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Table of contents
1.
Introduction
2.
Beginner Level Spark Interview Questions
3.
Frequently Asked Questions
3.1.
Which module is used for Structured Data Processing?
3.2.
Which one is the most popular language that Spark supports and why?
3.3.
What are the types of operations RDD supports?
4.
Conclusion
Last Updated: Mar 27, 2024

Spark Interview Questions-1

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Introduction

Apache Spark is a data processing framework that can perform processing tasks on very large data sets efficiently, as well as distribute data processing activities over several computers, either on its own or in combination with other distributed computing technologies. These two characteristics are critical in the areas of big data and machine learning, which demand huge computational power to break through large data repositories.

If you are preparing for a Spark interview in the future and are looking for a quick guide before your interview, then you have come to the right place.

The whole series consists of 90 Spark Interview Questions and is divided into three parts. This blog is Part 1, which covers the first 30 Spark Interview questions for the beginner level. You can refer to Part 2 for the Intermediate Level Spark Interview Questions and Part 3 for the Advanced level of Spark Interview questions. Now, let us start with some important Spark interview questions at the beginner level.

Must Recommended Topic, Pandas Interview Questions

Beginner Level Spark Interview Questions

1. According to you, what is Apache Spark all about?
Apace is an open-source framework engine whose execution engine supports the in-memory computation, and cyclic data flow is Spark. It has built-in modules for Machine learning, Streaming, SQL, etc.

2. Do you know in which language Spark is developed?

Spark is developed in the Scala language.

3. Name some sources from where data can be present in Spark Streaming?

Some sources from where data can be taken are Kafka, Flume, Kinesis, etc.

4. Say something on Hadoop.

A framework that is open source and allows storing and processing of Big Data. It is a Java-based platform in which we distribute large data sets across nodes.

5. What is MapReduce?

MapReduce is a software framework and a programming model used to process huge datasets. MapReduce can be split into two parts: Map and Reduce. Data Splitting and Data Mapping are looked after by Map, while Shuffling and Reduction of Data are handled by Reduce.

6. Is there any benefit of learning MapReduce?
Yes, there are numerous benefits of learning MapReduce. Tools such as Pig and Hive convert their queries into MapReduce for better optimization.

7. What parameters are defined to specify window operation?

‘Window length’ and ‘Sliding interval’ are the parameters defined to specify window operation.

8. What is in-memory processing?

With in-memory processing, you can instantly access data from physical memory whenever it is required. It significantly reduces the time consumed in transferring data.

9. Which algorithms are present in MLlib?

Algorithms such as Regression, Classification, Clustering, Pattern Mining, and Collaborative filtering are present in MLlib.

10. What do you mean by RDD?

RDD stands for Resilient Distribution Datasets. It is a fault-tolerant collection of elements in operation that run parallelly. The two types of RDDs in Spark are Parallelized collections and Hadoop Datasets.

11. Define Partitions.

Partitions in Spark are similar to Splits in MapReduce. When a huge chunk of data is partitioned into smaller and logical units, these units are known as partitions. They speed up the processing of data.

12. Mention two methods of creating RDD.

The two methods are-

  • By making use of SparkContext’s ‘parallelize’ method val.
  • Load an external database from external storage.

 

13. What is DStream?

DStream is a Continuous Stream of RDD, that is, Resilient Distributed Dataset.

14. Mention some output operations on DStream.

Some output operations on DStream include ‘SaveAsTextFiles’, ‘ForeachRDD’, and ‘ReduceByKeyAndWindow’.

15. Mention the default storage level of cache().

MEMORY_ONLY

16. What does Spark Streaming do?

Spark Stream provides the Spark Core’s fast scheduling capability to perform streaming analytics.

17. What is the reason for Spark being more speedy than MapReduce?

DAG execution engine and in-memory computation are the most prominent features of Spark and the reasons behind Spark being more speedy.

18. Can you tell how many tasks can run on each partition in Spark?

Not more than one task can run on each partition in Spark.

19. Which algorithms are solutions for the Multiclass Classification problem?

Algorithms such as Naive Bayes, Random Forests, and Decision Trees solve the Multiclass Classification problem.

20. Which algorithms are solutions to the Regression problem?

Algorithms such as Logistic Regression, Decision Trees, and Gradient-Boosted Trees solve the Regression problem.

21. Which Cluster Managers do Spark support?

Standalone Cluster Manager, MESOS, and YARN are some of the Cluster Managers Spark support.

22. What do you mean by YARN?

YARN is a platform for central resource management that delivers scalable operations along the whole cluster.

23. Is it necessary to install YARN on all the nodes of the YARN cluster?

No, there is no such necessity.

24. Say something on Spark Datasets.

These data structures in Spark provide Spark SQL-optimised execution engine and JVM object benefits of RDD.

25. What is Spark DataFrames?

When datasets are organized in columns, they are called DataFrame. Spark DataFrames are mainly designed for Big Data.

26. What do you know about Catalyst Optimizer?

Catalyst is designed to add new optimization techniques and features to Spark SQL easily. The optimizer helps us run queries much faster than their counter RDD part.

27. Which type of optimization does Catalyst Optimizer support?

Catalyst Optimizer supports either rule-based or cost-based optimization depending upon the requirement of the scenario.

28. What is the role of receivers?

As the name suggests, Receivers receive data from several sources and then move it to Spark. Each receiver is configured so that it uses up only a single core.

29. Briefly explain the types of receivers.

The two types of receivers are-

  • Reliable receivers: Sends acknowledgment to the data source.
  • Unreliable receivers: Do not send any acknowledgment to the data source.

 

30. How do we import SparkContext?

We can import SparkContext by using the following package-

‘import org.apache.spark.SparkContext’

 

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Frequently Asked Questions

Which module is used for Structured Data Processing?

There are several modules available for different purposes in Spark. For Structured Data Processing, we use Spark SQL.

Which one is the most popular language that Spark supports and why?

Scala is the most popular language Spark supports. This is because Spark is written in that language.

What are the types of operations RDD supports?

The two types of operations RDD supports are

  • Transformations
  • Actions

Conclusion

We hope that you have gained some insights on Spark Interview Questions through this article. We hope that this will help you excel in your interviews and enhance your knowledge regarding Apache Spark and related stuff. This was the basic level of Spark Interview Questions. For intermediate Spark Interview Questions, refer to this link, and for advanced Spark Interview Questions refer to this link

Recommended Reading:

Power Apps Interview Questions

 

For peeps out there who want to learn more about Data Structures, Algorithms, Power programming, JavaScript, or any other upskilling, please refer to guided paths on Coding Ninjas Studio. Enroll in our courses, go for mock tests, solve problems, and interview puzzles. Also, you can put your attention towards interview stuff- interview experiences and an interview bundle for placement preparations. Do upvote our blog to help other ninjas grow.

Happy Coding!

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