{"id":23561,"date":"2021-12-26T12:06:00","date_gmt":"2021-12-26T06:36:00","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/spark-interview-questions\/"},"modified":"2024-10-15T01:12:52","modified_gmt":"2024-10-14T19:42:52","slug":"spark-interview-questions","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/spark-interview-questions\/","title":{"rendered":"Top 10 Spark Interview Questions and Answers"},"content":{"rendered":"\n

Spark is an open-source framework that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. If you're facing a Spark Interview and wish to enter this field, you must be well prepared. This blog will help you understand the top spark interview questions and help you prepare well for any of your upcoming interviews. The blog will cover questions that range from the basics to intermediate questions.<\/p>\n\n\n

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Top Spark Interview Questions:<\/strong><\/h2>\n\n\n\n

Q1) What is Apache Spark?<\/strong><\/h3>\n\n\n\n

Apache Spark is an Analytics engine for processing data at large-scale<\/strong>. It provides high-level APIs (Application Programming Interface) in multiple programming languages like Java, Scala, Python and R. It provides an optimized engine that supports general execution of graphs. It also supports an upscale set of higher-level tools including Spark SQL for SQL and structured processing of data, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for incremental computation and stream processing.<\/p>\n\n\n\n

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Spark interview Questions PDF<\/a><\/div>\n<\/div>\n\n\n\n

Q2) What is an RDD in Apache Spark?<\/strong><\/h3>\n\n\n\n

RDD Stands for Resilient Distributed Dataset<\/strong>. From a top-level perspective, every Spark application consists of a driver program that runs the user\u2019s main function and executes various parallel operations on a cluster. RDD is an abstract term provided by Spark, which means a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel so they automatically recover from node failures making them<\/strong> fault-tolerant<\/strong>. <\/p>\n\n\n\n

RDD\u2019s can be created in two ways: <\/p>\n\n\n\n

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  1. Parallelizing<\/strong> an existing collection<\/strong> in your driver program.<\/li>\n\n\n\n
  2. Referencing<\/strong> a dataset from an external<\/strong> storage system<\/strong>, such as a shared filesystem, HDFS, HBase, or any data source offering a Hadoop Input Format.<\/li>\n<\/ol>\n\n\n\n

    RDD\u2019s support two types of operations:<\/p>\n\n\n\n

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    1. Transformations<\/strong>: which create a new dataset from an existing one, e.g.: MAP.<\/li>\n\n\n\n
    2. Actions<\/strong>: which return a value to the driver program after running a computation on the dataset. e.g.: REDUCE.<\/li>\n<\/ol>\n\n\n\n

      All transformations in Spark are lazy, meaning, they do not compute their results right away. Instead, they just remember the transformations applied to some base dataset. The transformations are only computed when an action requires a result to be returned to the driver program. This design enables Spark to run more efficiently.<\/p>\n\n\n\n

      One of the most important capabilities in Spark is persisting<\/strong> (or caching<\/strong>) a dataset in memory across operations. When you persist an RDD, each node stores any partitions of it that it computes in memory and reuses them in other actions on that dataset (or datasets derived from it). This allows future actions to be much faster (often by more than 10x).<\/p>\n\n\n\n

      Q3) Why use Spark on top of Hadoop?<\/strong><\/h3>\n\n\n\n

      While Apache Hadoop is a framework which allows us to store and process big data<\/strong> in a distributed environment, Apache Spark is only a data processing engine<\/strong> developed to provide faster and easy-to-use analytics<\/strong> than Hadoop MapReduce. So, we store data in the Hadoop File System and use YARN for resource allocation on top of which we use Spark for processing data fast. Hadoop Map Reduce can\u2019t process data fast and Spark doesn\u2019t have its own Data Storage so they both compensate for each other\u2019s drawbacks and come strong together.<\/p>\n\n\n\n

      Note: We can use Spark Core or Hadoop Map Reduce as a Computing Engine.<\/p>\n\n\n\n

      Q4) How to install Spark on windows?<\/strong><\/h3>\n\n\n\n

      Prerequisites:<\/strong><\/p>\n\n\n\n

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      1. A system running Windows 10<\/li>\n\n\n\n
      2. A user account with administrator privileges (required to install software, modify file permissions, and modify system PATH)<\/li>\n\n\n\n
      3. Command Prompt or Powershell<\/li>\n\n\n\n
      4. A tool to extract .tar files, such as 7-Zip<\/li>\n\n\n\n
      5. Already installed Java<\/li>\n\n\n\n
      6. Already installed Python<\/li>\n<\/ol>\n\n\n\n

        Install Apache Spark on Windows<\/strong><\/h3>\n\n\n\n

        Step 1: Download Apache Spark<\/strong><\/h3>\n\n\n\n

        1. Open a browser and navigate to https:\/\/spark.apache.org\/downloads.html<\/a>.<\/p>\n\n\n\n

        2. Under the Download Apache Spark <\/em>heading, there are two drop-down menus. Use the current non-preview version.<\/p>\n\n\n\n