apache pig interview questions and answers - Wikitechy

What is the difference between Pig, Hive and HBase ?

Editor — Mon, 12 Jul 2021 08:40:35 +0000

Difference between Pig, Hive and HBase

Pig	Hive	Hbase
It is used for semi structured data.	Hive is query engine	HBase is a data storage particularly for unstructured data.
Pig Hadoop Component is generally used by Researchers and Programmers.	Apache Hive is mainly used for batch processing i.e. OLAP and creating reports.	HBase is extensively used for transactional processing where in the response time of the query is not highly interactive i.e. OLTP
Pig Hadoop Component operates on the client side of any cluster.	Hive Hadoop Component operates on the server side of any cluster.	Operations in HBase are run in real-time on the database
Avro supported for Pig.	Hive does not support Avro.	The client which is reading/writing the data has to deal with the avro schemas, after HBase delivered the raw data to it.
Pig Hadoop is a great ETL tool for big data because of its powerful transformation and processing capabilities.	Hive Hadoop Component is helpful for ETL.	Hbase Component is helpful for ETL.
Pig are high-level languages that compile to MapReduce.	Hive is also a high-level languages that compile to MapReduce.	HBase allows Hadoop to support the transactions on key value pairs.
Pig is also SQL-like but varies to a great extent and thus it will take some time efforts to master Pig.	Hive directly leverages SQL expertise and thus can be learnt easily.	HBase allows you to do quick random versus scan all of data sequentially, do insert/update/delete from middle, and not just add/append.

What is the difference between Pig and Sqoop in Hadoop ?

Editor — Mon, 12 Jul 2021 08:36:31 +0000

Difference between Pig and Sqoop in Hadoop

Pig	Sqoop
Apache Pig is a tool for analytics which is used to analyze data stored in HDFS.	Apache Sqoop is a tool to importing structured data from RDBMS to HDFS or exporting data from HDFS to RDBMS.
We can import the data from Sql databases into hive rather than NoSql Databases.	It can integrate with any external data sources with HDFS i.e Sql , NoSql and Data warehouses as well using this tool at the same time we export it as well since this can be used as bi-directional ways
Pig can be used for following purposes ETL data pipeline, Research on raw data.	Important Sqoop control commands to import RDBMS data are Append, Columns and Where
The pig Metastore stores all info about the tables. And we can execute spark sql queries because spark can interact with pig Metastore.	Sqoop metastore is a tool for using hosts in a shared metadata repository. Multiple users and remote users can define and execute saved jobs defined in metastore.
The scalar data types in pig are int, float, double, long, chararray, and bytearray. The complex data types in Pig are map, tuple, and bag.	It basically converts CHAR(x), VARCHAR(x), NUMERIC(x,y) to string (with lengh 32767), and it converts DATETIME to BIGINT.

What is the difference between Pig, Hive and MapReduce ?

Editor — Mon, 12 Jul 2021 08:26:57 +0000

Difference between Pig, Hive and MapReduce

Pig	Hive	Hadoop mapreduce
Pig is a scripting language	SQL like query language	It is a compiled language
Higher level of abstraction	Higher level of abstraction	Hadoop mapreduce used lower level of abstraction
Comparatively less line of codes than mapreduce	Comparatively less lines of code than mapreduce and apache pig	More lines of code
Development effort is less code efficiency is relatively less	Development effort is less code efficiency is relatively less	More development , effort is involved
Code efficiency is relatively less	Code efficiency is relatively less	Code efficiency is high when compared to pig and hive
Pig is open source	Hive open source	Hadoop MapReduce were built so that hadoop developers could do the same thing in Java in a less verbose way by writing only fewer lines of code that is easy to understand
Disadvantage of pig is that commands are not executed unless either you dump or store an intermediate or final result.This increases the iteration between debug and resolving the issue.	Disadvantages of hive is that No real time access to data, Updating data is complicated	The only drawback that developers need to write several lines of basic java code

What is the difference between Pig and Hive ?

Editor — Mon, 12 Jul 2021 08:22:29 +0000

Difference between pig and Hive

Pig	Hive
Pig Hadoop Component is generally used by Researchers and Programmers.	Hive Hadoop Component is mainly used by data analysts.
Pig Hadoop Component is used for semi structured data.	Hive Hadoop Component is used for completely structured Data.
Pig Hadoop Component has a procedural data flow language (Pig Latin).	Hive Hadoop Component has a declarative Structured Query Language (HiveQL).
Pig can’t start reliance based server.	Hive can start reliance based server,send queries from any nook and corner directly to the Hive server which will execute them.
Pig is also SQL like but it varies from great extent and it will take some time efforts to master Pig.	Hive directly borrowed SQL expertise it learnt easily.
Avro supported for Pig.	Avro does not support for Hive.
Pig Hadoop Component operates on the client side of any group.	Hive Hadoop Component operates on the server side of any group.
Pig Hadoop Component is highly used for programming.	Hive Hadoop Component is mainly used for generating reports.
Pig Hadoop is a great ETL tool for big data because of its powerful transformation and processing capabilities.	Hive Hadoop Component is helpful for ETL(Extract, Transform and Load).
In Pig there is no dedicated metadata database and the schemas or data types will be defined in the script itself.	Hive makes use of exact variation of the SQL DLL language by defining the tables beforehand and storing the schema details in any local database.
Pig Hadoop component does not have any notion for partitions though might be one can achieve this through filters.	The Hive Hadoop component has a provision for partitions so that you can process the subset of data by date or in an alphabetical order.
Pig Hadoop Component renders users with sample data for each scenario and each step through its “Illustrate” function.	This feature is not incorporated with the Hive Hadoop Component.

What is the difference between group and cogroup in Pig Latin ?

Editor — Mon, 12 Jul 2021 08:13:18 +0000

Difference between group and cogroup in Pig Latin

Group	Cogroup
For readability GROUP is used in statements involving one relation	Cogroup used as a statements involving two or more relations. Cogroup should no exceed 127 relations at a time.
The group statement collects together records with the same key	cogroup is a general statement of group. Instead of collecting records of 1 input it collects records of N inputs based on a key
Syntax: grunt> Group_data = GROUP Relation_name BY age;	Syntax: alias = CROSS alias, alias [, alias …] [PARTITION BY partitioner] [PARALLEL n];

What is the advantages of pig in Hadoop ?

Editor — Mon, 12 Jul 2021 08:04:01 +0000

Advantages of pig in Hadoop

- 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.

What is default numbers of reducers while executing a pig query ?

Editor — Mon, 12 Jul 2021 07:14:15 +0000

Set Number of Reducer in Pig:

SET default_parallel XXX

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.

For Example:

In GROUP operator the PARALLEL class has been used.

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

Here 18 is number of reducer.

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

The properties values has been specified

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.

What is pig Latin ?

Editor — Mon, 12 Jul 2021 07:08:15 +0000

Pig Latin

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.

What is Pig in Hadoop ?

Editor — Mon, 12 Jul 2021 06:43:27 +0000

Pig in Hadoop

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

Need Apache Pig:

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.

What is workflow when using Pig & R ?

Editor — Mon, 12 Jul 2021 06:37:19 +0000

Workflow when using Pig & R

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.

What is the history of Pig in Hadoop ?

Editor — Mon, 12 Jul 2021 05:12:12 +0000

History of Pig in Hadoop

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.