This article will discuss a list of the most asked Top 20 Pyspark interview questions and answers. These questions can help you prepare for your following Pyspark interview. Check out the complete list of questions below.
Pyspark interview questions for Freshers
Q1. What is PySpark?
PySpark is the Python API for Apache Spark. It is an open-source distributed system that is used for big data processing.
Q2. What is the difference between RDD, DataFrame, and Dataset in PySpark?
Resilient Distributed Datasets is a basic data structure in PySpark. It represents a distributed collection of objects. The Dataset is a high-level abstraction that provides a more organized way of manipulating data. DataFrame is a collection of data organized into named columns.
Q3. How do you create an RDD in PySpark?
We can create an RDD in PySpark by loading data from a file. We can also create it using the parallelize() function from an existing collection.
Q4. What is lazy evaluation in PySpark?
Lazy evaluation is a feature in PySpark that defers the execution of code until it is needed. This is used to optimize the performance of PySpark by decreasing the amount of data that needs to be processed.
Q5. What is a transformation in PySpark?
A transformation is an operation that takes one RDD as input and produces another RDD as output. Some examples of transformations are map(), filter(), and groupBy().
Q6. What is an action in PySpark?
An action is an operation in Pyspark that triggers the execution of transformations and produces a result. Some examples of actions in pyspark are count(), collect(), and saveAsTextFile().
Q7. How do you handle missing data in PySpark?
Missing data can be handled using the dropna() function to drop rows with missing values. We can also handle it by filling in missing values using the fillna() function.
Q8. How do you join two DataFrames in PySpark?
You can join two DataFrames in PySpark using the join() function. It takes the two DataFrames as input and a join condition.
Q9. How do you handle skewed data in PySpark?
Skewed data can be handled using the skew join optimization technique. This technique involves splitting data into multiple partitions based on the join key.
Q10. How do you optimize PySpark performance?
PySpark performance can be optimized by using lazy evaluation, reducing data shuffling. It can also be optimized using the appropriate data structure for the job, for example, RDDs, DataFrames, or Datasets.