Top 30+ Spark Interview Questions and Answers

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Apache Spark is an open-source data processing engine built for speed and ease of use. It was developed in 2009 by Matei Zaharia at UC Berkeley’s AMPLab and later donated to the Apache Software Foundation. Spark gained popularity for its fast in-memory computing – making it ideal for big data tasks. If you are preparing for a data engineering or analytics role, Spark often comes up in interviews. That’s why we have compiled 30+ of the most asked Spark interview questions and answers in one place.

Fun Fact – Apache Spark can process data up to 100x faster than Hadoop MapReduce when using in-memory computing.

Note – We have categorized the interview questions on Spark into basic-level, intermediate-level, advanced-level, coding-based, and company-specific sections for easy preparation.

Basic Level Spark Interview Questions

These Spark interview questions and answers cover the core concepts every beginner should know before attending an interview.

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

Apache Spark is an open-source big data engine built for fast, in-memory processing. Unlike Hadoop MapReduce, which writes intermediate results to disk, Spark keeps data in memory. This speeds up processing, especially for iterative workloads like machine learning.

2. Explain the role of RDDs in Spark.

RDDs (Resilient Distributed Datasets) are the basic data structure in Spark. They represent a distributed collection of objects that can be processed in parallel across a cluster. RDDs are fault-tolerant and support in-memory computation.

3. What are transformations and actions in Spark?

Transformations (like map or filter) define operations on RDDs and return a new RDD. Actions (like collect or count) trigger execution and return results to the driver or external storage.

4. How does lazy evaluation work in Spark?

Spark doesn’t run transformations immediately. It builds a logical plan and waits until an action is called. This allows it to optimize the overall workflow.

5. What are the different cluster managers Spark supports?

Spark supports four cluster managers: Standalone, YARN (Hadoop), Apache Mesos, and Kubernetes. Kubernetes is now widely adopted for Spark jobs.

6. Can you list the main components of the Spark ecosystem?

The core components include Spark Core, Spark SQL, Spark Streaming, MLlib (machine learning), and GraphX (graph processing).

7. What is the difference between SparkContext and SparkSession?

SparkContext was used to initialize Spark in older versions. SparkSession, introduced in Spark 2.0, combines SQLContext and HiveContext and is now the standard entry point for working with Spark.

Intermediate Level Interview Questions on Spark

Here are the common intermediate-level interview questions on Apache Spark to help you understand slightly advanced concepts.

8. How does Spark handle data partitioning?

Spark divides data into partitions, which are processed in parallel across nodes. By default, it uses hash partitioning, but custom partitioning can be applied using functions like partitionBy() in key-value RDDs or DataFrames.

9. What is the difference between persist() and cache()?

Both store data in memory. cache() uses default memory storage. persist() lets you choose the storage level—memory, disk, or both. Use persist() when you need more control over storage behavior.

10. Explain how broadcast variables work in Spark.

Broadcast variables send a read-only copy of data to all worker nodes. This is useful when you have a small lookup table that needs to be used across multiple tasks without repeatedly shipping it.

11. When would you use RDDs over DataFrames?

I prefer RDDs when I need fine-grained control over data or want to work with unstructured or complex data types that don’t fit well into a tabular format. I use them when I need custom logic that DataFrames don’t support directly.

12. How does Spark handle schema inference in DataFrames?

Spark can automatically infer schema when reading structured data like JSON or CSV. It samples records to detect field types. For better performance or accuracy, users can also define the schema manually.

13. What are accumulators in Spark and how are they used?

Accumulators are variables used for counting or summing across executors. They’re mainly used for tracking metrics like error counts or processed records but don’t affect program logic.

14. What causes a stage to be created in Spark?

A stage is created when there’s a wide transformation like groupByKey or reduceByKey that involves a shuffle between partitions.

Advanced Level Spark Interview Questions for Experienced

These Apache Spark interview questions for experienced professionals focus on performance tuning, optimization, and complex use cases.

15. How would you optimize a Spark job running slowly due to shuffling?

I start by reviewing the query plan using .explain(). I try to reduce the number of shuffles by filtering early, using broadcast joins when one table is small, and avoiding wide transformations unless necessary. Repartitioning smartly can also help.

16. What is a wide transformation and how does it affect performance?

A wide transformation, like groupByKey or join, requires data to be shuffled across the network. This involves disk I/O and network latency, which slows down the job and increases resource usage.

17. How does Spark handle skewed data during joins?

Spark can struggle with data skew where one key has too many records. To deal with this, I use salting techniques, broadcast joins (if one side is small), or custom partitioners to distribute the load more evenly.

18. Can you explain Spark’s Catalyst Optimizer?

Catalyst is Spark’s query optimization engine. It builds a logical plan, applies rule-based optimizations, and generates a physical plan. It automatically reorders operations, pushes filters down, and simplifies expressions to speed up execution.

19. How do Tungsten and whole-stage code generation improve performance?

Tungsten improves memory management using off-heap storage and binary processing. Whole-stage code generation compiles parts of the execution plan into Java bytecode. This reduces overhead by minimizing function calls and object creation.

20. What strategies can you use to reduce data shuffling?

Avoid wide transformations if possible. Use reduceByKey instead of groupByKey. Apply filters early. Use map-side reductions. Broadcast small datasets. Partition data smartly based on usage patterns.

Spark Coding Interview Questions

Here are some important Spark programming interview questions focusing on practical coding tasks.

21. Write PySpark code to count word frequency in a text file.

rdd = sc.textFile(“file.txt”)

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

word_counts = words.map(lambda word: (word, 1)).reduceByKey(lambda a, b: a + b)

word_counts.collect()

22. How do you join two DataFrames in Spark using PySpark?

df1 = spark.read.csv(“file1.csv”, header=True)

df2 = spark.read.csv(“file2.csv”, header=True)

joined_df = df1.join(df2, df1[“id”] == df2[“id”], “inner”)

joined_df.show()

23. Write a Spark job to remove duplicates from a dataset.

df = spark.read.csv(“data.csv”, header=True)

unique_df = df.dropDuplicates()

unique_df.show()

24. How do you filter rows in a DataFrame based on a condition?

df = spark.read.csv(“people.csv”, header=True)

adults = df.filter(df[“age”] >= 18)

adults.show()

25. Write code to read a JSON file and display its schema.

df = spark.read.json(“data.json”)

df.printSchema()

Note – Spark code interview questions often include data transformations, RDD operations, DataFrame queries, and performance optimization techniques.

Other Important Spark Interview Questions

This section includes additional interview questions on Apache Spark that are commonly asked across various roles and industries.

Hadoop Spark Interview Questions

Here are some of the most asked Hadoop Spark interview questions that test your knowledge of both ecosystems and their integration.

1. What are the key differences between Spark and Hadoop MapReduce?
2. How does Spark use HDFS for data storage?
3. How does Spark complement the Hadoop ecosystem?
4. Why is Spark better than Hadoop for iterative processing?

Spark Scala Interview Questions

These are some commonly asked Apache Spark Scala interview questions to test your understanding of Spark’s core API in Scala.

1. What are the advantages of using Scala with Spark?
2. How do you define an RDD in Scala?
3. What is the role of case classes in Spark Scala apps?
4. How do you create and use DataFrames in Scala?

Note – Spark and Scala interview questions include topics like RDDs, DataFrames, Spark transformations, lazy evaluation, and functional programming concepts in Scala.

Spark SQL Interview Questions

1. What is Spark SQL and how is it different from Hive?
2. How do you register a DataFrame as a temporary SQL table?
3. How do you run SQL queries on structured data?
4. What is the use of the ‘explain’ function in Spark SQL?

Spark Architect Interview Questions

1. How do you choose between RDDs, DataFrames, and Datasets?
2. What factors affect Spark job performance at scale?
3. How would you design a fault-tolerant Spark pipeline?
4. What are common Spark architecture patterns for batch processing?

Spark Streaming Interview Questions

These are some important interview questions on Spark Streaming.

1. What is the difference between Spark Streaming and Structured Streaming?
2. How does Spark handle stateful streaming operations?
3. How do you handle late data in streaming?
4. What is watermarking in Structured Streaming?

Spark Optimization Techniques Interview Questions 

1. What is predicate pushdown and how does Spark use it?
2. How can you reduce the number of shuffles in Spark?
3. What is the role of coalesce and repartition?
4. What is whole-stage code generation?

Databricks PySpark Interview Questions

1. How is PySpark on Databricks different from local use?
2. How do you manage notebooks and versions in Databricks?
3. What are widgets in Databricks notebooks?
4. How does Delta Lake improve reliability in Databricks?

Apache Flink Interview Questions

1. How does Apache Flink differ from Spark in streaming?
2. What is Flink’s checkpointing mechanism?
3. What are stateful operators in Flink?
4. When is Flink preferred over Spark?

Also Read - Top 15+ PySpark Interview Questions and Answers (2025)

Company-Specific Spark Interview Questions

Here are company-specific interview questions on Spark designed to reflect real questions asked by top tech firms.

Accenture Apache Spark Interview Questions

These are the commonly asked Accenture Spark interview questions.  

1. Describe a scenario where Spark helped process large datasets.
2. How do you handle schema evolution in Spark jobs?
3. How do you manage project dependencies for Spark in production?
4. What tuning techniques have you applied in your Spark jobs?

Amazon Spark Interview Questions

1. How do you use Spark on AWS EMR?
2. What are the benefits of using S3 with Spark?
3. How would you optimize a Spark job for cost on AWS?
4. How does Amazon Glue compare to Spark?

Cognizant Spark Interview Questions 

1. What data sources have you connected with Spark?
2. How do you process large files using Spark?
3. How do you troubleshoot failed Spark jobs?
4. What role does Spark play in data lake architectures?

Deloitte Spark Interview Questions 

1. How have you used Spark for data transformation?
2. How do you build reusable components in Spark projects?
3. What’s your approach to testing Spark code?
4. What tools do you use to monitor Spark performance?

Infosys Spark Interview Questions

1. How did you migrate from traditional ETL to Spark?
2. How do you integrate Spark with relational databases?
3. How do you maintain and document Spark code?
4. What’s your experience using Spark with Hive?

Tips to Prepare for Your Spark Interview

Here are some great tips to help you prepare for your Spark interview. 

• Revise RDDs, DataFrames, and Spark SQL differences
• Practice PySpark coding for basic data tasks
• Read Spark job logs to understand failures
• Know when to use broadcast joins or caching
• Review questions on optimization and partitioning
• Practice with real-world datasets if possible
• Stay updated with Spark 3.x and Kubernetes support

Wrapping Up

These 30+ Spark interview questions cover the key topics interviewers often ask in real technical rounds. Going through them gives you a clear understanding of how Spark works in practical scenarios and what kind of questions to expect.

Looking for Spark roles? Hirist is a dedicated tech job portal where top Spark job openings across India are updated regularly.

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