Pig is a platform for a data flow programming on large data sets in a parallel environment. It consists of a language to specify these programs, Pig Latin, a compiler for this language, and an execution engine to execute the programs.

Pig runs on hadoop MapReduce, reading data from and writing data to HDFS, and doing processing via one or more MapReduce jobs.

## Design

This section gives a very high overview of the design of the Pig system. Throughout the documents you can see design for that package or class by looking for the Design heading in the documentation.

### Overview

Pig's design is guided by our pig philosophy.

Pig shares many similarities with a traditional RDBMS design. It has a parser, type checker, optimizer, and operators that perform the data processing. However, there are some significant differences. Pig does not have a data catalog, there are no transactions, pig does not directly manage data storage, nor does it implement the execution framework.

### High Level Architecture

Pig is split between the front and back ends of the engine. In the front end, the parser transforms a Pig Latin script into a logical plan. Semantic checks (such as type checking) and some optimizations (such as determining which fields in the data need to be read to satisfy the script) are done on this Logical Plan. The Logical Plan is than transformed into a {@link org.apache.pig.backend.hadoop.executionengine.physicalLayer.plans.PhysicalPlan}. This Physical Plan contains the operators that will be applied to the data. This is then divided into a set of MapReduce jobs by the {@link org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MRCompiler} into an {@link org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.plans.MROperPlan}. This MROperPlan (aka the map reduce plan) is then optimized (for example, the combiner is used where possible, jobs that scan the same input data are combined where possible, etc.). Finally a set of MapReduce jobs are generated by the {@link org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.JobControlCompiler}. These are submitted to Hadoop and monitored by the {@link org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MapReduceLauncher}.

On the backend, each {@link org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.PigGenericMapReduce.Map}, {@link org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.PigCombiner.Combine}, and {@link org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.PigGenericMapReduce.Reduce} use the pipeline of physical operators constructed in the front end to load, process, and store data.

### Programmatic Interface

In addition to the command line and grunt interfaces, users can connect to {@link org.apache.pig.PigServer} from a Java program.

Pig makes it easy for users to extend its functionality by implementing User Defined Functions (UDFs). There are interfaces for defining functions to load data {@link org.apache.pig.LoadFunc}, storing data {@link org.apache.pig.StoreFunc}, doing evaluations on fields (including collections of data, so user defined aggregates are possible) {@link org.apache.pig.EvalFunc} and filtering data {@link org.apache.pig.FilterFunc}.