Top 10 Apache Spark Interview Questions

Apache Spark is a powerful open-source unified analytics engine designed for big data processing. It has built-in modules for SQL, streaming, machine learning, and graph processing and is known for its speed, ease of use, and versatility. 

If you're preparing for a job interview that involves Apache Spark, it's essential to understand its core concepts, features, and best practices. This blog post covers some of the most commonly asked Apache Spark interview questions and answers to help you prepare effectively.

1. What is Apache Spark, and what are its main features?

Answer: Apache Spark is an open-source unified analytics engine for big data processing, known for its speed, ease of use, and versatility. It provides APIs for Java, Scala, Python, and R and supports various big data operations, including batch processing, interactive querying, real-time stream processing, machine learning, and graph processing.

Main Features:

  • Speed: In-memory computing capabilities make Spark up to 100 times faster than Hadoop MapReduce for certain applications.
  • Ease of Use: Provides high-level APIs in Java, Scala, Python, and R and includes an interactive shell for each language.
  • Advanced Analytics: Supports complex analytics like machine learning, graph processing, and stream processing.
  • Unified Engine: A single-engine for different workloads, including batch processing, interactive querying, real-time stream processing, and machine learning.

2. Explain the core components of Apache Spark.

Answer: The core components of Apache Spark include:

  • Spark Core: The foundation of the Spark platform, responsible for basic I/O functionalities, job scheduling, and task dispatching.
  • Spark SQL: Module for structured data processing, providing DataFrames and an SQL interface.
  • Spark Streaming: Module for real-time stream processing.
  • MLlib: Scalable machine learning library.
  • GraphX: API for graph processing and graph-parallel computation.

3. What is an RDD in Spark, and what are its characteristics?

Answer: RDD (Resilient Distributed Dataset) is the fundamental data structure in Spark, representing an immutable distributed collection of objects that can be processed in parallel.


  • Immutability: Once created, RDDs cannot be changed; transformations create new RDDs.
  • Partitioned: RDDs are divided into partitions, which can be processed in parallel across the cluster.
  • Fault-Tolerant: RDDs can recover lost data using lineage information.
  • Lazy Evaluation: Transformations on RDDs are not executed immediately but are recorded as lineage information until an action is called.

4. What is the difference between transformations and actions in Spark?

Answer: In Spark, transformations and actions are two types of operations performed on RDDs.


  • Definition: Operations that create a new RDD from an existing RDD.
  • Lazy Evaluation: Transformations are not executed immediately; they are recorded as a lineage graph.
  • Examples: map(), filter(), flatMap(), reduceByKey(), groupByKey().


  • Definition: Operations that trigger the execution of transformations to return a result or write to external storage.
  • Immediate Execution: Actions trigger the execution of the recorded transformations.
  • Examples: collect(), count(), first(), take(), saveAsTextFile(), reduce().

5. How does Spark handle fault tolerance?

Answer: Spark handles fault tolerance using RDD lineage. Each RDD maintains a lineage graph of transformations that were used to build it. If a partition of an RDD is lost due to node failure, Spark can recompute that partition using the lineage information from the original data source or previous RDDs. Additionally, Spark can use checkpointing to save RDDs to stable storage, reducing the recomputation cost in complex workflows.

6. What is the difference between Spark SQL and DataFrame API?

Answer: Both Spark SQL and the DataFrame API are used for structured data processing, but they differ in their interfaces and usage.

Spark SQL:

  • Interface: SQL-like query language.
  • Usage: Allows querying data using SQL syntax.
  • Example:
    val df ="data.json")
    val result = spark.sql("SELECT * FROM data WHERE age > 30")

DataFrame API:

  • Interface: High-level API similar to data frames in R and Python (Pandas).
  • Usage: Provides a more programmatic approach to data manipulation.
  • Example:
    val df ="data.json")
    val result = df.filter($"age" > 30)

7. What is Spark Streaming, and how does it work?

Answer: Spark Streaming is a Spark module for real-time stream processing. It processes live data streams and performs computations similar to batch processing. Spark Streaming ingests data in mini-batches, processes each batch to produce a result, and updates the result.

How It Works:

  • Data Ingestion: Spark Streaming receives data from various sources like Kafka, Flume, HDFS, or TCP sockets.
  • Mini-Batch Processing: Data is divided into mini-batches, which are processed using Spark's batch processing capabilities.
  • Output Operations: Results are written to external systems like databases, file systems, or dashboards.


import org.apache.spark.streaming._
import org.apache.spark.streaming.kafka._

val conf = new SparkConf().setAppName("SparkStreamingExample")
val ssc = new StreamingContext(conf, Seconds(10))
val kafkaStream = KafkaUtils.createStream(ssc, "localhost:2181", "group", Map("topic" -> 1))


8. What is the Catalyst optimizer in Spark SQL?

Answer: The Catalyst optimizer is a query optimization framework in Spark SQL. It optimizes SQL queries and DataFrame/Dataset operations to improve performance. Catalyst uses rule-based and cost-based optimization techniques to transform logical plans into optimized physical plans.


  • Rule-Based Optimization: Applies a series of transformation rules to optimize the query plan.
  • Cost-Based Optimization: Uses statistics and cost models to choose the most efficient query execution plan.
  • Extensible: Allows users to add custom optimization rules.

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

Answer: Both persist() and cache() are used to store RDDs in memory for faster access, but they have different flexibility in storage levels.


  • Default Storage Level: Stores RDD in memory only (MEMORY_ONLY).
  • Example:
    val rdd = spark.textFile("data.txt")


  • Custom Storage Levels: Allows specifying different storage levels (e.g., MEMORY_ONLY, MEMORY_AND_DISK, DISK_ONLY).
  • Example:
    val rdd = spark.textFile("data.txt")

10. How do you optimize Spark jobs?

Answer: To optimize Spark jobs, consider the following best practices:

  • Data Partitioning: Ensure data is partitioned appropriately to avoid data skew and improve parallelism.
  • Avoid Shuffling: Minimize shuffling operations (e.g., groupByKey()) by using more efficient transformations like reduceByKey().
  • Broadcast Variables: Use broadcast variables for read-only data that needs to be shared across tasks.
  • Memory Management: Tune Spark memory configuration to allocate sufficient memory for execution and storage.
  • Caching and Persistence: Cache and persist intermediate RDDs to avoid recomputation.
  • Use DataFrames/Datasets: Prefer DataFrames/Datasets over RDDs for better optimization through Catalyst.
  • Use coalesce() and repartition(): Adjust the number of partitions using coalesce() and repartition() to balance the workload.
  • Resource Allocation: Allocate appropriate resources (e.g., executors, cores, memory) based on the job requirements.


Apache Spark is a powerful and versatile big data processing engine that offers numerous features for batch processing, real-time stream processing, machine learning, and graph processing. Understanding its core concepts, features, and best practices is crucial for any developer working with big data. This blog post covered some of the most commonly asked Apache Spark interview questions, helping you prepare effectively for your next interview. By mastering these concepts, you will be well-equipped to tackle any Spark-related challenges you may encounter.