Top 127 Best Spark Interview Questions 2023 [Updated]

There is no doubt that IT sectors are growing day by day and so does the software jobs. And more and more students are taking this field in the hope to get the job as well. If you too are one of them who is looking at the spark jobs then you are not alone. There are a lot of things that you would need to get the job. Apart from the academic qualifications, you would need to clear the interview as well to get the job.

And the interview is one of the hardest steps to get any job and especially the spark jobs. Things might be a little more challenging if you are taking the interview for the first time. You might not know what kind of questions asked and how you should prepare yourself. Well, we are here to exactly help you with that.

Here we have collected a bunch of commonly asked spark interview questions that you should prepare. These questions would certainly help you to ace the interview. So let’s not waste anymore of your time and introduce you to the best spark interview questions that you might be asked in your forthcoming interview.

also check- nursing interview questions / teacher interview questions

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Spark Interview Questions

What is Shark?

Most of the data users know only SQL and are not good at programming. Shark is a tool, developed for people who are from a database background – to access Scala MLib capabilities through Hive like SQL interface. Shark tool helps data users run Hive on Spark – offering compatibility with Hive metastore, queries and data.

List some use cases where Spark outperforms Hadoop in processing.

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 – For processing logs and detecting frauds in live streams for alerts, Apache Spark is the best solution.

What is a Sparse Vector?

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.

What is RDD?

RDDs (Resilient Distributed Datasets) are 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, collection of records, that are –

Immutable – RDDs cannot be altered.

Resilient – If a node holding the partition fails the other node takes the data.

Explain about transformations and actions in the context of RDDs.

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.

What are the languages supported by Apache Spark for developing big data applications?

Scala, Java, Python, R and Clojure

Can you use Spark to access and analyse data stored in Cassandra databases?

Yes, it is possible if you use Spark Cassandra Connector.

Is it possible to run Apache Spark on Apache Mesos?

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

Is it possible to run Spark and Mesos along with Hadoop?

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

What is lineage graph?

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

How can you trigger automatic clean-ups in Spark to handle accumulated metadata?

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.

What are the benefits of using Spark with Apache Mesos?

It renders scalable partitioning among various Spark instances and dynamic partitioning between Spark and other big data frameworks.

What is Catalyst framework?

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.

Spark Interview Questions for 3 years Experience

What are the key features of Apache Spark?

Here is a list of the key features of Apache Spark:

Hadoop Integration

Lazy Evaluation

Machine Learning

Multiple Format Support

Polyglot

Real-Time Computation

Speed

What are the components of Spark Ecosystem?

Here are the core components of the Spark ecosystem:

Spark Core: a Base motor for vast scale parallel and appropriated information preparing

Spark Streaming: Used for preparing ongoing gushing information

Spark SQL: Integrates social preparing with Spark’s practical programming API

GraphX: Graphs and diagram parallel calculation

MLlib: Performs machine learning in Apache Spark

– Spark interview questions and answers for 3 years experience

What are the languages supported by Apache Spark and which is the most popular one?

Apache Spark supports the accompanying four languages: Scala, Java, Python and R. Among these languages, Scala and Python have intuitive shells for Spark. The Scala shell can be gotten to through ./canister/start shell and the Python shell through ./receptacle/pyspark. Scala is the most utilized among them since Spark is composed in Scala and it is the most prominently utilized for Spark.

What are the multiple data sources supported by Spark SQL?

Apache Spark SQL is a popular ecosystem or interfaces to work with structured or semi-structured data. The multiple data sources supported by Spark SQL includethe text file, JSON file, Parquet file etc.

– Spark interview questions and answers for 3 years experience

How is machine learning implemented in Spark?

MLlib is a versatile machine learning library given by Spark. It goes for making machine adapting simple and versatile with normal learning calculations and utilize cases like grouping, relapse separating, dimensional decrease, and alike.

What is YARN?

Like Hadoop, YARN is one of the key highlights in Spark, giving a focal and asset administration stage to convey versatile activities over the group. YARN is an appropriated compartment supervisor, as Mesos for instance, though Spark is an information preparing instrument. Spark can keep running on YARN, a similar way Hadoop Map Reduce can keep running on YARN. Running Spark on YARN requires a parallel dissemination of Spark as based on YARN support.

Does Spark SQL help in big data analytics through external tools too?

Yes, Spark SQL helps in big data analytics through external tools too. Let us see how it is done actually.

It access data using SQL statements in both ways either data is stored inside the Spark program or data needs to access through external tools that are connected to Spark SQL through database connectors like JDBC or ODBC.

It provides rich integration between a database and regular coding with RDDs and SQL tables. It is also able to expose custom SQL functions as needed.

– Spark interview questions and answers for 3 years experience

How is Spark SQL superior from others – HQL and SQL?

Spark SQL is advance database component able to support multiple database tools without changing their syntax. This is the way how Spark SQL accommodates both HQL and SQL superiorly.

Do real-time data processing is possible with Spark SQL?

Real-time data processing is not possible directly but obviously, we can make it happen by registering existing RDD as a SQL table and trigger the SQL queries on priority.

Explain the concept of Resilient Distributed Dataset (RDD).

RDD is an abbreviation for Resilient Distribution Datasets. An RDD is a blame tolerant accumulation of operational components that keep running in parallel. The divided information in RDD is permanent and distributed in nature. There are fundamentally two sorts of RDD:

Parallelized Collections: Here, the current RDDs run parallel with each other.

Hadoop Datasets:

They perform works on each document record in HDFS or other stockpiling frameworks.

RDDs are essential parts of information that are put away in the memory circulated crosswise over numerous hubs. RDDs are sluggishly assessed in Spark. This apathetic assessment is the thing that adds to Spark’s speed.

spark sql interview questions for experienced

Q. What is Catalyst framework?

Answer: 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.

Q. Why is BlinkDB used?

Answer: 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.

– Spark interview questions and answers for 3 years experience

Q. How can you compare Hadoop and Spark in terms of ease of use?

Answer: 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.

Q. What are the various data sources available in SparkSQL?

Answer: Parquet file

JSON Datasets

Hive tables

SparkSQL is a Spark component that supports querying data either via SQL or via the Hive Query Language. It originated as the Apache Hive port to run on top of Spark (in place of MapReduce) and is now integrated with the Spark stack. In addition to providing support for various data sources, it makes it possible to weave SQL queries with code transformations which results in a very powerful tool. Below is an example of a Hive compatible query:

– Spark interview questions and answers for 3 years experience

Q. Name a few commonly used Spark Ecosystems.

Answer: Spark SQL (Shark)

Spark Streaming

GraphX

MLlib

SparkR

Q. What is “Spark SQL”?

Answer: Spark SQL is a Spark interface to work with structured as well as semi-structured data. It has the capability to load data from multiple structured sources like “text files”, JSON files, Parquet files, among others. Spark SQL provides a special type of RDD called SchemaRDD. These are row objects, where each object represents a record.

Q. Can we do real-time processing using Spark SQL?

Answer: Not directly but we can register an existing RDD as a SQL table and trigger SQL queries on top of that.

Q. Explain about the major libraries that constitute the Spark Ecosystem

Answer: Spark MLib- Machine learning library in Spark for commonly used learning algorithms like clustering, regression, classification, etc.

Spark Streaming – This library is used to process real time streaming data.

Spark GraphX – Spark API for graph parallel computations with basic operators like join Vertices, subgraph, aggregate Messages, etc.

Spark SQL – Helps execute SQL like queries on Spark data using standard visualization or BI tools.

– Spark interview questions and answers for 3 years experience

Q. What is Spark SQL?

Answer: SQL Spark, better known as Shark is a novel module introduced in Spark to work with structured data and perform structured data processing. Through this module, Spark executes relational SQL queries on the data. The core of the component supports an altogether different RDD called SchemaRDD, composed of rows objects and schema objects defining data type of each column in the row. It is similar to a table in relational database.

Q. What is a Parquet file?

Answer: Parquet is a columnar format file supported by many other data processing systems. Spark SQL performs both read and write operations with Parquet file and consider it be one of the best big data analytics format so far.

Q. List the functions of Spark SQL.

Answer: Spark SQL is capable of:

Loading data from a variety of structured sources

Querying data using SQL statements, both inside a Spark program and from external tools that connect to Spark SQL through standard database connectors (JDBC/ODBC). For instance, using business intelligence tools like Tableau

Providing rich integration between SQL and regular Python/Java/Scala code, including the ability to join RDDs and SQL tables, expose custom functions in SQL, and more

Apache spark scala interview questions: Shyam Mallesh

Q. Who designed Scala? Which is the latest version?

At the time of writing, Scala 2.12.6 is the latest version. The interviewer may ask you this to find out whether you keep yourself updated. Martin Odersky, a German computer scientist, began designing it in 2001 at EPFL, Switzerland.

Q. What are the advantages of Scala?

Among various other benefits of the language, here are a few:

It is highly scalable

It is highly testable

It is highly maintainable and productive

It facilitates concurrent programming

It is both object-oriented and functional

It has no boilerplate code

Singleton objects are a cleaner solution than static

Scala arrays use regular generics

Scala has native tuples and concise code

Q. What is ofDim in Scala?

ofDim() is a method in Scala that lets us create multidimensional arrays. Since these let us store data in more than one dimension, we can store data like in a matrix. Let’s take an example.

scala> import Array.ofDim

import Array.ofDim

scala> var a=ofDim[Int](3,3)

a: Array[Array[Int]] = Array(Array(0, 0, 0), Array(0, 0, 0), Array(0, 0, 0))

scala> var k=1

k: Int = 1

scala> for(i<-0 to 2){

    | for(j<-0 to 2){

    | a(i)(j)={i+k}

    | k+=1

    | }

    | k-=1

    | }

scala> a

res12: Array[Array[Int]] = Array(Array(1, 2, 3), Array(4, 5, 6), Array(7, 8, 9)).

Q. What do you have to say about exception propagation in Scala?

When a function experiences an exception, it looks for a handler to deal with it. When it fails to find one, it searches for one in the caller method. Failing there, it looks for yet another in the next caller in the chain. Whenever it does find a handler, it makes it catch the exception. This is exception propagation.

Q. What is a BitSet?

A bitset is a set of non-negative integers depicted as arrays. These arrays are variable in size and packed into 64-bit words. The largest number in a bitset determines its memory footprint. Let’s take an example.

scala> import scala.collection.immutable._

import scala.collection.immutable._

scala> var nums=BitSet(7,2,4,3,1)

nums: scala.collection.immutable.BitSet = BitSet(1, 2, 3, 4, 7)

scala> nums+=9 //Adding an element

scala> nums

res14: scala.collection.immutable.BitSet = BitSet(1, 2, 3, 4, 7, 9)

scala> nums-=4 //Deleting an element

scala> nums

res16: scala.collection.immutable.BitSet = BitSet(1, 2, 3, 7, 9)

scala> nums-=0 //Deleting an element that doesn’t exist

scala> nums

res18: scala.collection.immutable.BitSet = BitSet(1, 2, 3, 7, 9)

Q. What is a vector in Scala?

A vector is a general-purpose data structure that is immutable. We can use it when we want to hold a huge number of elements and want random access to them. This data structure extends the trait IndexedSeq and the abstract class AbstractSeq.

scala> import scala.collection.immutable._

import scala.collection.immutable._

scala> var v1=Vector.empty

v1: scala.collection.immutable.Vector[Nothing] = Vector()

scala> var v2=Vector(7,2,4,3,1)

v2: scala.collection.immutable.Vector[Int] = Vector(7, 2, 4, 3, 1)

scala> var v3:Vector[Int]=Vector(8,2,6,5,9)

v3: scala.collection.immutable.Vector[Int] = Vector(8, 2, 6, 5, 9)

scala> v3=v3 :+7 //Adding a new element

v3: scala.collection.immutable.Vector[Int] = Vector(8, 2, 6, 5, 9, 7)

scala> v2++v3 //Merging two vectors

res19: scala.collection.immutable.Vector[Int] = Vector(7, 2, 4, 3, 1, 8, 2, 6, 5, 9, 7)

scala> v3.reverse //Reversing a vector

res20: scala.collection.immutable.Vector[Int] = Vector(7, 9, 5, 6, 2, 8)

scala> v3.sorted //Sorting a vector

res21: scala.collection.immutable.Vector[Int] = Vector(2, 5, 6, 7, 8, 9)

In results 20 and 21, we do not assign the expression to any variable, so not that this doesn’t change the original vectors.

Cts spark interview questions

Q. What do you understand by Lazy Evaluation?

Spark is intellectual in the manner in which it operates on data. When you tell Spark to operate on a given dataset, it heeds the instructions and makes a note of it, so that it does not forget – but it does nothing, unless asked for the final result. When a transformation like map () is called on a RDD-the operation is not performed immediately. Transformations in Spark are not evaluated till you perform an action. This helps optimize the overall data processing workflow.

Q. Define a worker node?

A node that can run the Spark application code in a cluster can be called as a worker node. A worker node can have more than one worker which is configured by setting the SPARK_ WORKER_INSTANCES property in the spark-env.sh file. Only one worker is started if the SPARK_ WORKER_INSTANCES property is not defined.

– Spark interview questions and answers for 3 years experience

Q. What do you understand by SchemaRDD?

An RDD that consists of row objects (wrappers around basic string or integer arrays) with schema information about the type of data in each column.

Q. How Spark uses Akka?

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

– Spark interview questions and answers for 3 years experience

Q. How can you achieve high availability in Apache Spark?

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

Q. Hadoop uses replication to achieve fault tolerance. How is this achieved in Apache Spark?

Data storage model in Apache Spark is based on RDDs. RDDs help achieve fault tolerance through lineage. RDD always has the information on how to build from other datasets. If any partition of a RDD is lost due to failure, lineage helps build only that particular lost partition.

Q. How Spark handles monitoring and logging in Standalone mode?

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 work directory of the slave nodes.

– Spark interview questions and answers for 3 years experience

Q. Does Apache Spark provide checkpointing?

Lineage graphs are always useful to recover RDDs from a failure but this is generally time consuming if the RDDs have long lineage chains. Spark has an API for check pointing i.e. a REPLICATE flag to persist. However, the decision on which data to checkpoint – is decided by the user. Checkpoints are useful when the lineage graphs are long and have wide dependencies.

– Spark interview questions and answers for 3 years experience

Q. How can you launch Spark jobs inside Hadoop MapReduce?

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

Times Spark Questions

Question. What Is Spark Core?

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

Question. How Can You Remove The Elements With A Key Present In Any Other Rdd?

Answer : Use the subtractByKey () function

Question. What Is The Difference Between Persist() And Cache()

Answer : persist () allows the user to specify the storage level whereas cache () uses the default storage level.

Question. What Are The Various Levels Of Persistence In Apache Spark?

Answer : Apache Spark automatically persists the intermediary data from various shuffle operations, however it is often suggested that users call persist () method on the RDD in case they plan to 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

Question. How Spark Handles Monitoring And Logging In Standalone Mode?

Answer : 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 work directory of the slave nodes.

Question. How Can You Launch Spark Jobs Inside Hadoop Mapreduce?

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

Question. How Spark Uses Akka?

Answer : Spark uses Akka basically for scheduling. All the workers request for a task to master after registering. The master just assigns the task. Here Spark uses Akka for messaging between the workers and masters.

Question. How Can You Achieve High Availability In Apache Spark?

Answer : Implementing single node recovery with local file system

Using StandBy Masters with Apache ZooKeeper.

Question. Hadoop Uses Replication To Achieve Fault Tolerance. How Is This Achieved In Apache Spark?

Answer : Data storage model in Apache Spark is based on RDDs. RDDs help achieve fault tolerance through lineage. RDD always has the information on how to build from other datasets. If any partition of a RDD is lost due to failure, lineage helps build only that particular lost partition.

Conclusion –

So these are some of the most commonly asked sparked interview questions that you can expect. Apart from the technical questions, you should certainly prepare before you step into that room. These questions would help you understand how you should prepare for your interview and how you should present yourself in front of the interviewers.

Thank you for visiting our page and we wish you the best of luck for your forthcoming spark interview.