Apache Pig Overview

Apache Pig Overview

• Apache Pig is a platform for analyzing large datasets.
• It uses a high-level language (Pig Latin) for data analysis programs.
• Pig provides an abstraction layer on top of Hadoop.
• Pig Latin programs are converted into MapReduce jobs for execution on Hadoop clusters.
• This simplifies data analysis for analysts, data scientists, and statisticians, since they don't need to write MapReduce code in Java.

Why Use Pig?

• Quick Analysis: Pig exploits Hadoop's parallel processing power.
• Easy to Learn: Pig's high-level language, Pig Latin, has a lower learning curve than MapReduce.
• Common Analysis Tasks: Pig includes predefined tasks for common data analyses.
• Flexible Structure Transformations: Pig facilitates easy data transformation.
• Custom Processing: Pig allows customization through User-Defined Functions (UDFs).
• Comparison to Relational Databases: Pig's schema flexibility and parallel processing outperform relational databases, especially for large datasets. Pig offers SQL-like querying and procedural programming flexibility. Relational databases are rigid in their schemas and typically do not handle parallelism as effectively.

Pig Latin - The Big Picture

• Pig Latin is the high-level language used in Pig.
• It's SQL-like, making it easier to learn if you know SQL.
• Pig Latin programs are executed by the Pig Framework after translation.
• Pig Latin statements are translated into MapReduce jobs which are executed by Hadoop.
• Pig simplifies the work for programmers by hiding MapReduce implementation details.

Features of Pig

• Rich Operators: Provides joins, sorts, filters, etc.
• Ease of Programming: Pig Latin is similar to SQL.
• Optimization: Pig automatically optimizes tasks.
• Extensibility: Allows users to create custom functions.
• UDFs: Support for custom functions written in Java (or other languages, where supported).
• Data Handling: Pig handles both structured and unstructured data, storing results in HDFS.
• Flexible Data Storage: Permits data storage anywhere in the pipeline (during execution).
• Declares Execution Plans: Pig defines the steps involved in the execution of a program.
• ETL support: Pig offers functions for Extract, Transform, and Load (ETL) processes.

Pig Architecture

• Pig programs (Pig Latin scripts) are parsed for syntax, type checking, etc,.
• The output is a directed acyclic graph (DAG).
• The DAG is optimized by the logical optimizer.
• The optimizer removes redundant steps, and does other optimization.
• The optimized DAG is compiled into MapReduce jobs executed on Hadoop.
• The MapReduce jobs produce the results.

Pig Latin Components

• Parser: Checks syntax, performs type checking, and creates a DAG, representing the program's logic.
• Optimizer: Performs logical optimizations like projections and pushdowns.
• Compiler: Compiles the optimized plan into MapReduce jobs.
• Execution Engine: Submits MapReduce jobs to Hadoop in sorted form for execution, obtaining the desired results.

Pig Data Model

• Atom: (Atomic value) data type including string, numbers.
• Tuple: An ordered set of fields.
• Bag: An unordered collection of tuples (similar to a table in a relational database).
• Relation: A bag of tuples.
• Map: A collection of key-value pairs, where the key is a character array.

Running Pig

• Script: Execute commands from a file.
• Grunt: Interactive shell for Pig commands (used when no script file is provided.)
• Embedded: Using the PigServer class, programmatically execute Pig commands.

LOAD Command

• LOAD 'data' [USING function] [AS schema];
• data: Input data file or directory.
• USING: Specifies the load function (e.g., PigStorage).
• AS: Assigns names and types to the loaded fields

Diagnostic Tools

• DESCRIBE: Displays bag structure.
• EXPLAIN: Produces logical and MapReduce plans showing transformation steps.
• ILLUSTRATE: Shows how Pig transforms the data.

TOKENIZE Function

• Splits strings into tokens.

STORE & DUMP Commands

• DUMP: Displays results to the screen.
• STORE: Saves results to a file usually within Hadoop (HDFS, HBase).

Joins in Pig

• Supports inner, outer, and full joins.
• Joins data based on matching keys.

User-Defined Functions (UDFs)

• Pig provides a way to write custom functions using Java (or other languages, where supported).
• UDFs enable complex data transformations.

Tools and Resources

• Various online resources (links provided in the original text) are useful for reference and problem-solving.
• PigPen Eclipse plugin.

Choosing Technologies for Data Processing

• MapReduce: Low-level, flexible, but time-consuming. Suitable for situations demanding high control and performance.
• Pig: Easier to write and deploy than MapReduce. Appropriate for most analysis and processing.
• Hive SQL : Very complex, running long queries..Suitable for complex data analysis.

Pig vs. Relational Databases

• Pig/Hive are optimized for large read-only datasets and batch processing.
• Relational databases are better for interactive use with smaller datasets and in-place data modifications.