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Top 30 Apache spark interview questions and answers

In the technical market, developers are always in search of advanced data processing tools that can process data faster to meet the flexible needs of the superior market. Also, advanced tools are able to handle real-time data processing within seconds. Here, Apache is getting quick momentum when it comes to enterprises and large-sized businesses that generally have plenty of big data to work on. Highlights of Apache Spark and why it has gone so popular during last few years??
  • Helps in generating complex big data solutions within seconds
  • Handles real-time data processing faster and more accurately
  • Most of large organizations like Amazon, eBay, Alibaba, and many others implanting Spark for big data deployment.
  • Event streaming performed by Spark is truly recommendable and more preferable than Hadoop big data platform.
  • Ensures full-fledged career options for Data Scientists, Spark Developers, and Data Analytics professionals.
The increasing demand of Apache Spark has triggered us to compile a list of Apache Spark interview questions and answers that will surely help you in the successful completion of your interview. These questions are good for both fresher and experienced Spark developers to enhance their knowledge and data analytics skills both. 

Apache Spark interview questions

  1. What are the key features of Apache Spark?
  2. What are the components of Spark Ecosystem?
  3. What are the languages supported by Apache Spark and which is the most popular one?
  4. What are the multiple data sources supported by Spark SQL?
  5. How is machine learning implemented in Spark?
  6. What is YARN?
  7. Does Spark SQL help in big data analytics through external tools too?
  8. How is Spark SQL superior from others – HQL and SQL?
  9. Do real-time data processing is possible with Spark SQL?
  10. Explain the concept of Resilient Distributed Dataset (RDD).
  11. What kind of operations does RDD support?
  12. What is a Parquet file?
  13. Why is Parquet file format taken best choice for various data processing systems?
  14. Spark SQL is parallel or distributed data processing framework?
  15. What is the catalyst framework in Spark SQL?
  16. What is Executor Memory in a Spark application?
  17. How to balance query accuracy and response time in Spark SQL?
  18. Which framework is more preferable in terms of usage either Hadoop or Spark?
  19. Are there any benefits of Apache Spark over Hadoop MapReduce?
  20. How Array and List can be differentiated in Scala?
  21. How to map data and forms together in Scala?
  22. Do private members of Companion classes can be accessed through companion objects in Scala?
  23. What is the significance of immutable design in Scala programming language?
  24. How can Auxiliary Constructors be defined in Scala?
  25. How will you explain yield keyword in Scala?
  26. How can functions be invoked silently without passing all the parameters?
  27. What do you mean by Scala Traits and how it can be used in Scala programming language?
  28. Is there any difference between parallelism and concurrency in Scala programming language?
  29. How are Monads useful for Scala developers?
  30. How can Transformations be defined in Apache Spark?
  31. What is the meaning of “Actions” in Apache Spark?
  32. Define Spark Core and how it is useful for Scala Developers?
  33. Define data streaming in Apache Spark?
  34. How can graphs be processed in Apache Spark?
  35. Is there any library function to support machine learning algorithms?
  36. Which File System is supported by Apache Spark?
  37. How many cluster modes are supported in Apache Spark?
  38. Is there any cluster management technology in Apache Spark?
  39. How can you create RDD in Apache Spark?
  40. What is the key distinction between Hadoop and Spark?

Apache Spark interview questions and answers

In the earlier section, we have given the list of 30 questions after careful research and analysis that will surely help you in interview selection. During later sections, we will provide answers to each question by dividing whole 30 questions into three sets – Apache Spark SQL interview questions, Apache Spark Scala interview questions, and Apache Spark Coding interview questions.

Q1). What are the key features of Apache Spark?

Here is a list of the key features of Apache Spark:
  • Hadoop Integration
  • Lazy Evaluation
  • Machine Learning
  • Multiple Format Support
  • Polyglot
  • Real-Time Computation
  • Speed

Q2). What are the components of Spark Ecosystem?

Here are the core components of the Spark ecosystem:
  • Spark Core: a Base motor for vast scale parallel and appropriated information preparing
  • Spark Streaming: Used for preparing ongoing gushing information
  • Spark SQL: Integrates social preparing with Spark's practical programming API
  • GraphX: Graphs and diagram parallel calculation
  • MLlib: Performs machine learning in Apache Spark

Q3). What are the languages supported by Apache Spark and which is the most popular one?

Apache Spark supports the accompanying four languages: Scala, Java, Python and R. Among these languages, Scala and Python have intuitive shells for Spark. The Scala shell can be gotten to through ./canister/start shell and the Python shell through ./receptacle/pyspark. Scala is the most utilized among them since Spark is composed in Scala and it is the most prominently utilized for Spark. 

Q4). What are the multiple data sources supported by Spark SQL?

Apache Spark SQL is a popular ecosystem or interfaces to work with structured or semi-structured data. The multiple data sources supported by Spark SQL includethe text file, JSON file, Parquet file etc.

Q5). How is machine learning implemented in Spark?

MLlib is a versatile machine learning library given by Spark. It goes for making machine adapting simple and versatile with normal learning calculations and utilize cases like grouping, relapse separating, dimensional decrease, and alike.

Q6). What is YARN?

Like Hadoop, YARN is one of the key highlights in Spark, giving a focal and asset administration stage to convey versatile activities over the group. YARN is an appropriated compartment supervisor, as Mesos for instance, though Spark is an information preparing instrument. Spark can keep running on YARN, a similar way Hadoop Map Reduce can keep running on YARN. Running Spark on YARN requires a parallel dissemination of Spark as based on YARN support.

Q7). Does Spark SQL help in big data analytics through external tools too?

Yes, Spark SQL helps in big data analytics through external tools too. Let us see how it is done actually –
  • It access data using SQL statements in both ways either data is stored inside the Spark program or data needs to access through external tools that are connected to Spark SQL through database connectors like JDBC or ODBC.
  • It provides rich integration between a database and regular coding with RDDs and SQL tables. It is also able to expose custom SQL functions as needed.

Q8). How is Spark SQL superior from others – HQL and SQL?

Spark SQL is advance database component able to support multiple database tools without changing their syntax. This is the way how Spark SQL accommodates both HQL and SQL superiorly.

Q9). Do real-time data processing is possible with Spark SQL?

Real-time data processing is not possible directly but obviously, we can make it happen by registering existing RDD as a SQL table and trigger the SQL queries on priority.

Q10). Explain the concept of Resilient Distributed Dataset (RDD).

RDD is an abbreviation for Resilient Distribution Datasets. An RDD is a blame tolerant accumulation of operational components that keep running in parallel. The divided information in RDD is permanent and distributed in nature. There are fundamentally two sorts of RDD:
  • Parallelized Collections: Here, the current RDDs run parallel with each other.
  • Hadoop Datasets:
  • They perform works on each document record in HDFS or other stockpiling frameworks.
RDDs are essential parts of information that are put away in the memory circulated crosswise over numerous hubs. RDDs are sluggishly assessed in Spark. This apathetic assessment is the thing that adds to Spark's speed.

Apache Spark SQL interview questions

Q11). What kind of operations does RDD support?

There are two types of operations that RDDs support: transformations and actions.
  • Transformations: Transformations make new RDD from existing RDD like guide, reduceByKey, and channel. Transformations are executed on interest. That implies they are registered lethargically.
  • Actions: Actions return last aftereffects of RDD calculations. Actions trigger execution utilizing genealogy diagram to stack the information into unique RDD, carry out every single transitional change and return last outcomes to Driver program or compose it out to document framework.

Q12). What is a Parquet file?

Parquet is a columnar arrangement record upheld by numerous other information preparing frameworks. Start SQL performs both read and write operations with Parquet document and think of it as an extraordinary compared to other enormous information examination arranges up until this point. 

Q13). Why is Parquet file format taken best choice for various data processing systems?

Parquet is a popular columnar file format compatible with almost all data processing systems. This is the reason why it is taken as one of the best choices for big data analytics so far. Spark SQL interface is able to perform read and write operation on Parquet file and it can be accessed quickly whenever required.

Q14). Spark SQL is parallel or distributed data processing framework?

Spark SQL is parallel data processing framework where batch streaming and interactive data analytics is performed altogether.

Q15). What is the catalyst framework in Spark SQL?

Catalyst framework is advanced functionality in Spark SQL for automatic transformation of SQL queries by addition of optimized functions that help in processing data faster and accurately than your expectations.

Q16). What is Executor Memory in a Spark application?

Each spark application has the same settled load estimate and settled a number of centers for a spark agent. The pile measure is the thing that alluded to as the Spark agent memory which is controlled with the spark.executor.memory property of the – agent memory signal. Each spark application will have one agent on every laborer hub. The agent memory is fundamentally a measure on how much memory of the specialist hub will the application use.

Q17). How to balance query accuracy and response time in Spark SQL?

To maintain query accuracy and response time in Spark SQL, you are advised to go with BlinkDB query engine. The engine renders queries with meaningful results and significant error to maintain the accuracy.

Q18). Which framework is more preferable in terms of usage either Hadoop or Spark?

The programming in Hadoop was really tough that has been made easier with Spark by usage of interactive APIs for the different programming language. Obviously, Spark is a preferable choice than Hadoop in terms of usage.

Q19). Are there any benefits of Apache Spark over Hadoop MapReduce?

Spark has the ability to perform data processing 100 times faster than MapReduce. Also, Spark has inbuilt memory processing and libraries to perform multiple tasks together like batch processing, streaming, interactive processing etc. The above discussion makes sure than Apache Spark is surely better than any other data processing frameworks exist as of now. 

Q20). How Array and List can be differentiated in Scala?

The Array is a mutable data structure that is sequential in nature while Lists are immutable data structures that are recursive in nature. Size of array is predefined while lists change its size based on operational requirements. In other words, Lists are variable in size while the array is fixed size data structure.

Apache Spark Scala interview questions

Q21). How to map data and forms together in Scala?

The most wonderful solution to map data and forms together in Scala is “apply” and “unapply" methods. As the name suggests, the apply method is used to map data while the unapply method can be used to unmap the data. The unapply method follows the reverse operation of the apply method. 

Q22). Do private members of Companion classes can be accessed through companion objects in Scala?

Yes, it is possible that private members of Companion classes can be accessed through companion objects in Scala.

Q23). What is the significance of immutable design in Scala programming language?

Every time when working with concurrent programs and other similar equality issues then immutable design in Scala programming language works amazingly. It helps in resolving coding related issues and makes programming easy for Scala developers.

Q24). How can Auxiliary Constructors be defined in Scala?

The keywords "def" and "this" is used to declare secondary or auxiliary constructors in Scala programming language. They are designed to overload constructors similar to Java. This is necessary to understand the working of each constructor deeply so that the right constructor can be invoked at the right time. Even declaration of constructor differs from each other in terms of data types or parameters.

Q25). How will you explain yield keyword in Scala?

Yield keyword can be used either before or after expressions. It is taken more useful when declared before expression. The return value from every expression will be stored as the collection. The returned value can either be used as a normal collection or iterate in another loop. 

Q26). How can functions be invoked silently without passing all the parameters?

In case, when we want to invoke functions silently without passing all the parameters, we should use implicit parameters. The parameters that you want to use implicit, you need to provide default values for the same.

Q27). What do you mean by Scala Traits and how it can be used in Scala programming language?

Scala trait is an advanced class in Scala that enables the use of multiple inheritances and it can be extended to multiple classes together. In other words, one class can have multiple Scala traits based on requirement. Traits are used commonly when you need dependency injection. You just need to initiate class with Scala traits and dependency will be injected immediately.

Q28). Is there any difference between parallelism and concurrency in Scala programming language?

Normal users are generally confused between two terms parallelism and concurrency in Scala programming language. Here, we will discuss in simple words how they are different from each other and their significance too. When processes are executed sequentially then it is termed as concurrency while processes are executed simultaneously then it is named as parallelism technology. There are several library functions available in Scala to achieve parallelism.

Q29). How are Monads useful for Scala developers?

If you want to understand Monads in simple words then it would not be wrong comparing them with a wrapper. As wrappers are used to protect any product and to make it attractive, Monads are used for the same purpose in Scala. They are used to wrap objects together and perform two important functions further. These functions are –
  • Identity through “unit” in Scala
  • Bind through “flatMap” in Scala 

Q30). How can Transformations be defined in Apache Spark?

Transformations are created early in programs and these are generally used along with RDD. These functions are applied on already existed RDD to make a new RDD. Transformations cannot be used without implementing actions in Apache Spark. The most popular examples of transformation are amap () and filter () that helps to create new RDD by selecting elements in available RDD.

Apache Spark Coding interview questions

Q31). What is the meaning of “Actions” in Apache Spark?

The data is taken back to the local machine from RDD with the help of “actions” in Apache Spark. The popular example of the action is folded () passes value again and again until the time it is left only one. The actions are executed with the assistance of transformations that are created early in programs. The most popular examples of transformation are amap () and filter () that helps to create new RDD by selecting elements in available RDD.

Q32). Define Spark Core and how it is useful for Scala Developers?

Spark Core in Apache Spark is used for memory management, job monitoring, tolerate faults, scheduling jobs and interactive storage features. RDD is an advanced feature in Spark Core suitable for tolerating faults. RDD is a collection of distributed objects available across multiple nodes that are generally manipulated in parallel.

Q33). Define data streaming in Apache Spark?

No framework can come to the top without the functionality of live data streaming or handling live events. This is the reason why Apache Spark has used the most advanced techniques to allow the same. For this purpose, Apache uses complex algorithms and high-level functions like reduce, map, join or window etc. These functions push data to file systems and live dashboards further.

Q34). How can graphs be processed in Apache Spark?

Out of all, one attractive feature supported in Apache Spark includes graph processing. Spark uses advanced multimedia component GraphX to create or explore graphs used to explore data more wisely and accurately.

Q35). Is there any library function to support machine learning algorithms?

Spark MLib is a popular library function in Apache Spark to support machine learning algorithms. The common learning algorithms and utilities included in MLib library functions are a regression, clustering, classification, dimensional reduction, low-level optimization, advance level pipelining APIs, and collaborative filtering etc. The main objective of the machine learning algorithm is recommendations, predictions and similar other functions.

Q36). Which File System is supported by Apache Spark?

Apache Spark is an advanced data processing system that can access data from multiple data sources. It creates distributed datasets from the file system you use for data storage. The popular file systems used by Apache Spark include HBase, Cassandra, HDFS, and Amazon S3 etc.

Q37). How many cluster modes are supported in Apache Spark?

The three popular cluster modes supported in Apache Spark include – Standalone, Apache Mesos, and YARN cluster managers. YARN is the cluster management technology in Apache Spark stands for yet another resource negotiator. The idea was taken from Hadoop where YARN technology was specially introduced to reduce the burden on MapReduce function.

Q38). Is there any cluster management technology in Apache Spark?

Yes, the cluster management technology in Apache Spark is popular with the name YARN technology. YARN stands for yet another resource negotiator. The idea was taken from Hadoop where YARN technology was specially introduced to reduce the burden on MapReduce function.

Q39). How can you create RDD in Apache Spark?

There are two popular techniques that can be used to create RDD in Apache Spark – First is Parallelize and other is text File method. Here is a quick explanation of how both methods can be used for RDD creation. val x= Array(5,7,8,9) val y= sc.parallelize(x) val input = sc.textFile(“input.txt”);

Q4). What is the key distinction between Hadoop and Spark?

The key distinction between Hadoop and Spark lies in the way to deal with processing: Spark can do it in-memory, while Hadoop MapReduce needs to peruse from and keep in touch with a disc. Thus, the speed of handling varies altogether – Spark might be up to 100 times quicker. Be that as it may, the volume of information prepared likewise varies: Hadoop MapReduce can work with far bigger informational indexes than Spark.

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