Showing posts with label spark. Show all posts
Showing posts with label spark. Show all posts

June 07, 2022

Top 20 Amazon EMR Interview Questions and Answers


    Using open source frameworks like as Apache Spark, Apache Hive, and Presto, Amazon EMR is the industry-leading cloud big data platform for data processing, interactive analysis, and machine learning. With EMR, you can perform petabyte-scale analysis for half the price of typical on-premises solutions and over 1.7 times quicker than ordinary Apache Spark.

AWS(Amazon Web Services) Interview Questions and Answers

AWS Cloud Interview Questions and Answers

Ques. 1): What are the benefits of using Amazon EMR?


Amazon EMR frees you up to focus on data transformation and analysis rather than maintaining computing resources or open-source apps, and it saves you money. You may supply as much or as little capacity on Amazon EC2 as you want using EMR, and build up scaling rules to handle changing compute demand. CloudWatch notifications may be set up to notify you of changes in your infrastructure so you can react quickly. You may use EMR to submit your workloads to Amazon EKS clusters if you utilise Kubernetes. Whether you employ EC2 or EKS, EMR's optimised runtimes help you save time and money by speeding up your analysis.

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Ques. 2): How do I troubleshoot a query that keeps failing after each iteration?


You may use the same tools that they use to troubleshoot Hadoop Jobs in the case of a processing failure. The Amazon EMR web portal, for example, may be used to locate and view error logs. Here's where you can learn more about troubleshooting an EMR task.

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Ques. 3): What is the best way to create a data processing application?


In Amazon EMR Studio, you can create, display, and debug data science and data engineering applications written in R, Python, Scala, and PySpark. You may also create a data processing task on your desktop and run it on Amazon EMR using Eclipse, Spyder, PyCharm, or RStudio. When spinning up a new cluster, you may also pick JupyterHub or Zeppelin in the software configuration and build your application on Amazon EMR utilising one or more instances.

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Ques. 4): Is it possible to perform many queries in a single iteration?


Yes, you may specify a previously ran iteration in subsequent processing by specifying the option. The approach ensures that subsequent runs on the same iteration use the exact same input records from the Kinesis stream as earlier runs.

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Ques. 5): In Amazon EMR, how is a computation done?


The Hadoop data processing engine is used by Amazon EMR to perform calculations using the MapReduce programming methodology. The customer uses the map() and reduce() methods to create their algorithm. A customer-specified number of Amazon EC2 instances, consisting of one master and several additional nodes, are started by the service. On these instances, Amazon EMR runs Hadoop software. The master node separates the input data into blocks and distributes the block processing to the subordinate nodes. The map function is then applied to the data that has been assigned to each node, resulting in intermediate data. The intermediate data is then sorted and partitioned before being transmitted to processes on the nodes that perform the reduction function locally.

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Ques. 6): What distinguishes EMR Studio from EMR Notebooks?


There are five major differences:

EMR Studio does not require access to the AWS Management Console. The EMR Studio server is not part of the AWS Management Console. If you don't want data scientists or engineers to have access to the AWS Management Console, this is a good option.

To log in to EMR Studio, you can utilise enterprise credentials from your identity provider using AWS Single Sign-On (SSO).

EMR Studio provides you with your first notebook encounter. Because EMR Studio kernels and applications operate on EMR clusters, you receive the benefit of distributed data processing with the Amazon EMR runtime for Apache Spark, which is designed for performance.

Attaching the laptop to an existing cluster or establishing a new one is all it takes to run code on a cluster.

EMR Studio features a user interface that is simple to use and abstracts hardware specifications. For instance, you can create cluster templates once and then utilise them to create future clusters.

EMR Studio facilitates debugging by allowing you to access native application user interfaces in one location with as few clicks as feasible.

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Ques. 7): What tools are available to me for debugging?


You may use a variety of tools to gather information about your cluster and figure out what went wrong. If you utilise Amazon EMR studio, you can leverage debugging tools like Spark UI and YARN Timeline Service. You can gain off-cluster access to persistent application user interfaces for Apache Spark, Tez UI, and the YARN timeline server through the Amazon EMR Console, as well as multiple on-cluster application user interfaces and a summary view of application history for all YARN apps. You may also use SSH to connect to your Master Node and inspect cluster instances using these web interfaces. See our docs for additional details.

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Ques. 8): What are the advantages of utilising Command Line Tools or APIs rather than the AWS Management Console?


The Command Line Tools or APIs allow you to programmatically launch and monitor the progress of running clusters, as well as build custom functionality for other Amazon EMR customers (such as sequences with multiple processing steps, scheduling, workflow, or monitoring) or build value-added tools or applications. The AWS Management Console, on the other hand, offers a simple graphical interface for starting and monitoring your clusters from a web browser.

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Ques. 9): What distinguishes EMR Studio from SageMaker Studio?


With Amazon EMR, you may utilise both EMR Studio and SageMaker Studio. EMR Studio is an integrated development environment (IDE) for developing, visualising, and debugging data engineering and data science applications in R, Python, Scala, and PySpark. Amazon SageMaker Studio is a web-based visual interface that allows you to complete all machine learning development phases in one place. SageMaker Studio provides you total control, visibility, and access to every step of the model development, training, and deployment process. You can upload data, create new notebooks, train and tune models, travel back and forth between phases to change experiments, compare findings, and push models to production all in one spot, increasing your productivity significantly.

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Ques. 10): Is it possible to establish or open a workspace in EMR Studio without a cluster?


Yes, a workspace may be created or opened without being attached to a cluster. You should only join them to a cluster when you need to execute. EMR Studio kernels and apps run on Amazon EMR clusters, allowing you to take advantage of distributed data processing with the Amazon EMR runtime for Apache Spark.

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Ques. 11): What computational resources can I use in EMR Studio to execute notebooks?


You may execute notebook code on Amazon EMR on Amazon Elastic Compute Cloud (Amazon EC2) or Amazon EMR on Amazon Elastic Kubernetes Service using EMR Studio (Amazon EKS). Notebooks can be added to either existing or new clusters. In EMR Studio, you can construct EMR clusters in two ways: by using an AWS Service Catalog pre-configured cluster template or by defining the cluster name, number of instances, and instance type.

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Ques. 12): What IAM policies are required to utilise EMR Studio?


To interact with other AWS services, each EMR studio requires permissions. Your administrators must build an EMR Studio service role using the specified policies to grant the essential access to your EMR Studios. They must also create a user role for EMR Studio that defines permissions at the Studio level. They may assign a session policy to a user or group when they add users and groups from AWS Single Sign-On (AWS SSO) to EMR Studio to apply fine-grained authorization constraints. Administrators may utilise session policies to fine-tune user rights without having to create several IAM roles. See Policies and Permissions in the AWS Identity and Access Management User Guide for further information on session policies.

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Ques. 13): What may EMR Notebooks be used for?


EMR Notebooks make it simple to create Apache Spark apps and conduct interactive queries on your EMR cluster. Multiple users may build serverless notebooks straight from the interface, attach them to an existing shared EMR cluster, or provision a cluster and begin playing with Spark right away. Notebooks can be detached and reattached to new clusters. Notebooks are automatically saved to S3 buckets, and you may access them from the console to resume working. The libraries contained in the Anaconda repository are preconfigured in EMR Notebooks, allowing you to import and utilise them in your notebooks code to modify data and show results. Furthermore, EMR notebooks feature built-in Spark monitoring capabilities, allowing you to track the status of your Spark operations and debug code directly from the notebook.

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Ques. 14): Is Amazon EMR compatible with Amazon EC2 Spot, Reserved, and On-Demand Instances?


Yes. On-Demand, Spot, and Reserved Instances are all supported by Amazon EMR.

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Ques. 15): What role do Availability Zones play in Amazon EMR?


All nodes for a cluster are launched in the same Amazon EC2 Availability Zone using Amazon EMR. Running a cluster in the same zone enhances work flow performance. By default, Amazon EMR runs your cluster in the Availability Zone with the greatest available resources. You can, however, define a different Availability Zone if necessary. You may also utilise On-Demand Capacity Reservations to optimise your allocation for the lowest-priced on-demand instances, best spot capacity, or lowest-priced on-demand instances.

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Ques. 16): What are node types in a cluster?


There are three sorts of nodes in an Amazon EMR cluster:

master node : A master node supervises the cluster by executing software components that coordinate the distribution of data and tasks among the other nodes for processing. The master node keeps track of task progress and oversees the cluster's health. A master node is present in every cluster, and it is feasible to establish a single-node cluster using only the master node.

core node : A core node is a node that contains software components that conduct jobs and store data in your cluster's Hadoop Distributed File System (HDFS). At least one core node exists in multi-node clusters.

task node: A task node is a node that only performs tasks and does not store data in HDFS. Task nodes are not required.

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Ques. 17): Can Amazon EMR restore a cluster's master node if it goes down?


Yes. You may set up an EMR cluster with three master nodes (version 5.23 or later) to offer high availability for applications like YARN Resource Manager, HDFS Name Node, Spark, Hive, and Ganglia. If the primary master node fails or important processes, such as Resource Manager or Name Node, crash, Amazon EMR immediately switches to a backup master node. You may run your long-lived EMR clusters without interruption since the master node is not a potential single point of failure. When a master node fails, Amazon EMR immediately replaces it with a new master node that has the same configuration and boot-strap activities.

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Ques. 18): What are the steps for configuring Hadoop settings for my cluster?


For most workloads, the EMR default Hadoop setup is sufficient. However, depending on the memory and processing needs of your cluster, changing these values may be necessary. If your cluster activities are memory-intensive, for example, you may want to employ fewer tasks per core and limit the size of your job tracker heap. A pre-defined Bootstrap Action is offered to configure your cluster on starting in this case. For setup information and usage instructions, see the Developer's Guide's Configure Memory Intensive Bootstrap Action. You may also use an extra preset bootstrap action to tailor your cluster parameters to whatever value you like.

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Ques. 19): Is it possible to modify tags directly on Amazon EC2 instances?


Yes, tags may be added or removed directly on Amazon EC2 instances in an Amazon EMR cluster. However, because Amazon EMR's tagging system does not immediately sync changes to a corresponding Amazon EC2 instance, we do not advocate doing so. To guarantee that the cluster and its associated Amazon EC2 instances have the necessary tags, we recommend using the Amazon EMR GUI, CLI, or API to add and delete tags for Amazon EMR clusters.

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Ques. 20): How does Amazon EMR operate with Amazon EKS?


Amazon EMR requires you to register your EKS cluster. Then, using the CLI, SDK, or EMR Studio, send your Spark tasks to EMR. The Kubernetes scheduler on EKS is used by EMR to schedule Pods. EMR on EKS creates a container for each task you perform. The container includes an Amazon Linux 2 base image with security updates, as well as Apache Spark and its dependencies, as well as your application's particular needs. Each Job is contained within a pod. This container is downloaded and executed by the Pod. If the container's image has already been deployed to the node, the download is skipped and a cached image is utilised instead. Log or metric forwarders, for example, can be deployed as sidecar containers to the pod. When the job finishes, the Pod finishes as well. You may continue debug the task using Spark UI after it has finished.

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November 17, 2021

Top 20 Apache Spark Interview Questions & Answers


Ques: 1). What is Apache Spark?


Apache Spark is an open-source real-time processing cluster computing framework. It has a vibrant open-source community and is now the most active Apache project. Spark is a programming language that allows you to programme large clusters with implicit data parallelism and fault tolerance.

Spark is one of the Apache Software Foundation's most successful projects. Spark has unquestionably established itself as the market leader in Big Data processing. Spark is used by many enterprises on clusters with thousands of nodes. Spark is now used by large companies like as Amazon, eBay, and Yahoo!

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Ques: 2). What advantages does Spark have over MapReduce?


Compared to MapReduce, Spark has the following advantages:

Spark implements processing 10 to 100 times quicker than Hadoop MapReduce due to the availability of in-memory processing, whereas MapReduce uses persistence storage for any of the data processing activities.

Unlike Hadoop, Spark has built-in libraries that allow it to do a variety of functions from the same core, including as batch processing, steaming, machine learning, and interactive SQL queries. Hadoop, on the other hand, only supports batch processing.

Hadoop is heavily reliant on discs, but Spark encourages caching and data storage in memory. Spark is capable of performing computations multiple times on the same dataset. This is called iterative computation while there is no iterative computing implemented by Hadoop.

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Ques: 3). What exactly is YARN?


YARN is a fundamental element in Spark, similar to Hadoop, in that it provides a central and resource management platform for delivering scalable operations across the cluster. YARN, like Mesos, is a distributed container manager, but Spark is a data processing tool. Spark can be run on YARN in the same way that Hadoop Map Reduce can. Running Spark on YARN needs the use of a Spark binary distribution with YARN support.

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Ques: 4). What's the difference between an RDD, a Dataframe, and a Dataset?


Resilient Distributed Dataset (RDD) - RDD stands for Resilient Distributed Dataset. It is the most basic data structure in Spark, consisting of an immutable collection of records partitioned among cluster nodes. It allows us to do fault-tolerant in-memory calculations on massive clusters.

RDD, unlike DF and DS, will not keep the schema. It merely stores data. If a user wants to apply a schema to an RDD, they must first build a case class and then apply the schema to the data.

We will use RDD for the below cases:

-When our data is unstructured, A streams of text or media streams.

-When we don’t want to implement any schema.

-When we don’t care about the column name attributes while processing or accessing.

-When we want to manipulate the data with functional programming constructs than domain specific expressions.

-When we want low-level transformation, actions and control on the dataset.


-Like RDD DataFrames are immutable collection of data.

-Unlike RDD DataFrame will have schema for their data making user to easily access/process large set of data which is distributed among the nodes of cluster.

-DataFrame provides a domain specific language API to manipulate distributed data and makes Spark accessible to a wider audience, beyond specialized data engineers.

-From Spark 2.x Spark DataFrames are nothing but Dataset[Row] or alias (Untyped API)

consider DataFrame as an alias for a collection of generic objects Dataset[Row], where a Row is a generic untyped JVM object


Dataset, by contrast, is a collection of strongly-typed JVM objects, dictated by a case class you define in Scala or a class in Java. To implement case class on RDD and use as Dataset[T].

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Ques: 5). Can you explain how you can use Apache Spark along with Hadoop?


Apache Spark provides the benefit of being Hadoop compatible. They make a powerful tech team when they work together. Using Apache Spark and Hadoop combines the processing capability of Spark with the best capabilities of HDFS and YARN. The following are some examples of how to use Hadoop Components with Apache Spark:

Batch & Real-Time Processing – MapReduce and Spark can work together, where the former handles batch processing, and the latter handles real-time processing.

HDFS – Spark can make use of the HDFS to leverage the distributed replicated storage.

MapReduce – Apache Spark can be used in conjunction with MapReduce in a similar Hadoop cluster or independently as a processing framework.

YARN – You can run Spark applications on YARN.

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Ques: 6). What is the meaning of the term "spark lineage"?


• In Spark, regardless of the actual data, all dependencies between RDDs will be logged in a graph. In Spark, this is referred to as a lineage graph.

• RDD stands for Resilient Distributed Dataset, with the term "resilient" referring to fault tolerance. We can re-compute the missing or damaged partition due to node failure using RDD Lineage Graph. When we generate new RDDs based on existing RDDs, we use lineage graph spark to handle the dependencies. Each RDD keeps a pointer to one or more parents, together with metadata describing the type of relationship it has with the parent RDD.

• RDD Lineage Graph in Spark can be obtained using the ToDebugString method.


Ques: 7). List the various components of the Spark Ecosystem.


These are the five types of components in the Spark Ecosystem:

GraphX: Enables graphs and graph-parallel computation.

MLib: It is used for machine learning.

Spark Core: A powerful parallel and distributed processing platform.

Spark Streaming: Handles real-time streaming data.

Spark SQL: Combines Spark's functional programming API with relational processing.


Ques: 8). What is RDD in Spark? Write about it and explain it.


Resilient Distributed Dataset (RDD) is an acronym for Resilient Distributed Dataset. RDDs are a fault-tolerant core data structure in Spark that is immutable. They've disseminated partitioned datasets among the cluster nodes.

Parallelizing and referencing a data set are the two methods for constructing RDDS. Lazy evaluation is the responsibility of the RDDS. The faster processing performance in Spark is due to the lazy evaluation of RDDs.


Ques: 9). In Spark, how does streaming work?


Spark gets data in real time that is separated into batches. The Spark Engine processes these batches of data, and the final stream of results is returned back in batches. DStream, or Discretized Stream, is the most basic stream unit in Spark.


Ques: 10). Is it feasible to access and analyse data stored in Cassandra databases using Apache Spark?


Yes, Apache Spark may be used to retrieve and analyse data stored in Cassandra databases. Apache Spark can access and analyse data contained in Cassandra databases using the Spark Cassandra Connector. Spark should have a functionality that allows Spark executors to communicate with local Cassandra nodes and request just local data.

Cassandra and Apache Spark can be connected to speed up queries by lowering network traffic between Spark executors and Cassandra nodes.


Ques: 11). What are the advantages of using Spark SQL?


Spark SQL carries out the following tasks:

Loads data from a variety of structured datasources, such as a relational database management system (RDBMS).

It may query data using SQL commands within the Spark programme as well as JDBC/ODBC connectors from third-party tools such as Tableau.

It can also provide SQL and Python/Scala code interaction.


Ques: 12). What is the purpose of Spark Executor?


The Executors are obtained on top of worker nodes in the clusters when a SparkContext is formed. Spark Executors are in charge of performing computations and storing data on the worker node. They are also in charge of returning the results to the driver.


Ques: 13). What are the advantages and disadvantages of Spark?


Advantages: Spark is known for real-time data processing, which may be employed in applications such as stock market analysis, finance, and telecommunications.

Spark's stream processing allows for real-time data analysis, which can aid in fraud detection, system alarms, and other applications.

Due to its lazy evaluation mechanism and parallel processing, Spark processes data 10 to 100 times quicker.

Disadvantages: When compared to Hadoop, Spark consumes greater storage space.

The task is distributed over numerous clusters rather than taking place on a single node.

Spark's in-memory processing might be costly when dealing with large amounts of data.

When compared to Hadoop, Spark makes better use of data.


Ques: 14). What are some of the drawbacks of utilising Apache Spark?


The following are some of the drawbacks of utilising Apache Spark:

There is no file management system built-in. To take benefit of a file management system, integration with other platforms such as Hadoop is essential.

Higher latency, but lower throughput as a result

It does not support the processing of real-time data streams. In Apache Spark, live data streams are partitioned into batches, which are then processed and turned back into batches. To put it another way, Spark Streaming is more like micro-batch data processing than true real-time data processing.

There are fewer algorithms available.

Record-based window requirements are not supported by Spark streaming. It is necessary to distribute work across multiple clusters instead of running everything on a single node.

Apache Spark's in-memory ability becomes a bottleneck when used for the cost-efficient processing of big data.


Ques: 15). Is Apache Spark compatible with Apache Mesos?


Yes. Spark can work on Apache Mesos-managed clusters, just as it works on YARN-managed clusters. Spark may run without a resource manager in standalone mode. If it has to execute on multiple nodes, it can use YARN or Mesos.


Ques: 16). What are broadcast variables, and how do they work?


Accumulators and broadcast variables are the two types of shared variables in Spark. Instead of shipping back and forth to the driver, the broadcast variables are read-only variables cached in the Executors for local referencing. A broadcast variable preserves a read-only cached version of a variable on each computer instead of delivering a copy of the variable with tasks.

Additionally, broadcast variables are utilised to distribute a copy of a big input dataset to each node. To cut transmission costs, Apache Spark distributes broadcast variables using efficient broadcast algorithms.

There is no need to replicate variables for each task when using broadcast variables. As a result, data can be processed quickly. In contrast to RDD lookup(), broadcast variables assist in storing a lookup table inside the memory, enhancing retrieval efficiency.


Ques: 17). In Apache Spark, how does caching work?


Caching RDDs in Spark speeds up processing by allowing numerous accesses to the same RDD. The function of Discretized Streams, or DStreams, in Spark streaming is to allow users to cache or retain data in memory.

The functions cache () and persist(level) are used to cache data in memory and cache memory based on the storage level specified, respectively.

The persist () without the level specifier is the same as cache, which means it caches the data in memory. The persist(level) method caches data at the provided storage level, such as on disc, on the server, or in off-heap memory.


Ques: 18).  What exactly is Akka? What does Spark do with it?


Akka is a Scala and Java framework for reactive, distributed, parallel, and robust concurrent applications. Akka is the foundation for Apache Spark.

When assigning tasks to worker nodes, Spark employs Akka for job scheduling and messaging between the master and the worker node.


Ques: 19). What applications do you utilise Spark streaming for?


When real-time data must be streamed into the Spark programme, this method is employed. It can be broadcast from a variety of places, such as Kafka, Flume, Amazon Kinesis, and others. For processing, the streamed data is separated into batches.

Spark streaming is used to conduct real-time sentiment analysis of customers on social media sites like as Twitter and Facebook, among others.

Live streaming data processing is critical for detecting outages, detecting fraud in financial institutions, and making stock market predictions, among other things.


Ques: 20). What exactly do you mean when you say "lazy evaluation"?


The way Spark works with data is intellectual. When you ask Spark to perform a task on a dataset, it follows your instructions and records them so that it doesn't forget them - but it doesn't do anything until you tell it to. When map() is invoked on an RDD, the operation is not done immediately. Transformations aren't evaluated by Spark until you use them. This aids in the overall data processing workflow optimization.