Apache Spark Interview Questions and Answers (2025) | JaganInfo

Apache Spark is an open-source unified analytics engine designed for large-scale data processing with built-in modules for streaming, SQL, machine learning, and graph processing.

Fast in-memory computation, ease of use, support for multiple languages (Scala, Java, Python, R), advanced analytics, and compatibility with Hadoop ecosystem.

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

Spark supports Scala, Java, Python (PySpark), and R languages.

The Spark Driver is the master node that manages the Spark application, schedules tasks, and coordinates worker nodes.

Transformations are lazy operations on RDDs producing new RDDs (e.g., map, filter), while actions trigger execution and return results (e.g., count, collect).

Spark delays computation until an action is called, optimizing execution plans and reducing unnecessary computation.

Spark supports Standalone, Apache Mesos, Hadoop YARN, and Kubernetes cluster managers to allocate resources and schedule tasks.

DataFrames are distributed collections organized into named columns (similar to tables), and Datasets provide statically typed APIs with compile-time type safety, building on DataFrames.

Through lineage information of RDD transformations, Spark can recompute lost partitions upon node failure.

Shuffle is the process of redistributing data across partitions and nodes, commonly needed in wide transformations such as reduceByKey or join, which can be expensive.

Broadcast variables allow the programmer to efficiently send large read-only data to all worker nodes for use in tasks.

Accumulators are variables that workers can only add to, used to implement counters and sums across tasks.

Spark architecture includes Driver, Cluster Manager, Executors running tasks, and storage layers for efficient distributed processing.

Spark SQL is a Spark module for structured data processing, supporting SQL queries, DataFrames, and Datasets.

Catalyst is Spark SQL’s query optimizer that applies advanced techniques to optimize logical and physical query plans.

Spark supports various storage levels for caching RDDs/DataFrames including MEMORY_ONLY, MEMORY_AND_DISK, DISK_ONLY, etc.

Spark Streaming performs micro-batch based processing while Storm processes real-time data streams record by record.

Structured Streaming provides scalable and fault-tolerant stream processing based on the Spark SQL engine using high-level APIs.

Spark can run on Hadoop YARN, read data from HDFS, and replace MapReduce jobs to increase performance.

DAG Scheduler divides operators into stages with shuffle boundaries and submits them as tasks to Task Scheduler for execution.

Tungsten is Spark’s low-level execution engine optimizing CPU and memory usage with bytecode generation and cache-aware computation.

Partitioning controls how data is distributed across the cluster and directly impacts parallelism and shuffling.

Schedules tasks on executors based on cluster resources and data locality to optimize execution.

Looking at driver and executor logs, web UI, Spark History Server, and using Spark event logs.

An interactive REPL environment to write and test Spark code in Scala or Python.

Checkpointing periodically saves streaming metadata and data to reliable storage to recover from failures.

Triggers define when microbatch queries run: continuous, one-time, or fixed intervals.

Spark uses unified memory management dividing heap into execution and storage memory regions to mitigate GC overhead.

It analyzes and optimizes query plans applying rules-based and cost-based transformations for efficient execution.

Catalyst uses abstract syntax trees, rule-based and cost-based optimization, and code generation for efficient query execution.

Fault tolerance is achieved by RDD lineage graphs enabling recomputation of lost partitions in case of failures.

Tungsten improves Spark’s CPU and memory efficiency using whole-stage code generation, cache-aware computation, and off-heap memory management.

Skewed data can be handled by salting keys, using custom partitioners, broadcasting small tables, and optimizing shuffle operations.

Narrow dependencies allow pipelined execution within a stage; wide dependencies involve shuffle between stages and require data redistributions.

Backpressure mechanisms throttle data ingestion rates to prevent overload and ensure stability in streaming jobs.

Watermarks specify how late data is allowed to arrive for event-time windows, helping manage state size and correctness.

Via MLlib, Spark offers scalable machine learning algorithms and pipelines with support for feature extraction, model training, and evaluation.

Driver creates RDD DAG, DAG Scheduler splits into stages, submits tasks to Task Scheduler, which runs them in executors; results collected and returned.

Use broadcast joins, cache DataFrames, avoid shuffles, filter early, and leverage Catalyst optimizer capabilities.

Tune memory, number of executor cores, parallelism, shuffle partitions, and leverage optimized serialization.

Spark uses unified memory regions for execution and storage with dynamic adjustment to handle caching and computation efficiently.

Cluster managers like YARN, Mesos, and Kubernetes allocate resources and schedule Spark executors across cluster nodes.

It applies logical optimization rules to rearrange and simplify query plans before physical planning.

Spark checkpoints metadata and intermediate data to reliable storage to enable recovery and fault tolerance in streaming jobs.

Off-heap memory allows Spark to store data outside JVM heap reducing garbage collection overhead and improving performance.

Tasks are scheduled in stages considering data locality and available resources by DAG and Task Schedulers.

Lineage provides a deterministic fault recovery by tracking the sequence of transformations to recompute lost data partitions.

Using techniques like salting keys, broadcasting small datasets, or using skew hints to reduce hot-spotting during join operations.

Spark SQL supports in-memory processing and interactive queries with Catalyst optimizer, whereas Hive uses MapReduce with higher latency; Spark SQL integrates better with streaming and machine learning.