Introduction

With the IT industry’s increasing need to calculate big data at high speeds, it’s no wonder that the Apache Spark mechanism has earned the industry’s trust. Apache Spark is one of the most common, general-purpose and cluster-computing frameworks. 

The open-source tool provides an interface for programming the entire computing cluster with implicit data parallelism and fault-tolerance capabilities. 

The thought of possible interview questions can shoot up your anxiety! But don’t worry, for we’ve compiled here a comprehensive list of Spark interview questions and answers.

Let us start by looking at the top 20 common Spark interview questions usually addressed in recruiting professionals. 

  1. Explain Shark.
  2. Can you explain the main features of Spark Apache?
  3. What is Apache Spark?
  4. Explain the concept of Sparse Vector.
  5. What is the method for creating a data frame?
  6. Explain what is SchemaRDD.
  7. Explain what are accumulators.
  8. Explain the core of Spark.
  9. Explain how data is interpreted in Spark.
  10. How many forms of transformations are there?
  11. What’s Paired RDD?
  12. What is implied by the treatment of memory in Spark?
  13. Explain the Directed Acyclic Graph.
  14. Explain the lineage chart.
  15. Explain the idle appraisal in Spark.
  16. Explain the advantage of a lazy evaluation.
  17. Explain the concept of “persistence”.
  18. What is Map-Reduce learning function?
  19. When processing information from HDFS, is the code performed near the data?
  20. Does Spark also contain the storage layer?

Here are the answers to the most commonly asked Spark interview questions.

1. Explain Shark.

Shark is for people from a Database background that can help them access Scala MLib through SQL accounting.

2. Can you explain the main features of Spark Apache?

  • Supports several programming languages Spark can be coded in four programming languages, i.e. Java, Python, R, and Scala. It also offers high-level APIs for them. Additionally, Apache Spark supplies Python and Scala shells. 
  • Lazy Evaluation Apache Spark uses the principle of lazy evaluation to postpone the evaluation before it becomes completely mandatory.
  • Machine Learning The MLib machine learning component of Apache Spark is useful for extensive data processing. It removes the need for different engines for processing and machine learning.
  • Modern Format Assistance Apache Spark supports multiple data sources, like  Cassandra, Hive, JSON, and Parquet. The Data Sources API provides a pluggable framework for accessing structured data through Spark SQL. 
  • Real-Time Computation – Spark is specifically developed to satisfy massive scalability criteria. Thanks to in-memory computing, Spark’s computing is real-time and has less delay.
  • Speed – Spark is up to 100x faster than Hadoop MapReduce for large-scale data processing. Apache Spark is capable of achieving this incredible speed by optimized  portioning. The general-purpose cluster-computer architecture handles data across partitions that parallel distributed data processing with limited network traffic.
  • Hadoop Integration – Spark provides seamless access to Hadoop and is a possible substitute for the Hadoop MapReduce functions. Spark is capable of operating on top of the existing Hadoop cluster using YARN for scheduling resources.

3. What is Apache Spark?

Apache Spark is a data processing framework that can perform processing tasks on extensive data sets quickly. This is one of the most frequently asked Apache Spark interview questions.

4. Explain the concept of Sparse Vector.

A vector is a one-dimensional array of elements. However, in many applications, the vector elements have mostly zero values that are said to be sparse. 

5. What is the method for creating a data frame?

A data frame can be generated using the Hive and Structured Data Tables.

6. Explain what SchemaRDD is.

A SchemaRDD is similar to a table in a traditional relational database. A SchemaRDD can be created from an existing RDD, Parquet file, a JSON dataset, or by running HiveQL against data stored in Apache Hive.

7. Explain what accumulators are.

Accumulators are variables used to aggregate information across the executors.

8. Explain what the core of Spark is.

Spark Core is a basic execution engine on the Spark platform.

9. Explain how data is interpreted in Spark?

Data can be interpreted in Apache Spark in three ways: RDD, DataFrame, and DataSet.

NOTE: These are some of the most frequently asked spark interview questions.

10. How many forms of transformations are there?

There are two forms of transformation: narrow transformations and broad transformations.

11. What’s Paired RDD?

Paired RDD is a key-value pair of RDDs.

12. What is implied by the treatment of memory in Spark?

In memory computing, we retain data in sloppy access memory instead of specific slow disc drives.

NOTE: It is important to know more about this concept as it is commonly asked in Spark Interview Questions.

13. Explain the Directed Acyclic Graph.

Directed Acyclic Graph is a finite collateral graphic with no alternating disc.

14. Explain the lineage chart.

Lineage map reports to the graph for the RDD parent as a whole.

15. Explain the idle assessment in Spark.

The idle assessment, known as call by use, is a strategy that defers compliance until one needs a benefit.

16. Explain the advantage of a lazy evaluation.

To expand the program’s manageability and features.

17. Explain the concept of “persistence”.

RDD persistence is an ideal technique that saves the results of the RDD assessment.

18. What is the Map-Reduce learning function?

Map Reduce is a model used for a vast amount of data design.

19. When processing information from HDFS, is the code performed near the data?

Yes, in most situations, it is. It creates executors that are close to paths that contain data.

20. Does Spark also contain the storage layer?

No, it doesn’t have a disc layer, but it lets you use many data sources.

These 20 Spark coding interview questions are some of the most important ones! Make sure you revise them before your interview!

21. Where does the Spark Driver operate on Yarn?

The Spark driver operates on the client computer.

22. How is machine learning carried out in Spark?

Machine learning is carried out in Spark with the help of MLlib. It’s a scalable machine learning library provided by Spark.

23. Explain what a Parquet file is.

Parquet is a column structure file that is supported by many other data processing classes.

24. Explain the lineage of the RDD.

The lineage of RDD is that it does not allow memory duplication of records.

25. Explain the Spark executor.

Executors are worker nodes’ processes in charge of running individual tasks in a given Spark job.

26. Explain the meaning of a worker’s node or route.

A worker node or path corresponds to any node that can stick the application symbol in many nodes.

27. Explain the Sparse Vector.

A sparse vector has two parallel formats, one for indices and the other for values.

28. Is it possible to stick with the Apache Spark on Apache Mesos?

Yes, you should adhere to the clusters of resources that have Mesos.

29. Explain the Apache Spark accumulators.

Accumulators are predictions that are taken away only by a non-linear method of thinking and alternate processes.

30. Why is there a need for transmitting variables while using Apache Spark?

Because it reads, except for variables, the relevant in-memory array on each machine tool.

31. Explain the import of Sliding Window performance.

Sliding Window withholds transmission of numerical information packets between different data networks on machines.

32. Explain the Discretized Stream of Apache Spark.

Discretized Stream is a fundamental abstraction acceptable to Spark Streaming.

Make sure you revise these Spark streaming interview questions before moving onto the next set of questions.

33. State the distinction between SQL and HQL.

SparkSQL is a critical component of the Spark Core engine, whereas HQL is a combination of OOPS with the Relational database concept.

NOTE: This is one of the most widely asked Spark SQL interview questions.

34. Explain the use of Blink DB.

Blink DB is a query machine tool that helps you to run SQL queries.

35. Explain the node of the Apache Spark worker.

The node of a worker is any path that can run the application code in a cluster.

NOTE: This is one of the most crucial Spark interview questions for experienced candidates.

36. Explain the framework of the Catalyst.

The Catalyst Concept is a modern optimization framework in Spark SQL.

37. Does Spark use Hadoop?

Spark has its own cluster administration list and only uses Hadoop for collection.

38. Why does Spark use Akka?

Spark simply uses Akka for scheduling.

39. Explain the worker node or pathway.

A node or route that can run the Spark program code in a cluster can be called a worker or porter node.

40. Explain what you understand about the RDD Schema?

Schema RDD consists of a row factor with schema data in both directions with details in each column.

41. What is the function of Spark Engine?

Spark Engine schedules for distribution and monitoring.

42. Which is the Apache Spark default level?

The cache() method is used for the default storage level, which is StorageLevel.

43. Can you use Spark to perform the ETL process?

Yes, Spark may be used for the ETL operation as Spark supports Java, Scala, R, and Python.

44. Which is the necessary data structure of Spark?

The Data Framework is essential for the fundamental development of Spark data.

45. Can you flee Apache Spark on Apache Mesos?

Yes, it can flee Apache Spark on the hardware clusters that Mesos charges.

46. Explain the Spark MLlib.

MLlib is the acronym of Spark’s scalable machine learning library.

47. Explain DStream.

D Stream is a high-level concentration described by Spark Streaming.

48. What is one advantage of Parquet files?

Parquet files are adequate for large-scale queries.

49. Explain the framework of the Catalyst.

The Catalyst is a structure that represents and manipulates a data frame graph.

50. Explain the set of data.

Spark Datasets is an extension of the Data Frame API.

51. What are DataFrames?

They are a list of data that is arranged in the named columns.

52. Explain the concept of the DDR (Resilient Distributed Dataset). Also, how can you build RDDs in Apache Spark?

The RDD or Resilient Distribution Dataset is a fault-tolerant array of operating elements capable of running parallel. Any partitioned data in the RDD can be distributed. There are two kinds of RDDs:

  1. Hadoop Datasets – Perform functions for each file record in HDFS (Hadoop Distributed File System) or other forms of storage structures.
  2. Parallelized  Collections – Extensive RDDs running parallel to each other

There are two ways to build an RDD in Apache Spark:

  • By paralleling the array in the Driver program. It uses the parallelize() function of SparkContext.
  • Through accessing an arbitrary dataset from any external storage, including HBase, HDFS, and a shared file system.

53. Define Spark.

Spark is a parallel system for data analysis. It allows a quick, streamlined big data framework to integrate batch, streaming, and immersive analytics.

54. Why use Spark?

Spark is a 3rd gen distributed data processing platform. It’s a centralized big data approach for big data processing challenges such as batch, interactive or streaming processing. It can ease a lot of big data issues.

55. What is RDD?

The primary central abstraction of Spark is called Resilient Distributed Datasets. Resilient Distributed Datasets are a set of partitioned data that fulfills these characteristics. The popular RDD properties are immutable, distributed, lazily evaluated, and catchable.

56. Throw some light on what is immutable.

If a value has been generated and assigned, it cannot be changed. This attribute is called immutability. Spark is immutable by nature. It does not accept upgrades or alterations. Please notice that data storage is not immutable, but the data content is immutable.

57. How can RDD spread data?

RDD can dynamically spread data through various parallel computing nodes.

58. What are the different Ecosystems of Spark?

Some typical Spark ecosystems are:

  • Spark SQL for developers of SQL
  • Spark Streaming for data streaming
  • MLLib for algorithms of machine learning
  • GraphX for computing of graph
  • SparkR to work on the Spark engine
  • BlinkDB, which enables dynamic queries of large data

GraphX, SparkR, and BlinkDB are in their incubation phase.

59. What are partitions?

Partition is a logical partition of records, an idea taken from Map-reduce (split) in which logical data is directly obtained to process data. Small bits of data can also help in scalability and fasten the operation. Input data, output data & intermediate data are all partitioned RDDs.

60. How does Spark partition data?

Spark uses the map-reduce API for the data partition. One may construct several partitions in the input format. HDFS block size is partition size (for optimum performance), but it’s possible to adjust partition sizes like Split.

61. How does Spark store data?

Spark is a computing machine without a storage engine in place. It can recover data from any storage engine, such as HDFS, S3, and other data services.

62. Is it obligatory to launch the Hadoop program to run a Spark?

It is not obligatory, but there is no special storage in Spark. Thus you must use the local file system to store the files. You may load and process data from a local device. Hadoop or HDFS is not needed to run a Spark program.

63. What’s SparkContext?

When the programmer generates RDDs, SparkContext connects to the Spark cluster to develop a new SparkContext object. SparkContext tells Spark to navigate the cluster. SparkConf is the central element for creating an application for the programmer.

64. How is SparkSQL different from HQL and SQL?

SparkSQL is a special part of the SparkCore engine that supports SQL and HiveQueryLanguage without modifying syntax. You will enter the SQL table and the HQL table.

65. When is Spark streaming used?

It is an API used for streaming data and processing it in real-time. Spark streaming collects streaming data from various services, such as web server log files, data from social media, stock exchange data, or Hadoop ecosystems such as Kafka or Flume.

66. How does the Spark Streaming API work?

The programmer needs to set a specific time in the setup, during which the data that goes into the Spark is separated into batches. The input stream (DStream) goes into the Spark stream. 

The framework splits into little pieces called batches, then feeds into the Spark engine for processing. The Spark Streaming API sends the batches to the central engine. Core engines can produce final results in the form of streaming batches. Production is in the form of batches, too. It allows the streaming of data and batch data for processing.

67. What is GraphX?

GraphX is a Spark API for editing graphics and arrays. It unifies ETL, analysis, and iterative graph computing. Its fastest graphics system offers error tolerance and easy use without the need for special expertise.

68. What is File System API?

The File System API can read data from various storage devices, such as HDFS, S3, or Local FileSystem. Spark utilizes the FS API to read data from multiple storage engines.

69. Why are partitions immutable?

Each transformation creates a new partition. Partitions use the HDFS API such that the partition is immutable, distributed, and error-tolerant. Partitions are, therefore, conscious of the location of the results.

70. Discuss what is flatMap and Map in Spark.

A map is a simple line or row to process the data. Each input object can be mapped to various output items in FlatMap (so the function should return a Seq rather than a unitary item). So most often, it is used to return the Array components.

71. Define broadcast variables.

Broadcast variables allow the programmer to have a read-only variable cached on each computer instead of sending a copy of it with tasks. Spark embraces two kinds of mutual variables: broadcast variables and accumulators. Broadcast variables are stored as Array Buffers, which deliver read-only values to the working nodes.

72. What are Spark Accumulators in context to Hadoop?

Off-line Spark debuggers are called accumulators. Spark accumulators are equivalent to Hadoop counters and can count the number of activities. Only the driver program can read the value of the accumulator, not the tasks.

73. When can Apache Spark be used? What are the advantages of Spark over MapReduce?

Spark is quite fast. Programs run up to 100x faster than Hadoop MapReduce in memory. It appropriately uses RAM to achieve quicker performance. 

In Map Reduce Paradigm, you write many Map-reduce tasks and then link these tasks together using the Oozie/shell script. This process is time-intensive, and the role of map-reducing has a high latency.

Frequently, converting production from one MR job to another MR job can entail writing another code since Oozie might not be enough.

In Spark, you can do anything using a single application/console and get the output instantly. Switching between ‘Running something on a cluster’ and ‘doing something locally’ is pretty simple and straightforward. All this leads to a lower background transition for the creator and increased efficiency.

Spark sort of equals MapReduce and Oozie when put in conjunction.

The above-mentioned Spark Scala interview questions are pretty popular and are a compulsory read before you go for an interview.

74. Is there a point of MapReduce learning?

Yes. It serves the following purposes:

  • MapReduce is a paradigm put to use by several big data tools, including Spark. So learning the MapReduce model and transforming a problem into a sequence of MR tasks is critical.
  • When data expands beyond what can fit into the cluster memory, the Hadoop Map-Reduce model becomes very important.
  • Almost every other tool, such as Hive or Pig, transforms the query to MapReduce phases. If you grasp the Mapreduce, you would be better able to refine your queries.

75. What are the drawbacks of Spark?

Spark uses memory. The developer needs to be cautious about this. Casual developers can make the following mistakes:

  • It might end up running everything on the local node instead of spreading work to the cluster.
  • It could reach some web services too many times by using multiple clusters.
  • The first dilemma is well addressed by the Hadoop Map reduce model.
  • A second error is also possible in Map-Reduce. When writing Map-Reduce, the user can touch the service from the inside of the map() or reduce() too often. This server overload is also likely when using Spark.

NOTE: Spark Interview Questions sometimes test the basics of the candidate and questions like advantages are drawbacks are frequently asked.

Final Word


These sample Spark interview questions can help you a lot during the interview. The interviewer would expect you to address complicated questions and have some solid knowledge of Spark fundamentals.

Conclusion

Organizations like Shopify, Alibaba, Amazon, and eBay are legally taking Apache Spark for their huge size data formation. The requirement for Spark developers is anticipated to rise exponentially.

If you are interested in making it big in the world of data and evolve as a Future Leader, you may consider our Integrated Program in Business Analytics, a 10-month online program, in collaboration with IIM Indore!

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