Interview Questions.

Q1. What Are The Commonalities Between Pig And Hive?

HiveQL and PigLatin each convert the instructions into MapReduce jobs.

They can not be used for OLAP tractions as it is tough to execute low latency queries.

Q2. What Does Flatten Do In Pig?

Sometimes there's statistics in a tuple or a bag and if we need to take away the extent of nesting from that records, then Flatten modifier in Pig can be used. Flatten un-nests bags and tuples. For tuples, the Flatten operator will replacement the fields of a tuple in place of a tuple, whereas un-nesting bags is a bit complicated as it requires creating new tuples.

Q3. Explain The Need For Mapreduce While Programming In Apache Pig.?

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

Q4. Explain About The Scalar Datatypes In Apache Pig.?

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

Q5. Explain About The Different Complex Data Types In Pig.?

Apache Pig supports 3 complex statistics sorts:

Maps- These are key, cost shops joined together the use of #.

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

Bags- Unordered series of tuples. Bag lets in multiple replica tuples.

Q6. You Have A File Employee.Txt In The Hdfs Directory With one hundred Records. You Want To See Only The First 10 Records From The Employee.Txt File. How Will You Do This?

The first step might be to load the document worker.Txt into with the relation name as Employee.

The first 10 data of the worker facts may be received the use of the restrict operator -

Result= restriction worker 10.

Q7. What Is The Usage Of Foreach Operation In Pig Scripts?

FOREACH operation in Apache Pig is used to use trformation to each element within the records bag, so that respective motion is executed to generate new statistics items.

Syntax- FOREACH data_bagname GENERATE exp1, exp2.

Q8. What Do You Mean By A Bag In Pig?

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

Q9. What Do You Know About The Case Sensitivity Of Apache Pig?

It is difficult to say whether or not Apache Pig is case sensitive or case insensitive. For instance, person defined capabilities, members of the family and subject names in pig are case touchy i.E. The characteristic  COUNT isn't always similar to feature matter or X=load ‘foo’ is not identical as x=load ‘foo’. On the alternative hand, key phrases in Apache Pig are case insensitive i.E. LOAD is equal as load.

Q10. What Is A Udf In Pig?

If the in-built operators do now not offer a few capabilities then programmers can put in force those functionalities by way of writing person described functions the usage 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.

Q11. How Do Users Interact With The Shell In Apache Pig?

Using Grunt i.E. Apache Pig’s interactive shell, users can have interaction with HDFS or the nearby report gadget.

To start Grunt, customers should invoke Apache Pig with no command:

Executing the command “pig –x local” will result in the set off -

grunt >

This is wherein PigLatin scripts may be run both in nearby mode or in cluster mode by means of placing the configuration in PIG_CLASSPATH.

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

Q12. Explain About The Bloommapfile.?

BloomMapFile is a category, that extends the MapFile class. It is used in HBase table layout to offer quick membership test for the keys using dynamic bloom filters.

Q13. Explain About The Execution Pl Of A Pig Script?<br> Or<br> Differentiate Between The Logical And Physical Plan Of An Apache Pig Script?

Logical and Physical pl are created at some stage in the execution of a pig script. Pig scripts are primarily based on interpreter checking. Logical plan is produced after semantic checking and basic parsing and no records processing takes vicinity in the course of the advent of a logical plan. For each line within the Pig script, syntax check is achieved for operators and a logical plan is created. Whenever an blunders is encountered in the script, an exception is thrown and this system execution ends, else for every announcement inside the script has its very own logical plan.

A logical plan includes series of operators within the script but does not comprise the rims between the operators.

After the logical plan is generated, the script execution movements to the physical plan wherein there may be an outline about the physical operators, Apache Pig will use, to execute the Pig script. A physical plan is more or less like a series of MapReduce jobs however then the plan does now not have any reference on how it will be performed in MapReduce. During the introduction of bodily plan, cogroup logical operator is transformed into 3 bodily operators namely –Local Rearrange, Global Rearrange and Package. Load and keep capabilities generally get resolved within the bodily plan.

Q14. I Have A Relation R. How Can I Get The Top 10 Tuples From The Relation R.?

TOP () characteristic returns the pinnacle N tuples from a bag of tuples or a relation. N is exceeded as a parameter to the characteristic pinnacle () along side the column whose values are to be compared and the relation R.

Q15. What Are The Debugging Tools Used For Apache Pig Scripts?

Describe and provide an explanation for are the crucial debugging utilities in Apache Pig.

Give an explanation for software is beneficial for Hadoop builders, when looking to debug errors or optimize PigLatin scripts. Provide an explanation for may be implemented on a selected alias inside the script or it could be implemented to the complete script in the grunt interactive shell. Explain application produces numerous graphs in textual content format which may be published to a file.

Describe debugging software is useful to builders when writing Pig scripts because it suggests the schema of a relation inside the script. For novices who're seeking to learn Apache Pig can use the describe application to understand how every operator makes changes to information. A pig script may have a couple of describes.

Q16. How Do Users Interact With Hdfs In Apache Pig?

Using the grunt shell.

Q17. What Is The Use Of Having Filters In Apache Pig?

Just like the wherein clause in SQL, Apache Pig has filters to extract records primarily based on a given condition or predicate. The document is handed down the pipeline if the predicate or the condition turn to actual. Predicate carries diverse operators like ==, <=,!=, >=.

Example:-

X= load ‘inputs’ as(call,deal with)

Y = clear out X with the aid of symbol fits ‘Mr.*’;

Q18. What Are The Various Diagnostic Operators Available In Apache Pig?

Dump Operator- It is used to display the output of pig Latin statements on the display screen, in order 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 query no -eleven

Q19. Differentiate Between Group And Cogroup Operators.?

Both GROUP and COGROUP operators are equal and might paintings with one or greater relations. GROUP operator is normally used to institution the statistics in a single relation for higher readability, whereas COGROUP may be used to institution the information in 2 or more family members. COGROUP is extra like a combination of GROUP and JOIN, i.E., it companies the tables primarily based on a column and then joins them on the grouped columns. It is possible to cogroup up to 127 family members at a time.

Q20. What Is Illustrate Used For In Apache Pig?

Executing pig scripts on massive information units, generally takes a long term. To address this, builders run pig scripts on sample information but there's opportunity that the pattern facts decided on, won't execute your pig script properly.

For instance, if the script has a be part of operator there must be at the least some records in the pattern data which have the identical key, in any other case the be a part of operation will not return any effects. To address those form of troubles, illustrate is used. Illustrate takes a sample from the facts and each time it comes throughout operators like be a part of or clear out that do away with information, it ensures that just a few information skip thru and some do not, by way of making changes to the facts such that they meet the circumstance. Illustrate just suggests the output of every level but does no longer run any MapReduce undertaking.

Q21. What Do You Understand By An Inner Bag And Outer Bag In Pig?

A relation internal a bag is referred to as inner bag and outer bag is only a relation in Pig.

Q22. Is Piglatin A Strongly Typed Language? If Yes, Then How Did You Come To The Conclusion?

In a strongly typed language, the user has to declare the sort of all variables upfront. In Apache Pig, while you describe the schema of the statistics, it expects the records to return within the equal layout you noted.

However, whilst the schema is not known, the script will adapt to truly statistics sorts at runtime. So, it could be stated that PigLatin is strongly typed in maximum instances however in uncommon cases it is lightly typed, i.E. It continues to work with statistics that doesn't stay up to its expectancies.

Q23. Differentiate Between Piglatin And Hiveql?

It is important to specify the schema in HiveQL, whereas it is optional in PigLatin.

HiveQL is a declarative language, whereas PigLatin is procedural.

HiveQL follows a flat relational records model, while PigLatin has nested relational records model.

Q24. Compare Apache Pig And Sql?

Apache Pig differs from SQL in its utilization for ETL, lazy assessment, store statistics at any given factor of time within the pipeline, aid for pipeline splits and express statement of execution pl. SQL is orientated round queries which produce a unmarried result. SQL has no in-built mechanism for splitting a statistics processing circulation and applying extraordinary operators to each sub-circulate.

Apache Pig permits consumer code to be protected at any point in the pipeline whereas if SQL in which for use statistics needs to be imported to the database first after which the technique of cleaning and trformation starts offevolved.

Q25. Can You Join Multiple Fields In Apache Pig Scripts?

Yes, it's far feasible to sign up for multiple fields in PIG scripts because the join operations takes data from one enter and joins them with some other input. This can be carried out by means of specifying the keys for every input and the two rows may be joined while the keys are equal.

Q26. What Are Some Of The Apache Pig Use Cases You Can Think Of?

Apache Pig big information equipment, is used specially for iterative processing, studies on uncooked records and for traditional ETL facts pipelines. As Pig can perform in circumstances wherein the schema isn't always acknowledged, inconsistent or incomplete- it is extensively used by researchers who need to make use of the information before it is wiped clean and loaded into the facts warehouse.

To construct behavior prediction models, for instance, it is able to be used by a website to track the reaction of the traffic to numerous varieties of commercials, snap shots, articles, and so on.

Q27. Does Pig Support Multi-line Commands?

Yes.

Q28. How Will You Merge The Contents Of Two Or More Relations And Divide A Single Relation Into Two Or More Relations?

This may be achieved using the UNION and SPLIT operators.

Q29. Explain The Difference Between Count_star And Count Functions In Apache Pig?

COUNT feature does not include the NULL fee whilst counting the number of elements in a bag, whereas COUNT_STAR (zero function consists of NULL values at the same time as counting.