• A UDF has input and output. Here is the different ways you can specify the output format of a Python UDF through use of the outputSchema decorator.

• chararray: like a string
• bytearray: a bunch of bytes in a row. Like a string but not as human friendly
• long: long integer
• int: normal integer
• double: floating point number
• datetime
• boolean
• No schema is specified;then the Pig assumes that the UDF outputs a bytearray.

• • The development time is Decrease.
  • It is a collection of Rich set of operators in order to perform operations such as join, filer, sort and many more.
  • Pig Latin is similar to SQL, it becomes very easy for developers to write a Pig script.
  • The task execution in Apache Pig it gets automatically optimized , hence the programmers need to focus on semantics language.

• Using the existing operators, users can easily develop their own functions to process,read, and write data.
• Pig provide the facility for creating User Defined Functions UDF’s, we can easily create a number of programming languages such as Java and invoke them in Pig Scripts.
• All types of data handling are having both structured & unstructured is provided by Apache Pig and the results are stored inside HDFS.
• You have UDFs which you want to parallellize and utilize for large amounts of data, then you are in luck. Use Pig as a base pipeline where it does the hard work and you just apply your UDF in the step that you want.

• Where XXX is the number of reducer.
• This command is used to set the number of reducers at the script level
• The coder need to write this configuration at top/beginning of their pig script.
• Alternatively, use the PARALLEL clause to set the number of reducers at the operator level.
• We set the value using the PARALLEL clause will override any value we specify through (“SET default parallel.”) to reduce phase you can include the PARALLEL clause with any operator.

• • COGROUP
  • CROSS
  • DISTINCT
  • GROUP
  • JOIN (inner)
  • JOIN (outer) and
  • ORDER BY.

• A = LOAD ‘myfile’ AS (t, u, v);
• B = GROUP A BY t PARALLEL 18

• If neither “set default parallel” nor the PARALLEL clause are used, using size of the input data Pig sets the number of reducers.

• pig.exec.reducers.bytes.per.reducer – Defines the number of input bytes per reduce; Pig reducer default value is 1000*1000*1000 (1GB).
• pig.exec.reducers.max – Defines the upper bound on the number of reducers; Pig reducer default is 999.

• User can perform all the data manipulation operations in Hadoop using Apache Pig.
• Apache Pig is an abstraction over MapReduce.

• Using Pig Latin, programmers can perform MapReduce tasks codes in Java.
• Apache Pig uses multi-query approach, it reducing the length of codes.
• For example, an operation that would require you to type 200 lines of code in Java can be easily done by typing just ten lines of codes in Apache Pig.
• Ultimately Apache Pig reduces the development time by almost sixteen times.

• Pig programming language is used for obtaining and manipulating data perhaps doing otherwise with UDFs.
• Pig is used for filtering, cleaning, transforming and otherwise preparing large data sets.
• Depending on the workflow, user might throw a Hive table on top of the Pig output and then use Hive to prepare subsets of data for R.
• R is used for analysis and modeling and it’s also act as prototype models.
• There are ways to connect directly to Hive from R using the Hive JDBC drivers.
• We use R for early data exploration and examination of results.
• In a normal workflow, Pig is used to perform the ETL(Extract, Transform, Load) functionality and transform it load the data into HDFS and analysts utilizes HIVE or extensions like RHIVE to perform the analysis on large volume of data available in HDFS.
• The Comparison between Pig & R is invalid for ever because the pig is for large scale data and R definitely is not.

• The FOREACH operator is used to generate specified data transformations based on the column data.

1. Simple FOREACH…GENERATE
2. Nested FOREACH {…GENERATE }

• This is simple to use the FOREACH…GENERATE in a single line of pig statement to generate the columns that we need.
• Relation_Name = FOREACH <Previous Relation Name> GENERATE column1, column2…..;

• Use operators such as DISTINCT, FILTER, LIMIT, ORDER and SAMPLE.

• • It should be taken set of expressions and it applied to every record in the data pipeline, hence define as FOREACH.
  • The pipeline to next operator.For those database, it is Pig’s projection operator.

For example, the code loads an entire record, then it removes all user and id fields from each record:

• • A = load ‘input’ as (user:chararray, id:long, address:chararray, phone:chararray, preferences:map[]);
  • B = foreach A generate user, id;

• It allows datatypes such as map and tuple and it representation of Pig Latin’s data model.

• Any single value in Pig Latin, irrespective of their data, type is known as an Atom.
• Values of Pig in Atom are number,int,long,float,double,chararray, and bytearray.
• Field define as a piece of data or a simple atomic value. Example − ‘xxx’ or ‘30’

• A record formed by ordered set of fields is known as a tuple. A tuple is similar to a row in a table of RDBMS. Example − (xxx, 30)

• Is an unordered set of tuples.
• A collection of tuples (non-unique) is known as a bag.Tuple have any number of fields (flexible schema).
• No need that every tuple contain the same number of fields and the same position (column) have the same type.
  • Example − {(xxx, 30), (yyyy, 45)}
• A Field contains bags in context its called inner bags.
  • Example − {xxx, 30, {98xxxxx22, xxxx@gmail.com,}}

• Map is a set of key-value pairs the key define as chararray and its unique.
• It is represented by ‘[]’ Example − [name#xxx, age#30]

• A relation is a bag of tuples.Pig Latin relations are unordered (there is no guarantee that tuples are processed in any particular order).