Top 20 Apache Spark Interview Questions and Answers

Wanna get a job using your Apache Spark skills, do you? How ambitious! Are you ready? You’re going to have to get the job first, right? And that means an interview. And questions. Lots of them. But fear not, we’re here to help you. In fact, we’re providing twenty top Spark interview questions and answers for you to study. Now – go get that position.

What’s that? Are you not sure you’re ready? Why not prepare a little first with a background course that will certify you impressively, such as our Big Data Hadoop Certification Training. In this course, you’ll learn or brush up on the concepts of the Hadoop framework and learn how the components of the Hadoop ecosystem, such as Hadoop 2.7, Yarn, MapReduce, HDFS, Pig, Impala, HBase, Flume, Apache Spark, etc. fit in with the Big Data processing lifecycle. You will also implement real-life projects in banking, telecommunication, social media, insurance, and e-commerce on CloudLab. Not to mention you’ll get a  certificate to hang on your wall and list on your resume and LinkedIn profile. Then, you’ll surely be ready to master the answers to these Spark interview questions.

It’s no secret the demand for Apache Spark is rising rapidly. With companies like Shopify, Amazon, and Alibaba already implementing it, you can only expect more to adopt this large-scale data processing engine in 2019. Because it can handle event streaming and process data faster than Hadoop MapReduce, it’s quickly becoming the hot skill to have. And the big bucks are in it. According to the 2015 Data Science Salary Survey by O’Reilly, in 2016, people who could use Apache Spark made an average of $11,000 more than programmers who didn’t.

Nice, huh? What are you waiting for? Know the answers to these common Spark interview questions and land that job. You can do it, Sparky.

Here are the top 20 Apache Spark Interview Questions and Answers.

  1. Compare Hadoop and Spark.

    • Of course, you’d better know this one. Kinda critical, right? Luckily, you can answer this in many ways, but keep it simple – act like you know it so well, the answer is your elevator pitch to success. Summarize it by saying that Spark offers better simplicity, flexibility, and performance. Then, back up these claims with the following:
    • Spark stores data in-memory, placing it in Resilient Distributed Databases (RDD), so it’s 100 times faster than Hadoop for big data processing.
    • Because it uses an interactive mode, Spark is easier to program.
    • It allows for complete recovery using lineage graph should anything go awry.

    Easy, peasy. You’ve got one Spark interview question done. Let’s go on.
  2. What is Shark?

    No – don’t bring up Jaws here. While you might find that amusing, your future employer might not. Stay focused! Remember your goal: answering these Spark interview questions right. Thus, tell him or her that Shark is was developed for people from a database background used for accessing Scala MLib capabilities through Hive like an SQL interface. The Shark tool helps data users run Hive on Spark and offers compatibility with the Hive metastore, queries, and data.
  3. List some use cases where Spark outperforms Hadoop in processing.

    • Sensor Data Processing –Apache Spark’s ‘In-memory computing’ works best here because it retrieves and combines data from different sources
    • Spark is better than Hadoop at real-time data querying 
    • Stream Processing – For processing logs and detecting frauds in live streams for alerts, Apache Spark works best.
  4. What is a Sparse Vector?

    Simply stated, it’s a vector with two parallel arrays, one for indices, one for values, used for storing non-zero entities to save space.
  5. What is an RDD?

    RDDs (Resilient Distributed Datasets) are basic abstractions in Apache Spark that represent the data entering the system in object formats. Used for in-memory computations on large clusters, they have a fault-tolerant manner. RDDs are read-only portioned, collections of records that are:

    • Immutable and cannot be altered;
    • Resilient, so that when a node holding the partition fails, the other node retrieves its data.
  6. Explain the transformations and actions in the context of RDDs.

    Say, you execute transformations on-demand as functions to produce a new RDD. You follow all transformations by actions. Some examples of transformations include a map, filter, and reduceByKey.

    Actions are the results of RDD computations or transformations. After you perform an action, the data from RDD moves back to the local machine. Some examples of actions include reduce, collect, first, and take.
  7. What languages are supported by Apache Spark for developing big data applications?

    No worries with this, one of the easier Spark interview questions: Scala, Java, Python, R, and Clojure.
  8. Can you use Spark to access and analyze data stored in Cassandra databases?

    Yes, when you use Spark Cassandra Connector, tell the interviewer.
  9. Is it possible to run Apache Spark on Apache Mesos?

    Give a tip from Nike here: say, “Just do it.”
  10. Explain the different cluster managers in Apache Spark.

    • Apache Mesos – With rich resource scheduling capabilities, it’s terrific to run Spark along with other applications. When several users run interactive shells, it scales down the CPU allocation between commands.
    • Standalone deployments – Great for new deployments that only run and are easy to set up.
  11. How can Spark be connected to Apache Mesos?

    Wow. These Spark interview questions are getting complex, eh? Let’s keep powering through. Here’s this answer:

    • Configure the Spark driver program to connect to Mesos. A Spark binary package should be in a location accessible by Mesos (or)
    • Install Apache Spark in the same location as that of Apache Mesos and configure the property ‘spark.mesos.executor.home’ to point to the location where it is installed.
  12. Why do you need broadcast variables when working with Apache Spark?

    Why, indeed? You better know. It’s easy, right? Because when working with Spark, the use of broadcast variables negates the need to ship copies of a variable for every task so that you can process data faster. Broadcast variables help store a lookup table inside the memory, enhancing the retrieval efficiency when compared with an RDD lookup.
  13. What is the lineage graph?

    A lineage graph, as you know, Sparky, is the representation of dependencies in between RDDs. Lineage graph information computes each RDD on-demand, so whenever a part of persistent RDD is lost, you can recover this data using the lineage graph information. But you knew that.
  14. What are the benefits of using Spark with Apache Mesos?

    You’ve got this one, too. It renders scalable partitioning among various Spark instances and dynamic partitioning between Spark and other big data frameworks.
  15. When running Spark applications, must you install Spark on all the nodes of YARN cluster?

    No, silly. Spark can execute on top of YARN or Mesos clusters without affecting any change to the cluster.
  16. Which Spark library allow reliable file sharing at memory speed across different cluster frameworks?

    One word: Tachyon.
  17. Compare Hadoop and Spark in terms of ease of use.

    Take your time with this one. It’s a crucial one. Say:

    Hadoop MapReduce requires programming in Java, which is tricky, though Pig and Hive make it a bit simpler. However, learning Pig and Hive syntax takes time. Spark has interactive APIs for different languages like Java, Python or Scala and includes Shark, i.e., Spark SQL for SQL lovers – making it comparatively easier to use than Hadoop.
  18. What are the disadvantages of using Apache Spark over Hadoop MapReduce?

    Ahh. Look. The interviewer is trying to put one over on you. Good thing we’ve prepped you. Remember, you’re ready for both positives and negatives. Report that Apache Spark does not scale well for computing-intensive jobs and consumes a lot of system resources. Also, Spark’s in-memory capability at times comes as a major roadblock for the cost-efficient processing of big data. In addition, Spark does have its own file management system and therefore, must be integrated with other cloud-based data platforms or Apache Hadoop.
  19. What makes Apache Spark good at low-latency workloads like graph processing and machine learning?

    Apache Spark stores data in-memory for faster model building and training. Machine learning algorithms require multiple iterations to create an optimal model, while graph algorithms traverse all the nodes and edges. These low latency workloads that need multiple iterations can lead to increased performance. Less disk access and controlled network traffic make a huge difference when a lot of data must be processed.
  20. What is Lazy Evaluation?

    Don’t be lazy! You’re almost done with all these Spark interview questions. The answer is:

    Spark is intellectual in how it operates on data. When you tell Spark to operate on a given dataset, it follows the instructions notes them without forgetting, but does nothing unless asked for the result. When a transformation like a map () is called on an RDD, the operation is not performed immediately. Transformations in Spark are not evaluated until you perform an action, which aids in optimizing the overall data processing workflow.

OK – enough Spark interview questions already!

We could go on, but these are plenty, don’t you think? Undoubtedly, they provide ample material to prepare. You might be asked more and/or some different ones. What? That makes you nervous? We can appease you. It’s quite simple.

Take our Apache Spark and Scala Certification Training, and you’ll have nothing to fear. It’s a wonderful course that’ll give you another superb certificate. In it, you’ll advance your expertise working with the Big Data Hadoop Ecosystem. Also, you’ll master essential skills of the Apache Spark open-source framework and the Scala programming language, including Spark Streaming, Spark SQL, machine learning programming, GraphX programming, and Shell Scripting Spark. You’ll also understand the limitations of MapReduce and the role of Spark in overcoming these limitations and learn Structured Query Language (SQL) using SparkSQL, among other highly valuable skills that will make answering any Spark interview questions a potential employer throws your way easy. Brilliant, no?

Get ready, Sparky. Take that certification course, study those answers and you’ll land that dream job. Any questions?

About the Author

Shivam AroraShivam Arora

Shivam Arora is a Senior Product Manager at Simplilearn. Passionate about driving product growth, Shivam has managed key AI and IOT based products across different business functions. He has 6+ years of product experience with a Masters in Marketing and Business Analytics.

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