Attending a PySpark interview and wondering what are all the questions and discussions you will go through? Before attending a PySpark interview, it’s better to have an idea about the types of PySpark interview questions that will be asked so that you can mentally prepare answers for them.
To help you out, I have created the top PySpark interview question and answers guide to understand the depth and real-intend of PySpark interview questions. Let’s get started.
As the name suggests, PySpark is an integration of Apache Spark and the Python programming language. Apache Spark is a widely used open-source framework that is used for cluster-computing and is developed to provide an easy-to-use and faster experience. Python is a high-level general-purpose programming language. It is mainly used for Data Science, Machine Learning and Real-Time Streaming Analytics, apart from its many other uses.
Originally, Apache spark is written in the Scala programming language, and PySpark is actually the Python API for Apache Spark. In this article, we will take a glance at the most frequently asked PySpark interview questions and their answers to help you get prepared for your next interview. If you are a beginner and interested to learn more about data science, check out our data analytics certification from top universities.
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PySpark Interview Questions and Answers
1. What is PySpark?
This is almost always the first PySpark interview question you will face.
PySpark is the Python API for Spark. It is used to provide collaboration between Spark and Python. PySpark focuses on processing structured and semi-structured data sets and also provides the facility to read data from multiple sources which have different data formats. Along with these features, we can also interface with RDDs (Resilient Distributed Datasets ) using PySpark. All these features are implemented using the py4j library.
2. List the advantages and disadvantages of PySpark? (Frequently asked PySpark Interview Question)
The advantages of using PySpark are:
- Using the PySpark, we can write a parallelized code in a very simple way.
- All the nodes and networks are abstracted.
- PySpark handles all the errors as well as synchronization errors.
- PySpark contains many useful in-built algorithms.
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The disadvantages of using PySpark are:
- PySpark can often make it difficult to express problems in MapReduce fashion.
- When compared with other programming languages, PySpark is not efficient.
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3. What are the various algorithms supported in PySpark?
The different algorithms supported by PySpark are:
- spark.mllib
- mllib.clustering
- mllib.classification
- mllib.regression
- mllib.recommendation
- mllib.linalg
- mllib.fpm
4. What is PySpark SparkContext?
PySpark SparkContext can be seen as the initial point for entering and using any Spark functionality. The SparkContext uses py4j library to launch the JVM, and then create the JavaSparkContext. By default, the SparkContext is available as ‘sc’.
5. What is PySpark SparkFiles?
One of the most common PySpark interview questions. PySpark SparkFiles is used to load our files on the Apache Spark application. It is one of the functions under SparkContext and can be called using sc.addFile to load the files on the Apache Spark. SparkFIles can also be used to get the path using SparkFile.get or resolve the paths to files that were added from sc.addFile. The class methods present in the SparkFiles directory are getrootdirectory() and get(filename).
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6. What is PySpark SparkConf?
PySpark SparkConf is mainly used to set the configurations and the parameters when we want to run the application on the local or the cluster.
We run the following code whenever we want to run SparkConf:
class pyspark.Sparkconf(
localdefaults = True,
_jvm = None,
_jconf = None
)
7. What is PySpark StorageLevel?
PySpark StorageLevel is used to control how the RDD is stored, take decisions on where the RDD will be stored (on memory or over the disk or both), and whether we need to replicate the RDD partitions or to serialize the RDD. The code for StorageLevel is as follows:
class pyspark.StorageLevel( useDisk, useMemory, useOfHeap, deserialized, replication = 1)
8. What is PySpark SparkJobinfo?
One of the most common questions in any PySpark interview. PySpark SparkJobinfo is used to gain information about the SparkJobs that are in execution. The code for using the SparkJobInfo is as follows:
class SparkJobInfo(namedtuple(“SparkJobInfo”, “jobId stageIds status ”)):
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9. What is PySpark SparkStageinfo?
One of the most common question in any PySpark interview question and answers guide. PySpark SparkStageInfo is used to gain information about the SparkStages that are present at that time. The code used fo SparkStageInfo is as follows:
class SparkStageInfo(namedtuple(“SparkStageInfo”, “stageId currentAttemptId name numTasks unumActiveTasks” “numCompletedTasks numFailedTasks” )):
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10. What is PySpark DataFrames?
This is one of the most common PySpark dataframe interview questions. PySpark DataFrames are the distributed assortment of well-organized data. They are identical to relational database tables and are included in named columns. Moreover, PySpark DataFrames are more efficiently optimized than Python or R programming languages. The reason is they can be created from various sources like Structured Data Files, Hive Tables, external databases, existing RDDs, etc.
The greatest advantage of using PySpark DataFrame is that the data in it is distributed over various machines in the cluster. The corresponding operations will run parallel on all the machines.
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11. What is PySpark Join?
PySpark Join helps combine two DataFrames. By binding these, it is easy to join multiple DataFrames. It enables all fundamental join type operations accessible in traditional SQL like INNER, RIGHT OUTER, LEFT OUTER, LEFT SEMI, LEFT ANTI, SELF JOIN, and CROSS. PySpark Joins are transformations that use data shuffling throughout the network.
12. How to rename a DataFrame column in PySpark?
It is one of the most frequently asked PySpark dataframe interview questions. You can use PySpark withColumnRenamed() to rename a DataFrame column. Frequently, you need to remain single or multiple columns on PySpark DataFrame. It can be done in multiple ways. DataFrame is an immutable collection, so you can’t update or rename a column instead when using withColumnRenamed(). This is because it prepares a new DataFrame with the updated column names. Two common ways to rename nested columns are –renaming all columns or renaming selected multiple columns.
13. Are PySpark and Spark the same?
These types of PySpark coding questions test the candidates’ basic knowledge of the PySpark fundamentals. PySpark has been launched to support the collaboration of Python and Apache Spark. Essentially, it is a Python API for Spark. PySpark assists you in interfacing with Resilient Distributed Datasets (RDDs) in Python programming language and Apache Spark.
14. What is PySparkSQL?
When preparing for PySpark coding interview questions, you must prepare for PySparkSQL. It is a PySpark library to implement SQL-like analysis on a large amount of either structured or semi-structured data. You can also use SQL queries with PySparkSQL. Moreover, it can be connected to Apache Hive, and HiveQL can also be implemented.
PySparkSQL works as a wrapper over the PySpark core. PySparkSQL introduced the DataFrame, a tabular illustration of structured data that is identical to that of a table from an RDBMS (relational database management system).
15. Are there any prerequisites to learning PySpark?
One of the fundamental PySpark coding questions is about the prerequisites to learn PySpark. It is assumed that the readers are aware of what a framework and a programming language are before moving towards different concepts in the PySpark tutorial. It is beneficial if the readers have some knowledge of Python and Spark in advance.
16. What do you understand by PySpark SparkFiles?
It is allowed to upload our files in Apache Spark by using sc.addFile. Here sc is the default SparkContext. It also assists in getting the path on a worker through SparkFiles.get. It also resolves the paths to files that are added via SparkContext.addFile().PySpark SparkFiles includes certain classmethods likeget(filename) and getrootdirectory().
17. What are the key characteristics of PySpark?
Knowing PySpark characteristics is important after you complete preparing for the PySpark coding interview questions. The four key characteristics of PySpark are as below. (i) Nodes are abstracted: You can’t access the individual worker nodes. (ii) APIs for Spark features: PySpark offers APIs for using Spark features. (iii) PySpark is dependent on MapReduce: PySpark is dependent on the MapReduce model of Hadoop. So, it lets a programmer provide the map and the reduced functions. (iv) Abstracted Network: Abstracted networks in PySpark allow implicit communication only.
18. What is SparkCore? What are the major functions of SparkCore?
SparkCore is the Spark platform’s general execution engine that supports all the functionalities. It provides in-memory computing capabilities to offer a decent speed and a universal execution model to support different applications. It also supports Scala, Java, and Python APIs to simplify the development process. The key functions of SparkCore include the basic I/O functions, monitoring, scheduling, effective memory management, fault tolerance, fault recovery, and interaction with storage systems.
19. What it means by PySpark serializers?
One of the mid-level PySpark interview coding questions can be around PySpark serializers. In PySpark, the serialization process is used to perform Spark performance tuning. PySpark incorporates serializers because you must constantly check the data sent or received across the network to the memory or disk. Two types of serializers in PySpark are as below. (i) PickleSerializer: It serializes the objects using Python’s PickleSerializer and class pyspark.PickleSerializer). It supports most of the Python objects. (ii) MarshalSerializer: It performs objects’ serialization. It can be employed through class pyspark.MarshalSerializer. It is faster than the PickleSerializer, but it supports limited types.
20. What is PySpark ArrayType?
PySpark ArrayType is a collection data type that outspreads PySpark’s DataType class (the superclass for all types). It only contains the same types of files. You can use ArraType()to construct an instance of an ArrayType. Two arguments it accepts are discussed below. (i) valueType: The valueType must extend the DataType class in PySpark. (ii) valueContainsNull: It is an optional argument that states whether a value can accept null and it is by default value, is True.
21. What is PySpark Partition? How many partitions can one make in PySpark?
You may be asked a PySpark interview question around PySpark Partition. It is a method that splits a huge dataset into smaller datasets depending on one or multiple partition keys. It improves the execution speed when the transformations on partitioned data operate faster. The reason is that every partition’s transformations run in parallel. PySpark allows two types of partitioning i.e. partitioning on disc (File system) and partitioning in memory (DataFrame). Its syntax is partitionBy (self, *cols) . Including 4x of partitions to the number of cores in the cluster accessible for application is recommended.
22. What is Parquet file in PySpark?
You may be asked PySpark interview coding questions on the file type in PySpark. The Parquet file in PySpark is a column-type format supported by different data processing systems. It helps Spark SQL to perform read and write operations. Its column-type format storage offers the following benefits. (i) It consumes less space. (ii)It allows you to retrieve specific columns for access. (iii)It employs type-specific encoding. (iv)It provides better-summarized data. (v)It supports limited I/O operations.
23. Why is PySpark faster than pandas?
This kind of PySpark interview question tests your in-depth knowledge of PySpark. PySpark is speedier than pandas because it supports parallel execution of statements in a distributed environment. PySpark can be implemented on different machines and cores not supported in Pandas.
Conclusion
We hope you went through all the frequently asked PySpark Interview Questions. Apache Spark is mainly used to handle BigData and is in very high demand as companies move forward to use the latest technologies to drive their businesses.
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What is Cluster Computing?
Cluster Computing consists of loosely coupled systems that interact, work, and perform operations as a single system. The various cluster nodes are connected via LAN (Local Area Network). Cluster computing ensures scalability, speed, resource management, and continuous availability of computing power. Clusters are of two types: Open and closed. Open clusters are those through which nodes can be accessed only via the Internet. In closed clusters, the nodes are hidden and secure. Each cluster computer consists of cluster nodes, cluster operating system, switches, and network-switching hardware.
What is the average salary of an Apache PySpark Developer in India?
A PySpark developer ensures that data is available for query processing. An Apache PySpark developer should be good at Python, Apache Spark, Java, and Scala. The demand for Apache Spark developers has been increasing. One can get more than 60000 search results of job opportunities for these roles. The salary, however, depends on many factors. These include work experience, skill set, demand in the market, organisation, location, etc. Based on these, the salary could range from INR 8 LPA to INR 20 LPA. The average wages for people with less than two years of experience range from INR 4.5 LPA to INR 15.7 LPA.
What is meant by RDD?
RDD stands for Resilient Distributed Dataset (RDD). It is a data structure that stores immutable objects. It supports the storage of objects of any language, like Python, Java, Scala, and other user-defined objects. MapReduce is used for massively parallel processing of data quickly. Spark uses RDD to perform MapReduce operations. RDDs can be created in 2 ways: either by parallelising a data set in your system or by referencing an external data storage system. RDD is fault-tolerant and supports parallel processing. It is mainly used to process and manipulate unstructured data. RDD is a distributed system. It follows the Lazy Evaluation Principle, i.e. transformations are applied only when we call it and not when the data is loaded.
