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Hadoop’s Pig Data Types and Syntax

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Pig Data Types

  • Every piece of data in PIG has one of these four types:
Data Atom: is a simple atomic DATA VALUE and it is stored as string but can be used either a string or a number.
Examples:‘’ and ‘1-0’
Tuple: is a data record consisting of a sequence of “fields” and each field is a piece of data of any type (data atom, tuple or data bag)
  • We denote tuples with <> bracketing
  •  Example of a hepde is
Data Bag: Is a set of tuples (duplicate tuples are allowed)
  • Think of it as a “table”, except that pig does not require that the tuple field types match, or even that the tuples has the same no. of fields. Bag could be {}
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Data Map: is a map from keys that are string literals to values that can be of any data type.
  • Think of it as a Hash map where X can be any of the 4 pig data types.
  • A data map supports the expected get and put interface.
Data Types in Pig:
 Other language Ping
Int Int
string char array
float float
long long
double double
boolean boolean

Different Transformations in Pig:

REGISTER- Register jar file with the pig runtime

DEFINE- Create an alias for a macro, UDF, Streaming script (or) command specification.

IMPORT- Import macros defined in separate file into a script.

Typical Transformations:

 Load: load data from the file system.

FILETER: Remove unwanted rows from a location

FOREACH: Particular column is displayed

GENERATE: Add or Remove fields from a Relation

GROUP: To group data in a single relation.

COGROUP: To group or join data in two or more relation

UNION: To merge the contents of two or more relations

SPLIT: To partition the contents of a relation into multiple relations

JOIN (Inner or Outer): To join the data in two or more relations

ORDER: Sort the relations by one or more fields

LIMIT: Limits the size of a relation to a maximum no. of tuples

Debugging Pig Latin:  

  • Pig Latin provides operators that help you debug the pig latin statements.
DUMP: To display the results to your terminal screen
DESCRISE: To review the schema of a relation.
EXPLAIN: To view the logical, physical or map reduce execution plans to compute a relation.
ILLUSTRATE: To view the step-by-step execution of a series of statements.

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Ravindra Savaram
About The Author

Ravindra Savaram is a Content Lead at His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter. Protection Status