100+ Apache Spark Interview Questions and Answers for 2023

Top 100 Spark Interview Questions and Answers for freshers and experienced professionals to nail any big data job interview and get hired. | ProjectPro

100+ Apache Spark Interview Questions and Answers for 2023
 |  BY ProjectPro
Are you an Apache Spark developer seeking to enhance your knowledge and skillset for the big data industry? Look no further! With the increasing demand for Spark developers to process big data faster, keeping up with the superior market of midsize big data sets for real-time data processing within seconds is crucial. That's why we've collaborated with big data industry experts to curate a list of the top 100 Apache Spark Interview Questions and Answers for 2023. Our comprehensive guide covers interview questions based on Spark Ecosystem components such as Spark SQL, Spark MLlib, Spark GraphX, and Spark Streaming to help you ace your next interview. 

Snowflake Real Time Data Warehouse Project for Beginners-1

Downloadable solution code | Explanatory videos | Tech Support

Start Project

However, relying solely on interview questions is not the key to crack any Spark job interview. It's also essential to gain practical experience by working on several enterprise-grade projects based on Apache Spark. This can help you stand out, as employers always look for candidates who can demonstrate their expertise in Spark and have a track record of implementing best practices to build complex big data solutions.

To build a strong portfolio and emphasize your practical skills, you can also mention Spark Streaming projects, Spark MLlib projects, and PySpark projects in your resume. These Apache Spark projects will help you showcase skills that make you eligible to apply for Spark developer job roles.

Companies like Amazon, Shopify, Alibaba, and eBay are adopting Apache Spark for their big data deployments, and the demand for Spark developers is expected to grow exponentially. Google Trends confirm "hockey-stick-like growth" in Spark enterprise adoption and awareness among organizations across various industries. Spark is becoming popular because it handles event streaming and processes big data faster than Hadoop MapReduce.

Therefore, 2023 is the best time to hone your Apache Spark skills and pursue a fruitful career as a data analytics professional, data scientist, or big data developer.

Top Apache Spark Interview Questions and Answers for 2023 

Preparation is crucial to reduce nervousness at any big data job interview. Regardless of the big data expertise and skills one possesses, every candidate dreads the face-to-face big data job interview. Though there is no way of predicting exactly what questions will be asked in any big data or spark developer job interview- these Apache spark interview questions and answers might help you prepare for these interviews better.

Spark Architecture Interview Questions and Answers

Apache Spark Architecture Interview Questions and Answers

Spark Architecture is a widely used big data processing engine that enables fast and efficient data processing in distributed environments. The commonly asked interview questions and answers are listed below to help you prepare and confidently showcase your expertise in Spark Architecture.

Three different cluster managers are available on Apache Spark. These are:

  • Standalone Cluster Manager: The Standalone Cluster Manager is a simple cluster manager responsible for managing resources based on application requirements. The Standalone Cluster Manager is resilient in that it can handle task failures. It is designed so that it has masters and workers who are configured with a certain amount of allocated memory and CPU cores. Using this cluster manager, Spark gives resources based on the core.
  • Apache Mesos: Apache Mesos uses dynamic resource sharing and isolation to handle the workload in a distributed environment. Mesos is useful for managing and deploying applications in large-scale clusters. Apache Mesos combines existing physical resources on the nodes in a cluster into a single virtual resource. 

Apache Mesos contains three components:

  1. Mesos masters: The Mesos master is an instance of the cluster. To provide fault tolerance, a cluster will have many Mesos masters. However, only one instance of the master is considered the leading master. The Mesos master is in charge of sharing the resources between the applications.
  2. Mesos agent: The Mesos agent manages the resources on physical nodes to run the framework.
  3. Mesos frameworks: Applications that run on top of Mesos are called Mesos frameworks. A framework, in turn, comprises the scheduler, which acts as a controller, and the executor, which carries out the work to be done
  • Hadoop YARN: YARN is short for Yet Another Resource Negotiator. It is a technology that is part of the Hadoop framework, which handles resource management and scheduling of jobs. YARN allocates resources to various applications running in a Hadoop cluster and schedules jobs to be executed on multiple cluster nodes. YARN was added as one of the critical features of Hadoop 2.0. 

ProjectPro Free Projects on Big Data and Data Science

2.  Explain the critical libraries that constitute the Spark Ecosystem. 

The Spark Ecosystem comprises several critical libraries that offer various functionalities. These libraries include:

  • Spark MLib - This machine learning library is built within Spark and offers commonly used learning algorithms like clustering, regression, classification, etc. Spark MLib enables developers to integrate machine learning pipelines into Spark applications and perform various tasks like data preparation, model training, and prediction.
  • Spark Streaming - This library is designed to process real-time streaming data. Spark Streaming allows developers to process data in small batches or micro-batches, enabling real-time streaming data processing. Spark applications can handle high-volume data streams with low latency with this library.
  • Spark GraphX - This library provides a robust API for parallel graph computations. It offers basic operators like subgraph, joinVertices, aggregateMessages, etc., that help developers build graph computations on top of Spark. With GraphX, developers can quickly build complex graph-based applications, including recommendation systems, social network analysis, and fraud detection.
  • Spark SQL - This library enables developers to execute SQL-like queries on Spark data using standard visualization or BI tools. Spark SQL offers a rich set of features, including a SQL interface, DataFrame API, and support for JDBC and ODBC drivers. With Spark SQL, developers can easily integrate Spark with other data processing tools and use familiar SQL-based queries to analyze data.

Here's what valued users are saying about ProjectPro

I am the Director of Data Analytics with over 10+ years of IT experience. I have a background in SQL, Python, and Big Data working with Accenture, IBM, and Infosys. I am looking to enhance my skills in Data Engineering/Science and hoping to find real-world projects fortunately, I came across...

Ed Godalle

Director Data Analytics at EY / EY Tech

I come from a background in Marketing and Analytics and when I developed an interest in Machine Learning algorithms, I did multiple in-class courses from reputed institutions though I got good theoretical knowledge, the practical approach, real word application, and deployment knowledge were...

Ameeruddin Mohammed

ETL (Abintio) developer at IBM

Not sure what you are looking for?

View All Projects
  • Spark provides advanced analytic options like graph algorithms, machine learning, streaming data, etc.
  • It has built-in APIs in multiple languages like Java, Scala, Python, and R. 
  • It has good performance gains, as it helps run an application in the Hadoop cluster ten times faster on disk and 100 times faster in memory.

Apache Spark is primarily used for

  • Stream processing,
  • Interactive data analytics, and processing.
  • Iterative machine learning.
  • Sensor data processing

Special operations can be performed on RDDs in Spark using key/value pairs, and such RDDs are referred to as Pair RDDs. Pair RDDs allow users to access each key in parallel. They have a reduceByKey () method that collects data based on each key and a join () method that combines different RDDs, based on the elements having the same key.

It has all the basic functionalities of Spark, like - memory management, fault recovery, interacting with storage systems, scheduling tasks, etc.

Use the subtractByKey () function

Spark has a web-based user interface for monitoring the cluster in standalone mode that shows the cluster and job statistics. The log output for each job is written to the working directory of the slave nodes.

Yes, Apache Spark provides checkpointing as a mechanism to improve the fault tolerance and reliability of Spark applications. When a Spark job is checkpointed, the state of the RDDs is saved to a reliable storage system, such as Hadoop Distributed File System (HDFS), to avoid recomputation in case of job failure. Checkpointing can be used to recover RDDs more efficiently, especially when they have long lineage chains. However, it is up to the user to decide which data should be checkpointed as part of the Spark job.

Spark uses Akka basically for scheduling. All the workers request a task from the master after registering, and the master assigns the task. Here Spark uses Akka for messaging between the workers and masters.

  • Implementing single-node recovery with the local file system
  • Using StandBy Masters with Apache ZooKeeper.

Apache Spark achieves fault tolerance by using RDDs as the data storage model. RDDs maintain lineage information, which enables them to rebuild lost partitions using information from other datasets. Therefore, if a partition of an RDD is lost due to a failure, only that specific partition needs to be rebuilt using lineage information.

  • Driver- The process that runs the main () method of the program to create RDDs and perform transformations and actions on them.
  • Executor – The worker processes that run the individual tasks of a Spark job.
  • Cluster Manager- A pluggable component in Spark to launch Executors and Drivers. The cluster manager allows Spark to run on top of other external managers like Apache Mesos or YARN.

Spark is intellectual in the manner in which it operates on data. When you tell Spark to run on a given dataset, it heeds the instructions and notes it so that it remembers - but it only does something if asked for the final result. When a transformation like a map () is called on an RDD-the operation is not performed immediately. Transformations in Spark are only evaluated once you act. This helps in the optimization of the overall data processing workflow. 

Download Apache Spark Interview Questions and Answers PDF 

A worker node is a component within a cluster that is capable of executing Spark application code. It can contain multiple workers, configured using the SPARK_WORKER_INSTANCES property in the spark-env.sh file. If this property is not defined, only one worker will be launched.

Executor Memory in a Spark application refers to the amount of memory allocated to an executor process. It stores data processed during Spark tasks. It can impact application performance if set too high or too low. It can be configured using a parameter called spark.executor.memory..

The Spark engine schedules, distribute, and monitors the data application across the spark cluster.

In Spark, map() transformation is applied to each row in a dataset to return a new dataset. flatMap() transformation is also used for each dataset row, but a new flattened dataset is returned. In the case of flatMap, if a record is nested (e.g., a column that is in itself made up of a list or array), the data within that record gets extracted and is returned as a new row of the returned dataset.

  • Both map() and flatMap() transformations are narrow, meaning they do not result in the shuffling of data in Spark.
  • flatMap() is a one-to-many transformation function that returns more rows than the current DataFrame. Map() returns the same number of records as in the input DataFrame.
  • flatMap() can give a result that contains redundant data in some columns.
  • flatMap() can flatten a column that contains arrays or lists. It can be used to flatten any other nested collection too.

Learn the A-Z of Big Data with Hadoop with the help of industry-level end-to-end solved Hadoop projects.

Spark SQL Interview Questions and Answers

Spark SQL Interview Questions

If you're preparing for a Spark SQL interview, you must have a solid understanding of SQL concepts, Spark's data processing capabilities, and the syntax used in Spark SQL queries. Check out the list of commonly asked Spark SQL interview questions and answers below to help you prepare for your interview and demonstrate your proficiency in Spark SQL.

19.  Can spark be used to analyze and access the data stored in Cassandra databases?

Yes, it is possible to use Spark Cassandra Connector. It enables you to connect your Spark cluster to a Cassandra database, allowing efficient data transfer and analysis between the two technologies.

The catalyst framework is a new optimization framework present in Spark SQL. It allows Spark to automatically transform SQL queries by adding new optimizations to build a faster processing system.

A Parquet file is a columnar format file that helps –

  • Limit I/O operations
  • Consumes less space
  • Fetches only required columns.
  • Parquet file
  • JSON Datasets
  • Hive tables

SchemaRDD is a data structure in Apache Spark that represents a distributed collection of structured data, where each record has a well-defined schema or structure. The schema defines the data type and format of each column in the dataset.

  • Spark SQL is faster than Hive.
  • Any Hive query can quickly be executed in Spark SQL but vice-versa is not true.
  • Spark SQL is a library, whereas Hive is a framework.
  • It is not mandatory to create a metastore in Spark SQL, but it is compulsory to create a Hive metastore.
  • Spark SQL automatically infers the schema, whereas, in Hive, the schema needs to be explicitly declared.

BlinkDB is an approximate query engine built on top of Hive and Spark. Its purpose is to allow users to trade off query accuracy for a shorter response time and, in the process, enable interactive queries on the data.

In Spark SQL, Scalar functions are those functions that return a single value for each row. Scalar functions include built-in functions, including array functions and map functions. Aggregate functions return a single value for a group of rows. Some of the built-in aggregate functions include min(), max(), count(), countDistinct(), avg(). Users can also create their own scalar and aggregate functions.

Temp views in Spark SQL are tied to the Spark session that created the view and will no longer be available upon the termination of the Spark session. 

Global temp views in Spark SQL are not tied to a particular Spark session but can be shared across multiple Spark sessions. They are linked to a system database and can only be created and accessed using the qualified name "global_temp." Global temporary views remain available until the Spark session is terminated.

Spark Streaming Interview Questions and Answers 

Spark Streaming Interview Questions and Answers

During a Spark interview, employers frequently ask questions about Spark Streaming, as it is a widely used real-time streaming engine built on top of Apache Spark that facilitates the processing of continuous data streams in real-time. Here is a list of the most frequently asked interview questions on Spark Streaming: 

Spark Streaming is a real-time processing framework that allows users to process data streams in real time. It ingests data from various sources such as Kafka, Flume, and HDFS, processes the data in mini-batches, and then delivers the output to other systems such as databases or dashboards.

On the other hand, batch processing processes a large amount of data at once in a batch. It is typically used for processing historical data or offline data processing. Batch processing frameworks such as Apache Hadoop and Apache Spark batch mode process data in a distributed manner and store the results in Hadoop Distributed File System (HDFS) or other file systems. 

Sliding Window is an operation that plays an important role in managing the flow of data packets between computer networks. It allows for efficient data processing by dividing it into smaller, manageable chunks. The Spark Streaming library also uses Sliding Window by providing a way to perform computations on data within a specific time frame or window. As the window slides forward, the library combines and operates on the data to produce new results. This enables continuous processing of data streams and efficient analysis of real-time data.

Discretized Stream is a sequence of Resilient Distributed Databases representing a data stream. DStreams can be created from various sources like Apache Kafka, HDFS, and Apache Flume. DStreams have two operations –

  • Transformations that produce a new DStream.
  • Output operations that write data to an external system.

In DStreams, there are two types of transformations - stateless and stateful.

Stateless transformations refer to the processing of a batch that is independent of the output of the previous batch. Common examples of stateless transformations include operations like map(), reduceByKey(), and filter().

On the other hand, stateful transformations rely on the intermediary results of the previous batch for processing the current batch. These transformations are typically associated with sliding windows, which consider a window of data instead of individual batches.

Apache Flume, Apache Kafka, Amazon Kinesis. 

DStream.

Receivers are unique entities in Spark Streaming that consume data from various data sources and move them to Apache Spark. Receivers are usually created by streaming contexts as long-running tasks on different executors and scheduled to operate round-robin, with each receiver taking a single core.

The number of nodes can be decided by benchmarking the hardware and considering multiple factors such as optimal throughput (network speed), memory usage, the execution frameworks being used (YARN, Standalone, or Mesos), and considering the other jobs that are running within those execution frameworks along with a spark.

The transform function in spark streaming allows developers to use Apache Spark transformations on the underlying RDDs for the Stream. The map function in Hadoop is used for an element-to-element transform and can be implemented using a transform. Ideally, the map works on the elements of Dstream and transforms developers to work with RDDs of the DStream. A map is an elementary transformation, whereas a transform is an RDD transformation.

Spark Streaming supports caching via the underlying Spark engine's caching mechanism. It allows you to cache data in memory to make it faster to access and reuse in subsequent operations.

To use caching in Spark Streaming, you can call the cache() method on a DStream or RDD to cache the data in memory. When you perform operations on the cached data, Spark Streaming will use the cached data instead of recomputing it from scratch.

Spark MLib Interview Questions and Answers 

Spark MLib Interview Questions and Answers

If you're preparing for a Spark MLib interview, you must have a strong understanding of machine learning concepts, Spark's distributed computing architecture, and the usage of MLib APIs. Here is a list of frequently asked Spark MLib interview questions and answers to help you prepare and demonstrate your proficiency in Spark MLib.

Spark MLlib is a machine learning library built on Apache Spark, a distributed computing framework. It provides a rich set of tools for machine learning tasks such as regression, clustering, classification, and collaborative filtering. Its key features include scalability, distributed algorithms, and easy integration with Spark's data processing capabilities.

Spark MLlib is designed for distributed computing, which means it can handle large datasets that are too big for a single machine. Scikit-Learn on the other hand, is intended for single-machine environments and needs to be better suited for big data. TensorFlow is a deep learning library focusing on neural networks and requires specialized hardware, such as GPUs, for efficient computation. Spark MLlib supports a broader range of machine learning algorithms than TensorFlow and integrates better with Spark's distributed computing capabilities.

Spark MLlib supports various machine learning algorithms, including classification, regression, clustering, collaborative filtering, dimensionality reduction, and feature extraction. It also includes tools for evaluation, model selection, and tuning.

Supervised learning involves labeled data, and the algorithm learns to make predictions based on that labeled data. Examples of supervised learning algorithms include classification algorithms.

Unsupervised learning involves unlabeled data, and the algorithm learns to identify patterns and structures within that data. Examples of unsupervised learning algorithms include clustering algorithms.

Spark MLlib provides several methods for handling missing data, including dropping rows or columns with missing values, imputing missing values with mean or median values, and using machine learning algorithms that can handle missing data, such as decision trees and random forests.

L1 and L2 regularization are techniques for preventing overfitting in machine learning models. L1 regularization adds a penalty term proportional to the absolute value of the model coefficients, while L2 regularization adds a penalty term proportional to the square of the coefficients. L1 regularization is often used for feature selection, while L2 regularization is used for smoother models. Both L1 and L2 regularization can be implemented in Spark MLlib using the regularization parameter in the model training algorithms.

Spark MLlib handles large datasets by distributing the computation across multiple nodes in a cluster. This allows it to process data that is too big for a single machine. Some best practices for working with big data in Spark MLlib include partitioning the data for efficient processing, caching frequently used data, and using the appropriate data storage format for the application.

Spark GraphX Interview Questions and Answers

Spark GraphX Interview Questions and Answers

Employers may ask questions about GraphX during a Spark interview. It is a powerful graph processing library built on top of Apache Spark, enabling efficient processing and analysis of large-scale graphs. Check out the list of essential interview questions below. 

Spark's GraphX is a distributed graph processing framework that provides a high-level API for performing graph computation on large-scale graphs. GraphX allows users to express graph computation as a series of transformations and provides optimized graph processing algorithms for various graph computations such as PageRank and Connected Components.

Compared to other graph processing frameworks such as Apache Graph and Apache Flink, GraphX is tightly integrated with Spark and allows users to combine graph computation with other Spark features such as machine learning and streaming. GraphX provides a more concise API and better performance for iterative graph computations.

Apache Spark GraphX provides three types of operators which are:

  • Property operators: Property operators produce a new graph by modifying the vertex or edge properties using a user-defined map function. Property operators usually initialize a graph for further computation or remove unnecessary properties.
  • Structural operators: Structural operators work on creating new graphs after making structural changes to existing graphs.  
  • The reverse method returns a new graph with the edge directions reversed.
  • The subgraph operator takes vertex predicates and edge predicates as input and returns a graph containing only vertices that satisfy the vertex predicate and edges satisfying the edge predicates and then connects these edges only to vertices where the vertex predicate evaluates to "true." 
  • The mask operator is used to construct a subgraph of the vertices and edges found in the input graph.
  • The groupEdges method is used to merge parallel edges in the multigraph. Parallel edges are duplicate edges between pairs of vertices.
  • Join operators: Join operators are used to creating new graphs by adding data from external collections such as resilient distribution datasets to charts. 

Spark GraphX comes with its own set of built-in graph algorithms, which can help with graph processing and analytics tasks involving the graphs. The algorithms are available in a library package called 'org.apache.spark.graphx.lib'. These algorithms have to be called methods in the Graph class and can just be reused rather than having to write our implementation of these algorithms. Some of the algorithms provided by the GraphX library package are:

  • PageRank
  • Connected components
  • Label propagation
  • SVD++
  • Strongly connected components
  • Triangle count
  • Single-Source-Shortest-Paths
  • Community Detection

Google's search engine uses the PageRank algorithm. It is used to find the relative importance of an object within the graph dataset, and it measures the importance of various nodes within the graph. In the case of Google, the importance of a web page is determined by how many other websites refer to it.

Unlock the ProjectPro Learning Experience for FREE

Scala Spark Interview Questions and Answers

Spark scala interview questions

Scala is a programming language widely used for developing applications running on the Apache Spark platform. If you're preparing for a Spark interview, you must understand Scala programming concepts. Here is a list of the most commonly asked spark scala interview questions

Most data users know only SQL and need to improve at programming. Shark is a tool developed for people from a database background - to access Scala MLib capabilities through a Hive-like SQL interface. Shark tool helps data users run Hive on Spark - offering compatibility with Hive metastore, queries, and data.

The Spark driver is the program that controls the execution of a Spark job. It runs on the master node and coordinates the distribution of tasks across the worker nodes.

RDDs (Resilient Distributed Datasets) are a basic abstraction in Apache Spark that represent the data coming into the system in object format. RDDs are used for in-memory computations on large clusters in a fault-tolerant manner. RDDs are read-only portioned collections of records that are –

  • Immutable – RDDs cannot be altered.
  • Resilient – If a node holding the partition fails, the other node takes the data.

The RDDs in Spark depend on one or more other RDDs. The representation of dependencies between RDDs is known as the lineage graph. Lineage graph information is used to compute each RDD on demand so the lost data can be recovered using the lineage graph information whenever a part of persistent RDD is lost.

A shuffle is a stage in a Spark job where data is redistributed across the worker nodes of a cluster. It is typically used to group or aggregate data.

In local mode, Spark runs on a single machine, while in cluster mode, it runs on a distributed cluster of machines. Cluster mode is typically used for processing large datasets, while the local mode is used for testing and development.

Transformations are functions executed on demand to produce a new RDD. All transformations are followed by actions. Some examples of transformations include map, filter, and reduceByKey.

Actions are the results of RDD computations or transformations. After an action is performed, the data from RDD moves back to the local machine. Some examples of actions include reduce, collect, first, and take.

groupByKey() groups the values of an RDD by key, while reduceByKey() groups the values of an RDD by key and applies a reduce function to each group. reduceByKey() is more efficient than groupByKey() for large datasets.

A DataFrame in Spark is a distributed set of data that is arranged into columns with specific names. It shares many similarities with a relational database table but has been optimized for distributed computing environments. 

A DataFrameWriter is a class in Spark that allows users to write the contents of a DataFrame to a data source, such as a file or a database. It provides options for controlling the output format and writing mode.

In Spark, a partition refers to a logical division of input data into smaller subsets or chunks that can be processed in parallel across different nodes in a cluster. The input data is divided into partitions based on a partitioning scheme, such as hash partitioning or range partitioning, which determines how the data is distributed across the nodes.

Each partition is a data collection processed independently by a task or thread on a worker node. By dividing the input data into partitions, Spark can perform parallel processing and distribute the workload across the cluster, leading to faster and more efficient processing of large datasets.

Repartition () shuffles the data of an RDD. It evenly redistributes it across a specified number of partitions, while coalesce() reduces the number of partitions of an RDD without shuffling the data. Coalesce () is more efficient than repartition() for reducing the number of partitions.

Access to a curated library of 250+ end-to-end industry projects with solution code, videos and tech support.

Request a demo

Hadoop Spark Interview Questions and Answers

Hadoop Spark Interview Questions and Answers

Hadoop and Spark are the most popular open-source big data processing frameworks today. Many organizations use Hadoop and Spark to perform various big data processing tasks. Thus, during a spark interview, employers might ask questions based on the integration between these two frameworks and their features and components. Check out the list of such essential questions below. 

 

Criteria

Hadoop MapReduce

Apache Spark

Memory 

Does not leverage the memory of the Hadoop cluster to the maximum.

Let's save data on memory with the use of RDD's.

Disk usage

MapReduce is disk oriented.

Spark caches data in-memory and ensures low latency.

Processing

Only batch processing is supported

Supports real-time processing through spark streaming.

Installation

Is bound to Hadoop.

Is not bound to Hadoop.

Simplicity, Flexibility, and Performance are the significant advantages of using Spark over Hadoop.

  • Spark is 100 times faster than Hadoop for big data processing as it offers in-memory data storage using Resilient Distributed Databases (RDD).
  • Spark is easier to program as it comes with an interactive mode.
  • It provides complete recovery using a lineage graph whenever something goes wrong.

Refer to Spark vs Hadoop

  • Sensor Data Processing –Apache Spark’s ‘In-memory computing’ works best here, as data is retrieved and combined from different sources.
  • Spark is preferred over Hadoop for real-time-querying of data. 
  • Stream Processing – Apache Spark is the best solution for processing logs and detecting frauds in live streams for alerts. 

To connect Spark with Mesos-

  • Configure the spark driver program to connect to Mesos. Spark binary package should be in a location accessible by Mesos. (or)
  • Install Apache Spark in the same location as that of Apache Mesos and configure the property 'spark.mesos.executor.home' to point to its installed location.

Using SIMR (Spark in MapReduce), users can run any spark job inside MapReduce without requiring any admin rights.

Yes, it is possible to run Spark and Mesos with Hadoop by launching each service on the machines. Mesos acts as a unified scheduler that assigns tasks to either Spark or Hadoop.

Spark need not be installed when running a job under YARN or Mesos because Spark can execute on top of YARN or Mesos clusters without affecting any change to the cluster.

Hadoop MapReduce requires programming in Java, which is difficult, though Pig and Hive make it considerably easier. Learning Pig and Hive syntax takes time. Spark has interactive APIs for different languages like Java, Python, or Scala and also includes Shark, i.e., Spark SQL for SQL lovers - making it comparatively easier to use than Hadoop.

Spark has its cluster management computation and mainly uses Hadoop for storage. 

The answer to this question depends on the given project scenario - as it is known that Spark uses memory instead of network and disk I/O. However, Spark uses a large amount of RAM and requires a dedicated machine to produce effective results. So the decision to use Hadoop or Spark varies dynamically with the project's requirements and the organization's budget.

Apache Spark may not scale as efficiently for compute-intensive jobs and can consume significant system resources. Additionally, the in-memory capability of Spark can sometimes pose challenges for cost-efficient big data processing. Also, Spark lacks a file management system, which means it must be integrated with other cloud-based data platforms or Apache Hadoop This can add complexity to the deployment and management of Spark applications.

No, it is unnecessary because Apache Spark runs on top of YARN. 

Starting Hadoop is not mandatory to run any spark application. As there is no separate storage in Apache Spark, it uses Hadoop HDFS, but it is not compulsory. The data can be stored in the local file system, loaded from the local file system, and processed.

Join the Big Data community of developers by gaining hands-on experience in industry-level Spark Projects.

PySpark Interview Questions and Answers

Python Spark Interview Questions and Answers”

PySpark is a Python API for Apache Spark that provides an easy-to-use interface for Python programmers to perform data processing tasks using Spark. Check out the list of important python spark interview questions below 

Scala, Java, Python, R and Clojure

def ProjectProAvg(x, y):

return (x+y)/2.0;

avg = ProjectPrordd.reduce(ProjectProAvg);

What is wrong with the above code, and how will you correct it?

The average function is neither commutative nor associative. The best way to compute the average is first to sum it and then divide it by count as shown below -

def sum(x, y):
return x+y;
total =ProjectPrordd.reduce(sum);
avg = total / ProjectPrordd.count();

However, the above code could overflow if the total becomes big. So, the best way to compute the average is to divide each number by count and then add it up as shown below -

cnt = ProjectPrordd.count();
def divideByCnt(x):
return x/cnt;
myrdd1 = ProjectPrordd.map(divideByCnt);
avg = ProjectPrordd.reduce(sum);

PySpark provides several functions to handle missing values in DataFrames, such as dropna(), fillna(), and replace(). These functions can remove, fill, or replace missing values in DataFrames.

A Shuffle is an expensive operation in PySpark that involves redistributing data across partitions, and it is required when aggregating data or joining two datasets. Shuffles can significantly impact PySpark's performance and should be avoided whenever possible.

PySpark MLlib is a PySpark library for machine learning that provides a set of distributed machine learning algorithms and utilities. It allows developers to build machine learning models at scale and can be used for various tasks, including classification, regression, clustering, and collaborative filtering.

PySpark can be integrated with other big data tools through connectors and libraries. For example, PySpark can be combined with Hadoop through the Hadoop InputFormat and OutputFormat classes or with Kafka through the Spark Streaming Kafka Integration library.

The map() transforms each element of an RDD into a single new element, while flatMap() transforms each element into multiple new elements, which are then flattened into a single RDD.

A Window function in PySpark is a function that allows operations to be performed on a subset of rows in a DataFrame, based on a specified window specification. Window functions help calculate running totals, roll averages, and other similar calculations.

Spark Optimization Interview Questions and Answers

Spark Optimization Interview Questions and Answers

Employers might consider asking questions based on Spark optimization during a Spark interview to assess a candidate's ability to improve the performance of Spark applications. Spark optimization is critical for efficiently processing large datasets, and employers may want to ensure that candidates deeply understand Spark's architecture and optimization techniques. Check out the questions below to have a strong grasp of Spark's optimization algorithms and performance-tuning strategies.

There are several techniques you can use to optimize Spark performance, such as:

  • Partitioning data properly to reduce data shuffling and network overhead
  • Caching frequently accessed data to avoid recomputing
  • Using broadcast variables to share read-only variables across the cluster efficiently
  • Tuning memory usage by adjusting Spark's memory configurations, such as executor memory, driver memory, and heap size
  • Using efficient data formats such as Parquet and ORC to reduce I/O and storage overhead
  • Leveraging Spark's built-in caching and persistence mechanisms such as memory-only, disk-only, and memory-and-disk.

Minimizing data transfers and avoiding shuffling helps write spark programs that run quickly and reliably. The various ways in which data transfers can be minimized when working with Apache Spark are:

  • Using Broadcast Variable- Broadcast variable enhances the efficiency of joins between small and large RDDs.
  • Using Accumulators – Accumulators help update the values of variables in parallel while executing.
  • The most common way is to avoid operations ByKey, repartition, or other operations that trigger shuffles.

persist () allows the user to specify the storage level, whereas cache () uses the default one.  

Apache Spark automatically persists the intermediary data from various shuffle operations, however, it is often suggested that users call persist () method on the RDD if they reuse it. Spark has various persistence levels to store the RDDs on disk or in memory, or as a combination of both with different replication levels.

The various storage/persistence levels in Spark are -

  • MEMORY_ONLY
  • MEMORY_ONLY_SER
  • MEMORY_AND_DISK
  • MEMORY_AND_DISK_SER, DISK_ONLY
  • OFF_HEAP

If the user does not explicitly specify, then the number of partitions is considered the default level of parallelism in Apache Spark.

Developers often make the mistake of-

  • Hitting the web service several times by using multiple clusters.
  • Run everything on the local node instead of distributing it.

Developers must be careful with this, as Spark uses memory for processing.

Shuffling is a mechanism by which data redistribution is performed across partitions in Spark. Spark performs shuffling to repartition the data across different executors or machines in a cluster. Shuffling, by default, does not change the number of partitions but only the content within the partitions. Shuffling is expensive and should be avoided as much as possible as it involves data being written to the disk and transferred across the network. Shuffling also involves deserialization and serialization of the data.

Shuffling is performed when a transformation requires data from other partitions. An example is to find the mean of all values in a column. In such cases, Spark will gather the necessary data from various partitions and combine it into a new partition.

Coalesce in Spark is a method to reduce the number of partitions in a DataFrame. Reduction of partitions using the repartitioning method is an expensive operation. Instead, the coalesce method can be used. Coalesce does not perform a full shuffle, and instead of creating new partitions, it shuffles the data using Hash Partitioner and adjusts the data into the existing partitions. The Coalesce method can only be used to decrease the number of partitions. Coalesce is to be ideally used in cases where one wants to store the same data in fewer files.

Spark Coding Interview Questions and Answers

spark technical interview questions

If you're preparing for a Spark technical interview or a Spark developer interview, you must be familiar with common Spark coding interview questions that assess your coding skills and ability to implement Spark applications efficiently. Here is a list of commonly asked Spark technical interview questions and their answers to help you prepare and confidently demonstrate your proficiency in Spark development during your interview.

  • The foremost step in a Spark program involves creating input RDDs from external data.
  • Use various RDD transformations like filter() to create new transformed RDD's based on the business logic.
  • persist() any intermediate RDDs which might have to be reused in the future.
  • Launch various RDD actions() like first(), and count() to begin parallel computation, which will then be optimized and executed by Spark.

These are read-only variables present in-memory cache on every machine. When working with Spark, using broadcast variables eliminates the need to ship copies of a variable for every task so that data can be processed faster. Broadcast variables help store a lookup table inside the memory, which enhances retrieval efficiency compared to an RDD lookup ().

Tachyon

One can identify the operation based on the return type -

  • The operation is an action if the return type is other than RDD.
  • The operation is transformed if the return type is the same as the RDD.

You can create an RDD (Resilient Distributed Dataset) in Spark by loading data from a file, parallelizing data collection in memory, or transforming an existing RDD. Here is an example of creating an RDD from a text file:

java

val rdd = sc.textFile("path/to/file.txt")

Spark code can be debugged using traditional debugging techniques such as print statements, logging, and breakpoints. However, since Spark code is distributed across multiple nodes, debugging can be challenging. One approach is to use the Spark web UI to monitor the progress of jobs and inspect the execution plan. Another method is to use a tool like Databricks or IntelliJ IDEA that provides interactive debugging capabilities for Spark applications.

Advanced Spark Interview Questions and Answers for Experienced Data Engineers

Spark advanced interview questions and answers

As a data engineer with experience in Spark, you might face challenging interview questions that require in-depth knowledge of the framework. Check out a set of Spark advanced interview questions and answers below that will help you prepare for your next data engineering interview. 

A sparse vector has two parallel arrays –one for indices and the other for values. These vectors are used for storing non-zero entries to save space.

Yes, Apache Spark can be run on the hardware clusters managed by Mesos.

You can trigger the clean-ups by setting the parameter ‘spark.cleaner.ttl’ or by dividing the long-running jobs into different batches and writing the intermediary results to the disk.

It enables the scalable distribution of tasks across multiple instances of Spark and allows for dynamic resource allocation between Spark and other big data frameworks.

BlinkDB is a query engine for executing interactive SQL queries on huge volumes of data and renders query results marked with meaningful error bars. BlinkDB helps users balance ‘query accuracy’ with response time. BlinkDB builds a few stratified samples of the original data and then executes the queries on the samples rather than the original data to reduce the time taken for query execution. The sizes and numbers of the stratified samples are determined by the storage availability specified when importing the data. BlinkDB consists of two main components:

  • Sample building engine: determines the stratified samples to be built based on workload history and data distribution.
  • Dynamic sample selection module: selects the correct sample files at runtime based on the time and/or accuracy requirements of the query.

No. Apache Spark works well only for simple machine-learning algorithms like clustering, regression, and classification.

Apache Spark stores data in memory for faster model building and training. Machine learning algorithms require multiple iterations to generate a resulting optimal model, and similarly, graph algorithms traverse all the nodes and edges. These low-latency workloads that need multiple iterations can lead to increased performance. Less disk access and  controlled network traffic make a huge difference when there is a lot of data to be processed.

  • Maintaining the required size of shuffle blocks.
  • Spark developers often make mistakes with managing directed acyclic graphs (DAGs.) 

Some best practices for developing Spark applications include: 

  • Designing a clear and modular application architecture
  • Writing efficient and optimized Spark code
  • Leveraging Spark's built-in APIs and libraries whenever possible
  • Properly managing Spark resources such as memory and CPU
  • Using a distributed version control system (VCS) such as Git for managing code changes and collaboration
  • Writing comprehensive tests for your Spark application to ensure correctness and reliability
  • Monitoring Spark applications in production to detect and resolve issues quickly.

Nail your Upcoming Spark Interview with ProjectPro’s Solved end-to-end Enterprise-grade projects 

Acing a Spark interview requires not only knowledge of interview questions and concepts but also practical experience in solving real-world enterprise-grade projects. While studying the interview questions and concepts is important, having practical experience with enterprise-grade projects is equally essential. These projects provide hands-on experience and demonstrate your ability to solve business problems using Spark and other big data technologies. But where can you find such projects? ProjectPro is your one-stop solution with over 270+ Solved end-to-end projects in data science and big data Working on these projects can improve your expertise and enhance your chances of acing your upcoming Spark interview. 

Access Data Science and Machine Learning Project Code Examples

FAQs on Spark Interview Questions and Answers 

What questions are asked in a Spark interview?

In a Spark interview, you can expect questions related to the basic concepts of Spark, such as RDDs (Resilient Distributed Datasets), DataFrames, and Spark SQL. Interviewers may also ask questions about Spark architecture, Spark streaming, Spark MLlib (Machine Learning Library), and Spark GraphX. Additionally, you may be asked to solve coding problems or work on real-world Spark use cases.

What are the 4 components of Spark?

The four components of Spark are:

  • Spark Core: The core engine provides basic functionality for distributed task scheduling, memory management, and fault recovery.
  • Spark SQL: A Spark module for structured data processing using SQL queries.
  • Spark Streaming: A Spark module for processing real-time streaming data.
  • Spark MLlib: A Spark module for machine learning tasks such as classification, regression, and clustering.

How to prepare for a spark interview? 

It's important to have a solid grasp of Spark's foundational ideas, including RDDs, DataFrames, and Spark SQL, to be well-prepared for a Spark interview. It's recommended to work on real-world Spark use cases and practice coding problems related to Spark. By gaining practical experience, you can demonstrate your problem-solving skills and ability to work with large-scale data processing systems. 

 

PREVIOUS

NEXT

Access Solved Big Data and Data Science Projects

About the Author

ProjectPro

ProjectPro is the only online platform designed to help professionals gain practical, hands-on experience in big data, data engineering, data science, and machine learning related technologies. Having over 270+ reusable project templates in data science and big data with step-by-step walkthroughs,

Meet The Author arrow link