Top 25 Interview Questions and Answers for Apache Spark

Apache Spark is one of the most in-demand big data frameworks used for large-scale data processing, analytics, machine learning, and real-time streaming. With its in-memory computation and distributed processing capabilities, Spark has become a core skill for data engineers and analytics professionals.

This blog covers the Top 25 Apache Spark Interview Questions and Answers, starting from fundamentals and progressing to advanced concepts—perfect for technical interviews, certifications, and job preparation.

1. What is Apache Spark?

Apache Spark is an open-source distributed data processing framework designed for fast and scalable big data analytics. It processes large datasets in memory, making it significantly faster than traditional disk-based systems like Hadoop MapReduce.

Spark supports multiple workloads such as batch processing, real-time streaming, machine learning, graph processing, and SQL analytics through a unified engine.

2. What are the main features of Apache Spark?

Apache Spark offers several powerful features:

• In-memory computation for faster processing
• Distributed and fault-tolerant architecture
• Support for multiple programming languages (Scala, Python, Java, R)
• Advanced analytics using SQL, MLlib, GraphX, and Streaming
• Easy integration with Hadoop, HDFS, Hive, and cloud platforms

3. What are the core components of Apache Spark?

Apache Spark consists of the following core components:

4. What is RDD in Apache Spark?

RDD (Resilient Distributed Dataset) is the fundamental data structure in Spark. It represents an immutable collection of objects distributed across a cluster.

Key properties of RDDs:

• Fault tolerant
• Immutable
• Distributed
• Lazy evaluated

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

6. What is lazy evaluation in Spark?

Lazy evaluation means Spark does not execute transformations immediately. Instead, it builds a logical execution plan and executes it only when an action is called.

This approach improves performance by optimizing execution plans and minimizing unnecessary computations.

7. What are transformations and actions in Spark?

Transformations create new datasets from existing ones (e.g., map, filter, flatMap).
Actions trigger execution and return results (e.g., collect, count, saveAsTextFile).

8. What is SparkContext?

SparkContext is the entry point for Spark functionality. It connects the application to the Spark cluster and allows interaction with RDDs.

In modern Spark versions, SparkContext is accessed via SparkSession.

9. What is SparkSession?

SparkSession is a unified entry point introduced in Spark 2.x that replaces:

• SparkContext
• SQLContext
• HiveContext

It allows developers to work with RDDs, DataFrames, and SQL using a single object.

10. What is a Spark job?

A Spark job is created when an action is invoked on an RDD or DataFrame. Each job is divided into stages, and stages are divided into tasks executed across cluster nodes.

11. What is a Spark stage?

A stage is a set of tasks that can be executed in parallel without data shuffling. Spark creates new stages whenever a shuffle operation occurs.

12. What is a Spark task?

A Spark task is the smallest unit of execution sent to an executor. Each task processes a partition of data.

13. What is partitioning in Spark?

Partitioning is the process of dividing data into smaller chunks across nodes. Proper partitioning improves parallelism and performance by minimizing data movement.

14. What is shuffling in Spark?

Shuffling refers to redistributing data across partitions, usually during operations like groupByKey or reduceByKey. Shuffling is expensive and can significantly impact performance.

15. What is caching and persistence in Spark?

Caching stores data in memory to avoid recomputation. Persistence allows storing data using different storage levels such as memory, disk, or both.

Example storage levels:

• MEMORY_ONLY
• MEMORY_AND_DISK
• DISK_ONLY

16. What is Spark Streaming?

Spark Streaming is used for real-time data processing. It processes data in micro-batches from sources like Kafka, Flume, and sockets.

17. What is Structured Streaming?

Structured Streaming is a high-level streaming API built on Spark SQL. It treats streaming data as an unbounded table and provides better fault tolerance and ease of use compared to Spark Streaming.

18. What is MLlib in Spark?

MLlib is Spark’s machine learning library that supports algorithms for:

• Classification
• Regression
• Clustering
• Recommendation systems
• Feature extraction

19. What is GraphX?

GraphX is a graph processing framework in Spark that allows computation on graph-structured data using vertices and edges.

20. How does Spark ensure fault tolerance?

Spark uses RDD lineage to track transformations. If a node fails, Spark recomputes lost data using the lineage information instead of replicating data.

21. What is a broadcast variable?

Broadcast variables allow efficient sharing of read-only data across all worker nodes, reducing network overhead.

22. What are accumulators in Spark?

Accumulators are variables used to aggregate information across executors, commonly used for debugging and counters.

23. What is the difference between reduceByKey and groupByKey?

reduceByKey performs local aggregation before shuffling, making it more efficient.
groupByKey shuffles all data and should be avoided for large datasets.

24. How is Spark better than Hadoop MapReduce?

Spark is faster due to in-memory processing, supports iterative algorithms, provides rich APIs, and allows interactive analytics—unlike MapReduce which is disk-based and slower.

25. What are common performance tuning techniques in Spark?

Key tuning techniques include:

• Using DataFrames over RDDs
• Avoiding unnecessary shuffles
• Proper partitioning
• Caching reusable datasets
• Using broadcast joins
• Adjusting executor memory and cores

Conclusion

Apache Spark is a powerful and versatile framework that dominates modern big data processing. Understanding both core concepts and advanced internals is essential to crack Spark interviews confidently.

These Top 25 Apache Spark Interview Questions and Answers provide a solid foundation for freshers, data engineers, and experienced professionals preparing for Spark-related roles.