Top 40 PySpark Interview Questions (2026)

Introduction

Did you know? More than 70% of data engineering roles now require knowledge of PySpark or Apache Spark. From companies like Amazon, Walmart, and top gaming startups to fintech and AI companies PySpark has become a must-have skill in 2026.

But here’s the reality 👇

Most professionals know basic syntax…

Very few understand how PySpark works in real-world distributed systems.

And that’s exactly what top tech companies test.

If you are:

→ A working professional preparing for a switch
→ A software engineer moving into data engineering
→ A fresher targeting product-based companies

This blog is for you, here you’ll find the 40 most important PySpark interview questions explained in a simple way, along with real interview-style problems and practical solutions commonly asked in top product companies.

Basic PySpark Interview Questions

These questions test your foundation. Many candidates fail here because they memorize syntax but don’t understand how Spark works internally.

1. What is PySpark and how is it different from Apache Spark?

PySpark is the Python API for Apache Spark. Spark is written in Scala and runs on JVM, while PySpark allows developers to write Spark programs using Python. Internally, PySpark uses Py4J to communicate between Python and JVM.

In interviews, companies test whether you understand that execution still happens on JVM, not pure Python.

2. What is the difference between RDD and DataFrame?

RDD is a low-level distributed data structure. It gives full control but no automatic optimization.

DataFrame is a higher-level abstraction built on Spark SQL. It provides Catalyst Optimizer and Tungsten engine optimizations, making it faster and more memory efficient.

Most product companies prefer DataFrames in production systems.

3. What are Transformations and Actions in Spark?

Transformations are lazy operations like map(), filter(), select() that create a new dataset but do not execute immediately.

Actions like count(), collect(), show() trigger execution and return results.

Understanding lazy evaluation is important for performance optimization.

4. What is Lazy Evaluation in PySpark?

Lazy evaluation means Spark does not execute transformations immediately. It builds a DAG (Directed Acyclic Graph) and executes only when an action is called.

This helps Spark optimize the execution plan before running it.

5. What is SparkSession?

SparkSession is the entry point for working with DataFrames and Spark SQL.

It replaces SQLContext and HiveContext in newer Spark versions and allows unified access to Spark functionality.

6. What is a Partition in Spark?

Partition is the smallest unit of parallelism in Spark. Each partition is processed by one task.

Proper partitioning improves performance and avoids data skew issues in distributed systems.

7. What is the difference between map() and flatMap()?

map() returns one output element for each input element.

flatMap() can return multiple elements for one input element, and it flattens the result.

Example:

rdd.flatMap(lambda x: x.split(" ")) 

8. What is cache() and persist()?

cache() stores DataFrame in memory for faster reuse.

persist() allows storing in memory, disk, or both, with different storage levels.

Used when the same DataFrame is reused multiple times in a pipeline.

9. What is the difference between narrow and wide transformations?

Narrow transformation: Data movement happens within a partition (e.g., map).

Wide transformation: Data shuffle occurs across partitions (e.g., groupBy, join). Wide transformations are expensive due to shuffle.

10. What is shuffle in Spark?

Shuffle is the process of redistributing data across partitions. It occurs during join, groupBy, reduceByKey operations.

Shuffle is expensive because it involves disk I/O and network transfer.

11. What is schema in DataFrame?

Schema defines the structure of DataFrame including column names, data types, and nullability.

Defining schema manually improves performance compared to inferSchema.

12. How to handle null values in PySpark?

Use:

fillna() to replace null values

dropna() to remove rows with nulls

isNull() to filter null rows

Handling nulls correctly is common in ETL interviews.

13. What is the difference between groupBy() and reduceByKey()?

reduceByKey() works on RDD and reduces data before shuffle (more optimized).

groupBy() on DataFrame groups data but may cause heavy shuffling if not optimized.

14. How to read a CSV file in PySpark?

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

In production systems, defining schema explicitly is recommended.

15. What is Parquet and why is it preferred?

Parquet is a columnar storage format optimized for big data processing. It provides better compression and faster query performance compared to CSV or JSON.

Most big tech companies store data in Parquet format in data lakes.

Understanding formats like Parquet is essential in real-world data engineering systems. If you want to learn how these concepts are used in production, join our free Data Engineering Masterclass below.

Intermediate PySpark Interview Questions

These questions are commonly asked to 2-5 years experienced professionals switching to product-based companies.

16. What is Catalyst Optimizer in Spark?

Catalyst Optimizer is Spark SQL’s internal query optimizer. It analyzes logical plans and transforms them into optimized physical execution plans.

It improves performance by:

→ Reordering filters
→ Pushing predicates down
→ Optimizing joins

Most MAANG interviews expect you to know this at a high level.

17. What is Tungsten in Spark?

Tungsten is Spark’s execution engine that improves memory and CPU efficiency.

It uses:

→ Off-heap memory management
→ Binary processing
→ Cache-aware computation

This reduces garbage collection overhead and increases performance.

18. Difference Between cache() and persist()

Use persist(StorageLevel.MEMORY_AND_DISK) when data does not fit fully in memory.

19. What is Data Skew and how do you handle it?

Data skew happens when some partitions contain significantly more data than others.

It causes:

→ Slow tasks
→ Executor imbalance
→ Performance bottlenecks

Solutions:

→ Salting keys
→ Repartitioning
→ Broadcast join
→ Skew join handling in Spark 3+

20. Difference Between repartition() and coalesce()

Use coalesce() when reducing partitions.

21. What is Broadcast Join?

Broadcast join sends a small dataset to all worker nodes.

Used when:

→ One table is small
→ Avoid shuffle

Example:

from pyspark.sql.functions import broadcast

df1.join(broadcast(df2), "id")

Very common in Amazon interviews.

22. How does Spark handle fault tolerance?

Spark uses a lineage graph.

If a partition fails:

→ Spark recomputes it using transformation history
→ No need to replicate full data

This makes RDD fault tolerant.

23. What is Window Function in PySpark?

Window functions perform calculations across rows related to the current row.

Example:

from pyspark.sql.window import Window
from pyspark.sql.functions import rank
windowSpec = Window.partitionBy("dept").orderBy("salary")
df.withColumn("rank", rank().over(windowSpec))      

24. What is the difference between groupByKey() and reduceByKey() in Spark?

Both functions are used on key-value RDDs, but their internal execution and performance impact are very different.

groupByKey()

→ Collects all values for a key across partitions.
→ Performs shuffle before aggregation.
→ Transfers large amounts of data across the network.
→ No pre-aggregation at the map side.
→ Higher memory consumption.
→ Can cause OutOfMemory (OOM) issues on large datasets.
→ Slower in production workloads.

reduceByKey()

→ Performs local aggregation first (map-side reduction).
→ Sends only partially reduced data during shuffle.
→ Reduces network I/O significantly.
→ More memory efficient.
→ Much better performance for large datasets.
→ Preferred in real production systems.

Always prefer reduceByKey() over groupByKey() for large-scale data processing because it minimizes shuffle and improves performance.

25. How do you optimize a slow Spark job?

When a Spark job runs slow in production, we should follow a clear and structured approach to find the root cause instead of randomly increasing memory or resources.

Spark provides the following ways to identify and optimize performance issues:

→ Spark UI: A web-based interface that helps us identify slow stages, long-running tasks, large shuffle operations, and uneven task distribution across executors.

→ Data Skew Handling: If some tasks are taking much longer than others, it usually indicates skewed data. We can fix this by using salting techniques, repartitioning data, or using skew join optimizations.

→ Improving Parallelism: Increasing the number of partitions improves task parallelism. Too few partitions can lead to underutilized resources and slower job execution.

→ Optimizing Joins: If one dataset is small, we can use broadcast join to reduce shuffle. Reducing shuffle operations significantly improves performance.

→ Avoiding Expensive Actions: Avoid using actions like collect() on large datasets because it pulls all data to the driver and can cause memory issues.

By systematically analyzing Spark UI, shuffle behavior, partitioning strategy, and join optimizations, we can significantly improve Spark job performance in production environments.

26. What is Partition Pruning?

Partition pruning is an optimization technique where Spark reads only required partitions instead of scanning the entire dataset.

How It Works:

Data is stored partitioned by a column (e.g., date).

When filtering on that column, Spark reads only matching partitions.

Example:

If table is partitioned by date:

SELECT * FROM sales WHERE date = '2025-01-01' 

Spark reads only the 2025-01-01 folder.

Benefits:

→ Reduces disk I/O.
→ Reduces data scanned.
→ Faster query execution.
→ Lower memory consumption.

Production Importance:

In large data lakes with TBs of data, partition pruning is critical for performance.

27. What is Predicate Pushdown?

Predicate pushdown means Spark pushes filter conditions to the data source level.

Example:

df = spark.read.parquet("data").filter("age > 30") 

Only rows with age > 30 are read from disk.

Benefits:

→ Less data loaded
→ Faster execution
→ Works well with Parquet and ORC

28. Difference Between Parquet and ORC

Both are columnar formats for big data.

Parquet

Widely used

→ Good compression
→ Works across many platforms

ORC

→ Hive optimized
→ Very strong compression
→ Good for Hive-based systems

29. What are Accumulators in Spark?

Accumulators are shared variables used to collect information or metrics from executors during distributed processing.

They are mainly used for monitoring and debugging purposes in large-scale Spark jobs.

Common Use Cases:

→ Counting bad or corrupted records
→ Tracking number of processed rows
→ Monitoring errors during ETL pipelines
→ Collecting custom job-level metrics

How They Work:

→ Executors can only add/update values
→ The driver program can read the final result
→ They are write-only from executor side

Important Note:

→ Not reliable for core business logic
→ May give incorrect results if tasks are retried
→ Best used only for monitoring and debugging

In production environments, accumulators are mainly used for tracking metrics rather than controlling application logic.

30. What are UDFs and why should they be avoided sometimes?

UDFs (User Defined Functions) allow developers to apply custom transformation logic when built-in Spark functions are not sufficient.

They are useful for handling complex business rules or special data processing needs.

Why Python UDFs Can Be Problematic:

→ Break Catalyst Optimizer’s query optimization
→ Slower compared to built-in functions
→ Require serialization between JVM and Python
→ Higher CPU and memory overhead

Better Alternatives:

→ Use built-in Spark SQL functions whenever possible
→ Use Pandas UDFs (vectorized UDFs) for better performance
→ Rewrite logic using native Spark expressions

In large production systems processing big data, avoiding regular Python UDFs can significantly improve performance and scalability.

If you want structured guidance with real-world projects and MAANG-focused preparation, explore our Data Engineer Program designed for working professionals

Advanced PySpark Interview Questions

31. Explain Spark Execution Flow

In a production environment, understanding Spark’s execution flow is very important. Spark does not execute code line by line. Instead, it builds a logical execution plan and optimizes it before running.

The execution flow works like this:

→ The Driver creates a DAG (Directed Acyclic Graph)
→ DAG is divided into stages
→ Each stage contains multiple tasks
→ Tasks are sent to Executors
→ Executors process partitions in parallel

This distributed execution model allows Spark to handle massive datasets efficiently.

32. What happens during Shuffle in Spark?

Shuffle happens when data needs to be redistributed across partitions, such as during joins or groupBy operations.

Shuffle involves:

→ Writing intermediate data to disk
→ Transferring data across the network
→ Sorting and merging data

Because shuffle includes disk I/O and network transfer, it is expensive and often becomes the biggest performance bottleneck in Spark jobs.

Reducing shuffle is one of the main optimization goals in production systems.

33. How would you design a large-scale ETL pipeline in Spark?

Designing a production ETL pipeline requires more than just writing transformations.

Key considerations include:

→ Use partitioned Parquet format for storage
→ Apply predicate pushdown and partition pruning
→ Use broadcast joins where possible
→ Avoid small files problem
→ Monitor using Spark UI and metrics

A good ETL design focuses on scalability, fault tolerance, and performance optimization.

34. What is Delta Lake and why is it used?

Delta Lake is a storage layer built on top of Spark that provides reliability and advanced data management features.

It provides:

→ ACID transactions
→ Schema evolution
→ Time travel (query old versions)
→ Merge and upsert support

In modern data engineering systems, Delta Lake is widely used to make data lakes more reliable and production-ready.

35. What is Adaptive Query Execution (AQE)?

Adaptive Query Execution is a Spark feature that optimizes queries during runtime.

Instead of using a fixed execution plan, Spark can:

→ Change join strategy dynamically
→ Adjust number of partitions
→ Handle skew automatically

AQE improves performance automatically without manual tuning, especially in large distributed workloads.

36. What is the Small Files Problem?

In big data systems, writing too many small files can cause performance issues.

Problems caused by small files:

→ High metadata overhead
→ Slower read performance
→ Increased memory usage

To solve this:

→ Use coalesce() before writing
→ Compact files periodically
→ Optimize write partitioning strategy

Managing file size is very important in data lake architectures.

37. How does Spark handle Streaming Data?

Spark supports streaming through Structured Streaming, which processes data in micro-batches.

Streaming systems typically:

→ Read data from sources like Kafka
→ Process data using transformations
→ Write results to storage systems

Structured Streaming ensures fault tolerance using checkpointing and maintains exactly-once processing guarantees.

38. Difference Between Batch and Streaming Processing

Spark supports both batch and streaming workloads.

Batch Processing

→ Works on static datasets
→ Higher latency
→ Used for daily ETL jobs

Streaming Processing

→ Works on real-time data
→ Low latency
→ Used for event processing and analytics

In modern systems, companies often combine both approaches.

39. What is Checkpointing in Spark?

Checkpointing is used to save intermediate state to stable storage.

It is mainly used in:

→ Long lineage chains
→ Streaming applications
→ Fault-tolerant pipelines

Checkpointing helps prevent recomputation from the beginning if failure occurs.

40. How do you debug Spark failures in production?

Debugging Spark jobs requires structured analysis.

First, check:

→ Spark UI for failed stages
→ Executor logs for error messages
→ Memory usage and GC time

Common issues include:

→ OutOfMemory errors
→ Data skew
→ Serialization errors
→ Shuffle failures

A strong candidate explains debugging using Spark UI, logs, metrics, and optimization strategies rather than guessing configuration changes.

PySpark Interview Questions for Data Engineers (Scenario-Based)

These questions are usually asked in working professional interviews where companies test real production thinking instead of theory.

41. How would you handle a join between a 1TB dataset and a 5MB dataset?

In this scenario, the best approach is to use a broadcast join. Since one dataset is very small (5MB), Spark can broadcast it to all executors. This avoids a full shuffle of the 1TB dataset and significantly reduces network I/O. In production systems, broadcast joins are one of the most effective ways to optimize large-small table joins. However, we must ensure the small dataset truly fits in memory before broadcasting.

42. Your Spark job is failing with OutOfMemory (OOM) errors. How do you fix it?

First, check Spark UI and executor logs to identify where memory pressure is happening. OOM errors usually occur due to large shuffle operations, skewed partitions, or improper use of actions like collect(). We can fix this by increasing partitions, optimizing joins, avoiding large data movement to the driver, and properly caching only required datasets. Simply increasing memory is not a long-term solution, optimization is the key.

43. How would you design incremental data processing in Spark?

In real-world data pipelines, processing full datasets every time is inefficient. Instead, we use incremental loading strategies. This can be done using partition columns such as date or timestamp. In streaming systems, watermarking is used to handle late-arriving data. If using Delta Lake, merge and upsert operations help manage incremental updates efficiently. This approach reduces processing time and improves scalability.

44. How do you validate data quality in a Spark ETL pipeline?

Data validation is critical in production. Before writing output data, we perform checks such as row count validation, null checks, schema validation, and duplicate detection. Many companies also implement custom validation rules based on business logic. Proper logging and monitoring ensure that bad data does not silently enter production systems.

45. How do you handle late-arriving data in streaming applications?

In streaming pipelines, late data is common. Spark Structured Streaming allows handling this using watermarking. Watermarking defines how long Spark should wait for late data before finalizing results. This ensures accuracy while preventing unlimited memory growth. Handling late data correctly is a common real-world requirement in event-based systems.

Final Thoughts

PySpark interviews in 2026 are no longer about writing simple transformations. Companies now expect candidates to understand:

• Distributed systems concepts
• Shuffle and partitioning strategies
• Performance optimization
• Production debugging
• Real-world ETL architecture

If you are preparing for a Data Engineer role, focus not only on syntax but also on understanding how Spark works internally and how to solve performance issues in production environments.

At Bosscoder Academy’s Data Engineering Program, professionals are trained on real-world Spark projects, large-scale data pipelines, and MAANG interview preparation. The focus is not just on theory, but on solving practical problems that top tech companies actually test.

If your goal is to switch to a high-paying Data Engineer role in 2026, structured preparation and real production-level understanding will make the difference.

Frequently Asked Questions (FAQs)