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.

At the present time, Pig’s infrastructure layer consists of a compiler that produces sequences of Map-Reduce programs.

Pig’s language layer currently consists of a textual language called Pig Latin, which has the following key properties:

  • Ease of programming. It is trivial to achieve parallel execution of simple, “embarrassingly parallel” data analysis tasks. Complex tasks comprised of multiple interrelated data transformations are explicitly encoded as data flow sequences, making them easy to write, understand, and maintain.
  • Optimization opportunities. The way in which tasks are encoded permits the system to optimize their execution automatically, allowing the user to focus on semantics rather than efficiency.
  • Extensibility. Users can create their own functions to do special-purpose processing.This Diagram explains how apache pig works.

Pig_archi

 

 

 

 

 

 

Execution Modes:

Pig has two execution modes or exectypes:

  • Local Mode – To run Pig in local mode, you need access to a single machine; all files are installed and run using your local host and file system. Specify local mode using the -x flag (pig -x local).
  • Mapreduce Mode – To run Pig in mapreduce mode, you need access to a Hadoop cluster and HDFS installation. Mapreduce mode is the default mode; you can,but don’t need to, specify it using the -x flag (pig OR pig -x mapreduce).

You can run Pig in either mode using the “pig” command (the bin/pig Perl script) or the “java” command (java -cp pig.jar …).