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Previous | Next | Audio Book for Spark Interview Questions is available here | Top 150 Latest Spark Interview Questions | Quickly go through Spark Training Python & Scala
Question: What all you need to load data from AWS S3 bucket in Spark?
Answer: You can download the data from AWS (Amazon Web Service) S3 bucket. You need following three things
- URL for file stored in a bucket
- AWS Accesss Key ID
- AWS Secret Access Keys
Once, you have this info, you can load data from S3 bucket as below.
Question: How do you relate to SparkContext, SQLContext and HiveContext?
Answer: SparkContext provide the entry point for Spark system, to create SQLContext you need SparkContext object. HiveContext provides a superset functionality provided by the basic SQLContext. However, since Spark 2.0 there is a SparkSession object, which is preferred to enter into Spark system. SparkSession is unification for all these three SparkContext, SQLContext and HiveContext.
Question: Can you describe which all projects of Spark you have used?
Answer: Spark has many other project than Spark Core as below
- Spark SQL: This project help you to work with structured data, you can mix both SQL queries and Spark programming API, for your expected results.
- Spark Structured Streaming: It is good for processing streaming data. It can help you to create fault-tolerant streaming applications.
- MLib: This API is quite reach for writing machine learning applications. You can use Python, Scala, R language for writing Spark Machine Library.
- GraphX/GraphFrame: API for graphs and graph parallel computations.
Question: What is the difference, when you run Spark Applications either on YARN or standalone cluster manager?
Answer: When you run Spark Applications using YARN then Application processes are managed by the YARN Resource Manager and Node Manager.
Similarly when you run on Spark standalone, then application processes are managed by Spark Master and Worker Nodes.
Question: How do you compare MapReduce and Spark Application?
Answer: Spark has many advantages over the Hadoop job, lets describe each one
MapReduce: The highest level unit of computation in MapReduce is a Job. Jobs responsibility includes to load data, applies map function and then shuffles it, after that run reduce function and finally write data to persistence storage.
Spark Application: Highest-level unit of computation is an application. 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. So a Spark application can consists of more than just a single MapReduce job.
MapReduce starts a process for each task. In contrast, a Spark application can have processes running on its behalf even when it is not running any application. And in case of Spark, multiple tasks can run within the same executor. Both by combining extremely fast task startup as well as in-memory data storage, resulting in orders of magnitude faster performance over MapReduce.
Previous | Next | Audio Book for Spark Interview Questions is available here | Top 150 Latest Spark Interview Questions | Quickly go through Spark Training Python & Scala
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