Whats new in version 0.11.0:
• This release includes hundreds of bug fixes and many new features including DateType datatype, RANK, CUBE and ROLLUP operators, Groovy UDFs, pluggable reducer estimation logic, additional UDF features, schema-based tuples and HCatalog DDL integration.
• New RANK, CUBE and ROLLUP operators
• New DateType data type
• Support for Groovy UDFs
• Support for loading macros from jars
• Support for custom PigReducerEstimators
• Support for custom PigProgressNotificatonListeners
• Support for schema-based Tuples for reduced memory footprint
• Support for passing environment variables to streaming jobs
• Support for invoking HCatalog DDL commands from Pig
• Support for .pigbootup file for defaults
• Improved support for working with Maps in Pig scripts
• Grunt improvements: history and clear
• New cleanupOnSuccess method in StoreFunc interface
• UDF timing utilities
• UDF lifecycle improvements
• UDFs for DateType support
• Performance improvements to merge join
• Performance improvements to local mode
• Performance improvements ...Publisher review:
pache 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. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets.
At the present time, Pig's infrastructure layer consists of a compiler that produces sequences of Map-Reduce programs, for which large-scale parallel implementations already exist (e.g., the Hadoop subproject). 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.
- Java 1.6.x or later
- Hadoop 0.18.x
Mac OS X