Spark Interview Questions and Answers

The fast, in-memory data processing engine Apache Spark is at the vanguard of large data processing. It is being utilized more and more for stream processing, machine learning, and data analytics. As more businesses embrace big data, there is an unprecedented need for qualified Apache Spark specialists. Understand the importance of the big data skills required to shine in the big data domain.

A thorough understanding of Apache Spark is essential, whether you’re a hiring manager trying to bring in top Spark developers or a developer hoping to advance your career in Spark. The frequently asked Apache Spark interview questions and answers, suitable for experts with varying experience levels, have been gathered in this blog.

In what ways does Apache Spark differ from Hadoop?

The open-source distributed computing framework Apache Spark offers a programming interface for implicit data parallelism and fault tolerance in clusters. It is not the same as Hadoop in a few ways:

Because Spark processes data in memory, it operates more quickly than Hadoop’s disk-based processing paradigm. While Hadoop primarily concentrates on batch processing using MapReduce, Spark offers a wider range of libraries and supports more programming languages.

What are the various Spark RDD creation methods?

RDDs may be created in Spark in three main ways:

by using the parallelize() function in the driver application to parallelize an already-existing collection.

by using the textFile() method to reference a dataset in an external storage system, like the Hadoop Distributed File System (HDFS).

by applying functions like map(), filter(), and reduceByKey() to already-existing RDDs.

Explain the distinction in Spark between actions and transformations.

Operations like map(), filter(), and reduceByKey() that create a new RDD from an existing one are known as transformations in Spark. Lazy transformations are those that are documented as a series of actions to be carried out when an action is called, rather than being conducted instantly.

Spark actions start transformations, write data to an external storage system, and return results to the driver program. Actions like count(), collect(), and saveAsTextFile() are a few examples. To compute a result, actions are eager and trigger the execution of all previously defined transformations.

What are the different cluster managers that Apache Spark offers?

This is a typical question for interviews at Spark. The managers of the cluster are:

Standalone Mode: The standalone mode cluster tries to utilize all of the available nodes while executing programs in FIFO order by default. A master and workers can be manually started to manually start a standalone cluster. These daemons can also be tested on a single system.

Apache Mesos: Apache Mesos is an open-source project that manages computer clusters and runs Hadoop applications. Scalable partitioning across multiple Spark instances and dynamic partitioning between Spark and other frameworks are two advantages of deploying Spark using Mesos.

Hadoop YARN: The cluster resource manager for Hadoop 2 is Apache YARN. It also supports Spark.

Kubernetes: This open-source program automates the scaling, deployment, and management of containerized applications.

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What is a Lineage Graph exactly?

Another typical question in Spark interviews is this one. A graph of dependencies between an existing RDD and a new RDD is called a lineage graph. It implies that a graph representing all of the dependencies between the RDD will be used in place of the original data.

When we need to compute a new RDD or recover lost data from a persistent RDD that has been lost, an RDD lineage graph is necessary. Spark does not support in-memory data replication. Consequently, RDD lineage can be used to restore any missing data. It is also known as an RDD dependency graph or an RDD operator graph.

Describe the function of a Spark driver application.

The primary program that specifies the RDDs, transformations, and actions to be carried out on a Spark cluster is the Spark driver program. It is in charge of establishing the SparkContext, which connects to the cluster manager, and it runs on the same system that the Spark application is submitted on. The driver application collects the results of the distributed computations and manages the tasks of the worker nodes.

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Does Apache Spark support Checkpoints?

This Spark code interview question is one that you may see frequently. Sure, Apache Spark has an API for creating and controlling checkpoints. Checkpointing is the process of making streaming programs error-resistant. You can use it to store metadata and data to a checkpointing directory. Spark can recover this data and carry on from where it left off in the event of a failure.

Two types of data can be used with checkpointing in Spark.

Checkpointing Metadata: Data about data is what metadata, or checkpointing metadata, is. It speaks about keeping the metadata in a system of fault-tolerant storage, like HDFS. Metadata includes things like configurations, DStream operations, and incomplete batches.

Data Checkpointing: Since some of the stateful transformations depend on the RDD, we keep it in a dependable storage place in this instance.

What advantages does Spark’s in-memory computing offer?

There are several benefits to Spark’s in-memory computing:

Faster processing: Spark achieves noticeably faster execution speeds by avoiding the disk I/O bottleneck associated with traditional disk-based processing by storing data in memory.

Interactive and iterative processing: Because interim findings can be stored in memory, in-memory computing enables interactive data exploration and effective iterative algorithms.

Simplified programming model: Without having to worry about disk I/O or data serialization/deserialization, developers may use the same programming APIs for batch, interactive, and real-time processing.

In Spark, define shuffling.

Shuffling is the process of distributing data between partitions, which could lead to data migration across executors. Spark handles the shuffle operation differently from Hadoop.

There are two crucial compression factors for shuffling:

Check whether the engine would compress shuffle outputs with the ‘spark.shuffle.compress’ function.

The ‘Spark.shuffle.spill.compress’ function determines if intermediate shuffle spill files should be compressed or not.

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Which several features does Spark Core support? (Important spark architecture interview questions.)

This Spark code interview question is one that you may see frequently. The Spark Core engine can process large amounts of data in distributed and parallel modes. Spark Core offers the following features:

• Memory management
• Job scheduling and monitoring
• Interacting with storage system
• Fault detection and recovery
• Task distribution, etc.

What role does SparkContext play in Spark?

Any Spark functionality in a Spark application starts with SparkContext. It facilitates the execution of operations on RDDs and represents the connection to a Spark cluster.

SparkContext provides access to input/output functions, cluster managers, and more setup options. It is in charge of coordinating the worker nodes’ task execution and liaising with the cluster manager.

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What distinguishes Apache Spark from Apache Flink? (This is one of the spark interview questions for experienced professionals.)

Although they are both distributed data processing frameworks, Apache Spark and Apache Flink differ in a few ways:

Processing model: Flink is meant for both batch and stream processing, whereas Spark is primarily meant for batch and interactive processing. Flink provides native support for event-time processing together with enhanced windowing and streaming functionalities.

APIs for data processing: Spark offers high-level APIs that abstract the underlying data structures, such as RDDs, DataFrames, and Datasets. The DataStream API, a unified streaming and batch API offered by Flink, gives users fine-grained control over event processing and temporal semantics.

Fault tolerance: Flink employs a technique known as checkpointing to periodically preserve the state of operators to enable recovery from failures, whereas Spark accomplishes fault tolerance through RDD lineage.

Memory management: Flink optimizes memory utilization by combining managed memory and disk-based storage, while Spark bases its in-memory computation on resilient distributed datasets (RDDs).

What role does the Spark driver node play?

The Spark driver node runs the driver program, which defines the RDDs, transformations, and actions to be performed on the Spark cluster. It operates on the system that receives the submitted Spark application.

To obtain resources and control the worker nodes’ job execution, it interacts with the cluster manager. The driver node gathers and either sends the results of the distributed computations back to the user or stores them on an external storage device.

Explain the Spark serialization concept.

In Spark, serialization is the process of transforming an object into a byte stream for network transmission, disk, or memory storage. Spark makes use of effective serialization frameworks, such as the more streamlined Kryo serializer or Java’s ObjectOutputStream.

Because it enables objects, divisions, and closures to be transported across the network and executed on remote worker nodes, serialization is essential to Spark’s distributed computing paradigm.

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What functions do SQLContext and SparkContext provide in Spark? (It is the frequently asked spark SQL interview question.)

SparkContext, the initial point of any Spark action, represents the connection to a Spark cluster. It controls the cluster’s task execution, specifies configuration parameters, and permits the development of RDDs.

A higher-level API called SQLContext offers a programming interface for utilizing Spark’s DataFrame and Dataset APIs to interact with structured data. By supporting SQL query execution, data reading from many sources, and relational operations on dispersed data, it expands SparkContext’s functionality.

Explain the distinction between Spark’s narrow and wide transformations. (This is one of the spark interview questions and answers for experienced professionals.)

Narrow transformations in Spark are those operations that only contribute one output partition from each input partition. Each division undergoes local, narrow modifications, negating the need to move data across the network. The narrow transformations map(), filter(), and union() are a few examples.

Conversely, wide transformations are operations that necessitate moving data between partitions. They entail tasks like combining, aggregating, and grouping data from several partitions. Wide transformations alter how many partitions there are and frequently call for network connectivity. The wide transformations join(), groupByKey(), and reduceByKey() are a few examples.

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Describe the Spark idea of the RDD lineage.

The series of steps and dependencies that specify how an RDD is generated from its parent RDDs or source data is referred to as the RDD lineage in Spark. For every RDD, Spark preserves the lineage information indicating the modifications made to the original data. This lineage information enables fault tolerance, enabling Spark to reapply the changes and restore destroyed partitions. Moreover, it enables effective RDD optimization and recomputation during execution.

How are the serialization and deserialization of data handled by Spark?

Spark uses a pluggable serialization framework to manage data serialization and deserialization. Java’s ObjectOutputStream is the default serializer in Spark, although it also offers the more effective Kryo serializer.

By default, Spark selects the suitable serializer by itself, taking into account the data and operations being carried out. By creating their own serializers or utilizing third-party serialization tools, developers can further personalize serialization.

What role does the Spark Master node play?

A Spark cluster’s entry point and central coordinator is the Spark Master node. It controls how memory and CPU are distributed to Spark apps that are executing on the cluster.

The master node keeps track of the worker nodes that are accessible, keeps an eye on their health, and arranges for tasks to be performed on them. To monitor the cluster and submit Spark applications, it also offers a web user interface and APIs.

Describe the goal of the DAG scheduler in Spark.

A Spark application’s logical execution plan is translated into a physical execution plan using Spark’s DAG (Directed Acyclic Graph) scheduler. By grouping several transformations into stages and maximizing data locality, it examines the relationships between RDDs and transformations and optimizes the execution plan.

The cluster manager schedules and carries out the phases that the DAG scheduler has divided up into the execution plan.

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Explain how to use Spark’s text file handling feature. (This is one of the spark interview questions for experienced professionals.)

When working with text files in Spark, you can read the text file and build an RDD or DataFrame with each line representing a separate record by using the spark.read.text() method. Spark offers a range of choices for managing text file types, including encoded files, multi-line records, and compressed files.

After the text file has been read, you can process and analyze the data by applying actions and transformations to the RDD, or DataFrame. The saveAsTextFile() function allows you to write the RDD or DataFrame back to text format as well.

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Bottom Line

We hope these spark questions and answers are useful for you. As a professional, you must be familiar with all the jargon and technology used in the big data industry, especially Apache Spark, one of the most widely used and sought-after big data technologies. Start your big data career off right by reviewing the given Apache Spark interview questions in advance of employment interviews. Learn at SLA Institute the best big data training in Chennai with 100% placement assistance.