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. The salient property of Pig programs is that their structure is amenable to substantial parallelization which enables them to handle very large data sets.
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. The salient property of Pig programs is that their structure is amenable to substantial parallelization which enables them to handle very large data sets.
Pig runs in two execution modes: Local mode and MapReduce mode. Pig script can be written in two modes: Interactive mode and Batch mode.
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, easy to write and understand.
- Optimization opportunities. The declarative way in which tasks are encoded permits the system to optimize their execution plan automatically, allowing the user to focus on semantics rather than efficiency.
- Extensibility. Users can create their own functions to do special-purpose processing.
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