PySpark Interview Questions!!! Are You Prepared for PySpark Interview Questions and Land Your Dream Job?

PySpark can be an incredible asset when it comes to interview preparation for those starting their data analysis and computational career journey, from beginners and seasoned professionals alike!

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PySpark Interview Questions and Answers:

1. What is PyPark, and what is it used for?

PyPark is a Python API for Apache Spark, a distributed computing framework for big data processing. It provides an efficient way for developers to perform complex data processing and analysis tasks using Spark’s powerful engine.

2. What makes Apache Spark different from other distributed computing frameworks like Hadoop?

Spark is known for its scalable, massively parallel in-memory execution environment, which makes it faster at processing data than MapReduce and Hadoop. It also offers real-time competition and low latency due to in-memory processing.

3. What are the main components of the Spark ecosystem?

The Spark ecosystem consists of the Spark core component, which is responsible for basic I/O functions, scheduling, and monitoring, and various libraries like Spark SQL, Spark streaming, machine learning library, and GraphX component.

4. What is Spark SQL component, and how does it work?

The Spark SQL component allows developers to perform SQL-like queries on Spark data using declarative queries and PMI storage. You can also read related PySpark SQL interview questions from this blog.

5. What is Spark streaming, and how does it help in data processing?

The Spark streaming component allows developers to perform batch processing and streaming of data in the same application, making it easier to process real-time data.

In addition read the PySpark interview questions for data engineer for better understanding.

 6. What is the machine learning library in Spark, and what are its features?

The machine learning library in Spark offers various components for developing scalable machine learning pipelines, including summary statistics, core relations, feature extraction, transformation functions, optimization algorithms, and more.

 7. What is the GraphX component, and how does it help in data processing?

The GraphX component allows data scientists to work with graph and non-graph sources for flexibility and resilience in graph construction and transformation.

 8. What programming languages does Spark support, and how does Scala fit in?

Spark supports Scala, Python, and R as programming languages. Scala is used as an interface, and Python and R are also supported for ease of use.

9. Where can data be stored in Spark, and what are the advantages of in-memory data sharing?

Data can be stored in HDFS, local file systems, Amazon’s free cloud, and SQL and NoSQL databases. In-memory data sharing is faster than network and disk sharing, making it 10 to 100 times faster for distributed computing.

10. What are Resilient Distributed Data Sets (RDDs), and how do they help in distributed computing?

RDDs are a fundamental data structure of Spark that can handle both structured and unstructured data. They are immutable collections of objects, containing any type of Python, Java, or Scala objects, including user-defined classes.

RDDs provide in-memory data sharing for faster data processing and help reduce issues with multiple operations or jobs in distributed computing.

11. What are RDDs in Apache Spark, and what are their key features?

RDDs (Resilient Distributed Data Sets) are a fundamental data structure of Spark that supports in-memory computation and lazy evaluation.

They provide tolerance to the system, track data lineage information, and support partitioning as the fundamental unit of parallelism.

12. How can RDDs be created in Spark?

RDDs can be created from parallelized collections using the parallize method, from existing RDDs using transformation operations like map and filter, or from external data sources like HDFS.

13. What are the two main operations supported by RDDs in Spark?

RDDs support two main operations: transformations and lazy evaluations. Transformations are operations applied on an RDD to create a new RDD, while lazy evaluations allow for the execution of operations anytime by calling an action on the data.

 14. What are the different workloads supported by Parcatus in Spark?

Parcatus supports three different workloads in Spark: batch mode, interactive mode, and streaming mode.

Batch mode involves scheduling jobs without manual intervention, interactive mode involves executing commands one by one, and streaming mode runs continuously, transforming and executing data.

15. How did Yahoo use Spark to overcome challenges in personalizing properties and updating relevance models?

Yahoo used Spark to improve iterative model training in order to overcome challenges in personalizing properties and updating relevance models due to changing stories, newsfeeds, and ads.

This was achieved by taking advantage of Spark’s in-memory processing and distributed computing capabilities.

16. How did Spark help Yahoo in personalizing news web pages and targeted advertising?

Yahoo uses Apache Spark for personalizing news web pages and targeted advertising by using machine learning algorithms to find news user interests and categorize news stories.

Spark’s machine learning algorithm for news personalization was ready for production use in just 30 minutes on 100 million data sets and has resilient in-memory storage options.

 17. What is the Spark architecture, and how does it work?

The Spark architecture involves creating a Spark context in the master node, which acts as a gateway to all Spark functionality. The driver program and Spark context manage various jobs across the cluster, with tasks being distributed across various nodes.

Worker nodes execute tasks on partitions and return results back to the main Spark context.

 18. How does increasing the number of workers help in executing jobs faster in Spark?

Increasing the number of workers can divide jobs into more partitions and execute them faster over multiple systems. This increases memory and caches the jobs, allowing them to be executed more efficiently.

 19. What is the master-slave architecture in Spark, and how does it work?

The Spark architecture follows the master-slave architecture, where the client submits Spark user application code.

The driver implicitly converts the user code into a logically directed graph called DHE, performs optimizations, and converts it into a physical execution plan with many stages.

It creates physical execution units called tasks underneath stage, which are bundled and sent to the cluster.

The driver communicates with the executors, who register themselves with the drivers and start executing their tasks based on data placement.

 20. What are the three different types of workloads in Spark architecture?

The Spark architecture supports three different types of workloads: batch mode, interactive mode, and streaming mode.

Batch mode involves writing a job and scheduling it through a queue or batch of separate jobs through manual intervention.

Interactive mode is an interactive shell where commands are executed one by one, similar to the SQL shell. Streaming mode is continuous running, where the program continuously runs and performs transformations and actions on the data.

21. How to create a Spark application using Scala in Spark shell?

To create a Spark application using Scala in Spark shell, first, check if all demons are running. Then, run the Spark shell and specify the WebUI port for the shell.

In the input text file, map functions and apply transformations and actions like flat map transformation and word count. Apply the action reduceByKey to start the execution process.

22. What is the purpose of the program that counts hard to count words and research words?

The program counts hard to count words and research words, with science count as two.

 23. How does the program divide the task and parallelize the execution?

The program uses a seed to parallelize one to 100 numbers and divides the task into five partitions.

24. What features does the WebUI of Spark display for the task execution?

The Web UI of Spark displays the job stages, partitions, timeline, tag representation, and other features for the task execution.

 25. What information is displayed in the dog visualization and event timeline?

The dog visualization shows the completed stages and duration of the task, while the event timeline shows the added executor, execution time, and output bytes.

 26. What information is displayed in the partitions for the task execution?

The partitions show the successful tasks, with the scheduler delay, shuffle rate time, executor computing time, results realization time, and getting result time displayed.

27. What is displayed in the WebUI for the word context example?

The WebUI displays the IDs of all five tasks, their success, locality level, executor, host, launch time, and duration for the word context example. Additionally, it shows partitions, ag visualizations, and other information.

28. Why is Spark considered a powerful big data tool?

Spark is a powerful big data tool due to its ability to integrate with Hadoop, meet global standards, and be faster than MapReduce.

It is a standalone project designed to run on Hadoop distributed file systems or HDFS, and can work with MapReduce and other clusters.

29. Why has the popularity of Spark increased recently?

The popularity of Spark has increased over the last year due to its mature open source components and expanding user community.

Enterprises are adopting Spark for its speed, efficiency, and ease of use, as well as its single integrated system for all data pipelines.

30. Why is there a growing demand for certified Spark developers?

With the adoption of Apache Spark by businesses large and small growing rapidly, the demand for certified Spark developers is also increasing.

Learning Spark can give a competitive edge by demonstrating recognized validation for one’s experience.

Pinout on PySpark developer interview questions for easy understanding for further related topics.

31. How can one become a certified Apache Spark developer?

To become a certified Apache Spark developer, start by taking a training and certification exam for beginners.

Learn about RDDs or Resilient Distributed Datasets, data frames, and major components of Apache Spark like Spark SQL, Spark MLlib, Spark GraphX, Spark R, and Spark Streaming.

Take the CCA175 certification and solve sample exam papers to develop certification skills.

31. What are the skills required for an excellent Spark developer?

Skills required for an excellent Spark developer include loading data from different platforms using various ETL tools, deciding on file formats, cleaning data, scheduling jobs, working on Hive tables, assigning schemas, deploying HBase clusters, executing big and Hive scripts, maintaining privacy and security, troubleshooting, and maintaining enterprise Hadoop environments.

32. What are the roles and responsibilities of a Spark developer?

Roles and responsibilities of a Spark developer include writing executable code for analytics services and components, knowledge in high-performance programming languages, being a team player with global standards, ensuring quality technical analysis, and reviewing code use cases.

33. What is Apache Spark and what companies use it?

Apache Spark is a widely used technology in the IT industry, helping companies like Oracle, Dell, Yahoo, CGI, Facebook, Cognizant, Capgemini, Amazon, IBM, LinkedIn, and Accenture achieve their current accomplishments.

34. What are the components of the Spark ecosystem?

The Spark ecosystem consists of components like Spark SQL, Spark streaming, MLlib, and the core API component.

35. What does Spark SQL optimize?

Spark SQL optimizes storage by executing SQL queries on Spark data presented in RDDs and other external sources.

36. What does the Spark streaming component allow?

The Spark streaming component allows developers to perform batch processing and streaming of data with ease in the same application.

37. What is the machine learning library used for?

The machine learning library eases the development and deployment of scalable machine learning pipelines, graphics, and components.

38. What is the role of the core component in Spark?

The core component, responsible for basic input output functions, scheduling, and monitoring, is the most vital component of the Spark ecosystem.

39. What is PISPA and what advantages does it offer?

PISPA is a Python API for Spark that allows users to harness the simplicity of Python and the power of Apache Spark to manage PIC data.

Python is easy to learn and use, dynamically typed, and comes with various options for visualization and a wide range of libraries for data analysis.

40. How to install PISPA Spark?

To install PISPA Spark, first, ensure that Hadoop is installed in a system and download the latest version of Spark from the Apache Spark official website.

Install PIP and Jupyter notebook using the PIP command, and the PISPA shell will automatically open a Jupyter notebook for you.

41. What is the life cycle of a Spark program?

A Spark program’s life cycle includes creating RDDs from external data sources or parallelizing a collection in the driver program, lazy transformation to transform base RDDs into new RDDs and caching some for future reuse, and performing actions to execute computation and produce results.

RDD stands for Resilient Distributed Data Set, which is considered the building block of any Spark application. Once created, RDD becomes immutable, but can be transformed by applying certain transformations.

42. What are the different types of operations that can be applied on RDDs in Spark?

The different types of operations that can be applied on RDDs in Spark include transformations such as map, flat map, filter, distinct, reduced by key, map partition, and sought by, and actions such as contrast, transactions, and records.

43. How to load a file into an RDD in Spark?

To load a file into an RDD in Spark, use the sc.textFile method of the Spark context and provide the path of the file.

44. What is the difference between a transformation and an action in Spark?

Transformations are operations that create a new RDD from an existing one, while actions instruct participants to apply computation and pass the result back to the driver.

45. How to remove stop words from RDD data in Spark?

To remove stop words from RDD data in Spark, create a list of stop words and use the flat map function to convert the data into lower case and split it according to word order.

46. What are shared variables in Spark and what are their types?

Shared variables in Spark are used for parallel processing and maintain high availability and fault tolerance. The two types of shared variables are broadcast and accumulator, which are used to save data on all nodes in a cluster.

47. What are data frames in Spark and how to create one?

Data frames in Spark are distributed collections of rows and columns similar to relation databases or Excel sheets.

To create a data frame in Spark, use the spark.read.csv method and provide the local path, using the info schema and header parameters to infer the schema from the file.

48. What are some common attributes of a data frame in Spark?

Some common attributes of a data frame in Spark include year, month, day, departure time, arrival time, and arrival delay.

49. How to calculate summary statistics for a column in a data frame in Spark?

To calculate summary statistics for a column in a data frame in Spark, use the describe function.

50. What are some common data formats used to create data frames in Spark?

Some common data formats used to create data frames in Spark include JSON, CSV, Hive, Cassandra, Parquet files, and CSV XML files.

51. What function allows for filtering out data based on multiple parameters and conditions in Spark?

The filter function and where clause can be used together to filter out data based on multiple parameters and conditions.

52. How can a temporary table be created in Spark for SQL queries?

A temporary table can be created in Spark by converting an existing RDD or DataFrame into a table using the createTempView method. You can have an eye on PySpark data frame interview questions. 

53. What is an example of a nested SQL query in Spark?

A nested SQL query can be used to find flights with the minimum airtime as 20 by using a subquery in the where clause of another query.

54. What machine learning algorithms are covered in the presentation?

The presentation covers various machine learning algorithms supported by the MLlib library, including collaborative filtering, clustering, frequent pattern matching, linear algebra, binary classification, and linear regression.

55. What is the hard disease prediction model, and how is it predicted using Spark MLlib?

The hard disease prediction model is a machine learning model used to predict the categories of diagnosis of hard disease. It is predicted using the decision tree algorithm with the help of classification and regression functions in Spark MLlib.

56. What is the process for initializing the Spark context, reading the UCI dataset, and cleaning the data in the presentation?

The presentation initializes the Spark context, reads the UCI dataset of the hard disease prediction using the textFile method, and cleans the data using pandas and the NumPy library.

57. What is the purpose of importing the MLLib.regression file and converting labels to 0 in the presentation?

The purpose of importing the MLLib.regression file and converting labels to 0 in the presentation is to perform classification using the decision tree algorithm with the help of the Labelled Point and Decision Tree Classifier functions.

58. What is the standard ratio used for splitting the data into training and testing sets in the presentation?

The standard ratio used for splitting the data into training and testing sets in the presentation is 70:30.

59. What is the maximum depth for classifying in the hard disease prediction model in the presentation?

The maximum depth for classifying in the hard disease prediction model in the presentation is three.

60. What is the process for installing Hadoop and Java on a Windows system?

To check if Hadoop or Java are installed on a Windows system, open the .bashrc file and type the version number if Hadoop is installed. To install Java, download the JDK and set the JAVA\_HOME and PATH environment variables.

61. How to install Apache Spark on a Windows system?

To install Apache Spark on a Windows system, download the stable version of Spark from the Apache official website, extract the TGC file, and add the Spark path to the Bashasifiile. Install Jupyter notebook using PIP and set the PATH environment variable for the Spark bin folder.

62. What libraries and files are found inside the Python folder in Spark?

The Python folder in Spark contains various libraries and files used to run various programs, including pyspark, sqlcontext, hive-context, and mllib.

63. What command is used to start the master and worker nodes in Spark?

The command to start the master and worker nodes in Spark is ./sbin/start-all.sh.

64. How to check if Spark is running?

You can check if Spark is running by using the command JPS.

65. What should you do after making changes to the .bashrc R file?

After making changes to the .bashrc R file, you should type in source .bashrc to save the path of the notebook and PIP.

66. What are the essential components for building robust, resilient distributed datasets in Spark?

The essential components for building robust, resilient distributed datasets in Spark are Apache Spark, Jupyter notebook, and R.

67. What is the role of the PyPark shell in Spark?

The PyPark shell is a fundamental data structure in Spark that supports iterative distributed computing, processing data over multiple jobs, reducing the number of input/output operations, and enabling fault-tolerant distributed in-memory computations.

68. What are RDDs in Spark?

RDDs, or resilient distributed data, are schemaless structures in Spark that can handle both structured and unstructured data and are highly resilient, recovering quickly from issues as the same data chunks are replicated across multiple executed nodes.

69. What are the two types of operations supported by RDDs in Spark?

RDDs in Spark support two types of operations: transformations and actions. Transformations are operations applied on an RDD to create a new RDD, and actions are operations applied on an RDD to instruct a Spark executor to apply computation and pass the result back to the driver.

70. What are the important features of PySpark RDDs?

PySpark RDDs have several important features, including memory computation, immutability of data, partitioning, and persistence.

71. What is the difference between the take method and the collect action in PySpark?

The take method in PySpark returns a specified number of values from an RDD, while the collect action returns all values in the RDD from the Spark workers to the driver. However, using the collect action has performance implications when working with large amounts of data, as it transfers large volumes of data.

72. How to read a text file and split it by a tab delimiter in Spark using the map transformation?

You can read a text file and split it by a tab delimiter in Spark by using the map transformation with a lambda function and the SC.txtFile method, which requires an absolute path.

73. What is the purpose of creating a user-defined function using the dot lower and dot split transformation in Spark?

Creating a user-defined function using the dot lower and dot split transformation in Spark allows you to convert data into lower case and divide paragraphs into words, respectively.

74. What is the name of the new RDD that is created after applying the map transformation to the split RDD?

The name of the new RDD that is created after applying the map transformation to the split RDD is not explicitly mentioned in the text.

75. What is the purpose of creating a stop world RDD in Spark?

Creating a stop world RDD in Spark allows you to remove all stop words from the RDD using the filter transformation and a lambda function.

76. What is the purpose of creating a sample RDD in Spark?

Creating a sample RDD in Spark allows you to see if there are contrasting rays in the original RDD by taking a sample from it.

77. What are some functions used in the text to perform operations on the input data in Spark?

Functions used in the text to perform operations on the input data in Spark include join, reduced, reduced by key, sort by key, and unions.

78. What are the arguments passed to the sample RDD method in Spark?

The arguments passed to the sample RDD method in Spark are the original data as an argument, false for the width replacement parameter, and 0.1 for the fraction of data to be taken as the output.

79. What is the output of the join method in Spark when called with A.join?

The output of the join method in Spark when called with A.join is the result of combining the data from RDDs A and B based on a common key.

80. What is the purpose of collecting the output of the join method in Spark?

The purpose of collecting the output of the join method in Spark is to gather the results from the Spark executors to the driver for further processing or analysis.

81. What method is used to calculate the page rank of web pages using a nested list of outbound links?

The page rank of web pages using a function that takes two arguments: the list of web pages and the rank of the web page accessed through the outbound links.

It calculates the number of elements in the list and the rank contribution to each URI, and then returns the contributed rank for each URI.

82. What are the two main RDDs created in the code?

The code creates a link data RDD and a pay RDD for the rank data, with a rank of one for each web page.

83. What is the role of the dampening factor in the page rank calculation process?

The dampening factor plays a crucial role in the page rank calculation process, with a value of c being even lesser than the values of a, b, and d. Its purpose is not clear from the text, but it is mentioned as being important.

84. What are data frames and what are they used for?

Data frames are tabular data structures designed for processing large collections of structured or semi-structured data, such as big data. They can handle petabytes of data and support a wide range of data formats and sources.

Data frames are distributed, fault-tolerant, and immutable, and support elaborate methods for slicing and dicing the data.

85. What are some of the classes used to create data frames in PySpark?

Data frames can be created using various classes in PySpark, such as PySpark SQL.SQLContext, PySpark SQL.DataFrame, PySpark SQL.column, PySpark SQL.CryptData, and others.

These classes help manage missing data, improve manageability, speed, complexities, and optimization.

86. What are some of the data formats that can be used to create data frames in PySpark?

Data frames can be created using various data formats, such as JSON, CSV, XML, Parquet files, existing RDD, Hive, Cassandra, and files residing in the file system or SDFS.

87. How can a data frame be converted into a table for SQL queries in PySpark?

A data frame can be converted into a table for SQL queries by registering a temporary table using the same name as the data frame in the SQL context.

88. What are some basic functions of SQL that can be applied to data frames in PySpark?

Some basic functions of SQL that can be applied to data frames in PySpark include filtering, selecting, ordering, and importing SQL queries.

89. How can a data frame be created using a CSV data file in PySpark?

A data frame can be created using a CSV data file in PySpark by using the spark.read.format(“csv”) function and specifying the file location.

90. What is the use case given in the text for demonstrating the use of data frames and SQL functions in PySpark?

The use case given in the text for demonstrating the use of data frames and SQL functions in PySpark is analyzing superhero comics data.

91. What columns are included in the superhero data frame?

The superhero data frame includes columns such as serial number, name, gender, I color, race, hair color, height, publisher, skin color, alignment, and weight.

92. How can the number of male and female superheroes be determined using the data frame in PySpark?

The number of male and female superheroes can be determined using the filter function and specifying the condition based on the gender column.

93. How can the data frame be sorted based on the weight column in PySpark?

The data frame can be sorted based on the weight column in PySpark using the orderBy function and specifying the column name in descending order.

94. How can the number of superheroes in each publisher be determined using the data frame in PySpark?

The number of superheroes in each publisher can be determined using the groupBy and count functions in PySpark.

95. What is the use of the pie spark SQL module in PySpark?

The pie spark SQL module is a higher level abstraction over the pie spark core in PySpark, used for processing structured and some semi-structured data sets. It provides an optimization for reading data from various file formats or databases.

96. What is the size limit of a data frame in PySpark?

A data frame in PySpark can load up to 4GB of data by default, but this limit can be increased by configuring the spark.sql.parquet.compression.codec property.

97. What is the schema of the NYC flight status database?

The schema includes columns such as NYC flight status, year, month, day, departure time, arrival time, delay, tail number, flight number, origin, air time, and distance.

98. Why are libraries like partners preferred for data visualization and machine learning in Python and Spark programs?

They make visualization and machine learning easier than in Scala, Java, or R.

In conclusion, PySpark is a strong open-source data processing engine based on Apache Spark.

It provides a simple Python interface for doing large-scale data processing operations such as data manipulation, transformations, and analytics.

PySpark’s support for several data sources, including CSV, JSON, Parquet, and SQL databases, provides flexibility and variety for data processing needs.

Furthermore, PySpark’s connection with Spark’s machine learning package, MLlib, allows for powerful data analytics and predictive modeling capabilities.

Overall, PySpark is a useful tool for data scientists, engineers, and analysts that want to process and analyze massive information fast and effectively.

I hope you will bang on your next interview.

All the Best!!!