A scripting platform for processing and analyzing large data sets
With YARN as the architectural center of ApacheTM Hadoop, multiple data access engines such as Apache Pig interact with data stored in the cluster. Apache Pig allows Apache Hadoop users to write complex MapReduce transformations using a simple scripting language called Pig Latin. Pig translates the Pig Latin script into MapReduce so that it can be executed within YARN for access to a single dataset stored in the Hadoop Distributed File System (HDFS).
Hortonworks Focus for Pig
The Apache Pig community is working on continued development in two major areas:
|Performance||Improved performance through further optimizations to existing code and support for Apache Spark as an optional runtime|
|Analytics||Deeper analytics through built-in operators and enhancements to related libraries such as DataFu|
Recent Progress in Pig
What Pig Does
Pig was designed for performing a long series of data operations, making it ideal for three categories of Big Data jobs:
- Extract-transform-load (ETL) data pipelines,
- Research on raw data, and
- Iterative data processing.
Whatever the use case, Pig will be:
|Extensible||Pig users can create custom functions to meet their particular processing requirements|
|Easily programmed||Complex tasks involving interrelated data transformations can be simplified and encoded as data flow sequences. Pig programs accomplish huge tasks, but they are easy to write and maintain.|
|Self-optimizing||Because the system automatically optimizes execution of Pig jobs, the user can focus on semantics.|
How Pig Works
Pig runs on Apache Hadoop YARN and makes use of MapReduce and the Hadoop Distributed File System (HDFS). The language for the platform is called Pig Latin, which abstracts from the Java MapReduce idiom into a form similar to SQL. While SQL is designed to query the data, Pig Latin allows you to write a data flow that describes how your data will be transformed (such as aggregate, join and sort).
Since Pig Latin scripts can be graphs (instead of requiring a single output) it is possible to build complex data flows involving multiple inputs, transforms, and outputs. Users can extend Pig Latin by writing their own functions, using Java, Python, Ruby, or other scripting languages. Pig Latin is sometimes extended using UDFs (User Defined Functions), which the user can write in any of those languages and then call directly from the Pig Latin.
The user can run Pig in two modes, using either the “pig” command or the “java” command:
- MapReduce Mode. This is the default mode, which requires access to a Hadoop cluster.
- Local Mode. With access to a single machine, all files are installed and run using a local host and file system.