Pig 0.9.3-SNAPSHOT API

Pig is a platform for a data flow programming on large data sets in a parallel environment.

See:
Description

pig

org.apache.pig	Public interfaces and classes for Pig.
org.apache.pig.backend
org.apache.pig.backend.datastorage
org.apache.pig.backend.executionengine
org.apache.pig.backend.hadoop
org.apache.pig.backend.hadoop.datastorage
org.apache.pig.backend.hadoop.executionengine
org.apache.pig.backend.hadoop.executionengine.mapReduceLayer
org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.partitioners
org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.plans
org.apache.pig.backend.hadoop.executionengine.physicalLayer	Implementation of physical operators that use hadoop as the execution engine and data storage.
org.apache.pig.backend.hadoop.executionengine.physicalLayer.expressionOperators
org.apache.pig.backend.hadoop.executionengine.physicalLayer.expressionOperators.regex
org.apache.pig.backend.hadoop.executionengine.physicalLayer.plans
org.apache.pig.backend.hadoop.executionengine.physicalLayer.relationalOperators
org.apache.pig.backend.hadoop.executionengine.physicalLayer.util
org.apache.pig.backend.hadoop.executionengine.util
org.apache.pig.backend.hadoop.hbase
org.apache.pig.backend.hadoop.streaming
org.apache.pig.builtin	This package contains builtin Pig UDFs.
org.apache.pig.classification
org.apache.pig.data	This package contains implementations of Pig specific data types as well as support functions for reading, writing, and using all Pig data types.
org.apache.pig.impl
org.apache.pig.impl.builtin
org.apache.pig.impl.io
org.apache.pig.impl.logicalLayer	The logical operators that represent a pig script and tools for manipulating those operators.
org.apache.pig.impl.logicalLayer.schema
org.apache.pig.impl.logicalLayer.validators
org.apache.pig.impl.plan
org.apache.pig.impl.plan.optimizer
org.apache.pig.impl.streaming
org.apache.pig.impl.util
org.apache.pig.newplan
org.apache.pig.newplan.logical
org.apache.pig.newplan.logical.expression
org.apache.pig.newplan.logical.optimizer
org.apache.pig.newplan.logical.relational
org.apache.pig.newplan.logical.rules
org.apache.pig.newplan.logical.visitor
org.apache.pig.newplan.optimizer
org.apache.pig.parser
org.apache.pig.pen
org.apache.pig.pen.util
org.apache.pig.scripting
org.apache.pig.scripting.js
org.apache.pig.scripting.jython
org.apache.pig.tools
org.apache.pig.tools.cmdline
org.apache.pig.tools.grunt
org.apache.pig.tools.parameters
org.apache.pig.tools.pigstats
org.apache.pig.tools.streams
org.apache.pig.tools.timer

contrib: Piggybank

org.apache.pig.piggybank.evaluation
org.apache.pig.piggybank.evaluation.datetime
org.apache.pig.piggybank.evaluation.datetime.convert
org.apache.pig.piggybank.evaluation.datetime.diff
org.apache.pig.piggybank.evaluation.datetime.truncate
org.apache.pig.piggybank.evaluation.decode
org.apache.pig.piggybank.evaluation.math
org.apache.pig.piggybank.evaluation.stats
org.apache.pig.piggybank.evaluation.string
org.apache.pig.piggybank.evaluation.util
org.apache.pig.piggybank.evaluation.util.apachelogparser
org.apache.pig.piggybank.storage
org.apache.pig.piggybank.storage.allloader
org.apache.pig.piggybank.storage.apachelog
org.apache.pig.piggybank.storage.avro
org.apache.pig.piggybank.storage.hiverc
org.apache.pig.piggybank.storage.partition

contrib: Zebra

org.apache.hadoop.zebra	Hadoop Table - tabular data storage for Hadoop MapReduce and PIG.
org.apache.hadoop.zebra.io	Physical I/O management of Hadoop Zebra Tables.
org.apache.hadoop.zebra.mapred	Providing InputFormat and OutputFormat adaptor classes for Hadoop Zebra Table.
org.apache.hadoop.zebra.mapreduce	Providing InputFormat and OutputFormat adaptor classes for Hadoop Zebra Table.
org.apache.hadoop.zebra.pig	Implementation of PIG Storer/Loader Interfaces
org.apache.hadoop.zebra.pig.comparator	Utilities to allow PIG Storer to generate keys for sorted Zebra tables
org.apache.hadoop.zebra.schema	Zebra Schema
org.apache.hadoop.zebra.tfile
org.apache.hadoop.zebra.types	Data types being shared between the io and mapred packages.

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 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 MRCompiler into an 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 JobControlCompiler. These are submitted to Hadoop and monitored by the MapReduceLauncher.

On the backend, each PigGenericMapReduce.Map, PigCombiner.Combine, and 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 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 LoadFunc, storing data StoreFunc, doing evaluations on fields (including collections of data, so user defined aggregates are possible) EvalFunc and filtering data FilterFunc.