Hortonworks HDP 2.1 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 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.
Pig has two execution modes or exectypes:
- Local Mode - To run Pig in local mode, you need access to a single machine;
all files are installed and run using your local host and file system. Specify
local mode using the -x flag (pig -x local).
- Mapreduce Mode - To run Pig in mapreduce mode, you need access to a Hadoop cluster
and HDFS installation. Mapreduce mode is the default mode; you can, but don't need to,
specify it using the -x flag (pig OR pig -x mapreduce).
This charm provides Pig client with both execution modes (above).
I was working on reviews and this charm came up on top, it looks like it is still in progress. When you are ready for review please change the state to "Fix Committed".