May 26, 2021 Apache Pig
Apache Pig is an abstraction of MapReduce. /b10> It is a tool/platform for analyzing large data sets and representing them as data streams. /b11> Pig is typically used with Hadoop; we can use Apache Pig to perform all data processing operations in Hadoop.
To write a data analyzer, Pig provides a high-level language called Pig Latin. /b10> The language provides a variety of operators that programmers can use to develop their own capabilities for reading, writing, and processing data.
To analyze data using Apache Pig, programmers need to script in the Pig Latin language. /b10> All of these scripts are internally converted to Map and Reduce tasks. /b11> Apache Pig has a component called Pig Engine that accepts Pig Latin scripts as input and converts them to MapReduce jobs.
Programmers who are not very good at Java are usually used to using Hadoop, especially when performing any MapReduce job. /b10> Apache Pig is a boon to all such programmers.
With Pig Latin, programmers can easily perform MapReduce jobs without having to type complex code in Java.
Apache Pig uses a multi-query method to reduce code length. /b10> For example, an operation that requires 200 lines of code (LoC) to be entered in Java can be done easily by entering as few as 10 LoCs in Apache Pig. /b11> In the end, Apache Pig reduced development time by nearly 16 times.
Pig Latin is an SQL-like language, and it's easy to learn Apache Pig when you're familiar with SQL.
Apache Pig provides many built-in operators to support data operations such as joy, filter, ordering, and more. In addition, it provides nested data types, such as tuple (tuple), bag (package), and map map missing from MapReduce.
Apache Pig has the following features:
Rich set of operators - It provides many operators to perform operations such as line, sort, filer, etc.
Easy to program - Pig Latin is similar to SQL, and if you're good at USQL, it's easy to write Pig scripts.
Optimization Opportunities - Tasks in Apache Pig automatically optimize their execution, so programmers only need to focus on the semantics of the language.
Scalability - With existing operators, users can develop their own features to read, process, and write data.
User-defined functions - Pig provides the ability to create user-defined functions in other programming languages, such as Java, and can be called or embedded in Pig scripts.
Working with a variety of data - Apache Pig analyzes a variety of data, whether structured or unstructured, and stores the results in HDFS.
The main differences between Apache Pig and MapReduce are listed below.
| Apache Pig | Mapreduce |
|---|---|
| Apache Pig is a data flow language. | MapReduce is a data processing mode. |
| It is a high-level language. | MapReduce is low-level and rigid. |
| It's easy to do Join in Apache Pig. | It is very difficult to perform a Jon operation between datasets in MapReduce. |
| Any new programmer with SQL basics can easily work with Apache Pig. | Exposing to Java is necessary to use MapReduce. |
| Apache Pig uses a multi-query approach, which greatly reduces the length of the code. | MapReduce will need almost 20 times the number of rows to perform the same task. |
| There is no need to compile. /b10> At execution time, each Apache Pig operator is internally converted to a MapReduce job. | MapReduce jobs have a long compilation process. |
The main differences between Apache Pig and SQL are listed below.
| Pig | Sql |
| Pig Latin is a program language. | SQL is a declared language. |
| In Apache Pig, the mode is optional. We can store data without designing patterns (values stored at $01, $02, etc.). | Patterns are required in SQL. |
| The data model in Apache Pig is a nested relationship. | The data model used in SQL is a flat relationship. |
| Apache Pig offers limited opportunities for query optimization. | There are more opportunities for query optimization in SQL. |
In addition to the above differences, Apache Pig Latin:
Both Apache Pig and Hive are used to create MapReduce jobs. /b10> In some cases, Hive runs on HDFS in a similar manner to Apache Pig. /b11> In the table below, we list a few important points to distinguish Apache Pig from Hive.
| Apache Pig | Hive |
|---|---|
| Apache Pig uses a language called Pig Latin (originally created by Yahoo). | Hive uses a language called HiveQL (originally created on Facebook). |
| Pig Latin is a data flow language. | HiveQL is a query processing language. |
| Pig Latin is a process language that fits the pipeline paradigm. | HiveQL is a declarative language. |
| Apache Pig can handle structured, unstructured, and semi-structured data. | Hive is primarily used for structured data. |
Apache Pig is typically used by data scientists to perform tasks involving specific processing and rapid prototyping. Use Apache Pig:
In 2006, Apache Pig was developed as a Yahoo research project, especially in creating and executing MapReduce jobs on each dataset. /b10> In 2007, Apache Pig was open sourced through apache incubators. /b11> In 2008, the first version of Apache Pig came out. /b12> In 2010, Apache Pig was awarded the Apache Top Project.