Top PySpark Interview Questions and Answers for 2026

PySpark Interview Questions and Answers (General)

A strong foundation of understanding is very important. Let's take a look into the PySpark interview question journey step by step.

1. What is PySpark?

 PySpark = Python + Spark.

You must have seen auditorium lights, but the man behind the scenes is the light controller. In PySpark, Spark is the auditorium lights, and you are the controller. Your role is to write code similar to the light controller, and Spark executes it for you. Spark handles big data, distributes processing, and speeds up computation.

Spark runs using Python. PySpark is also the language used in Databricks PySpark notebooks, which makes it important for anyone working in that environment.

2. What are RDDs in PySpark?

RDD stands for Resilient Distributed Datasets, which are data structures that help process data across all the nodes in a cluster. This is known as processing. RDD is immutable, which means once it is created, it cannot be modified. Also, if any RDD fails, it can be recovered automatically.

Efficient and flexible: You can carry out many operations on RDDs to accomplish different tasks.

3. What is a DataFrame? (This is an important PySpark interview question.)

A DataFrame is a distributed collection of data structures segregated into rows and columns. These columns are named. It is far more optimized than R or Python. PySpark data can run on various machines within the span of a single second. It passes the efficiency criteria, as it makes handling collected data much easier.

4. What are the key characteristics of PySpark?

•   Nodes are abstracted—you cannot directly access individual worker nodes.
•   PySpark provides APIs to use Spark’s important features.
•  Based on MapReduce, PySpark follows the MapReduce model, letting you define map and reduce functions.
• Abstracted network—Network communication happens implicitly, without manual handling.

A PySpark interview question could be, "Is PySpark faster than pandas?"

  PySpark is faster than Pandas. PySpark has the option to run tasks in parallel on many machines. That too in one go. This feature is not available in Pandas.

5. What are PySpark serializers?

 Serialization is mainly built for performance tuning. It tunes or changes the data into a format that can be stored on a disk or sent through a network.

The two types of serializers are 

The pickleSerializer takes the help of Python’s pickle to serialize objects. It supports all Python objects.

The MarshalSerializer is quicker, but it supports only limited object types.

6. What is a Parquet file in PySpark? 

This question is very important under PySpark Interview Questions. 

A Parquet file is a columnar storage format where columns, instead of rows, organize data. Spark  reads and writes data. It works good for large datasets, as it is faster and more space-efficient. It also reduces overall storage requirements.

7. What is PySpark SparkContext? 

 SparkContext gives the entry point to any Spark feature. It allows your Spark application to access the cluster.

8. How to filter data in PySpark DataFrames?

Use the filter(). Where() methods in PySpark to pick rows that match a condition:

 df_filtered = df.filter(df["column"] > value)

9. How to join two DataFrames in PySpark?

 Use the join() method in PySpark to merge two DataFrames:

df_joined = df1. join(df2, df1["key"] == df2["key"] "inner")

 PySpark supports join types such as inner, left, right, full, semi, and anti joins.

10. What is PySpark StorageLevel?

PySpark StorageLevel tells how an RDD is stored. It decides where data is kept in memory on disk or in both. It also checks whether RDD partitions are replicated and whether data should be serialized.

11. What are Spark files?

 SparkFiles is a tool used to load files into your Spark application.

You can use:

sc.addFile() to add files

SparkFiles.get() to retrieve or resolve the file path.

The two class methods in the SparkFiles directory are getRootDirectory() and get(filename).

PySpark Interview Questions for Experienced

 PySpark Interview Questions for Professionals.

Under this topic, you will come across various advanced questions. Advanced PySpark interview questions for lead developer 2025-2026 

1. How can you calculate executor memory in Spark?

To calculate executor memory, you need to know the following:

Total cores per node

Number of executors per node

Available memory per node

Formula

Reserved memory is usually 5–10% of the total node memory (kept aside for system processes and Hadoop daemons).

Example Calculation

Total node memory = 64 GB

Reserved memory = 8 GB (for OS + daemons)

Executors per node = 4

 Every executor gets 14 GB of memory.

This ensures that Spark jobs use memory without getting OutOfMemory (OOM) errors.

 Convert Spark RDD to a DataFrame.

 There are two ways to convert an RDD into a DataFrame in Spark:

Using the toDF() helper function

import com.mapr.db.spark.sql._

val df = sc.loadFromMapRDB()

where(field("first_name") === "Peter")

.select("_id", "first_name")

 .toDF()

Using SparkSession. createDataFrame

def createDataFrame(RDD, schema: StructType)

2. How to use window functions in PySpark to calculate row-wise metrics?

The Window class is used to perform operations such as ranking, running totals, or moving averages.

from pyspark.sql import SparkSession

from pyspark. sql.window import Window

from pyspark.sql import functions as F 

spark = SparkSession.builder.appName("WindowFunctions"). getOrCreate()

data = [("Alice", "HR", 3000),

 ("Bob", "HR", 4000),

 ("Charlie", "IT", 3500),

 ("David", "IT", 4500),

 ("Eve", "Sales", 5000)]

columns = ["Name", "Department", "Salary"]

df = spark. createDataFrame(data, columns)

# Define window partitioned by department and ordered by salary

windowSpec = Window.partitionBy("Department"). orderBy(F.desc("Salary"))

# Rank employees by salary within each department

ranked_df = df.withColumn("Rank", F.rank().over(windowSpec))

ranked_df.show()

 It is an advanced PySpark interview question because window functions are commonly used in analytics pipelines.

3. How to perform a broadcast join in PySpark?

Broadcast joins are used to optimize joins when one dataset is small enough to fit in memory.

 from pyspark.sql import SparkSession

from pyspark.sql import functions as F

spark = SparkSession.builder.appName("BroadcastJoin"). getOrCreate()

# Large dataset

data_large = [(1, "Alice"), (2, "Bob"), (3, "Charlie"), (4, "David")]

df_large = spark.createDataFrame(data_large, ["ID", "Name"])

# Small dataset

data_small = [(1, "HR"), (2, "IT"), (3, "Sales"), (4, "Finance")]

df_small = spark. createDataFrame(data_small, ["ID", "Department"])

# Perform broadcast join

df_joined = df_large. join(F.broadcast(df_small), "ID")

df_joined.show()

  Interview Questions and Answers for Pyspark coding

1. How to Determine Spark's Total Unique Word Count?

 Steps:

Load the text file as an RDD.

lines = sc.textFile("hdfs://Hadoop/user/test_file.txt")

Split each line into words.

words = lines. flatMap(lambda line: line.split())

Convert every word to a key-value pair (word, 1)

wordTuple = words.map(lambda word: (word, 1))

Count word occurrences using reduceByKey().

counts = wordTuple. reduceByKey(lambda x, y: x + y)

Collect and print the results.

print(counts.collect())

How to Use Spark to see if a Keyword Is Present in a Large Text File?

 lines = sc.textFile("hdfs://Hadoop/user/test_file.txt")

foundBits = lines.map(lambda line: 1 if "my_keyword" in line else 0)

total = foundBits. reduce(lambda x, y: x + y)

if total > 0:

 print("Found")

else:

 print("Not Found") 

2. How to Link Hive and Spark SQL?

 Steps:

Place the Hive configuration file.

Copy hive-site.xml into Spark’s conf/ directory so Spark can read Hive settings.

Use SparkSession to query Hive tables.

from pyspark.sql import SparkSession

# Enable Hive support

spark = SparkSession.builder \

 .appName("HiveConnection") \

enableHiveSupport() \

.getOrCreate()

# Run Hive query

result = spark.sql("SELECT * FROM ")

result.show() 

3. How to remove duplicate rows in a PySpark DataFrame?

You can use the dropDuplicates() or dropDuplicates(subset=[...]) method.

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("RemoveDuplicates"). getOrCreate()

data = [("Alice", 25), ("Bob", 30), ("Alice", 25), ("David", 40)]

columns = ["Name", "Age"]

df = spark. createDataFrame(data, columns)

# Remove all duplicates

df_no_duplicates = df.dropDuplicates()

df_no_duplicates.show()

# Remove duplicates based only on 'Name'

df_no_duplicates_name = df.dropDuplicates(["Name"])

df_no_duplicates_name.show()

This is a very common PySpark interview question since deduplication is widely used in ETL pipelines.

4. How do you calculate aggregate functions like average, sum, and count in PySpark?

You can use the groupBy() with aggregation functions (avg, sum, and count).

from pyspark.sql import SparkSession

from pyspark.sql import functions as F

spark = SparkSession.builder.appName("Aggregations"). getOrCreate()

data = [("Alice", "Sales", 2000),

 ("Bob", "Sales", 3000),

 ("Charlie", "HR", 4000),

 ("David", "HR", 2500),

 ("Eve", "IT", 2200)]

columns = ["Name", "Department", "Salary"]

df = spark. createDataFrame(data, columns)

# Group by Department and calculate aggregates

agg_df = df.groupBy("Department").agg(

 F.avg("Salary").alias("Average_Salary"),

F.sum("Salary").alias("Total_Salary"),

 F.count("Name").alias("Employee_Count")

)

agg_df.show()

This type of coding question is asked to test knowledge of groupBy and aggregations in PySpark DataFrames.

5. How to Work with Structured Data in Spark SQL Using Domain-Specific Language (DSL)?

 Example:

val df = spark.read.json("examples/src//resources/people.json")

 df.show()

 df.select("name").show()

 df.select($"name" $"age" + 1). show()

 df.filter($"age" > 21). show()

 df.groupBy("age").count().show()

 Using DSL in Spark SQL, you can select, filter, transform, and aggregate data in a clean and intuitive way.

 Spark Program to Check if a Given Keyword Exists in a Huge Text File

 from pyspark import SparkContext

 sc = SparkContext("local" "Keyword Search")

 textFile = sc.textFile("path/to/your/text/file.txt")

 keyword = "yourKeyword"

 exists = textFile.filter(lambda line: keyword in line). count() > 0

if it exists:

 print(f"The keyword '{keyword}' exists in the file.")

 else:

 print(f"The keyword '{keyword}' does not exist in the file.")

5. How to Work with Different Data Formats in PySpark

 from pyspark.sql import SparkSession

 spark = SparkSession.builder.appName("DataFormats"). getOrCreate()

 df_json = spark.read.json("path/to/file.json")

 df_parquet = spark.read.parquet("path/to/file.parquet")

 df_avro = spark.read.format("avro"). load("path/to/file.avro")

df_json.write.json("path/to/output/json")

df_json.write.parquet("path/to/output/parquet")

df_json.write.format("avro").save("path/to/output/avro")

Lazy evaluation in PySpark

Spark memorizes the instructions whenever it is being operated on any dataset. RDD does not execute a transformation, like a map(), immediately when it is called. Lazy evaluation is a feature of Spark that helps optimize the entire data processing workflow for better performance and efficiency. If you want to practice or prepare similar concepts for interviews, tools like an AI answer question generator can help you quickly generate and refine technical Q&A scenarios.

Conclusion

We have covered the topics of DataFrames, RDDs, and Lazy Evaluation in this blog. We have also included PySpark coding interview questions and answers so that you will be code-ready. We hope this PySpark interview questions guide will be helpful in your preparation.

Sprintzeal offers courses on Big Data Analytics and Big Data Hadoop. Don’t forget to check it out.

Today in a world that relies on data learning, big data and analytics can make you a unique problem solver. The Big Data Hadoop Certification will give you hands-on experience with data systems. The Data Science Master Program will help you turn data into useful insights, giving you future‑ready analytical skills that lift your career.

 Subscribe to our newsletter for the insights and updates.

Good luck with your interview

Faqs

Can I expect coding-based PySpark interview questions?

Yes. Many interviews include coding tasks like filtering, joining DataFrames, or writing word count programs.

Are PySpark interview questions mostly theoretical or practical?

Normally, PySpark interview questions include both. Basic theory, hands-on coding, and real-world problem solving are asked.

What are some advanced PySpark interview questions?

Advanced questions often involve optimization, Spark architecture, shuffle operations, and broadcast joins.

What are PySpark scenario-based interview questions?

Scenario-based PySpark interview questions are mostly real-world problems. Examples include handling skewed data or optimizing a slow job.

What is a good PySpark coding question to practice?

I think a good PySpark coding question to practice is a word count program. The word count program asks you to read a text file, split each line into words, count how many times each word appears, and then write the results to disk. You can also practice filtering text files, removing duplicate rows with dropDuplicates(), and writing groupBy aggregations. Practicing these PySpark coding interview questions builds the muscle memory you need for interview rounds because interviewers often ask you to write the logic live, not just explain it.

How is PySpark used in Databricks? 

Databricks PySpark is one of the ways companies run PySpark in production. Databricks gives you managed Spark clusters, notebooks, and built‑in optimizations. Many interviewers ask specifically about PySpark and Databricks workflows if the role involves working on that platform.

What are PySpark interview questions for data engineers scenario-based?

PySpark interview questions for data engineers that are scenario-based usually go beyond syntax and test judgment on live systems. You could be asked how you'd redesign an ETL pipeline that keeps failing due to schema mismatches, how you'd approach reprocessing only the changed partitions in a daily batch job, or how you'd decide between repartitioning and coalescing when a job is underperforming. These questions matter to interviewers because senior data engineers are expected to make trade-off decisions on performance, cost, and reliability, not write working code.

What is a PySpark coding test?

Yes, many companies include a PySpark assessment or coding test as part of the interview process, either as a coding round or a take-home task. These usually cover the fundamentals shown in the coding section above. Filtering DataFrames, joining datasets, removing duplicates, and writing groupBy aggregations. Some companies also add a timed PySpark coding test through platforms, like HackerRank or a custom portal, so it's worth practicing writing this logic without relying on autocomplete or documentation