Pig Latin – Basics


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Pig Latin is the language used to analyze data in Hadoop using Apache Pig. In this chapter, we are going to discuss the basics of Pig Latin such as Pig Latin statements, data types, general and relational operators, and Pig Latin UDF’s.

Pig Latin – Data Model

As discussed in the previous chapters, the data model of Pig is fully nested. A Relation is the outermost structure of the Pig Latin data model. And it is a bag where −

  • A bag is a collection of tuples.
  • A tuple is an ordered set of fields.
  • A field is a piece of data.

Pig Latin – Statemets

While processing data using Pig Latin, statements are the basic constructs.

  • These statements work with relations. They include expressions and schemas.

  • Every statement ends with a semicolon (;).

  • We will perform various operations using operators provided by Pig Latin, through statements.

  • Except LOAD and STORE, while performing all other operations, Pig Latin statements take a relation as input and produce another relation as output.

  • As soon as you enter a Load statement in the Grunt shell, its semantic checking will be carried out. To see the contents of the schema, you need to use the Dump operator. Only after performing the dump operation, the MapReduce job for loading the data into the file system will be carried out.

Example

Given below is a Pig Latin statement, which loads data to Apache Pig.

grunt> Student_data = LOAD 'student_data.txt' USING PigStorage(',')as 
   ( id:int, firstname:chararray, lastname:chararray, phone:chararray, city:chararray );

Pig Latin – Data types

Given below table describes the Pig Latin data types.

S.N. Data Type Description & Example
1 int

Represents a signed 32-bit integer.

Example : 8

2 long

Represents a signed 64-bit integer.

Example : 5L

3 float

Represents a signed 32-bit floating point.

Example : 5.5F

4 double

Represents a 64-bit floating point.

Example : 10.5

5 chararray

Represents a character array (string) in Unicode UTF-8 format.

Example : ‘tutorials point’

6 Bytearray

Represents a Byte array (blob).

7 Boolean

Represents a Boolean value.

Example : true/ false.

8 Datetime

Represents a date-time.

Example : 1970-01-01T00:00:00.000+00:00

9 Biginteger

Represents a Java BigInteger.

Example : 60708090709

10 Bigdecimal

Represents a Java BigDecimal

Example : 185.98376256272893883

Complex Types
11 Tuple

A tuple is an ordered set of fields.

Example : (raja, 30)

12 Bag

A bag is a collection of tuples.

Example : {(raju,30),(Mohhammad,45)}

13 Map

A Map is a set of key-value pairs.

Example : [ ‘name’#’Raju’, ‘age’#30]

Null Values

Values for all the above data types can be NULL. Apache Pig treats null values in a similar way as SQL does.

A null can be an unknown value or a non-existent value. It is used as a placeholder for optional values. These nulls can occur naturally or can be the result of an operation.

Pig Latin – Arithmetic Operators

The following table describes the arithmetic operators of Pig Latin. Suppose a = 10 and b = 20.

Operator Description Example
+

Addition − Adds values on either side of the operator

a + b will give 30

Subtraction − Subtracts right hand operand from left hand operand

a − b will give −10
*

Multiplication − Multiplies values on either side of the operator

a * b will give 200
/

Division − Divides left hand operand by right hand operand

b / a will give 2
%

Modulus − Divides left hand operand by right hand operand and returns remainder

b % a will give 0
? :

Bincond − Evaluates the Boolean operators. It has three operands as shown below.

variable x = (expression) ? value1 if true : value2 if false.

b = (a == 1)? 20: 30;

if a = 1 the value of b is 20.

if a!=1 the value of b is 30.

CASE

WHEN

THEN

ELSE END

Case − The case operator is equivalent to nested bincond operator.

CASE f2 % 2

WHEN 0 THEN 'even'

WHEN 1 THEN 'odd'

END

Pig Latin – Comparison Operators

The following table describes the comparison operators of Pig Latin.

Operator Description Example
==

Equal − Checks if the values of two operands are equal or not; if yes, then the condition becomes true.

(a = b) is not true
!=

Not Equal − Checks if the values of two operands are equal or not. If the values are not equal, then condition becomes true.

(a != b) is true.
>

Greater than − Checks if the value of the left operand is greater than the value of the right operand. If yes, then the condition becomes true.

(a > b) is not true.
<

Less than − Checks if the value of the left operand is less than the value of the right operand. If yes, then the condition becomes true.

(a < b) is true.
>=

Greater than or equal to − Checks if the value of the left operand is greater than or equal to the value of the right operand. If yes, then the condition becomes true.

(a >= b) is not true.
<=

Less than or equal to − Checks if the value of the left operand is less than or equal to the value of the right operand. If yes, then the condition becomes true.

(a <= b) is true.
matches

Pattern matching − Checks whether the string in the left-hand side matches with the constant in the right-hand side.

f1 matches '.*tutorial.*'

Pig Latin – Type Construction Operators

The following table describes the Type construction operators of Pig Latin.

Operator Description Example
()

Tuple constructor operator − This operator is used to construct a tuple.

(Raju, 30)
{}

Bag constructor operator − This operator is used to construct a bag.

{(Raju, 30), (Mohammad, 45)}
[]

Map constructor operator − This operator is used to construct a tuple.

[name#Raja, age#30]

Pig Latin – Relational Operations

The following table describes the relational operators of Pig Latin.

Operator Description
Loading and Storing
LOAD To Load the data from the file system (local/HDFS) into a relation.
STORE To save a relation to the file system (local/HDFS).
Filtering
FILTER To remove unwanted rows from a relation.
DISTINCT To remove duplicate rows from a relation.
FOREACH, GENERATE To generate data transformations based on columns of data.
STREAM To transform a relation using an external program.
Grouping and Joining
JOIN To join two or more relations.
COGROUP To group the data in two or more relations.
GROUP To group the data in a single relation.
CROSS To create the cross product of two or more relations.
Sorting
ORDER To arrange a relation in a sorted order based on one or more fields (ascending or descending).
LIMIT To get a limited number of tuples from a relation.
Combining and Splitting
UNION To combine two or more relations into a single relation.
SPLIT To split a single relation into two or more relations.
Diagnostic Operators
DUMP To print the contents of a relation on the console.
DESCRIBE To describe the schema of a relation.
EXPLAIN To view the logical, physical, or MapReduce execution plans to compute a relation.
ILLUSTRATE To view the step-by-step execution of a series of statements.


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