Pyspark Interview Questions

PySpark is an open-source distributed computing software. It helps to frame more scalable Analytics and pipelines to enhance processing speed. It also acts as a library for large-scale data processing in real time. When you utilize PySpark, you may expect a 10x increase in disc processing performance and a 100x increase in-memory processing speed.

But, before we begin with the PySpark interview questions 2023, allow us to present in front of you some essential facts about PySpark:

➤ From 2019 to 2026, the PySpark service market is expected to increase at a CAGR of 36.9%, reaching $61.42 billion. This shows that the demand for Big Data Engineers and Specialists will skyrocket in the coming years.

➤ The latest version PySpark is 3.0, which just released.

➤ PySpark Developer's salary range is from $124,263 per year as per March 22 report.

Now that you know the demand for PySpark let's begin with the list of PySpark Interview Questions to help you boost your professional spirit.

PySpark 2023 (Updated) questions and solutions weblog had been created through us into stages; they are:

• Freshers
• Experienced
• FAQ's

Top 10 Pyspark Interview Question And Answers

1. Explain PySpark.
2. What are the main characteristics of PySpark?
3. What is PySpark Partition?
4. Tell me the different SparkContext parameters.
5. Tell me the different cluster manager types in PySpark.
6. Describe PySpark Architecture.
7. What is PySpark SQL?
8. Can we use PySpark as a programming language?
9. Why is PySpark helpful for machine learning?
10. List the main attributes used in SparkConf.

PySpark interview questions and answers for freshers

1. Explain PySpark.

PySpark is software based on a python programming language with an inbuilt API. It was developed in Scala and released by the Spark community. It supports the Data Science team in working with Big Data. PySpark is a good learn for doing more scalability in analysis and data science pipelines.

2. Tell me the differences between PySpark and other programming languages.

• It has an inbuilt API, whereas, in other programming languages, we need to integrate API externally from a third party.
  
• Implicit communication can be done in PySpark, but it is impossible in other programming languages.
  
• Developers can use the map to reduce functions as PySpark is map-based.
  
• We can address multiple nodes in PySpark, which is impossible in other programming languages.

3. Why should we use PySpark?

• Due to the most helpful ML algorithms implemented in PySpark, we can use it in Data Science.
  
• We can manage synchronization points and errors.
  
• Easy problems can be resolved quickly because all code is parallelized.
  

4. What are the main characteristics of PySpark?

The primary characteristics of PySpark are listed below:

• Nodes are abstracted - This means we can’t address an individual node.
  
• Network is abstracted - Only implicit communication is possible here.
  
• Based on Map-Reduce - Additionally, programmers provide a reduce and map function.
  
• API for Spark - PySPark is a Spark API.
  

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5. What are the advantages of PySpark?

• Easy to write - For simple problems, it’s easy to write parallelized codes.
  
• Error handling - Framework easily handles errors when it comes to synchronization points.
  
• Algorithms - Most of the algorithms are already implemented in Spark.
  
• In-memory computation - Through in-memory processing, Spark helps you to increase the processing speed. And the best thing is data is cached, thus allowing to fetch data from the disk every time, saving time.
  
• Swift processing - One of the significant benefits of working with Spark is it provides a high processing speed of 10x faster on the disk and 100x faster in memory.
  
• Fault-tolerance - Spark is specially designed to manage the malfunction of any worker node in the cluster, assuring that data loss is decreased to zero.

6. Tell me the disadvantages of PySpark.

• While using the Mapreduce process, we may face some errors.
  
• It is more efficient for a significant amount of data, so we can face less accuracy when dealing with a small data set.
  

7. What do you mean by SparkContext?

SparkContext is the software entry point for PySpark developers. When the developers try to launch this software, CparkContext will launch JVM using Py4J ( One of Python Library). This is a default process to provide as'sc' to the PySpark API.

8. Explain SparkConf and how does it work?

Once the developer wants to run the Spark API locally in a cluster, they need to use SparkConf to configure the declared data parameters. We can write conf=new SparkConf().setMaster(local[2]) to declare the particular parameters.

9. What do you know about SparkFiles?

To get the actual path of a file inside Apache Spark, we need to use SparkFiles. This is one of the Spark objects and can be added through SparkConf. We can access Spark jobs using SparkFiles. We can get the directory path through SparkFiles. We can set the recursive value to true so that directory will open.

10. Why do we need to mention the filename?

Developers can find out the files by their filenames as the file extension is attached... Developers can understand file names by the filename first portion. For say, "setup" is the first part of setupact.log, so the file name is a setup that developers can understand easily.

11. Describe getrootdirectory ().

The developers can obtain the root directory by using getrootdirectory().

It assists in obtaining the root directory, which contains the files added using SparkContext.addFile().

12. What is PySpark Storage Level?

Storage level defines how RDD( Resilient Distributed Dataset) will be stored in a database. It also determines the storage capacity and focuses on data serialization.

13. Explain broadcast variables in PySpark.

Developers can save the data as a copy into all nodes. All the data are variable fetched from machines and not sent back to devices. Broadcast variables will do code block to save the data copy as one of the classes of PySpark. 

14. Why does the developer needs to do Serializers in PySpark?

We can manage the data by serializers to tune the process. cPickle serializers are most effective for Python PySpark. It can handle any Python object. There are other serializers like Marshal, which doesn't support all Python objects.

15. When do you use Spark Stage info?

In PySpark, developers can see the information about the Spark stages by using spark stage info. This is a physical unit that executes multiple tasks in computation. Spark stage info is controlled by DAG(Directed Acyclic Graph to process and transform any data.

16. Which specific profiler do we use in PySpark?

Only one profiler is supported in PySpark and manages the usages of the custom profiler data. That means we can configure another profiler to maintain the output. We need to also declare the required methods for custom profilers :

• Add: we can add another profiler or add to an existing profile. SparkContext build-up usually initiates the different profile classes.
  
• Dump: To dump all the profiles to a particular path, we need to use the Dump profiler.
  
• Stats: We can get back the gathered stats by using this stats profiler.
  
• Profile: We need to use this to create a system profile as a defined object.

17. How would you like to use Basic Profiler?

By default, this is the standard profiler. We can use this while doing conjunction in cProfile and the accumulator.

18. Can we use PySpark in the small data set?

We should not use PySaprk in the small data set. It will not help us so much because it's typical library systems that have more complex objects than more accessible. It's best for the massive amount of data set.

19. What is PySpark Partition?

PySpark Partition allows you to split a large dataset into smaller ones using one or more partition keys. You can also use partitionBy() to create a partition on multiple columns by simply passing columns you want to partition as an argument.

Syntax:

Syntax: partitionBy(self, *cols)

20. How many partitions can you make in PySpark?

PySpark/Spark creates a task for each partition. You can transfer data from one partition to another using Spark Shuffle operations. By default, 200 partitions are created by DataFrame shuffle operations.

PySpark interview questions and Answers for experienced:

1. Tell me a few algorithms which support PySpark.

There few algorithms which we can use in PySpark:

• mllib. classification
• mllib. clustering
• smllib.fpm
• mllib. linalg
• smllib. recommendation
• spark. Mllib
• Mllib. Regression

2. Tell me the different SparkContext parameters.

Please find out the different SparkContext parameters:

• The cluster's master URL from which it connects.
• Our job's name is appName.
• Py files These are the.zip or.py files that need to be sent to the cluster and added to the PYTHIONPATH.
• Variables in the context of worker nodes.
• RDD serializer is a serializer for RDD data.
• Conf is an object of LSparkConf that allows you to set all of the Spark properties.
• JSC is a joint-stock company. It's a JavaSparkContext object.

3. What is RDD? How many types of RDDs are in PySpark?

The complete form of RDD is Resilient Distributed Datasets which are the elements used to run and operate on multiple nodes simultaneously on the same cluster. It can perform parallel processing as they use immutable characteristics. Once developers create an RDD, they can not change it anymore. Once any failure happens, this RDD will be recovered automatically.

There are two types of RDD:

• Transformation: This type of RDD is applicable in creating a new RDD or transforming any filter or map.
• Action: This type of RDD performs some computations on the return values. It sends data from the executor to the driver.

4. Tell me the different cluster manager types in PySpark.

There are many types of the cluster, few of them are:

• Local: It simplifies the running mode for Spark application through API.
• Kubernetes: It helps in automated deployment and data scaling as an open-source cluster.
• Hadoop YARN: This type of cluster manages the Hadoop environment.
• Apache Mesos: In this cluster, we can run Map-reduce.
• Standalone: This cluster can operate the Spark API.

5. What do you understand about PySpark DataFrames?

DataFrames can create Hive tables, structured data files, or RDD in PySpark. As PySpark is based on the rational database, this DataFrames organized data in equivalent tables and placed them in named columns. As a result, it has better optimization to compare the data set.

6. Explain SparkSession in PySpark.

We use usually get entry in PySpark through SparkContext in version 2.0. But from version 3.0, we can get into it by using SparkSession. It acts as the starting point to access all PySpark functionalities like RDD or DataFrames. We can also use this to unified API.

7. What do you know about PySpark UDF?

The complete form of UDF is User Defined Functions. It will be created when no functionalities do not support the PySpark library. Developers can create UDF by using the Python function and wrapping. SQL or DataFrames can reject it.

8. Describe PySpark Architecture.

This architecture is mainly based on mater slave pattern. Here driver means master node, and worker means slave nodes. Worker nodes are the main operational point. The cluster manager can manage the whole operation on the worker nodes.

9. What do you know about the PySpark DAGScheduler?

The complete form of DAG is Direct Acyclic Graph. It controls the scheduling layer of Spark for executing the stage-oriented scheduled tasks. This scheduler executes stages DAG for each job. Developers can keep track of all stages in RDD. Even this DAG scheduler reduces the running time.

10. Which workflow do we need to follow in PySpark?

The typical workflows are: 

• We need to create input RDD on the external data. These data can be taken from another source.
• Intermediate RDD needs to be created for later purposes.
• parallel computation is present in this workflow.

11. Tell me how RDD is created in PySpark?

• Apply sparkContext.parallelize(): Method parallelize() of SparkContext is used to create RDD by loading existing collection from the driver and then parallelizing. If the data is present in memory, we can only use this process.
• Apply sparkContext.textFile(): If we are going to read from the text file and transfer them into RDD, we can use this method.
• Apply sparkContext.wholeTextFiles(): The value of the file and file path can find out by using this method.
• Empty RDD with no partition using sparkContext.emptyRDD: one empty RDD can be created by the method.
• Empty RDD with partitions using sparkContext.parallelize: New empty RDD can be created without data in the partition.

12. Can we create a Data frame using an external database?

We can create the data frame locally in HDFC, HBase, MySQL, and any cloud.

Check Out: Steps To Set-Up Your MySQL Reporting

13. Explain Add method.

14. What is PySpark SQL?

Spark SQL is a module in Spark for structured data processing. It offers DataFrames and also operates as a distributed SQL query engine. PySpark SQL may also read data from existing Hive installations. Further, data extraction is possible using an SQL query language.

15. Do you think PySpark is similar to SQL?

In SQL database is maintained in tabular form. As well as in PySpark API, all information is stored in Data Frames. This Data Frame is immutable and stored in columns. That's why this is similar to SQL.

16. Why use Akka in PySpark?

Spark makes use of Akka for scheduling primarily. After registration, all workers request a task to complete. The master simply assigns the work. Spark uses Akka to communicate between workers and masters in this case.

17. How is PySpark exposed in Big Data?

The PySpark API is attached with the Spark programming model to Python and Apache Spark. Apache Spark is open-source software, so the most popular Big Data framework can scale up the process in a cluster and make it faster. Big Data use distributed database system in-memory data structures to smoother the processing.

Most Commonly PySpark FAQs

1. Why do we use PySpark?

Python and its set of libraries in real-time for large-scale data. It can be used through an open-source Apache Spark. Software industries are using this PySpark as Python API.

2. Do you think that PySpark and Python are similar?

Yes, they are directly related. It is a Python-based API that is based on the Spark framework. As a programming language, Python helps Spark manage big data.

3. Can we use PySpark as a programming language?

No, we can not use PySpark as a programming language. It's a computing framework.

4. Which one is the faster, PySpark or Pandas?

The processing speed depends upon the platform we are using to manage the vast amount of data. As PySpark is easy to use through inbuilt API, as a result, speed is faster. However, at the same time, Pandas is not running with any API; as a result, the rate is slower than PySpark.

5. Why is PySpark helpful for machine learning?

As PySpark is working with Machine Learning on a distributed database system so they can work together efficiently. We can use PySpark in extensive data analysis by using ML and Python. It also runs smoothly with Tableau. Moreover, we can run different machine learning algorithms due to the PySpark ML library.

6. What do you think PySpark is important in Data Science?

Data Science is based on two programming languages like Python and ML. PySpark is built into Python. It has the interface and inbuilt environment to use Python and ML both. That's why PySpark is an essential tool in Data Science. Once we process the data set, prototype models will be converted into production-grade workflows.

7. Name a few of the companies that are using PySpark?

Most of the E-commerce industry, Banking Industry, IT Industry, Retail industry, etc., are using PySpark. A few of the companies' names are Trivago, Amazon, Walmart, Runtastic, Sanofi, etc.

8. What are the different MLlib tools available in Spark?

MLlib can perform machine learning in Apache Spark. The different MLlib tools available in Spark are listed below:

• ML Algorithms: The core of MLlib is ML Algorithms. These include common learning algorithms like classification, clustering, regression, and collaborative filtering.
• Featurization: It includes extraction, transformation, selection, and dimensionality reduction.
• Pipelines: Pipelines provide tools to construct, evaluate, and tune ML Pipelines.
• Persistence: It aids in saving and loading models, algorithms, and pipelines.
• Utilities: Utilities for statistics, algebra, and handling data. 

Check out: Machine Learning Tutorial

9. Explain the function of SparkCore.

SparkCore is the base engine for distributed data processing and large-scale parallel computation. SparkCore performs vital functions like memory management, fault-tolerance, job scheduling and monitoring, and interaction with storage systems. Furthermore, additional libraries built at the top of the core allow diverse SQL and machine learning workloads.

10. List the main attributes used in SparkConf.

Below-listed are the most commonly used attributes of SparkConf:

• set(key, value) - It sets the configuration property.
• setMaster(value) - It sets the master URL.
• setAppName(value) - It sets the application name.
• get(key, defaultValue=None) - It gets the configuration value of a key.
• setSparkHome(value)

Conclusion

Enhance your technical skill on PySpark as popularity has risen in recent years, and many businesses are capitalizing on its benefits by creating a plethora of job possibilities for PySpark Developers. We are confident that this blog will surely assist you in better understanding of PySpark and help you qualify for the job Interview.