Spark Interview Questions and Answers

The most common questions will be covered in this set of Apache Spark interview questions and answers, along with relevant article links for further information. It’s always a good idea to have an in-depth understanding of the subject when looking for a job in Apache Spark. We can give you the knowledge you need to succeed in Spark interviews. Explore what is in store for you in our big data training syllabus, which gives you broader skills for a promising career.

What is Apache Spark?

The Hadoop ecosystem’s data is processed via the open-source Apache Spark framework, an in-memory computing processing engine. It uses distributed and parallel processing to handle data in both batch and real-time modes.

Which various cluster administrators does Spark support?

Any Spark application can be split up into separate processes and run in parallel on a cluster; the driver program’s SparkContext object manages this coordination. In particular, SparkContext links to many cluster managers, including Standalone, Hadoop Yarn, and Apache Mesos, to run in a cluster and distribute resources between applications. Spark obtains executors—processes that handle and store data for the application—on worker nodes after it is connected. Moreover, SparkContext provides executors with application code. Ultimately, tasks are sent to executors via SparkContexts to be processed.

  • Standalone cluster manager
  • Yarn
  • Mesos

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How does Spark differ from MapReduce?

MapReduce reads from and writes to disk with a lot of input and output. Processing is done in batches. MapReduce is written exclusively in Java. It lacks interactive and iterative features. Compared to Spark, MapReduce is capable of handling larger data sets.

Spark is an incredibly quick in-memory computing process engine that is 10 times quicker to disk and 100 times faster than MapReduce. Spark supports many languages, including Scala, Python, R, and Java. Real-time and batch data processing are both handled by Spark. Explore the reasons behind the bright future of Python.

Which modules or parts make up Apache Spark?

SparkCore, Spark SQL, Spark Streaming, Spark MlLib, and GraphX are all included with Apache Spark.

  • Spark Core
  • Spark SQL
  • Spark Streaming
  • MLib
  • GraphX

What are the various Spark installation methods?

Spark can be set up in three distinct ways, such as standalone mode, pseudo-distribution mode, and multi-cluster mode.

Describe SparkSession.

In Spark 2.0, SparkSession was released. It serves as a gateway to the core Spark capabilities, enabling the programmatic creation of Spark RDD, DataFrame, and DataSet. The default variable in spark-shell is the SparkSession object spark, which may be constructed programmatically with the SparkSession builder pattern.

How is an object for a SparkSession created?

You must call the getOrCreate() method and utilize the builder pattern method builder() in Scala or Python to construct a SparkSession. Creates a new SparkSession or returns an existing one if SparkSession already exists.

val spark = SparkSession.builder()




Is it possible to generate more than one SparkSession object in the same application?

Indeed, in a Spark application, you can construct an infinite number of SparkSession objects. If you wanted to maintain the logical separation of Spark tables (relational entities), you would need a lot of Spark session objects.

Describe what resilient distributed datasets (RDDs)

Resilient Distributed Dataset, or RDD for short, is the basic Spark data structure that symbolizes an immutable, partitioned set of records. RDDs can automatically recover from failures since they are fault-tolerant.

They make distributed data processing possible by permitting parallel processing among a cluster of devices. They can be made by converting pre-existing RDDs or using data kept in local file systems, the Hadoop Distributed File System (HDFS), or both.

How does Spark manage fault tolerance?

RDDs are how Spark achieves fault tolerance. Because they trace the history of changes made to a base dataset, RDDs are robust. Spark is capable of automatically reconstructing a lost partition of an RDD by reapplying the modifications from the original dataset. Without explicitly replicating data, Spark can handle errors and guarantee fault tolerance because of this lineage information.

What do you think of Spark’s DStreams?

This Spark code interview question is one that you may see frequently. The basic abstraction in Spark Streaming is called a Discretized Stream (DStream), which is just a series of consecutive RDDs. These RDD sequences are a continuous stream of data in all of the same categories. Each RDD contains data from a given period.

Spark’s DStreams can receive input from multiple sources, such as TCP connections, Flume, Kafka, and Kinesis. By altering the input stream, it can also be utilized to create a data stream. It offers fault tolerance and a high-level API, which is helpful to developers.

Why is Spark such a powerful tool for low-latency tasks like machine learning and graph processing?

To facilitate quicker processing and the creation of machine learning models, Apache Spark caches data in memory. Machine learning algorithms need multiple iterations and separate conceptual processes to build an optimal model. Graph algorithms examine each node and edge to build a graph. These low-latency jobs can improve performance but need a lot of iterations.

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What does Spark’s lazy evaluation mean?

Transformations on RDDs are not performed instantly in Spark due to lazy evaluation. Instead, Spark creates a directed acyclic graph (DAG) to represent the computation by logging the series of changes applied to an RDD. With this method, Spark can more effectively optimize and schedule the execution plan. Only when an action is invoked and the outcomes are required are the transformations idly assessed.

An executor of Spark is what?

A Spark cluster’s worker nodes are home to a process known as a Spark executor. The driver software assigns tasks to executors, and it is their responsibility to complete them. Every executor oversees the memory and storage resources allotted to the various tasks it is doing concurrently. To process data in parallel, executors coordinate and communicate with the driver software.

In Spark, what function do accumulators serve?

Variables called accumulators are employed to combine data from different executors. This data may include API diagnostics or information about the data itself, such as the number of damaged records or the number of calls made to a library API.

Describe the Spark Streaming caching concept.

Caching, sometimes referred to as persistence, is a method for making Spark computations more efficient. Similar to RDDs, DStreams allow developers to store the data in the stream in memory. In other words, using the persist() method on a DStream causes all of its RDDs to be automatically stored in memory. To enable their usage in subsequent phases, interim, incomplete results should be stored. The default persistence level for input streams that receive data over the network is configured to replicate the data to two nodes to provide fault tolerance.

Explain the idea behind a Spark shuffle operation.

In Spark, a shuffle operation is the process of dispersing data among partitions, usually carried out when the data’s partitioning is altered. A new partitioning technique requires that records be shuffled to their proper partitions, which necessitates data transmission between the cluster’s nodes. Shuffles can affect Spark application performance because they are costly operations in terms of disk and network I/O.

How do you keep RDDs around in Spark?

The persist() and cache() functions in Spark allow RDDs to be stored either in memory or on disk. Depending on the storage level chosen, an RDD’s partitions may be kept in memory or on disk when it persists. Reducing the requirement for recomputation, persisting RDDs provide quicker access and reuse of intermediate results across successive computations.

How can the status of a Spark application be tracked?

Spark offers many tools to track an application’s development:

Spark web UI: Spark initiates a web UI immediately, including comprehensive details about the application, such as job status, stages, tasks, and resource utilization.

Logs: Spark keeps thorough records of all the application’s activities, mistakes, and alerts. You can monitor the application by accessing and analyzing these logs.

Interfaces for cluster managers: If Spark is operating on a cluster manager such as Mesos or YARN (Yet Another Resource Negotiator), its separate user interfaces can offer information on the state of the application and how resources are being used.

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Describe the Spark idea of lineage.

In Spark, “lineage” describes the past of the series of changes made to an RDD. The Spark documentation of each RDD’s lineage describes how the RDD was formed through the conversion of its parent RDDs.

Spark can automatically recreate deleted RDD partitions by reapplying the modifications from the original data, all thanks to the preservation of this lineage information. Data recovery and fault tolerance are thus guaranteed.

In Spark DataFrames, how may missing data be handled?

There are several ways to handle missing or null values in Spark DataFrames:

Dropping rows: The drop() method can be used to remove rows that have missing values.

Filling in missing values: The fillna() method allows you to fill in missing values with a specified default value.

Imputing missing values: Based on other non-missing values in the column, Spark has functions like Imputer that can substitute missing values with statistical measures like mean, median, or mode.

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What are the benefits of Spark compared to conventional MapReduce?

Compared to conventional MapReduce, Spark has the following advantages:

Computation in memory: Spark reduces disk I/O and boosts performance by carrying out calculations in memory.

Faster data processing: Spark’s DAG execution engine produces a more efficient processing model and optimizes the execution plan, which leads to faster data processing.

Rich library: Spark offers many libraries to support various data processing workloads, including real-time stream processing (Spark Streaming), graph processing (GraphX), and machine learning (MLlib).

Processing that is interactive and iterative: Spark enables real-time exploration and quicker development cycles by supporting interactive queries and iterative algorithms.

Fault tolerance: By automatically providing fault tolerance and data recovery, Spark’s RDD lineage does away with the necessity for explicit data replication.

Describe the distinctions between Spark’s local and cluster modes.

Spark operates in local mode on a single system, usually the one that executes the driver software. Spark worker processes operating in the same JVM as the driver software enable local parallelism on many cores or threads.

When Spark is used in cluster mode, a collection of machines is used, with the driver software operating on one device (referred to as the driver node) and Spark workers performing tasks on other devices (referred to as worker nodes). The driver program manages the task execution of the worker nodes. The cluster distributes data so that it can be processed in parallel.

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How may skewed data be handled in Spark?

In Spark, skewed data can be handled using methods such as:

Data skew detection: Examine the distribution of the data to find skewed keys or partitions. Methods for identifying skewed data include partition size analysis, sampling, and histograms.

Skewed join handling: To balance the load, you can utilize methods like data replication, which involves replicating the data of skewed keys and partitions. For smaller, skewed datasets, broadcast joins are an alternative.

Data partitioning: By altering the partitioning strategy, the skewed data can be distributed more fairly. To disperse the data, custom partitioning functions, or bucketing, can be applied.

What does the Spark Shell intend to accomplish?

Users can easily engage with prototype code and Spark using the interactive command-line tool known as the Spark Shell. It offers a comfortable setting for running Spark scripts and creating interactive Spark apps.

The Python and Scala programming languages can be used using the read-evaluate-print-loop (REPL) interface that the Spark Shell offers. This interface facilitates interactive data exploration and experimentation.

Which multiple Spark storage levels are available?

Spark has multiple storage levels to manage RDD persistence on disk or in memory. Among the storage levels are:

RDD partitions are kept in memory via MEMORY_ONLY.

MEMORY_AND_DISK: Keeps RDD partitions in memory and, if needed, spills them to disk.

RDD partitions are kept in memory as serialized objects by MEMORY_ONLY_SER.

MEMORY_AND_DISK_SER: RDD partitions are serialized objects stored in memory that can overflow to disk when needed.

DISK_ONLY: RDD partitions are stored exclusively on disk.

OFF_HEAP: RDD partitions are serialized and stored off-heap.

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Describe the steps involved in using CSV files with Spark.

When working with CSV files in Spark, you can read the CSV file and produce a DataFrame by using the method. Spark lets you directly supply a schema or it can infer one from the data. Numerous options are available for selection, including delimiter, header presence, and null value handling.

After reading the CSV file into a DataFrame, you may process and analyze the data by applying actions and transformations to the DataFrame. The df.write.csv() function allows you to write the ‘DataFrame’ back to CSV format as well.

In Spark, how can dynamic allocation be enabled?

Depending on the workload, Spark’s dynamic allocation enables the dynamic acquisition and release of cluster resources. The following configuration parameters must be set to enable dynamic allocation:

To enable dynamic allocation, set the attribute Spark. dynamicAllocation.enabled to true. minExecutors and maxExecutors of Spark. dynamicAllocation: Decide how many executors Spark can allocate dynamically, minimum and maximum, depending on workload.

When dynamic allocation is enabled, Spark dynamically modifies the number of executors according to the cluster’s resource availability and resource requirements.

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Bottom Line

The Spark interview questions and answers cover a variety of subjects and let you show off your expertise and understanding of Spark. They will increase your probability of success and help you prepare more effectively for your interview. Coding exercises help strengthen your understanding of Spark concepts. Join our big data training in Chennai at SLA Jobs and accelerate your career in the data science domain.