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Oct 7, 2020 · You cannot compare Yarn and Spark directly per se. Yarn is a distributed container manager, like Mesos for example, whereas Spark is a data processing tool. Spark can run on Yarn, the same way Hadoop Map Reduce can run on Yarn.
Spark can run with any persistence layer. For spark to run it needs resources. In standalone mode you start workers and spark master and persistence layer can be any - HDFS, FileSystem, cassandra etc. In YARN mode you are asking YARN-Hadoop cluster to manage the resource allocation and book keeping.
- Apache Spark Basic Interview Questions
- CORE Concepts
- Spark Programming
- Spark Architecture
- Spark Ecosystem
- Performance Tuning and Optimization
- Integration and Data Sources
- Security and Authentication
- Cluster Management and Deployment
- Monitoring and Logging
What is Apache Spark?
Apache Spark is an Open source framework, an in-memory computing processing engine that processes data on the Hadoop Ecosystem. It processes both batch and real-time data in a parallel and distributed manner.
Difference between Spark and MapReduce?
MapReduce: MapReduce is I/O intensive read from and writes to disk. It is batch processing. MapReduce is written in java only. It is not iterative and interactive. MapReduce can process larger sets of data compared to spark. Spark: Spark is a lighting-fast in-memory computing process engine, 100 times faster than MapReduce, 10 times faster to disk. Spark supports languages like Scala, Python, R, and Java. Spark Processes both batch as well as Real-Time data.
What are the components/modules of Apache Spark?
Apache Spark comes with SparkCore, Spark SQL, Spark Streaming, Spark MlLib, and GraphX 1. Spark Core 2. Spark SQL 3. Spark Streaming 4. MLib 5. GraphX
What is Apache Spark, and how does it differ from Hadoop MapReduce?Explain the key features of Apache Spark.What is the Spark Driver, and what role does it play in a Spark application?What is a Spark Executor, and how does it relate to Spark tasks?How do you create an RDD in Spark?Explain the difference between map() and flatMap()transformations.What is a broadcast variable, and when would you use it?How can you persist an RDD in Spark, and why is it important?What is the Spark Cluster Manager, and name some common cluster managers used with Spark.Describe the Spark Master and Worker nodes in a cluster.Explain the role of the Cluster Manager, Application Master, and Executor in Spark’s execution model.What is the difference between YARN, Mesos, and Standalone cluster managers in Spark?What is Spark Streaming, and how does it process real-time data?Explain the key components of Spark MLlib (Machine Learning Library).What is GraphX in Spark, and what are its use cases?Describe the functionality of SparkR for R users.What are the few things you will check to improve Spark performance?What are some common techniques for optimizing Spark applications?How can you control the level of parallelism in Spark?What is speculative execution in Spark, and how does it help in fault tolerance?How can you read data from external data sources like HDFS or S3 in Spark?Explain how to write data back to external storage from Spark.What is the purpose of Spark connectors, and provide some examples.How can you connect Spark to a relational database like MySQL or PostgreSQL?What security features are available in Spark to protect data?Explain the role of authentication and authorization in a Spark cluster.How can you enable authentication and encryption in Spark using Kerberos?Describe the use of Spark’s built-in security manager.How can you deploy a Spark application in a standalone cluster mode?Explain the steps to submit a Spark application to a YARN cluster.What are some common issues and considerations when configuring Spark on a cluster?Describe the differences between cluster deploy mode and client deploy mode.What tools and utilities are available for monitoring Spark applications?Explain the purpose of Spark’s built-in web UI.How can you access Spark application logs and view them?What metrics and statistics are important to monitor in a Spark cluster?Jun 27, 2024 · Essential Spark interview questions with example answers for job-seekers, data professionals, and hiring managers. Jun 27, 2024. Apache Spark is a unified analytics engine for data engineering, data science, and machine learning at scale. It can be used with Python, SQL, R, Java, or Scala.
In addition to being a potential replacement for the Hadoop MapReduce functions, Spark is able to run on top of an extant Hadoop cluster by means of YARN for resource scheduling. Question: What advantages does Spark offer over Hadoop MapReduce? Answer:
- Spark Core acts as the base engine for large-scale parallel and distributed data processing. It is the distributed execution engine used in conjunc...
- GraphX – Implements graphs and graph-parallel computationMLib – Used for machine learningSpark Core – Base engine used for large-scale parallel and...
- GraphX is the API used for implementing graphs and graph-parallel computing in Apache Spark. It extends the Spark RDD with a Resilient Distributed...
- Spark SQL modules help in integrating relational processing with Spark’s functional programming API. It supports querying data via SQL or HiveQL (H...
- Parquet is a columnar format that is supported by several data processing systems. With it, Spark SQL performs both read as well as write operation...
- Apache Mesos – Commonly used cluster managerStandalone – A basic cluster manager for setting up a clusterYARN – Used for resource management
- Yes, it is possible to use Apache Spark for accessing as well as analyzing data stored in Cassandra databases using the Spark Cassandra Connector....
- Enhanced Speed – MapReduce makes use of persistent storage for carrying out any of the data processing tasks. On the contrary, Spark uses in-memory...
- Having compatibility with Hadoop is one of the leading advantages of Apache Spark. The duo makes up for a powerful tech pair. Using Apache Spark an...
Dec 19, 2023 · YARN mode in Apache Spark enables integration with Hadoop YARN for resource management. Allocate resources to Spark application using YARN’s ResourceManager and NodeManagers.
People also ask
What is the difference between yarn and spark?
What is the difference between a spark job and a YARN application?
Can I run Apache Spark on yarn?
Can spark run on yarn?
What is Apache Spark?
What is spark yarn mode?
Jul 24, 2018 · A Spark application can be used for a single batch job, an interactive session with multiple jobs, or a long-lived server continually satisfying requests. A Spark job can consist of more than just a single map and reduce. On the other hand, a YARN application is the unit of scheduling and resource-allocation.
