Spark is a widely used, general-purpose cluster computing framework. The open-source software provides an interface for programming a full computer cluster with implicit parallel computing and fault-tolerance capabilities. Spark's popularity has skyrocketed in recent years, and many businesses are taking advantage of its benefits by creating a plethora of job opportunities for the Spark profile. The global Big Data as a service market is expected to increase, which means that in the coming years, the demand for Big Data Engineers and Specialists will skyrocket. There are currently around 42k+ big data jobs available around the world, with the number expected to continue to rise.

Cracking the Spark with a Python interview, on the other hand, is a difficult task and takes extensive preparation. We have compiled the Top 35 Apache Spark Interview Questions and Answers 2023 to assist you.

All of these Spark Interview Questions and Answers have been compiled by top-tier experienced professionals to assist you in clearing the interview and landing your dream job as a Spark developer. So, to take your profession to the next level, use our Top 35 Spark with Python interview questions and answers segregated into the three sections as follows.

Apache Spark Interview Questions and Answers

Apache Spark Interview Questions For Beginners

1. Explain about Spark?

Apache Spark is the Spark Python API. It's used to allow Spark and Python to work together. Spark is a data processing framework based on structured and semi-structured data sets and the ability to read data from several sources in various formats. We can also use Spark to interact with RDDs (Resilient Distributed Datasets) in addition to these functionalities. The py4j library is used to implement all of these features.

2. What are the characteristics of Spark?

Following are some of the characteristics of Spark:

• Nodes are abstracted in this case, implying that it is impossible to address a single node.
• Furthermore, because the Network is abstracted, only implicit interaction is feasible.
• Furthermore, it is Map-Reduce driven, implying that the programmer needs to provide both a map and a reduce method.
• Spark is one of Spark's APIs.

3. What is meant by Apache Spark?

• Apache Spark is a free and open-source real-time processing cluster computing framework.
• It is the most active Apache project with a robust open-source community.
• Spark is a programming interface that gives implicit data parallelism and faults tolerance to entire programming clusters.
• One of the Apache Software Foundation's most successful projects is Spark.
• Spark has risen to the top of the Big Data processing leaderboard.
• Thousands of nodes are used by many enterprises to run Spark. Amazon, eBay, and Yahoo! are among the companies implementing Spark.

4. Explain the key features of Spark?

The Following are the Apache Spark features:

• Support for various languages: Spark's high-level APIs are available in Java, Scala, Python, and R. Any of these four languages can be used to write Spark code. It comes with a Scala and Python shell. In the installed directory, use./bin/spark-shell to access the Scala shell and./bin/Spark to access the Python shell.
• Speed: Spark is up to 100 times quicker for large-scale data processing than Hadoop MapReduce. Spark can accomplish this performance by partitioning data in a controlled manner. It organizes data using divisions, allowing distributed data processing to be parallelized while reducing network traffic.
• Multiple Data Formats: Spark supports various data formats, including Parquet, JSON, Hive, and Cassandra. The Data Sources API is a pluggable technique for using Spark SQL to access structured data. More than simply simple pipelines that transform data and draw it into Spark can be used as data sources.
• Lazy Evaluation: Apache Spark uses a lazy evaluation method, deferring evaluation until it is essential. This is one of the most important aspects influencing its speed. Spark adds alterations to a computation DAG, and this DAG is only run when the driver requests data.
• Real-time Computation: Spark's calculation in real-time has a low latency because of its in-memory computing. Spark is built to scale massively, and the Spark team has recorded users operating operational clusters with thousands of nodes. It also supports a variety of computational models.
• Hadoop Integration: Apache Spark integrates seamlessly with Hadoop. This is a huge win for those Big Data engineers who started their careers using Hadoop. Spark is a useful substitute for Hadoop's MapReduce capabilities, and it can run on top of an existing Hadoop cluster using the YARN resource scheduling system.
• Machine Learning: Spark's MLlib is a machine learning module that comes help when analyzing large amounts of data. It eliminates the requirement for two tools: one for processing and the other for machine learning. Spark is a powerful, unified engine for data engineers and data scientists that is both quick and simple to use.

 

5. Tell us about the pros and cons of Spark?

The following are some of the benefits of utilizing Spark:

• We may develop parallelized code in a very straightforward way with Spark.
• The nodes and networks have all been abstracted.
• All faults, including synchronization errors, are handled by Spark.
• Spark comes with a number of helpful built-in algorithms.

The following are some of the drawbacks of utilizing Spark:

• It can be challenging to articulate difficulties in MapReduce using Spark.
• Spark is inefficient when compared to other programming languages.

6. Explain RDD and tell us how to create RDDs in Apache Spark?

A Resilient Distribution Dataset, or RDD, is a fault-tolerant set of operational pieces that can execute parallel. In an RDD, any partitioned data is distributed and unchangeable.
RDDs are essentially pieces of data kept in memory dispersed across multiple nodes. These RDDs are reviewed slowly in Spark, which is one of the key reasons behind Apache Spark's faster performance. There are two types of RDDs:

1. Datasets in Hadoop - In HDFS (Hadoop Distributed File System) or other storage systems, perform functions on each file record.
2. Parallelized Collections — Existing RDDs that run in parallel.

In Apache Spark, there are two ways to make an RDD:

• In the Driver program, you can parallelize a collection. It takes advantage of the parallelize() feature of the SparkContext. Consider the following example: method val val DataArray = Array(22,24,46,81,101) DataArray = Array(22,24,46,81,101) sc.parallelize = DataRDD (DataArray)
• Loading an arbitrary dataset from an external storage structure, such as a shared file system, HBase, or an HDFS.

7. Describe Apache Spark architecture and how it runs the spark application?

• The Apache Spark application is made up of two projects: a Driver program and a Workers program.
• In the middle, a group of administrators will connect with these two bunch hubs. With the help of the Cluster Manager, Sparkle Context will stay in touch with the worker hubs.
• Spark Context has the appearance of an ace, and Spark laborers have the appearance of slaves.
• Workers house the agents who will carry out the operation. If any conditions or contentions need to be met, SparkContext will take care of them. The Spark Executors will be the focus of RDDs.
• You can also execute Spark apps locally using a string, and if you need to exploit specific conditions, you can use S3, HDFS, or another storage structure.

8. Explain about various functions of spark cores?

SparkCore is the main engine for distributed and parallel processing of large volumes of data. The Spark core's distributed execution engine provides APIs for building distributed ETL applications in Scala, Python, and Java.

The Spark Core is responsible for memory management, task monitoring, fault tolerance, storage system interactions, job scheduling, and support for all basic I/O activities. There are a number of additional libraries that work with Spark Core to enable SQL, streaming, and machine-learning applications. They have to:

• Fault recovery
• Memory management and Storage system interactions
• Job monitoring, scheduling, and distribution
• Basic I/O functions

9. Explain the benefits of spark over Mapreduce?

In comparison to MapReduce, Spark offers the following advantages:

• Spark is 10 to 100 times faster than Hadoop MapReduce because it uses in-memory computation, whereas MapReduce uses perseverance storage for all computational data requirements.
• Unlike Hadoop, Spark has built-in libraries for batch processing, streaming, machine learning, and immersive SQL queries. Hadoop, on the other hand, only works in batches.
• Spark supports caching and in-memory data storage, whereas Hadoop is mainly disk-dependent.
• On the same dataset, Spark can perform various computations. Iterative computation is referred to as Hadoop does not support it.

10. What is meant by YARN?

YARN is a fundamental element in Spark, similar to Hadoop, in that it provides a centralized resource management platform for delivering scalable operations across the cluster. For instance, like Mesos, YARN is a distributed container management, although Spark is a data management tool. Spark can operate on YARN in the same way that Hadoop Map Reduce can. Implementing Spark on YARN needs a Spark binary distribution with YARN support.

11. Is there any API available for implementing graphs in Spark?

Spark's graph and graph-parallel processing API is called GraphX. It adds a Resilient Distributed Property Graph to the Spark RDD.

A directed multi-graph with numerous edges running in parallel is the property graph. User-defined characteristics that are associated with every edge and vertex. Multiple associations between the same vertices are possible thanks to the parallel edges in this case. The Robust Distributed Property Graph, a directed multigraph with properties attached to each edge and vertex, is a high-level extension of the Spark RDD abstraction introduced by GraphX.

GraphX exposes a collection of basic operators (such as mapReduceTriplets, joinVertices, and subgraph) as well as an efficient version of the Pregel API to facilitate graph processing. GraphX also comes with a growing library of graph algorithms and architects that simplify graph analytics chores.

Scenario-Based Apache Spark Interview Questions

12. Explain how you can use Apache Spark along with Hadoop?

One of the most significant advantages of Apache Spark is its compatibility with Hadoop. The combo makes for a formidable tech duo. Using Apache Spark and Hadoop together allows you to take advantage of Spark's unrivaled processing power while also utilizing Hadoop's HDFS and YARN capabilities.

The following are some examples of how Hadoop Components can be used with Apache Spark:

• Real-time Processing: MapReduce and Spark can be used together for batch and real-time processing, with the former handling batch processing and the latter handling real-time processing.
• HDFS: Spark can run on top of HDFS to utilize distributed replicated storage.
• MapReduce: Apache Spark can collaborate with MapReduce in the same Hadoop framework or on its own as a Hadoop framework.
• YARN: YARN can execute Spark apps.

13. What is the difference between RDD, a DataSet, and a Data frame?

RDD	DataFrame	DataSet (A subset of DataFrames)
The structural square of Spark and all datasets and data frames are included within RDDs.	It makes the structure, such as lines and segments, visible. It's comparable to a database table.	It has the perfect encoding element, and, unlike metadata edges, it allows for systematized time security.
RDDs can be effectively reserved if a consistent data structure needs to be computed again.	The catalyst analyzer is used to build query strategies, which are then optimized.	If you need more data security at compile-time, or if you want typed JVM objects, Dataset is the way to go.
Low-level modifications, actions, and monitoring of a dataset can be beneficial.	Compile Time Wellbeing is one of the limits of data frames, i.e., while the structure of information is uncertain, no governance of information is available.	You can also use data points in circumstances where you want to take advantage of Catalyst enhancement or when you want to reap the benefits of Tungsten's fast code generation.
Rather than domain-specific expressions, functional programming elements are more typically employed to change data.	Additionally, if you're using Python, start with DataFrames and then go to RDDs if you need more functionality.	-

 

14. Explain about Spark Array type with an example?

Spark ArrayType is a data type for collections that implements Spark's DataType class, which is the superclass for all types. All ArrayType items should have the same item types. To create an object of an ArrayType, use the ArrayType() function. It takes valueType and one alternative parameter, valueContainsNull, which is set to True by default and determines if a value can accept null. In Spark, valueType should extend the DataType class.

import Spark.sql.types from Spark.sql.types arrayCol = ArrayType(StringType(),False), StringType, ArrayType

15. What is meant by the Spark Partition?

A huge dataset is partitioned into smaller portions using one or more partition keys in Spark. Spark develops a DataFrame in memory with a given number of segments based on specified parameters when developing a DataFrame from a file or table. Because each partition's changes are conducted in sequence, modifications on partitioned data run faster. Spark supports memory partitioning (DataFrame) and disk partitioning (File system).

16. Explain about the executor memory in Spark?

Spark executors have the same fixed number of cores and heap size as Spark apps. The heap size refers to the amount of memory used by the Spark executor, which is governed by the spark.executor.memory parameter of the -executor-memory flag. One processor is allocated to each worker node on which Spark runs. The executor memory represents the memory used by the worker node of the program.

17. List out some of the functions of SparkCore?

Spark Core is the foundation for parallel and distributed data processing on a large scale. The distributed implementation engine is at its core, and the Python, Java, and Scala APIs provide a foundation for developing distributed ETL applications. Memory management, interfacing, fault tolerance, job monitoring, and work schedule with repository systems are all functions that SparkCore does. Additionally, supplementary libraries built on top of the core enable various streaming, SQL, and machine learning activities.

• Memory management and fault recovery are two of its responsibilities.
• On a cluster, jobs are scheduled, monitored, and distributed.
• Interacting with storage systems is one of the most time-consuming aspects of the job.

18. List out a few limitations of incorporating Spark into applications?

Although Spark is a powerful data processing platform, it has some disadvantages when used in applications.

1. As it is relative to Hadoop or MapReduce, Spark uses more data storage, which could cause memory problems.
2. Because it uses "in-memory" computation, Spark might be a stumbling block for cost-effective massive data analysis.
3. Because the efficient method shuffles among multiple worker nodes related to resources available, files on the directory of the provincial filesystem must be accessible in the same place on all multiple processors while operating in cluster mode.
4. The files must be copied across all worker nodes, or a different network-mounted storage device must be deployed.

19. Explain Caching play in Spark Streaming?

The foundation of Spark Streaming is the partitioning of a data stream's payload into clusters of X seconds, known as DStreams. These DStreams enable developers to cache data in memory, which is useful if a DStream is used several times. Data can be cached using the cache() function or the persist() function with suitable persistence settings. The standard persistence level setting for input streams receiving data over networks such as Flume, Kafka, and others is designed to perform data replication on two nodes to complete fault tolerance.

var cacheDfrm = dframe.cache1 is the cache method ()
val persistDfrm = dframe.persist1(StorageLevel.MEMORY ONLY) val persistDfrm = dframe.persist1(StorageLevel.MEMORY ONLY1)
val persistDf = dframe.persist(StorageLevel1)

The following are some of the most important advantages of caching:

• Cost-effectiveness: Because Spark computations are expensive, caching facilitate data reuse, which leads to reuse computations, cutting operating costs.
• Time-saving: We can save a fortune of time by recycling computations.
• Jobs Achieved: Workforce nodes can execute/perform more operations by lowering computational execution time.

 

20. Explain about different levels of persistence that exist in Spark?

Spark preserves transitional data from assorted shuffle procedures automatically. Regardless, it is recommended that you use the RDD persist() function. RDDs can be stored on the disc, memory or both, with different levels of persistence and replication. The persistent levels obtainable in Spark are as follows:

• ONLY IN MEMORY: This is the standard persistence tier, and it saves RDDs as deserialized Java entities on the JVM. The segments are not cached if the RDDs are too long to serve in memory and therefore must be updated accordingly as required.
• MEMORY AND DISK: RDDs are stored as deserialized Java entities on the JVM. If memory is insufficient, segments that do not fit in memory will be maintained on disc, and data will be recovered as needed.
• MEMORY ONLY SER: The RDD is serialized Java Objects that are one byte per segment.
• ONLY ON DISK: RDD segments are saved only on disc.
• OFF HEAP: This level is identical to MEMORY ONLY SER, only the file is stored in off-heap memory instead of on-heap memory. Persistence levels are used via the persist() method, which has the subsequent syntax: df. continue (StorageLevel).

 

21. Explain about the checkpoint feature in Apache Spark?

Yes, checkpoints have their API in Spark. Checkpointing allows streaming apps to be more error-resistant. A checkpointing repository can be used to hold the metadata and data. In the event of a fault, the spark may recover this data and continue from where it left off.

Checkpointing can be used in Spark for the supporting data types:

• Checking the metadata: The term "metadata" refers to data about data. It's when metadata is stored in a fault-tolerant storage system like HDFS. Configurations, DStream operations, and uncompleted batches are all examples of metadata.
• Checkpoints for data: We save the RDD to safe storage because some of the stateful activities require it. In this scenario, the RDDs define the RDD for the future batch based on past clusters.

Apache Spark Interview Questions For Experienced

22. What are the broadcast variables in Spark? Why do we need them in Apache Spark?

Instead of shipping a copy of a read-only variable with tasks, the programmer can use broadcast variables to maintain a read-only variable cached on each computer. They utilized to efficiently distribute a copy of a big input dataset to each node. Spark also tries to reduce communication costs by distributing broadcast variables utilizing efficient broadcast methods.

As a result, data may be processed rapidly. When compared to an RDD lookup(), broadcast variables help to store a lookup table in memory, which improves retrieval speed.

23. Explain the profiles which you use in Spark.

Custom profiles can be created in Spark and used to develop predictive prototypes. In general, profilers are computed using each column's maximum and minimum values. It's a useful data evaluation tool for ensuring that the data is correct and suitable for future use.
For a custom profiler, the subsequent methods should be written or inherited.

• profile- it is the same as the system profile.
• add- This command allows users to put a profile to an already existing profile.
• dump- is a command that exports all of the profiles to a file.
• stats- delivers a list of the stats gathered.

 

24. Explain about the SparkConf in Spark? List a few attributes of SparkConf?

SparkConf assists in installing and configuring a Spark application, whether it is run remotely or in a cluster. In other words, it provides options for executing a Spark application. Below are some of the most important aspects of SparkConf:

• set(key, value): This attribute facilitates the setting of configuration properties.
• setSparkHome(value): You can use this functionality to define the directory in which Spark will be deployed on worker nodes.
• setAppName(value): The application's name is specified using this element.
• setMaster(value): This property can be used to set the master URL.
• get(key, defaultValue=None): This attribute helps retrieve the configuration value.

 

25. What module is used for implementing SQL in Spark?

Spark SQL is a new Spark module that combines relational processing and the functional programming API. It allows you to query data using SQL or the Hive Query Language. Spark SQL will be a smooth transfer from your previous tools, allowing you to push the boundaries of standard relational data processing for those already familiar with relational databases.

Spark SQL combines relational processing with functional programming in Spark. It also supports a variety of data sources and allows you to combine SQL queries with code transformations, resulting in a powerful tool. The four Spark SQL libraries are listed below:

• Data Source API
• DataFrame API
• Interpreter & Optimizer
• SQL Service.

26. What are Sparse Vectors? How are they different from dense vectors?

Sparse vectors are made up of two parallel arrays, one for indexing and storing values and the other for indexing and storing values. Non-zero values are stored in these vectors to conserve space.

val sparseVec: Vector = Vectors.sparse(5, Array(0, 4), Array(1.0, 2.0)) val sparseVec: Vector = Vectors.sparse(5, Array(0, 4), Array(1.0, 2.0)) val sparseVec: Vector = Vectors.spars

Although the vector in the preceding example is of size 5, non-zero values can only be found at indices 0 and 4. When there are only a few non-zero values, sparse vectors come in handy. Dense vectors should be used instead of sparse vectors if there are only a few zero values, as sparse vectors would result in indexing cost, hurting efficiency. An example of a dense vector is as follows:

denseVec = Vectors.dense = val denseVec = val denseVec = val denseVec (4405d,260100d,400d,5.0,4.0,198.0,9070d,1.0,1.0,2.0,0.0)

The use of sparse or dense vectors does not affect the results of computations, but when they are utilized incorrectly, they impact the amount of memory used and the computation time.

27. What is meant by SparkSession in Spark? How to create SparkSession in Spark?

Spark 2.0 introduces the SparkSession class (Spark.sql import SparkSession). SparkSession was a unifying class for all of the many contexts we had before the 2.0 release (SQLContext and HiveContext, etc). SparkSession can now replace SQLContext, HiveContext, and other contexts defined before version 2.0.

It's a means to delve into Spark's fundamental technology and programmatically create Spark RDDs and DataFrames. Spark is the default object in Spark-shell, and SparkSession can be used to construct it programmatically.

To construct SparkSession programmatically (in an a.py file), we must use the builder pattern function builder() in Spark, as shown below. The getOrCreate() function either retrieves an existing SparkSession or creates one if none exists.

28. What operations does RDD support?

Spark's fundamental logical data unit is the RDD (Resilient Distributed Dataset). An RDD has dispersed a collection of objects. The term "distributed" refers to the fact that each RDD is divided into many divisions. Each of these partitions can be stored in memory or on the disks of different cluster devices. RDDs are read-only (immutable) data structures.

The original RDD cannot be changed, but it can be transformed into a new RDD with any alterations you choose. Transformations and actions are two types of operations supported by RDDs.

• Transformations: Transformations, such as map, reduceByKey, and filter, build new RDDs from existing RDDs. On-demand transformations are carried out. That is, they are computed lazily.
• Actions: RDD computations' ultimate results are returned by actions. Actions cause the data to be loaded into the original RDD, all intermediate transformations to be performed, and the final results to be returned to the Driver application or written to the file system via the lineage graph.

29. Explain about Actions in Spark?

An action aids in the data transfer from RDD to the local machine. The consequence of all successfully made transformations is the implementation of an action. Actions cause the data to be loaded into the original RDD, all subsequent transformations to be performed, and the final results to be returned to the Driver application or written to the file system via the lineage graph.

Reduce() is an action that repeats the function until there is only one value left—the take() operation copies all RDD's values to a local node.

moviesData.saveAsTextFile(“MoviesData.txt”)

The moviesData RDD is stored into a text document called MoviesData.txt, as shown.

30. What do you understand about SchemaRDD in Spark RDD?

• SchemaRDD is an RDD made up of row objects (wrappers around simple string or integer arrays) that contain schema information about the data type in each column.
• SchemaRDD was created to make code debugging and unit testing on the SparkSQL core module easier for developers. The goal is to use a formal definition comparable to the relational database schema to describe the data structures within RDD.
• SchemaRDD provides certain simple relational query interface functions that are realized by SparkSQL, in addition to all of the fundamental capabilities offered by conventional RDD APIs.
  On Spark's newest trunk, it has been renamed to DataFrame API.

31. Explain about the Parquet file?

Various data processing systems support Parquet as a columnar format file. Spark SQL can read and write data with a Parquet file, making it one of the best-advanced analytics formats.
Many data processing systems support the columnar format Parquet. The following are some of the benefits of columnar storage:

• IO operations are limited by columnar storage.
• It can retrieve specific columns that you require.
• Columnar storage takes up less room.
• It provides improved data summaries and adheres to type-specific encoding.

32. How will you minimize the data transfers when working with Spark?

Writing spark programs that run quickly and reliably requires minimizing data transfers and avoiding shuffling. When dealing with Apache Spark, there are several techniques to reduce data transfers:

• The use of a broadcast variable improves the effectiveness of joins over small and big RDDs.
• Accumulators are used to updating variables' values in parallel while the program runs.
• The most popular method is to avoid actions like ByKey, repartition, and other shuffle-inducing actions.

33. Explain how DAG works in Spark?

A direct Acyclic Graph (DAG) is a graph with a finite set of edges and vertices. The edges represent the operations to be done sequentially on RDDs, while the vertices represent RDDs. The DAG is then sent to the DAG Scheduler, which divides the graphs into phases of activities based on the data transformations. The RDDs of that stage are detailed in the stage view.

• The initial step is using an interpreter to interpret the code. The Scala interpreter interprets the Scala code if you use it.
• When you type the code into the Spark console, Spark constructs an operator graph.
• The operator graph is sent to the DAG Scheduler when the action is invoked on Spark RDD.
• The DAG Scheduler organizes the operators into task stages. The stage consists of detailed operations on the input data that are carried out step by step. After that, the operators are connected via pipeline.
• The stages are then handed to the Task Scheduler, which begins the task via the cluster manager, allowing each stage to work independently.
• After that, the task is completed by worker nodes. Each RDD maintains a pointer to one or more parent RDDs, as well as its association with them. Consider the RDD childB that keeps track of its parentA using the val childB=parentA.map() method. This is known as RDD lineage.

34. What is meant by piping in Spark?

Apache Spark supports the pipe() method on RDDs, according to the UNIX Standard Streams, which enables you to compose discrete pieces of projects in any language. The pipe() method can be used to read each object of the RDD as a String, which is how the RDD transformation is constructed. These can be changed as required, and the outcomes can be displayed as Strings.

35. Explain about the Dstream in Spark?

Spark Streaming's primary abstraction is the Discretized Stream (DStream). It's a never-ending stream of information. It comes from a data source or a data stream that has been processed by altering the input stream. A DStream is internally represented as a continuous series of RDDs, each carrying data from a certain interval. Any operation on a DStream corresponds to operations on the RDDs beneath it.
DStreams can be built using various tools, including HDFS, Apache Flume, and Apache Kafka. DStreams perform two tasks:

• Transformations that result in the formation of a new DStream.
• Data is written to an external system using output operations.

In Spark Streaming, there are numerous DStream transforms available. Take a peek at filter (func). Filter (func) creates a new DStream from the records of the source DStream for which func returns true.

Conclusion

Here we complete the list of frequently asked 35 Apache Spark Interview Questions and this helps you to check your knowledge as well as help to prepare for your next Spark interview. Got a question or suggestion for us? Please let us know in the comment box and we'll get back to you ASAP.

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