig provides a set of functions that can be used to manipulate bags and tuples. These functions allow you to filter, join, project, and group data. They also provide support for user-defined functions, which allow you to extend the capabilities of Pig.
1. Bag Functions:
a) SIZE: Returns the size of a bag.
b) ISEMPTY: Returns TRUE if a bag is empty.
c) FLATTEN: Flattens a bag into a tuple.
d) FOREACH: Applies an expression to each element of a bag, returning a new bag.
e) DISTINCT: Returns a bag with unique tuples.
2. Tuple Functions:
a) SIZE: Returns the size of a tuple.
b) CONCAT: Concatenates two or more tuples.
c) ISEMPTY: Returns TRUE if a tuple is empty.
d) INDEXOF: Returns the position of the first occurrence of a given element in a tuple.
e) LAST_INDEX_OF: Returns the position of the last occurrence of a given element in a tuple.
Apache Pig – String Functions
1. CONCAT: combines two strings together.
2. LOWER: converts a string to lowercase.
3. UPPER: converts a string to uppercase.
4. SUBSTRING: extracts part of a string.
5. TRIM: removes leading and trailing white space from a string.
6. REGEX_EXTRACT: extracts a sub string that matches a regular expression.
7. REGEX_EXTRACT_ALL: extracts all sub strings that match a regular expression.
8. REPLACE: replaces a sub string with another.
9. STRSPLIT: splits a string into multiple sub strings based on a delimiter.
10. TOKENIZE: tokenizes a string into multiple words.
Apache Pig – Date-time Functions
Apache Pig provides a set of date-time functions that are useful for working with date-time values. The functions include:
– GetDay():
Returns the day of the month for a given date.
– GetMonth():
Returns the month of the year for a given date.
– GetYear():
Returns the year for a given date.
– GetHour():
Returns the hour of the day for a given date.
– GetMinute():
Returns the minute of the hour for a given date.
– GetSecond():
Returns the second of the minute for a given date.
– GetWeek():
Returns the week of the year for a given date.
– GetDayOfWeek():
Returns the day of the week for a given date.
– GetWeekOfYear():
Returns the week of the year for a given date.
– GetMonthName():
Returns the name of the month for a given date.
– GetDayName():
Returns the name of the day for a given date.
– ToDate():
Converts a string containing the date in a specified format to a date.
– ToString():
Converts a date to a string containing the date in a specified format.
– DateDiff():
Returns the number of days between two given dates.
– DateAdd():
Returns a date that is a number of days after a given date.
– IsLeapYear():
Returns true if the year is a leap year.
Apache Pig – Math Functions
Apache Pig provides several built-in math functions to perform mathematical calculations. These functions can be used in Pig Latin statements to perform calculations on numeric data. Some of the commonly used math functions are:
1. ABS – returns the absolute value of a number.
2. MAX – returns the maximum value from a set of numbers.
3. MIN – returns the minimum value from a set of numbers.
4. ROUND – rounds a number to the nearest integer.
5. CEIL – returns the smallest integer greater than or equal to a number.
6. FLOOR – returns the largest integer less than or equal to a number.
7. SQRT – returns the square root of a number.
8. LOG – returns the natural logarithm of a number.
9. EXP – returns the exponential of a number.
10. SIN – returns the trigonometric sine of an angle.
11. COS – returns the trigonometric cosine of an angle.
12. TAN – returns the trigonometric tangent of an angle.
13. ASIN – returns the inverse sine of a number.
14. ACOS – returns the inverse cosine of a number.
15. ATAN – returns the inverse tangent of a number.
Apache Pig – User Defined Functions
Apache Pig is an open source data analysis system for Apache Hadoop. It is used for data processing and analysis of large datasets on a distributed computing platform. Pig provides a high-level language, Pig Latin, that allows users to easily write data analysis programs. It also provides a library of user-defined functions (UDFs) that can be used to extend the capabilities of Pig Latin. UDFs allow users to write custom functions to perform specific tasks, such as data analysis, extraction, and transformation. UDFs can also be used to extend the functionality of Pig Latin, such as by adding new data types or processing functions.
Types of UDF’s in Java
Filter –
Filter is a useful operation that allows you to select a subset of elements from a collection. It is a function that takes a predicate (boolean function) as an argument and returns a new collection containing only the elements that satisfy the predicate.
Eval –
Eval is a generic term for a function that takes a string as an argument and evaluates it according to the language it is written in. This can be used to evaluate expressions, execute code, and generate values.
Algebraic Functions UDF’s in Java –
Algebraic functions are mathematical functions that can be used to perform operations on variables. Java provides several built-in algebraic functions, such as the sin, cos, and tan functions. Additionally, developers can create user-defined algebraic functions that can be used in their programs. These UDFs are written in Java and can be used to perform calculations on numeric data.
Writing UDF’s using Java
User Defined Functions (UDFs) written in Java can be used in Apache Hive queries to extend the functionality of Hive. UDFs are functions that take one or more columns from a row in the dataset as input and output a single value based on the logic implemented in the UDF.
To create a UDF using Java, the following steps should be followed:
1. Create a Java class that implements the UDF Interface. The UDF interface provides the necessary methods for implementing a UDF.
2. Implement the evaluate() method. This method is used to process a row of data and return a single value based on the logic implemented in the UDF.
3. Compile the Java class into a JAR file. This JAR file must be added to the classpath of the Hive server before the UDF can be used.
4. Create the UDF in Hive. The syntax for creating a UDF in Hive is “CREATE FUNCTION <function_name> AS ‘<fully_qualified_class_name>’ USING JAR <path_to_jar_file>”.
5. Execute the UDF in a Hive query. Once the UDF has been created, it can be used in a Hive query by using the following syntax: “SELECT <function_name>(<column_name>) FROM <table_name>”.
Apache Pig – Running Scripts
Apache Pig is a programming language used to analyze and manipulate large datasets in a distributed environment. It is used to process data stored in HDFS (Hadoop Distributed File System), and other data sources such as Cassandra, MongoDB, and relational databases. Pig scripts are written in Pig Latin, which is a data flow language. Pig Latin scripts are executed in a multi-stage execution model, where each stage is a Pig Latin statement.
To run a Pig script, the user must first compile it using the Pig compiler. The compiler parses the script to create an execution plan, which is then passed to the Pig runtime environment (Pig Latin interpreter). The interpreter then executes the plan, accessing data from the underlying data sources and transforming the data according to the instructions in the Pig Latin script.
For example, a script may start by loading data from an HDFS file, then filtering out unwanted records, performing calculations on the remaining data, and finally storing the results in another HDFS file. The script can also be used to join data from multiple sources, or to aggregate data in order to produce statistical summaries.
Once the Pig script is compiled and executed, the results can be visualized using the Pig Latin Visualizer, or stored in a file for further analysis.
Comments in Pig Script
Comments in Pig Script are similar to other programming languages, and are written with a ‘#’ sign. For example:
# This is a comment
Any text after the ‘#’ sign will be ignored by the interpreter.
Executing Pig Script in Batch mode
Pig can be executed in batch mode with the help of the “pig” command.
The command for executing a Pig script in batch mode is:
pig [options] -f <script_file>
Options
-x exectype: Specifies the execution mode for the script.
-param_file: The name of the parameter file.
-param: Specifies the value for a parameter used in the script.
-l logfile: Specifies the log file.
-w|-stop_on_failure: Specifies whether to stop execution on failure or not.
Example:
pig -x mapreduce -param_file mypigparams.txt -param my_param=10 -l mypiglog.log -w myscript.pig
Overview
What is Apache Pig?
Apache Pig is an open source platform for data analysis. It is a high-level scripting language used for extracting and transforming large datasets. Pig enables users to write complex data analysis programs in a simple scripting language and execute them on Apache Hadoop.
What are the features of Apache Pig?
• Pig Latin – A high-level scripting language used to write and execute data analysis programs
• Flexible Data Model – Allows users to store data in various formats such as text files, CSV, JSON, and XML
• Extensibility – Allows users to extend Pig with custom functions
• Scalability – Can easily scale to process large datasets
• Optimization – Performs automatic optimization to reduce the amount of data processing
• Runtime Environment – Supports both local and distributed environment for data processing.
What are the benefits of using Apache Pig?
• Easy to learn – Pig Latin is easy to learn and use
• Cost-effective – Pig is an open source platform that is free to use
• Speed – Pig enables users to quickly process large datasets
• Flexibility – Pig allows users to easily process data in various formats
• Scalability – Pig can easily scale to process large datasets
• Optimization – Pig performs automatic optimization to reduce the amount of data processing
Apache Pig Vs MapReduce
Apache Pig is a high-level platform for creating programs that run on Apache Hadoop. It is a data flow language which uses a SQL-like scripting language to process and query large data sets. Pig is designed to provide a simpler interface to MapReduce, making it easier to write programs to process and analyze large data sets.
MapReduce is a programming model and an associated implementation for processing and generating large data sets with a parallel, distributed algorithm on a cluster. It is a scalable, fault-tolerant system for data-intensive distributed computing.
The main difference between Apache Pig and MapReduce is that Apache Pig is a high-level platform for creating programs that run on Apache Hadoop, while MapReduce is a programming model and an associated implementation for processing and generating large data sets with a parallel, distributed algorithm on a cluster. Pig makes it easier to write programs to process and analyze large data sets, while MapReduce is a scalable, fault-tolerant system for data-intensive distributed computing.
Apache Pig Vs SQL
Apache Pig and SQL are two different technologies used for data analysis.
Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs called Pig Latin, which is compiled into MapReduce jobs. It provides a simple and high-level language to create data pipelines that are easy to maintain and extend. Pig is used for tasks such as data extraction, transformation, loading, and analytics.
SQL, on the other hand, is a structured query language used to access and manipulate data stored in relational databases. It is used to retrieve and manipulate data from tables, create new tables, and perform data analysis. SQL is used to query and analyze data in a structured way.
In comparison, Apache Pig is better suited for data transformation and analysis, while SQL is better suited for data querying, retrieval, and manipulation. Pig is generally simpler and faster than SQL, and can be used to build complex pipelines for large data sets. SQL is more structured and requires more code to write, but is better suited for small data sets.
Applications of Apache Pig
1. Web Log Analysis: Apache Pig can be used to analyze web logs and generate useful insight into web traffic and user behavior.
2. ETL Processing: Apache Pig can be used to extract, transform, and load data from various sources such as relational databases, Hadoop Distributed File System (HDFS), NoSQL databases, etc.
3. Text Mining: Apache Pig can be used for text mining tasks such as document classification, clustering, and text analysis.
4. Machine Learning: Apache Pig can be used for machine learning tasks such as predictive analytics, recommendation systems, and data mining.
5. Data Analysis: Apache Pig can be used for data analysis tasks such as data aggregation, data sorting, data filtering, and data visualization.
Apache Pig – History
Apache Pig was originally developed by Yahoo! in 2006 as an internal project called Pig Latin, which was used in production by the Webmap, Nutch Indexing, and Crawl teams. In 2007, Yahoo! open-sourced the project. Since then, Apache Pig has been an Apache top-level project under the Apache Software Foundation.
Apache Pig is a high-level platform for creating programs that run on Apache Hadoop. The language for this platform is called Pig Latin. It is a data flow language which allows for the creation of data analysis programs using a language that is similar to SQL. Pig Latin programs are translated into MapReduce jobs for execution on a Hadoop cluster. Pig Latin offers a much higher level of abstraction than MapReduce, allowing users to write complex data analysis programs without having to dive into the details of MapReduce.
Apache Pig – Architecture
Apache Pig is a platform for analyzing large datasets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets.
At the core of Pig is a compiler that produces sequences of Map-Reduce programs for execution on Hadoop. The language, Pig Latin, is a simple data flow language. Pig Latin statements are compiled into a Directed Acyclic Graph (DAG) of Map-Reduce jobs. Pig has a wide variety of built-in operators (e.g., for filtering, sorting, joining, etc.) that can be combined together to form Pig Latin programs.
The Pig architecture is centered around the compiler that takes Pig Latin programs as input and produces Map-Reduce jobs as output. The Pig Latin compiler is written in Java and consists of several components. The components of the Pig compiler include the parser, optimizer, optimizer rule set, operator tree optimizer, physical plan generator, map-reduce generator, and the translator.
The parser takes the Pig Latin program as input and parses it into an abstract syntax tree (AST). The optimizer then takes the AST and performs various transformations and optimizations to produce an optimized plan. This optimized plan is then used by the physical plan generator to generate a physical plan for executing the program.
The physical plan is then used by the map-reduce generator to generate a series of Map-Reduce jobs. The map-reduce generator also includes the translator component that is used to translate the Pig Latin program into an equivalent Map-Reduce program. Finally, the generated Map-Reduce jobs are then submitted to the Hadoop cluster for execution.
Apache Pig Components
1. Pig Latin: Pig Latin is a high-level data processing language used for querying and transforming data stored in Apache Hadoop. It is a procedural language used to express data analysis programs.
2. Pig Engine: The Pig Engine is the execution component of Apache Pig that is responsible for executing Pig Latin scripts.
3. Grunt Shell: The Grunt Shell is an interactive command-line shell used to submit Pig Latin scripts and commands to the Pig Engine.
4. Piggy Bank: The Piggy Bank is a library of user-defined functions (UDFs) written in Java, Python, and JavaScript that can be used to extend the functionality of Pig Latin.
5. Pig Storage: The Pig Storage component is used to read and write data stored in Hadoop. It supports a variety of file formats such as CSV, Avro, and Sequence Files.
Pig Latin Data Model
A Pig Latin data model is a structured way of representing data using the Pig Latin language. The model is based on a set of abstractions and associated operations that allow data to be manipulated in a consistent and predictable manner. The model consists of three components: data, operations, and statements. Data is represented by variables, which can be either scalar or collections of data. Operations are the functions that manipulate the data and can include filtering, aggregation, and transformation. Statements are the instructions that define the flow of control and the order in which operations are performed.
Apache Pig – Installation
Apache Pig is an open-source data processing framework based on the MapReduce programming model. It is used to analyze and manipulate large datasets in a distributed computing environment. Pig can be installed on both Linux and Windows operating systems.
Step 1: Download Pig
Download the latest version of Pig from the Apache website. The download file will be a .tgz file.
Step 2: Extract Pig
Extract the .tgz file using an archive manager such as 7Zip. This will create a folder with the same name as the .tgz file.
Step 3: Set Environment Variables
Set the following environment variables in the Terminal or Command Prompt:
PIG_HOME – The location of the Pig installation directory
PIG_CLASSPATH – The location of the Pig libraries
PIG_CONF_DIR – The location of the Pig configuration directory
Step 4: Configure Pig
Open the configuration file, pig.properties, located in the PIG_CONF_DIR directory and update the necessary settings.
Step 5: Start Pig
Start the Pig interpreter using the command pig -x local. This will launch the Pig command line shell. You can now start writing PigLatin scripts to process and analyze data.
Apache Pig – Execution
Apache Pig uses a procedural language called Pig Latin to process data. Pig Latin consists of commands, functions, and expressions that are used to describe a series of data transformations. To run a Pig Latin script, a user submits the script to the Pig engine, which parses and compiles the script into a series of MapReduce jobs and submits them to the Hadoop cluster for execution. The output of each job is stored in HDFS, which is then used as input for the next job in the sequence. Once all the jobs are completed, the results are displayed in the console.
Local Mode:
Apache Pig can be run in local mode using the Pig command line interface. In local mode, Pig operates using the local file system and does not require access to a distributed file system. To run Pig in local mode, you must provide the “-x local” option when invoking the pig command, as in the following example:
$ pig -x local
HDFS Mode:
Apache Pig can also be run in HDFS (Hadoop Distributed File System) mode. In this mode, Pig uses the HDFS file system to access and process data. To run Pig in HDFS mode, you must provide the “-x hdfs” option when invoking the pig command, as in the following example:
$ pig -x hdfs
Apache Pig Execution Mechanisms
Apache Pig provides two execution mechanisms:
Interactive Mode: Interactive mode allows you to interactively write and execute Pig Latin statements from the Pig command line.
Batch Mode: Batch mode allows you to write Pig Latin statements in a file and then execute the statements from the command line
Embedded Mode: Embedded mode allows you to write and execute Pig Latin statements from within a Java program.
Summary
Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets. Pig was initially developed at Yahoo! Research and open-sourced in 2007.