• 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.

• It contains easy programming.
• It is little to achieve parallel execution of simple, “embarrassingly parallel” data analysis tasks.
• For Complex tasks it comprised the multiple interrelated data transformations are explicitly encoded as data flow sequences, making them easy to write, understand, and maintain.
• To upgrade their execution automatically, allowing the user to focus on semantics rather than efficiency and Extensibility.
• A special-purpose processing to create own functions.
• For efficient working pig and hive can be used together.
• Pig is best tool for parsing (ETL) kind of job, and even pig supports UDF better than hive.
• It can help you to develop you own framework using pig where you can call Hive ql and map-reduce job for better functionality.

• • 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.

• Pig Latin is not a language but its a language game that all use to speak in code.
• Altering words in English Pig Latin is formed.
• Pig Latin can be extended using user-defined functions which the user can write these languages Java, Python, JavaScript, Ruby or Groovy and then call directly.
• It execute Hadoop jobs in MapReduce, Apache Tez, or Apache Spark
• Pig Latin abstracts the programming from the Java MapReduce idiom which makes MapReduce programming as a high level, its similar to that SQL for relational database management systems.

• 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;

• Pig UDFs-Pig supported the languages: Java, Python, and JavaScript.
• It give high importance for Java functions.
• It include the parts of data load, column transformation, and aggregation.
• The coder can customize all parts of the processing which including data load, column transformation and aggregation.
• In addition to pre-defined function, Apache Pig support for User Defined Function.
• The own function define by User Defined Function.
• It supports programming languages, like Java, Jython, Python, JavaScript, Ruby and Groovy.
• For writing User Defined Function, it complete support is provided in Java and limited support providing for remaining languages.
• Since Apache Pig written in Java, the User Defined Function written using Java language work efficiently compared to all other languages.
• The coder have a Java repository for User Defined Function is termed as Piggybank.
• Using Piggybank, the coder access Java User Defined Function written by others, and contribute our own User Defined Function.

• All User Defined Function must extend "org.apache.pig.EvalFunc"
• All functions must override "exec" method.

• Joining skewed data using apache Pig skewed join.In a distributed processing environment Data skew is a serious problem,and occurs when the data is not evenly divided among the key tuples from the map phase.
• To help the data skew issue with joins Apache Pig is used.

• Using two-table skewed join works.
• Construct the join Used “skewed”‘ to force it used skewed join. pig.skewed join.reduce.memusage
• specifies the reducer to perform the join.
• Pig forces low fraction for more reducer but increases copying cost.
• Difficult to presence Parallel joins for underlying data.
• The underlying data is sufficiently skewed, load too much of the parallelism gains.
• Skewed join does not have restriction on the size of the input keys.
• It accomplishes by dividing one of the input on the join and other input.

• Skewed join it translates into two map/reduce jobs.
• The root job samples the input records and computes the underlying key space.
• The second job modules the input table and performs a join on the predicate.
• In order to join two tables, the first tables is partitioned and another is streamed to the reducer.
• The map task uses the pig.keydist file to define the number of reducers per key.
• It sends the key to each of the reducers in a round robin(RR)fashion. Skewed joins happen in the reduce phase of the join job.

• 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).

• In 2006 Pig was developed by Yahoo Research for particular way of creating and executing MapReduce jobs on very large data sets.
• Apache Software Foundation was moved into 2007.
• Pig started a project in Yahoo! Research, where Yahoo! scientists designed it and produced an implementation.
• The MapReduce paradigm presented by Hadoop “is too low-level and rigid, and leads to a great deal of custom user code that is hard to maintain and reuse.”
• Users were not comfortable with MapReduce languages such as SQL.
• Sort the problem a new language called Pig Latin that we have designed to fit between the declarative style of SQL, and the low-level, procedural style of MapReduce.”
• Hadoop users adopt Pig. So, a team of development engineers was assembled to take the research prototype and build into a production-quality product. About this same time, in fall 2007, Pig was open sourced via the Apache Incubator.
• The first Pig release in September 2008, were Pig graduated from the Incubator and became a subproject of Apache Hadoop.