Top PySpark Interview Questions and Answers

Preparing for a PySpark interview in 2025? This guide covers 70+ PySpark interview questions with answers for freshers and experienced data engineers. You must be ready for both basic and advanced concepts. Interview questions on PySpark often go beyond syntax and test how you solve problems in the real world. 

Freshers are usually asked about basics like DataFrames, joins, RDDs, and transformations, while experienced professionals should expect optimization techniques, partitioning, fixing memory, and skew handling issues.

Table of Contents:

• PySpark Interview Questions and Answers for Freshers
• Intermediate PySpark Interview Questions
• Advanced PySpark Interview Questions for Experienced Data Engineers
• Top 10 PySpark Coding Interview Questions
• PySpark Scenario-Based Interview Questions
• Frequently Asked PySpark Interview Questions and Answers for Aspiring Data Engineers

PySpark Interview Questions and Answers for Freshers

1. What is PySpark?

PySpark is an Apache Spark interface written in Python.  It allows you to collaborate with Spark via Python APIs.  It also supports Spark technologies such as Spark DataFrame, Spark SQL, Spark Streaming, Spark MLlib, and Spark Core.  It has an interactive PySpark shell for analyzing structured and semi-structured data in a distributed setting.  PySpark can read data from numerous sources and formats.  It also simplifies the use of RDDs (Resilient Distributed Datasets).  The py4j package in Python implements PySpark capabilities.

PySpark can be installed using PyPi with the following command:

pip install pyspark

2. What is PySpark UDF?

A UDF (User Defined Function) in PySpark gives a way for you to apply custom Python logic to Spark DataFrames when a built-in function is not available. In those cases, you can create a normal Python function, register it as a UDF using pyspark.sql.functions.udf, and then apply it to DataFrame columns just like any other Spark function. You can then use UDFs on any number of SQL expressions or DataFrame transformations.

While UDFs extend Spark’s functionality, they can be slower than native functions because they run in Python. For better performance, PySpark provides Pandas UDFs (vectorized UDFs) that process data in batches using Apache Arrow.

For example:

from pyspark.sql.functions import udf
from pyspark.sql.types import StringType

def upper_case(name):
    return name.upper()

upper_udf = udf(upper_case, StringType())
df = df.withColumn("upper_name", upper_udf(df["name"]))

This creates a new column upper_name by applying the custom function.

3. What is the difference between RDD, DataFrame, and Dataset?

While working with Spark, you would come across its three main abstractions RDDs, DataFrames, and Datasets. They all process data in a distributed way, but work at different levels to solve the same problem.

• RDD (Resilient Distributed Dataset):

An RDD or Resilient Distributed Dataset is Spark’s core data structure. It’s a distributed collection of objects across different machines in the cluster with no fixed schema. You use functions like map, filter, or reduce to transform the data. RDDs give you all the control you need over how the computation is done, but it doesn’t have built-in optimizations, so they are often slower. They are mainly used when you need custom transformations or logic that the higher-level APIs would not cover.

• DataFrame:

A DataFrame represents data into rows and columns, like a table in a database or a defined schema. DataFrames focus on what you want to do with the data rather than how Spark should do it. Now Spark uses its catalyst optimizer to figure out how the execution can take place in the best possible way. This makes DataFrames faster than RDDs for most tasks. In PySpark, DataFrames are a default choice since they offer SQL queries, built-in functions, and integration combined with MLlib and Streaming. The only catch is that type errors will only show at runtime and not while coding.

• Dataset:

A Dataset extends from DataFrames itself and only adds compile-time type safety to it. This means the compiler can catch errors while writing the code instead of runtime errors, but it is available only in Scala and Java, not in Python (Doesn’t support it). Hence, datasets are not for PySpark users and therefore they fall back to RDDs when they need more control over execution.

4. How would you optimize a PySpark DataFrame operation that involves multiple transformations and is running too slowly on a large dataset?

The first step is always to look at the Spark UI, which tells you where to identify bottlenecks like skew, shuffles, and wide transformations. Common fixes include:

• Use caching/persisting for intermediate DataFrames if they are reused multiple times.
• Apply the predicate pushdown filter data as early as possible to reduce data size before expensive operations.
• Avoid UDFs when possible and rely on built-in functions, as they are much faster.
• Reduce shuffle cost by tuning partitions:
  

  spark.conf.set("spark.sql.shuffle.partitions", "200")

Always filter early, cache smartly, avoid UDFs, and tune partitions.

5. Given a large dataset that doesn’t fit in memory, how would you convert a Pandas DataFrame to a PySpark DataFrame for scalable processing?

If the dataset is larger than memory, you should not push it through Pandas first. The scalable way is:

• Save the Pandas data to distributed storage like S3, CSV/Parquet on HDFS, or a data lake.
• Load it back using Spark:
  

  df = spark.read.parquet("s3://bucket/large_dataset/")

• Alternatively, you can use createDataFrame(), which is great only for small Pandas objects:
  

  spark_df = spark.createDataFrame(pandas_df)

• For large datasets, you should always run PySpark instead of Pandas.

6. How do you optimize data partitioning in PySpark? When and how would you use repartition() and coalesce()?

Partitioning is like splitting your dataset into slices so Spark can process them in parallel. If the slices are uneven (too many or too few), Spark will struggle to complete the task.

• repartition(n)

• • It performs a complete shuffle, helping to redistribute the data across n partitions.
  • It is useful when you have too few partitions and just want to increase parallelism (e.g., after reading a single large file).
    Example:
    

    df = df.repartition(100, "user_id") # For parallelism

• • This ensures Spark groups rows by user_id and balances the work across executors.

• coalesce(n)

• • It reduces the number of partitions without a shuffle (just merges existing ones).
  • It is useful when you’re writing output and want to avoid thousands of small files.
    Example:
    

    df = df.coalesce(10)    # For efficient write
    df.write.parquet("s3://bucket/output/")

Note: Always repartition before heavy operations (joins/aggregations) for balance, and coalesce before writing to control file sizes.

If you’re looking for a good resource to learn PySpark in a more structured manner, check out this Introduction to PySpark training course certification.

7. How can you create a DataFrame using an existing RDD and from a CSV file?

1. Using an Existing RDD
You can convert an RDD to a DataFrame with the toDF() By default, the resulting DataFrame assigns column names _1, _2, and so on.

dfFromRDD1 = rdd.toDF()
dfFromRDD1.printSchema()

Output schema:

root
 |-- _1: string (nullable = true)
 |-- _2: string (nullable = true

To provide custom column names, pass them to toDF() as a list:

columns = ["language", "users_count"]
dfFromRDD1 = rdd.toDF(columns)
dfFromRDD1.printSchema()

2. Schema with custom names:

root
 |-- language: string (nullable = true)
 |-- users_count: string (nullable = true)

From a CSV File
You can use read.csv() to create a DataFrame from a CSV file:

dfFromCSV = spark.read.csv("path/to/csvfile.csv", header=True, inferSchema=True)
dfFromCSV.printSchema()

This reads the CSV, uses the first row as headers, and infers column types automatically.

8. How do you create a SparkSession in PySpark? What are its main uses?

In PySpark, a SparkSession is the entry point for working with Apache Spark. It allows you to create DataFrames, run SQL queries, manage configurations, and access the core Spark functionalities. In fact, without a SparkSession, you cannot perform most operations in PySpark.

You can create a SparkSession in PySpark using the SparkSession.builder API:

from pyspark.sql import SparkSession
# Create SparkSession
spark = SparkSession.builder 
        .appName("MySparkApp") 
        .master("local[*]") 
        .getOrCreate()

Main uses of SparkSession in PySpark:

• Create DataFrames from RDDs, structured data files (CSV, JSON, Parquet), or external databases.
• Run Spark SQL queries to process structured and semi-structured data.
• Configure Spark properties such as cluster settings, memory, and shuffle partitions.
• Manage SparkContext internally, so you don’t need to handle it separately.
• Enable access to Spark libraries like Spark SQL, Spark Streaming, MLlib, and GraphX.

SparkSession in PySpark is your way to work with all of Spark’s APIs in Python, making it one of the most important concepts for both freshers and experienced data engineers in interviews.

9. Write a PySpark code snippet to calculate the moving average of a column using window functions.

from pyspark.sql.window import Window
from pyspark.sql.functions import avg

windowSpec = Window.partitionBy("category").orderBy("date").rowsBetween(-2, 0)

df.withColumn("moving_avg", avg("sales").over(windowSpec)).show()

This calculates a rolling 3-row average per category.

10. Explain the use of StructType and StructField classes in PySpark with examples.

StructType and StructField are used to define DataFrame schemas, including nested or complex structures.

• StructType: A collection of StructField objects that describes the schema.
• StructField: Defines column name, data type, nullability, and metadata.

Example code:

from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType, IntegerType

spark = SparkSession.builder.master("local[1]").appName("Example").getOrCreate()

data = [
    ("James", "", "William", "36636", "M", 3000),
    ("Michael", "Smith", "", "40288", "M", 4000),
    ("Robert", "", "Dawson", "42114", "M", 4000),
    ("Maria", "Jones", "", "39192", "F", 4000)
]

schema = StructType([
    StructField("firstname", StringType(), True),
    StructField("middlename", StringType(), True),
    StructField("lastname", StringType(), True),
    StructField("id", StringType(), True),
    StructField("gender", StringType(), True),
    StructField("salary", IntegerType(), True)
])

df = spark.createDataFrame(data=data, schema=schema)
df.printSchema()
df.show(truncate=False)

Schema output:

root
 |-- firstname: string (nullable = true)
 |-- middlename: string (nullable = true)
 |-- lastname: string (nullable = true)
 |-- id: string (nullable = true)
 |-- gender: string (nullable = true)
 |-- salary: integer (nullable = true)

Here, StructType holds the schema definition, while each StructField specifies details for a column.

Intermediate PySpark Interview Questions

11. What is a Spark Driver in PySpark, and what are its responsibilities?

The Spark Driver is the main process that runs your PySpark application. It is responsible for converting your code into tasks and coordinating their execution across worker nodes.

Responsibilities of Spark Driver:

• Convert user code into a logical plan for execution
• Request resources from the cluster manager
• Split the job into stages and tasks, and send them to executors
• Track task execution and handle retries on failure
• Collect and return results to the user

12. What is a DAG in Spark?

A DAG (Directed Acyclic Graph) is the execution plan Spark builds before running a job. It records the sequence of transformations on the data and the dependencies between them.

Why DAG is important:

• It lets Spark optimize the job before execution
• Reduces unnecessary computations via optimizations such as pipelining and predicate pushdown
• Ensures tasks are executed in the best possible way across the cluster

Key points about DAG:

• Directed: Each step flows in a fixed direction from input to output
• Acyclic: No loops; once a step is complete, Spark moves forward
• Graph: Each node represents a dataset (RDD/DataFrame), and edges represent transformations

Example:

df = spark.read.csv("data.csv", header=True, inferSchema=True)

df = df.filter(df.age > 30).select("name", "age")

df.show()

Here, Spark builds a DAG with nodes for reading the CSV, filtering rows, and selecting columns. The show() action triggers execution of this DAG.

13. Explain the concept of window functions in PySpark and provide an example.

PySpark window functions let you perform operations across a set of rows, called a window, while returning a value for every row in the original dataset. They are commonly used for ranking, analytics, and aggregation tasks.

from pyspark.sql.window import Window
from pyspark.sql.functions import row_number
# Define the window function
window = Window.orderBy("discounted_price")
# Apply the window function
df = df_from_csv.withColumn("row_number", row_number().over(window))

In this example, each row in the DataFrame receives a row_number based on the discounted_price column, while all other data remains intact.

14. What is the purpose of checkpoints in PySpark?

Checkpointing in PySpark means saving RDDs to disk at a specific point in the computation. This allows the system to reference the checkpointed RDD instead of recomputing it from the original source. Checkpoints are useful for recovering from failures because the driver can restart from a previously computed state, reducing recomputation and improving reliability.

15. What are the different types of cluster managers available in Spark?

In Spark, cluster managers play a critical role in distributing resources, scheduling jobs, and ensuring efficient execution across a cluster. Spark supports multiple cluster managers, each suited for different environments and use cases. 

The Standalone cluster manager is lightweight and easy to set up, making it ideal for small to medium clusters or development environments where simplicity is preferred. 

Hadoop YARN integrates Spark with the broader Hadoop ecosystem, enabling Spark to coexist with other Hadoop workloads while leveraging YARN’s resource scheduling and fault-tolerance capabilities. 

Kubernetes offers a modern approach by allowing Spark applications to run in containerized environments with automated scaling, deployment, and resource isolation, which is especially useful in cloud-native architectures. 

Apache Mesos provides fine-grained resource management across multiple applications, making it suitable for large, multi-tenant clusters where multiple frameworks need to share resources efficiently. 

16. How do you handle errors and exceptions in PySpark?

Handling errors and exceptions in PySpark requires careful attention because failures can occur at both the transformation and action stages. A practical approach is to wrap critical code blocks in try-except statements, which allows you to catch exceptions and respond appropriately without halting the entire job. For RDD operations, the foreach method can be used to process elements individually while handling errors on a per-row basis. This approach ensures that a single problematic record does not cause the entire computation to fail, making pipelines more robust and easier to debug.

17. How does PySpark handle schema inference, and how can you define a schema explicitly?

When reading structured data, PySpark can automatically infer the schema by scanning the dataset. While this is convenient, explicit schema definition is often preferred for better control, performance, and reliability, especially with large datasets. Schemas can be defined using StructType and StructField to specify data types and nullability for each column. For example:

from pyspark.sql.types import StructType, StructField, IntegerType, StringType
schema = StructType([
    StructField("id", IntegerType(), True),
    StructField("name", StringType(), True)
])
df = spark.read.csv("data.csv", schema=schema, header=True)

Defining the schema explicitly not only ensures data consistency but also reduces the overhead of schema inference, which can improve performance when working with large datasets.

Advanced PySpark Interview Questions for Experienced Data Engineers

18. What are the key differences between Spark SQL and Hive SQL?

Spark SQL is Spark’s module designed for working with structured and semi-structured data using SQL queries while leveraging Spark’s distributed execution engine for high performance. It allows seamless integration with DataFrames, Datasets, and other Spark components, making it ideal for both batch and streaming analytics. Spark SQL also supports advanced optimizations through the Catalyst query optimizer and Tungsten execution engine, enabling faster query execution.

Hive SQL, on the other hand, is part of the Apache Hive ecosystem, which was built primarily for querying data stored in Hadoop’s HDFS using a SQL-like syntax. Hive SQL typically relies on MapReduce or Tez for execution, which can make it slower compared to Spark SQL. While Hive excels in batch analytics and long-running ETL jobs, Spark SQL provides better real-time capabilities, in-memory processing, and integration with modern data pipelines. Choosing between them depends on performance needs, data volume, and the broader ecosystem in use.

19. How does PySpark handle data skew?

Data skew occurs when certain partitions in a dataset are significantly larger than others, causing uneven workload distribution and slow tasks. PySpark provides several strategies to mitigate skew:

• Salting: Introduce a random key prefix to heavily skewed keys before performing joins, then remove it after the operation.
• Repartitioning: Redistribute data using repartition() or coalesce() to balance partitions evenly.
• Skew-aware joins: Use broadcast joins for small tables to avoid shuffling large skewed datasets.
• Custom partitioning: Apply a custom partitioner or bucketing strategy to spread hot keys across multiple partitions.
  

Handling data skew helps maintain performance in large-scale Spark jobs, ensuring that no single executor becomes a bottleneck.

20. Explain the concept of lineage in PySpark.

Lineage in PySpark represents the directed acyclic graph (DAG) of all transformations applied to an RDD or DataFrame. It tracks the sequence of operations from the original data source to the final result. Lineage provides several practical benefits:

• Fault tolerance: If a partition is lost, Spark can recompute it using its lineage instead of restarting the entire job.
• Debugging: By understanding the chain of transformations, developers can trace errors and identify performance bottlenecks.
• Optimizations: Spark uses lineage information to optimize execution plans and tries to reduce unwanted computations.
  

Essentially, lineage allows Spark to balance efficiency with reliability, making it a core feature for distributed data processing.

21. How can you perform incremental processing with PySpark?

Incremental processing enables Spark to handle only new or changed data rather than reprocessing entire datasets. Common techniques include:

• Checkpointing: Persist intermediate results to disk to avoid recomputation in iterative jobs.
• Structured Streaming with triggers: Process new data in micro-batches or continuous streams, using event-time processing and watermarking.
• Metadata management: Track processed records using unique identifiers, timestamps, or versioned datasets to ensure only fresh data is processed.
• Partition-based ingestion: Organize data by time or category partitions and process only the newest partitions during each run.
  

Incremental processing is essential for improving efficiency, reducing compute costs, and maintaining near real-time analytics in large-scale pipelines.

22. What are the best practices for managing large-scale data processing using PySpark?

Thorough planning and optimization are necessary for efficient PySpark development.

Here we are listing some best practices that normally include:

1. Optimize data partitions: Ensure partitions are neither too small (causing overhead) nor too large (causing memory issues). Use repartition() and coalesce() strategically.
2. Use file formats that are efficient: Prefer columnar formats like Parquet or ORC for faster read/write performance and predicate pushdown.
3. Cache intermediate results: Persist frequently accessed DataFrames or RDDs to memory or disk to avoid recomputation.
4. Tune Spark configurations: Adjust executor memory, cores, shuffle partitions, and other Spark settings based on workload.
5. Monitor job performance: Use Spark UI, logs, and metrics that help to identify issues, skew, or long-running stages.
6. Minimize shuffles: Avoid wide transformations where possible, or use broadcast joins to reduce network overhead.
7. Handle data skew proactively: Detect and mitigate skewed partitions to ensure even workload distribution.

Comparison of how data moves in the cluster is as shown:

23. What are the different algorithms used in PySpark?

PySpark offers a wide range of algorithms through its MLlib library, covering several machine learning and data processing tasks. Some of the key modules include:

1. mllib.clustering – algorithms for grouping similar data points.
2. mllib.classification – algorithms for predicting categorical labels.
3. mllib.regression – algorithms for predicting continuous values.
4. mllib.recommendation – algorithms for building collaborative filtering and recommendation systems.
5. mllib.fpm – algorithms for frequent pattern mining and pattern discovery.
6. mllib.linalg – linear algebra utilities that support machine learning computations.
7. spark.mllib – the core library providing access to all MLlib functionalities.

These modules allow PySpark to handle a variety of large-scale machine learning tasks efficiently while leveraging distributed computing.

24. How would you handle null values in a PySpark DataFrame when different columns require different strategies?

There’s no one-size-fits-all. But you can follow the below-mentioned solutions to handle null values in PySpark DataFrame. Here, some columns you will drop, some you will fill, and some you may require to impute. 

• Drop nulls where values are not useful:
  

  df.dropna(subset=["user_id"])   # Drop if user_id is null

• Replace with constants:
  

  df.fillna({"age": 0, "city": "Unknown"})        # Replace

• For numeric features in ML pipelines, you often have no choice but to use imputation with mean and median. It depends on the business use case.

25. Explain the differences between narrow and wide transformations in PySpark.

In PySpark, transformations are classified as narrow or wide based on how data moves between partitions. Narrow transformations occur when each input partition contributes to at most one output partition, meaning no data needs to be shuffled across the cluster. Examples include map(), filter(), and union(). These operations are generally faster and more efficient because Spark can execute them within a single partition without network overhead. Wide transformations, on the other hand, involve input partitions contributing to multiple output partitions. These operations require data shuffling across the cluster and are typically used for operations like groupBy(), join(), and sortBy(). Wide transformations are more resource-intensive, and understanding the difference between narrow and wide operations is essential for optimizing Spark performance.

26. What is a Catalyst optimizer in Spark, and how does it work?

The Catalyst optimizer is Spark SQL’s query optimization engine. It analyzes SQL queries or DataFrame operations and automatically transforms them into a smooth and effective execution plan. Catalyst applies a series of rule-based and cost-based optimizations, such as predicate pushdown, constant folding, and reordering of operations, to reduce data movement and computation time. By generating a physical plan tailored to the dataset and query structure, Catalyst helps Spark execute queries efficiently without requiring manual tuning, making it a key component for performance optimization in Spark applications. The image shows flow of how PySpark optimizes queries before execution.

27. When would you use a broadcast join in PySpark? Give an example.

Use broadcast joins when one DataFrame is small  (< 500MB). It avoids shuffling cost by sending small dataset to all executors.

from pyspark.sql.functions import broadcast
df_large.join(broadcast(df_small), "id").show()

Think of it like handing out a cheat sheet (small table) to every executor, instead of passing around huge datasets.

The only limitation of broadcast join in PySpark is  Memory-intensive if the broadcasted DataFrame is too large.

28. When should you use UDFs instead of built-in PySpark functions, and how do you optimize them?

• Use UDFs only when no equivalent built-in function exists in Spark as built-ins are always faster.
• Always prefer Pandas UDFs (vectorized) because they run with Arrow and are much quicker.

For Example:

from pyspark.sql.functions import pandas_udf, col

@pandas_udf("double")
def square(x: pd.Series) -> pd.Series:
    return x ** 2

df.withColumn("squared", square(col("value")))

29. What is lazy evaluation in PySpark?

Lazy evaluation in PySpark simply means Spark doesn’t execute your transformations immediately. Instead, it waits until you call an action like .show(), .count(), or .collect() before running the actual computation.

The above image shows how Spark builds a DAG of transformations, then executes only when an action is triggered.

This approach makes PySpark more efficient because:

• It builds a logical execution plan (DAG) first.
• Spark can then optimize the entire plan before doing any heavy lifting.
• Only the necessary operations get executed, saving time and resources.

30. Differentiate between transformations and actions in PySpark.

This is one of the most common PySpark interview questions because it tests if you truly understand Spark’s execution model.

1. Transformations:

• • Operations like filter(), map(), select(), or withColumn().
    
  • They’re lazy — they only describe what should happen to the data.
    
  • Nothing is executed yet.

2. Actions: 

• • Operations like count(), collect(), show(), or write().
    
  • They trigger execution — Spark now processes the DAG and returns results.

Example:

df = spark.read.csv("data.csv", header=True)
df_filtered = df.filter(df.age > 30)   # Transformation (lazy)
print(df_filtered.count())             # Action (triggers execution)

Easy way to remember:

• Transformations = the plan.
• Actions = get it done.

31. Discuss the deployment of PySpark applications in a production environment.

Writing PySpark code is one thing, but running it in production is where things get real. Deployment means making sure your PySpark applications are scalable, fault-tolerant, and easy to manage on large datasets. The diagram below shows an end-to-end view of job submission in production.

Key aspects of PySpark deployment:

1. Cluster Managers – Decide where the job runs:
   • YARN (commonly used in Hadoop environments).
   • Kubernetes (cloud-native, scalable, great for containerized workloads).
   • Standalone (for smaller setups or dev/test).
2. Job Submission – Package your PySpark code and run it using spark-submit. For enterprise, mainly pipelines, as well as tools like Airflow, Oozie, or Luigi, are used for scheduling and orchestration.
3. Resource Management – Fine-tune executors, memory, cores, and partitions so jobs don’t hog cluster resources.
4. Monitoring & Logging – Use Spark UI, logs, or external monitoring tools (Prometheus, Ganglia) to track performance and debug issues.
5. Best Practices:
   • Use Parquet/ORC (columnar formats) for efficiency.
   • Minimize shuffles and cache data smartly.
   • Add checkpointing for fault-tolerance.
   • Handle data skew proactively.

Top 10 PySpark Coding Interview Questions

32. How to Read Data from CSV and Perform Basic Transformations?

from pyspark.sql import SparkSession

spark =
SparkSession.builder.appName("DataEngineer").get
OrCreate()

# Read CSV
df = spark.read.csv("path_to_file.csv", header=True,
inferSchema=True)

# Basic transformations
df = df.withColumnRenamed("oldColumn",
"newColumn").filter(df["age"] > 25)
df.show()

33. How to Use Window Functions for Ranks?

from pyspark.sql.window import Window
from pyspark.sql.functions import rank

windowSpec =
Window.partitionBy("department").orderBy(df["salary"].desc())

df = df.withColumn("rank", rank().over(windowSpec))
df.show()

34. How to Handle Missing Data in a DataFrame?

df = df.na.fill({"column1": "default_value", "column2": 0})

# Fill missing values
df.show()

35. How to Join Two DataFrames on a Key?

joined_df = df1.join(df2, df1["id"] == df2["id"], "inner")
joined_df.show()

36. How to Filter Rows Using filter() and where()?

df.filter(df["age"] > 25).show()
df.where(df["salary"] > 50000).show()

37. How to Create New Columns Using withColumn()?

from pyspark.sql.functions import col
df = df.withColumn("new_column", col("existing_column") * 2)
df.show()

38. How to Write Data to Parquet Format?

df.write.parquet("output_path")

39. How to Repartition Data for Optimization?

df = df.repartition(4)
# Adjust based on data size and processing needs

40. How to Drop Duplicates Based on a Specific Column?

df = df.dropDuplicates(["columnName"])
df.show()

41. How to Group By and Perform Aggregations?

from pyspark.sql.functions import avg, count

result_df =
df.groupBy("columnName").agg(avg("salary").alias("avg_salary"), 
                             count("*").alias("count"))
result_df.show()

42. How to Implement a Custom Transformation in PySpark

In PySpark, a custom transformation is when you create your own function to change or add new columns in a DataFrame. Instead of repeating the same logic everywhere, you wrap it in a function and apply it using .transform().

Steps:

• Start by writing a Python function that would take an input, a DataFrame specifically.
• In the function, define the logic (for example, adding a new column).
• Use .transform() to apply this function to your DataFrame.

Example:

# Custom function to calculate discounted price
def get_discounted_price(df):
    return df.withColumn(
        "discounted_price",
        df.price - (df.price * df.discount) / 100
    )
# Apply the transformation
df_discounted = df_from_csv.transform(get_discounted_price)

PySpark Scenario-Based Interview Questions

43. You have a large dataset with a highly skewed distribution. How would you handle data skewness in PySpark?

Highly skewed data means some partitions are getting way more rows than others. That may lead to slow jobs or even failures at times. 

To fix it:

• Use salting: add random keys to spread skewed keys across partitions.
• Use broadcast joins if one side of the join is small.
• Apply skew hinting:
  

  df.join(df2.hint("skew"), "key")

• Repartition on balanced keys to reduce shuffle skew. The key here is to not let one executer do all the heavy lifting.

44. How would you troubleshoot out-of-memory errors in PySpark?

Out-of-memory errors usually mean Spark is trying to hold too much data in RAM. Here’s how you approach it in real life:

• Diagnostics via Spark UI

Identify whether failures are isolated to a single executor (due to possible skew) or cluster-wide (due to resource shortage).

• Memory Tuning

• • • Increase executor memory:
      

      --executor-memory 8G --driver-memory 4G

    • Increase cores if possible; as too many cores per executor amplify GC overhead.

• Optimize Transformations:

• • • Avoid wide transformations (like joins/groupBy) on massive datasets without repartitioning. Also, replace wide transformations with narrow ones when possible.

• • • Drop unused columns early (select) to reduce shuffle size.

• • • Use persist with disk strategically with MEMORY_AND_DISK instead of only in-memory:
      

      df.persist(StorageLevel.MEMORY_AND_DISK)

• • • Handle skewed data: Apply partitioning or salting if one key has too many rows, otherwise, one executor will get overloaded.

45. What strategies would you use to efficiently process multiple large files in PySpark?

Processing large volumes of files requires balancing format choice, partition strategy, and execution planning.

1. Adopt Columnar Formats

• • • Use wholeTextFiles() only for small files; other times, prefer spark.read.parquet() or csv().

      df = spark.read.parquet("s3://bucket/2025-data/")

2. Partition Size Management

• • • The default block size is 128 MB. For large datasets, tuning will memory will improve task parallelism:

      spark.conf.set("spark.sql.files.maxPartitionBytes", 268435456)  # 256 MB

3. Small File Mitigation

• • • Merge outputs with coalesce() or repartition() to prevent excessive metadata overhead.

      df.coalesce(50).write.parquet("s3://bucket/output/")

4. Repartition for Parallelism

• • • Increase partitions for wide workloads:

      df = df.repartition(200)

5. Leverage Lazy Evaluation

• • • Spark scans metadata first, not entire files, ensuring scalable planning.

Frequently Asked PySpark Interview Questions and Answers for Aspiring Data Engineers

46. Describe different types of joins in PySpark (inner, outer, left, right) and their use cases.

In PySpark, joins work just like they do in SQL. They let you combine two different DataFrames that are present in the same column. Here are the main types:

• Inner Join

• • • Keeps only the rows where the join key exists in both DataFrames.
    • Use case: When you only want the matched data.

      df1.join(df2, df1.id == df2.id, "inner")

• Left Join (or Left Outer Join)

• • • It keeps all the rows from the left DataFrame, and matches them, till the right rows.
    • If there’s no match, the right side shows null.
    • You should use it when your main data is in the left DataFrame, and you only want extra info if it exists.

      df1.join(df2, df1.id == df2.id, "left")

• Right Join (or Right Outer Join)

• • • It is the opposite of a left join, which is to retain all rows from the right DataFrame along with the corresponding rows from the left.
    • Use case: Rare, but useful if your main data is in the right DataFrame.

      df1.join(df2, df1.id == df2.id, "right")

• Full Outer Join

• • • Keeps all rows from both DataFrames.
    • If a row doesn’t have a match, the missing side shows null.
    • Use case: When you want a complete view of data from both sources, even if some don’t match.

      df1.join(df2, df1.id == df2.id, "outer")

47. How do you perform aggregations and group by operations in PySpark?

Aggregations in PySpark are like asking: “Can you group this data and give me some totals or averages?”

You use the groupBy() function along with built-in aggregation functions like count(), sum(), avg(), max(), min().

Example: Find the total sales and average sales per region.

from pyspark.sql.functions import sum, avg
df.groupBy("region") 
  .agg(
      sum("sales").alias("total_sales"),
      avg("sales").alias("avg_sales")
  ) 
  .show()

This groups the data by region, then calculates the total and average sales for each region.

Other common aggregations:

• count(“*”) → number of rows.

• max(“col”) → highest value.

• min(“col”) → lowest value.

48. What challenges have you faced when working with large datasets in PySpark? How did you overcome them?

This is an exemplary answer using STAR. You can implement this technique on all other scenario-based questions.

• Situation:
  “In one of my projects, I was working with very large datasets where daily ingestions were in the range of terabytes. Running transformations on this data in PySpark often caused performance bottlenecks and memory errors.”
• Task:
  “My goal was to ensure the pipeline ran within the expected SLA and that the jobs were stable in production without frequent failures.”
• Action:
  “To solve this, I first analyzed the job execution using Spark UI and identified bottlenecks during wide transformations and shuffles. I addressed data skew by repartitioning the dataset and, in some cases, applying salting techniques. I also cached intermediate results that were reused multiple times, optimized joins with broadcast variables for smaller tables, and tuned executor and memory configurations. Additionally, I worked with the team to design better partitioning strategies at the data storage layer, which reduced load imbalance.”
• Result:
  “With these optimizations, the job runtime was reduced by almost 40%, memory errors stopped occurring, and the pipeline became much more reliable. It also scaled smoothly as the dataset continued to grow.”