Quick Prep Big Data Interview Archives | Page 13 of 23 | 800+ Java & Big Data Interview Q&As

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13: Q98 – Q104 Apache Hive Basics Interview questions & answers

Q98. What is Hive? A98. Hive is used for accessing and analyzing data in Hadoop using SQL syntax. It is known as the HiveQL. Q99. What is the difference between Hive internal tables & external tables? A99. … Read more ›...

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13A: Scala Semigroup interview Q&As

Semigroups, Monoids, Functors, Monads, Applicatives, etc are concepts or patterns that abstract complexities in computations, and NOT traits or classes. You can think of them as type classes like List[T], Option[T], Futures[T], MyContainer[T], etc that fulfil requirements of functors, monads, etc. Let’s start with monoids first. … Read more ›...

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13B: Q81 – Q83 Scala monoids interview Q&As

Monoids, Functors, Monads, Applicatives, etc are concepts or patterns that abstract complexities in computations, and NOT traits or classes. You can think of them as container type classes like List[T], Option[T], Futures[T], MyContainer[T], etc that fulfil requirements of functors, monads, etc. Let’s start with monoids first. … Read more ›...

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13B: Scala foldLeft, foldRight, scanLeft, scanRight, reduceLeft, reduceRight, etc are Monoids

This extends 13: Q81 – Q83 Scala monoids interview Q&As. Given the below code

package com.myapp

object FoldIsAMonoid extends App {

  val xs: List[Double] = List(1.0,2.0,3.0, 4.0)
  val ys: List[Int] = List(11,22, 33)

  val map1 = Map ("John" -> 22,
    "Peter" -> 33,
    "Sam"-> 97
  )

  val map2 = Map ( "John" -> 7,
    "Peter" -> 9
  )
}

package com.myapp

object FoldIsAMonoid extends App {

val xs: List[Double] = List(1.0,2.0,3.0, 4.0)

val ys: List[Int] = List(11,22, 33)

val map1 = Map ("John" -> 22,

"Peter" -> 33,

"Sam"-> 97

)

val map2 = Map ( "John" -> 7,

"Peter" -> 9

)

}

Q01. How will you combine the two lists xs & xy? A01. The foldLeft(.)(..) to the rescue as shown below:

 override def foldLeft[B](z: B)(op: (B, A) => B): B

1	override def foldLeft[B](z: B)(op: (B, A) => B): B

 val result = ys.foldLeft(xs)((xs_acc,y) => xs_acc.:+(y.asInstanceOf[Double]))
  println(result);

val result = ys.foldLeft(xs)((xs_acc,y) => xs_acc.:+(y.asInstanceOf[Double]))

println(result);

:+ is a method name alias for the append:


final def:+[B >: A](elem: B): List[B]

final def:+[B >: A](elem: B): List[B]

Output:

List(1.0, 2.0, 3.0, 4.0, 11.0, 22.0, 33.0)

1 2	List(1.0, 2.0, 3.0, 4.0, 11.0, 22.0, 33.0)

Q02....

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14: 50+ SQL scenarios based interview Q&As on order of SQL executions

This post reiterates the importance of understanding the order of execution of the SQL statements. Also, always think of the data granularity (aka grain). This post also demonstrates that the final query may look complex, but if you break it down it is much easier to comprehend.

…

14: Coding Scala Way – Scala concurrency with Future

Scala concurrency with Future explains step by step blocking vs unblocking calls and how implicit and apply concepts work under the covers. Scala extends the Java programming language and runtime in many ways, including concurrency where Scala version of Future<T> is more flexible than the Java version as you can create futures directly from blocks of code,

…

14: PySpark with Apache Kafka Interview Q&As with tutorials – part 2

This extends 13: PySpark with Apache Kafka Interview Q&As with tutorials – part 1 Q42: How would you go about displaying the array of JSON data in a tabular format? A42 In this post we will expand & … Read more ›...

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14: Python Closures, Partial functions and Currying tutorial

Closures In Python you have a closure when a nested function references a value in its enclosing scope as shown below. The inner function “inner_sum” references the enclosing function “outer_sum”‘s variable “num1”.

$ python3
>>> def outer_sum(num1):
...     def inner_sum(num2):
...          return(num1 + num2)
...     return inner_sum
...
>>> add_5 = outer_sum(5)    # num1 will be closed over as 5
>>> type(add_5)
<type 'function'>
>>> add_5(10)
15
>>> add_5(12)
17
>>>
>>> add_3 = outer_sum(3)    # num1 will be closed over as 3
>>> add_3(6)
9
>>> add_5(add_3(6))
14
>>> add_3(add_5(9))
17
>>>

$ python3

>>> def outer_sum(num1):

... def inner_sum(num2):

... return(num1 + num2)

... return inner_sum

...

>>> add_5 = outer_sum(5) # num1 will be closed over as 5

>>> type(add_5)

>>> add_5(10)

>>> add_5(12)

>>>

>>> add_3 = outer_sum(3) # num1 will be closed over as 3

>>> add_3(6)

>>> add_5(add_3(6))

>>> add_3(add_5(9))

>>>

The criteria that must be met to create a closure in Python : 1) You must have...

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14: Q105 – Q108 Spark “map” vs “flatMap” interview questions & answers

Q105. What is the difference between “map” and “flatMap” operations in Spark?
A105. The map and flatMap are transformation operations in Spark.

map transformation is applied to each element of RDD and it returns the result as a new RDD.

…

14: Q84 – Q87 Category Theory With Scala Interview Q&As

Q84. What is a category theory from a functional programming language perspective? A84. The category theory is all about abstractions. Composition “f” and “g” are functions, and “A” and “B” are types. f: A => … Read more ›...

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15: 50+ SQL scenarios based interview Q&As on pivoting & aggregate functions

In this post let’s look at an SQL scenario, and three different ways to solve this scenario. There could be more ways to solve an SQL scenario. Problem Statement Given the below “orders” table write a query to find the order status.


line_item_id    order_id     line_item_status
----------------------------------------------
1               ORD123       DELIVERED
2               ORD123       DELIVERED
3               ORD123       DELIVERED
1               ORD124       DELIVERED
2               ORD124       SHIPPED
1               ORD125       PENDING
2               ORD125       SHIPPED
3               ORD125       DELIVERED
1               ORD126       SHIPPED
2               ORD126       SHIPPED
1               ORD127       PENDING
2               ORD127       PENDING

line_item_id order_id line_item_status

----------------------------------------------

1 ORD123 DELIVERED

2 ORD123 DELIVERED

3 ORD123 DELIVERED

1 ORD124 DELIVERED

2 ORD124 SHIPPED

1 ORD125 PENDING

2 ORD125 SHIPPED

3 ORD125 DELIVERED

1 ORD126 SHIPPED

2 ORD126 SHIPPED

1 ORD127 PENDING

2 ORD127 PENDING

1) If all line items are ‘DELIVERED’...

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15: Coding Scala Way – Scala concurrency & closure vs function definition

This post not only covers “onSuccess” method on a “Future”, but also clarifies the difference between executing a closure and a function definition. Is anything wrong with the following Scala concurrency code?


import scala.concurrent.ExecutionContext.Implicits.global //thread pool execution context
import scala.concurrent.{Await, Future}
import scala.actors.threadpool.AtomicInteger
import scala.concurrent.duration._ // 5 seconds

object SimpleFuture extends App {
  
  val counter = new AtomicInteger
  
  //can succeed or fail
  val futureInc = Future {
    Thread.sleep(2000) // 2000ms processing time
    println("Incrementing .....")
    counter.incrementAndGet() 
  }
  
  val f1: Future[Int] = futureInc
  val f2: Future[Int] = futureInc
  
  f1.onSuccess {
    
    case val1 => {
      
      println("val1 = " + val1)
      
      f2.onSuccess {
       
        case val2 => 
          println("val2 = " + val2)
          println("Sum of generated numbers = " + (val1 + val2))
      }
    }
  }
  
 Thread.sleep(5000)
  
}

import scala.concurrent.ExecutionContext.Implicits.global //thread pool execution context

import scala.concurrent.{Await, Future}

import scala.actors.threadpool.AtomicInteger

import scala.concurrent.duration._ // 5 seconds

object SimpleFuture extends App {

val counter = new AtomicInteger

//can succeed or fail

val futureInc = Future {

Thread.sleep(2000) // 2000ms processing time

println("Incrementing .....")

counter.incrementAndGet()

}

val f1: Future[Int] = futureInc

val f2: Future[Int] = futureInc

f1.onSuccess {

case val1 => {

println("val1 = " + val1)

f2.onSuccess {

case val2 =>

println("val2 = " + val2)

println("Sum of generated numbers = " + (val1 + val2))

}

Thread.sleep(5000)

}

Output:


Incrementing .....
val1 = 1
val2 = 1
Sum of generated numbers = 2

Incrementing .....

val1 = 1

val2 = 1

Sum of generated numbers = 2

“ … Read more ›...

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15: PySpark with Apache Kafka Interview Q&As with tutorials – part 3

This extends PySpark with Apache Kafka Interview Q&As with tutorials – part 1 and PySpark with Apache Kafka Interview Q&As with tutorials – part 2 Q46: How will you go about writing the streamed structured to a parquet file? A46: In the last tutorial we wrote a console sink, …...

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Blog Archives

13: Q98 – Q104 Apache Hive Basics Interview questions & answers

13A: Scala Semigroup interview Q&As

13B: Q81 – Q83 Scala monoids interview Q&As

13B: Scala foldLeft, foldRight, scanLeft, scanRight, reduceLeft, reduceRight, etc are Monoids

14: 50+ SQL scenarios based interview Q&As on order of SQL executions

14: Coding Scala Way – Scala concurrency with Future

14: PySpark with Apache Kafka Interview Q&As with tutorials – part 2

14: Python Closures, Partial functions and Currying tutorial

14: Q105 – Q108 Spark “map” vs “flatMap” interview questions & answers

14: Q84 – Q87 Category Theory With Scala Interview Q&As

15: 50+ SQL scenarios based interview Q&As on pivoting & aggregate functions

15: Coding Scala Way – Scala concurrency & closure vs function definition

15: PySpark with Apache Kafka Interview Q&As with tutorials – part 3

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