How the Pig programming gets converted into MapReduce jobs? In short
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Pig programming is a high-level data manipulation language designed to work with Apache Hadoop. Pig scripts are written using Pig Latin, a language that abstracts the complexities of MapReduce programming and provides a simpler way to express data transformations and analysis.
Here is a brief overview of how Pig Latin scripts are converted into MapReduce jobs:
Pig Latin Script: First, you write a Pig Latin script that describes the data transformations and operations you want to perform on your input data. The script is composed of a series of statements that specify the data sources, transformations, and outputs.
Pig Compiler: When you submit the Pig script, it goes through the Pig compiler. The compiler performs syntax and semantic checks on the script, ensuring that it follows the Pig Latin language rules.
Logical Plan: The Pig compiler then generates a logical plan, which represents the data flow and transformations specified in the script. The logical plan is a directed acyclic graph (DAG) that captures the operations to be performed on the data.
Physical Plan: The logical plan is optimized by the Pig optimizer, which rearranges and optimizes the operations to improve performance. The result is a physical plan, which is another DAG that represents the optimized execution steps.
MapReduce Jobs: The physical plan is then translated into one or more MapReduce jobs. Pig Latin operations such as filtering, grouping, joining, and aggregation are mapped to corresponding MapReduce operations. The Pig runtime generates the necessary Map and Reduce functions to execute the operations defined in the script.
Execution: The MapReduce jobs generated from the physical plan are submitted to the Hadoop cluster for execution. The jobs are distributed across the cluster, and each node processes a portion of the input data.
Output: After the MapReduce jobs complete, the output is written to the specified output location, which can be a file, directory, or another data store.
In summary, Pig Latin scripts are compiled and optimized into a series of MapReduce jobs that are executed on a Hadoop cluster. This allows you to express complex data transformations using a high-level language, while the underlying MapReduce framework handles the distributed processing of the data.
