Without a doubt, the job market for data scientists is competitive. If you aspire to stand out as one, you’ll want to obtain skills and capabilities with popular big data tools and frameworks such as PySpark.
The demand for data scientists with PySpark skills is increasing steadily. Many top recruiters include questions about this software interface when interviewing for data science roles.
Tackling the PySpark interview questions with confidence requires solid preparation. While a good data science bootcamp equips you with the fundamentals, you’ll need the practice to master PySpark questions.
This guide will review the most common PySpark interview questions and answers and discuss the importance of learning PySpark. Whether a beginner or an experienced professional, you’ll find this guide helpful.
Reasons for Data Engineers to Know About PySpark
Almost 43 percent of IT decision-makers worry that their IT infrastructure may be incapable of processing massive amounts of data in the coming years. There is a need for alternative methods to handle the data, one of which is to use advanced APIs to enhance the capacity of existing computational engines. PySpark is an API that enables the interface of Python programming language with Resilient Distributed Datasets (RDD) in Apache Spark.
Here’s a list of reasons why it should matter to data scientists:
- Enhances data processing speeds by 10-fold on storage and 100-fold in memory
- Superior maintenance and improved readability and familiarity with the code
- Generation of an all-encompassing and straightforward interface
- Simplification of the machine learning process
- Easy to learn and simple syntax
- The fundamental components of the data science libraries in R language can be converted to Python
Top PySpark Interview Questions for Newbies
What is PySpark?
The Apache Spark community developed PySpark to enable collaboration between Apache Spark and Python. It works with the help of an API written in Python and supports features such as Spark SQL, Spark DataFrame, Spark Streaming, Spark Core, and Spark MLlib.
Why is PySpark required for data science?
Data Science utilizes Python and Machine learning as the two main programming languages. Python enables integration of PySprak, which is helpful in interfaces and built-in environments based on machine learning and Python. PySpark enables an efficient transformation of the prototype into a production-ready workflow.
What is RDD?
RDD stands for Resilient Distributed Datasets. RDDS are elements that make up the central data structure of PySpark. They are fault-tolerant and immutable. They are run and processed on several nodes for parallel processing in a cluster.
In what ways is PySpark different from other programming languages?
PySpark does not require integrating APIs like other programming languages, as it has built-in APIs. Other languages do not support implicit communications, but PySpark does.
Developers can utilize the map-based feature of PySpark to reduce functions.
Name some advantages and disadvantages of PySpark.
- If you know Python and Apache Spark, you can learn and use PySpark to write parallelized code easily
- It handles errors easily and manages sync points
- Some abundant libraries and algorithms can be used for data visualization
- The problems in PySpark cannot be managed in MapReduce models
- It is slower than a Scala program.
- Scala’s Streaming API is more efficient than PySpark’s Streaming API.
Describe the main characteristics of PySPark.
The main characteristics of PySpark are:
- Spark functions are provided with an API by PySpark
- Nodes are abstracted, and individual nodes are inaccessible.
- Based on Hadoop’s MapReduce model, PySpark can provide maps and reduced functions.
- PySpark allows abstracted networks and implicit communications only.
Describe PySpark SparkFiles.
PySpark SparkFiles is a function under SparkContext that can be called with sc.addFile(). You can use it to load files into Apache Spark and resolve paths to the files. The function SparkFile.get() can be used to get the path. The functions getrootdirectory() and get(filename) are the class methods in the SparksFiles directory.
Name some key differences between an RDD, a DataFrame, and a Dataset.
RDD: RDD (Resilient Distributed Dataset) is a low-level data structure that performs low-level operations, transformations, and controls. It is also useful for altering data using functional programming structures. It contains all the data sets and DataFrames.
DataFrame: DataFrame is similar to a relational table in Spark SQL, enabling the visualization of the structure. It is preferred to begin with DataFrames and go on to RDDs in Python. However, it has poor Compile Time well-being, leading to a lack of control over unknown information structure.
DataSet: A collection of data is called a DataSet, and it is a subset of DataFrames. It comprises superior encoding components and enables organized time security. It is a recently added feature in Spark 1.6. A higher level of type safety at compile-time is possible, and the Catalyst optimization can be used. It can also be used to utilize Tungsten’s fast code generation optimally.
Top PySpark Interview Questions for Experienced Candidates
What is piping in PySpark?
Piping refers to using the pipe() function on the RDDs in Apache Spark to enable the assembly of the various parts of the job, irrespective of the programming language. It can be used to transform RDD and read it as a string.
What typical workflow does a Spark program follow?
The main steps in the common workflow of a Spark program are:
- Creation of input RDDs based on external data and from various data sources.
- Using RDD transformation operations such as filter() or map() to create new RDDS based on business logic.
- Retain any intermediate RDDs that may be reused later.
- Launch action operations such as first() or count() to initiate parallel computation.
Describe PySpark architecture.
Pyspark architecture follows the master-slave pattern, wherein the master node is referred to as the Driver, while the slave nodes are referred to as the workers.
During the run of a Spark application, the Spark driver generates SparkContext to work as the entry point to the application. The further operations are performed at the worker nodes. Cluster Managers manage the resources needed to execute these operations.
What are the different algorithms used in PySpark?
Some algorithms that can be used in PySpark are:
- mllib. clustering
- mllib. linalg
- mllib. classification
- spark. Mllib
- Mllib. Regression
- smllib. recommendation
How to cache data in PySpark? Name some benefits of caching.
The function cache() can be used for caching data. The main benefit is performance improvement by reducing the number of times reading the data from the disk.
What is DAGScheduler?
DAG stands for Direct Acyclic Graph. The DAGSchedular is the scheduling layer that enables the implementation of task scheduling in a phase-oriented way with the help of tasks and phases. It follows an event queue architecture. There is a transformation of the execution plan from logical to physical.
The DAG of the phases for each task is calculated, the generation of realized RDDS from phases is tracked, and the minimum schedule for task execution is determined. The TaskScheduler then runs these stages. Further, the DAG execution is computed by the DAGScheduler, which specifies the specific task locations and reads and executes the phases in a topological order.
Describe Pyspark SQL.
PySpark SQL is a Spark module offering DataFrames and operating as a distributed SQL query engine. The data from existing Hive installations can also be read. PySpark SQL thus facilitates data processing and extraction.
Name some SparkContext parameters.
Some of the main SparkContext parameters are:
- JSC is a JavaSprakContext object.
- RDD data can be serialized using an RDD serializer.
- The name of the job is given in the format ‘appName’.
- Py files are .zip or .py files. They are required to be transferred to the cluster and added to PYTHIONPATH.
- Variables are based on the context of worker nodes.
- Spark properties can be set using LSparkConf.
Name the different Persistence levels in Apache Spark.
The different Persistence levels in Apache Spark are:
- MEMORY_ONLY : You can store the RDD object as a de-serialized Java object in JVM. It will be recomputed if it does not fit in the memory.
- MEMORY_AND_DISK : The storage of RDD is similar to the previous level, but if it does not fit in the memory, it will be stored on the disk rather than recomputing.
- MEMORY_ONLY_SER : You can store RDD as a serialized object rather than a de-serialized one, making it more efficient.
- MEMORY_AND_DISK_SE: The storage of RDD is similar to the previous level, but it is stored on the disk in case it does not fit in the memory.
- DISK_ONLY : Here, you can store the RDD object only on Disk.
How to Prepare for a PySpark Interview?
Regardless of the experience, one should remember a few tips unique to PySpark while preparing for an interview. Here are some of them:
- Gain a thorough understanding of the Spark architecture, DataFrames, RDDs, and Spark SQL.
- Read up on PySpark’s built-in functions.
- Practice writing custom PySpark codes by solving examples and contributing to open-source projects. You can also try building applications.
- Make it a habit to read the problem carefully, note the requirements, and divide the problem into the major before beginning the solution.
A well-designed data science program will train you in the methodology of assessing a problem and the various steps in writing code to arrive at the best possible solution.
Take the Next Steps
Data science has emerged as a high-paying career, potentially placing you in the six-figure bracket with an average salary of $103,500 . This field has a massive scope for freshers and experienced IT professionals looking to give their careers a new direction.
Enrolling in an online data science program can be a fantastic way to advance your career. But for those who want to take a shorter route to an industry-recognized data science certificate, a structured data science bootcamp is ideal. Regardless of your route, formal training will prepare you to answer PySpark interview questions well.