Interview Questions.

Question 1. Compare Apache Pig And Sql?

Answer :

Apache Pig differs from SQL in its utilization for ETL, lazy assessment, keep facts at any given point of time within the pipeline, support for pipeline splits and specific announcement of execution plans. SQL is oriented around queries which produce a single result. SQL has no in-constructed mechanism for splitting a statistics processing move and applying one-of-a-kind operators to every sub-move.

Apache Pig permits person code to be protected at any point within the pipeline whereas if SQL wherein to be used records needs to be imported to the database first and then the process of cleaning and transformation begins.

Question 2. Explain The Need For Mapreduce While Programming In Apache Pig.?

Answer :

Apache Pig packages are written in a query language referred to as Pig Latin that is just like the SQL question language. To execute the query, there may be a want for an execution engine. The Pig engine converts the queries into MapReduce jobs and consequently MapReduce acts because the execution engine and is needed to run the applications.

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Question three. Explain About The Bloommapfile.?

Answer :

BloomMapFile is a class, that extends the MapFile class. It is used in HBase table format to offer brief membership take a look at for the keys using dynamic bloom filters.

Question four. What Do You Mean By A Bag In Pig?

Answer :

Collection of tuples is referred as a bag in Apache Pig.

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Question 5. What Is The Usage Of Foreach Operation In Pig Scripts?

Answer :

FOREACH operation in Apache Pig is used to apply transformation to every detail within the records bag, so that respective motion is carried out to generate new statistics objects.

Syntax- FOREACH data_bagname GENERATE exp1, exp2.

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Question 6. Explain About The Different Complex Data Types In Pig.?

Answer :

Apache Pig supports three complicated data kinds:

Maps- These are key, value stores joined collectively the usage of #.

Tuples- Just just like the row in a table, where unique objects are separated by way of a comma. Tuples could have more than one attributes.

Bags- Unordered series of tuples. Bag allows multiple replica tuples.

Question 7. What Does Flatten Do In Pig?

Answer :

Sometimes there may be records in a tuple or a bag and if we want to eliminate the level of nesting from that information, then Flatten modifier in Pig may be used. Flatten un-nests bags and tuples. For tuples, the Flatten operator will alternative the fields of a tuple in location of a tuple, whereas un-nesting bags is a touch complex as it requires developing new tuples.

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Question 8. How Do Users Interact With The Shell In Apache Pig?

Answer :

Using Grunt i.E. Apache Pig’s interactive shell, users can interact with HDFS or the nearby record machine.

To begin Grunt, users need to invoke Apache Pig and not using a command:

Executing the command “pig –x nearby” will result in the spark off -

grunt >

This is wherein PigLatin scripts may be run both in neighborhood mode or in cluster mode by using putting the configuration in PIG_CLASSPATH.

To go out from grunt shell, press CTRL+D or simply type go out.

Question 9. What Are The Debugging Tools Used For Apache Pig Scripts?

Answer :

describe and give an explanation for are the vital debugging utilities in Apache Pig.

Give an explanation for software is beneficial for Hadoop developers, while seeking to debug errors or optimize PigLatin scripts. Give an explanation for may be applied on a specific alias in the script or it is able to be applied to the entire script in the grunt interactive shell. Provide an explanation for application produces several graphs in text layout which may be published to a record.

Describe debugging application is helpful to builders whilst writing Pig scripts as it shows the schema of a relation inside the script. For beginners who're seeking to analyze Apache Pig can use the describe application to recognize how every operator makes alterations to facts. A pig script will have more than one describes.

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Question 10. What Is Illustrate Used For In Apache Pig?

Answer :

Executing pig scripts on massive records units, commonly takes a long term. To tackle this, builders run pig scripts on sample records but there is opportunity that the sample data decided on, won't execute your pig script properly.

For example, if the script has a be part of operator there have to be as a minimum some information in the pattern statistics that have the identical key, in any other case the join operation will now not return any effects. To tackle those type of problems, illustrate is used. Illustrate takes a pattern from the statistics and each time it comes across operators like join or clear out that remove facts, it ensures that just a few records skip thru and a few do no longer, by way of making changes to the statistics such that they meet the circumstance. Illustrate simply shows the output of each degree but does no longer run any MapReduce project.

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Question 11. Explain About The Execution Plans Of A Pig Script?<br> Or<br> Differentiate Between The Logical And Physical Plan Of An Apache Pig Script?

Answer :

Logical and Physical plans are created throughout the execution of a pig script. Pig scripts are based totally on interpreter checking. Logical plan is produced after semantic checking and fundamental parsing and no data processing takes vicinity in the course of the creation of a logical plan. For every line in the Pig script, syntax check is executed for operators and a logical plan is created. Whenever an error is encountered inside the script, an exception is thrown and this system execution ends, else for each statement inside the script has its own logical plan.

A logical plan consists of collection of operators in the script however does not contain the rims among the operators.

After the logical plan is generated, the script execution movements to the bodily plan wherein there's an outline about the bodily operators, Apache Pig will use, to execute the Pig script. A physical plan is greater or much less like a series of MapReduce jobs but then the plan does now not have any reference on how it'll be achieved in MapReduce. During the advent of physical plan, cogroup logical operator is converted into three physical operators particularly –Local Rearrange, Global Rearrange and Package. Load and shop capabilities typically get resolved in the bodily plan.

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Question 12. What Do You Know About The Case Sensitivity Of Apache Pig?

Answer :

It is difficult to mention whether Apache Pig is case sensitive or case insensitive. For example, user defined capabilities, family members and field names in pig are case touchy i.E. The characteristic  COUNT isn't the same as function count or X=load ‘foo’ isn't equal as x=load ‘foo’. On the opposite hand, keywords in Apache Pig are case insensitive i.E. LOAD is identical as load.

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Question thirteen. What Are Some Of The Apache Pig Use Cases You Can Think Of?

Answer :

Apache Pig massive information equipment, is used specially for iterative processing, studies on raw data and for traditional ETL facts pipelines. As Pig can perform in circumstances wherein the schema isn't regarded, inconsistent or incomplete- it is widely utilized by researchers who want to make use of the information before it's miles wiped clean and loaded into the information warehouse.

To construct conduct prediction fashions, for example, it may be utilized by a website to track the reaction of the traffic to various forms of advertisements, photos, articles, and many others.

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Question 14. Differentiate Between Piglatin And Hiveql?

Answer :

It is vital to specify the schema in HiveQL, while it's far optionally available in PigLatin.
HiveQL is a declarative language, whereas PigLatin is procedural.
HiveQL follows a flat relational statistics model, whereas PigLatin has nested relational records version.
Question 15. Is Piglatin A Strongly Typed Language? If Yes, Then How Did You Come To The Conclusion?

Answer :

In a strongly typed language, the user has to declare the kind of all variables prematurely. In Apache Pig, while you describe the schema of the information, it expects the facts to come back inside the identical layout you referred to.

However, while the schema isn't regarded, the script will adapt to certainly information types at runtime. So, it is able to be said that PigLatin is strongly typed in most cases but in rare instances it is gently typed, i.E. It maintains to paintings with records that doesn't stay up to its expectations.

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Question 16. What Do You Understand By An Inner Bag And Outer Bag In Pig?

Answer :

A relation inner a bag is known as inner bag and outer bag is only a relation in Pig.

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Question 17. Differentiate Between Group And Cogroup Operators.?

Answer :

Both GROUP and COGROUP operators are identical and might work with one or greater family members. GROUP operator is generally used to group the statistics in a single relation for higher clarity, while COGROUP may be used to institution the statistics in 2 or more members of the family. COGROUP is extra like a aggregate of GROUP and JOIN, i.E., it corporations the tables based totally on a column after which joins them at the grouped columns. It is viable to cogroup as much as 127 members of the family at a time.

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Question 18. Explain The Difference Between Count_star And Count Functions In Apache Pig?

Answer :

COUNT function does now not include the NULL fee whilst counting the range of factors in a bag, whereas COUNT_STAR (zero feature includes NULL values whilst counting.

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Question 19. What Are The Various Diagnostic Operators Available In Apache Pig?

Answer :

Dump Operator- It is used to show the output of pig Latin statements at the screen, so that developers can debug the code.
Describe Operator-Explained in apache pig interview query no- 10
Explain Operator-Explained in apache pig interview query no -10
Illustrate Operator- Explained in apache pig interview question no -11
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Question 20. How Will You Merge The Contents Of Two Or More Relations And Divide A Single Relation Into Two Or More Relations?

Answer :

This may be carried out using the UNION and SPLIT operators.

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Question 21. I Have A Relation R. How Can I Get The Top 10 Tuples From The Relation R.?

Answer :

TOP () feature returns the top N tuples from a bag of tuples or a relation. N is passed as a parameter to the feature pinnacle () together with the column whose values are to be compared and the relation R.

Question 22. What Are The Commonalities Between Pig And Hive?

Answer :

HiveQL and PigLatin both convert the commands into MapReduce jobs.
They cannot be used for OLAP transactions as it's far tough to execute low latency queries.
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Question 23. What Are The Different Types Of Udf’s In Java Supported By Apache Pig?

Answer :

Algebraic, Eval and Filter capabilities are the diverse types of UDF’s supported in Pig.

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Question 24. You Have A File Employee.Txt In The Hdfs Directory With 100 Records. You Want To See Only The First 10 Records From The Employee.Txt File. How Will You Do This?

Answer :

The first step would be to load the file employee.Txt into with the relation call as Employee.

The first 10 statistics of the employee statistics may be obtained using the limit operator -

Result= restriction employee 10.

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Question 25. Explain About The Scalar Datatypes In Apache Pig.?

Answer :

integer, drift, double, lengthy, bytearray and char array are the available scalar datatypes in Apache Pig.

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Question 26. How Do Users Interact With Hdfs In Apache Pig?

Answer :

Using the grunt shell.

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Question 27. What Is The Use Of Having Filters In Apache Pig?

Answer :

Just just like the where clause in SQL, Apache Pig has filters to extract statistics primarily based on a given situation or predicate. The file is handed down the pipeline if the predicate or the circumstance flip to genuine. Predicate consists of diverse operators like ==, <=,!=, >=.

Example:-

X= load ‘inputs’ as(name,address)

Y = clear out X by way of image suits ‘Mr.*’;

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Question 28. What Is A Udf In Pig?

Answer :

If the in-constructed operators do now not offer a few capabilities then programmers can implement the ones functionalities by means of writing user described capabilities the use of different programming languages like Java, Python, Ruby, and many others. These User Defined Functions (UDF’s) can then be embedded right into a Pig Latin Script.

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Question 29. Can You Join Multiple Fields In Apache Pig Scripts?

Answer :

Yes, it's miles feasible to sign up for a couple of fields in PIG scripts because the be a part of operations takes records from one input and joins them with any other enter. This can be performed via specifying the keys for each enter and the two rows will be joined when the keys are equal.

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Question 30. Does Pig Support Multi-line Commands?

Answer :

Yes.