Are you planning to apply for an Apache Spark job? Then, you have come to the right place as we are going to list the top 11 Apache Spark interview questions. We will cover all the answers to the top questions so that you can get a good idea of how to answer Apache Spark interview questions.
Also Read: 10 Best Books for Learning Apache Spark
The demand for Apache Spark experts is increasing every single day as companies now understand the importance of big data. This article will surely help you to get prepared for the Apache Spark interview that you are planning to appear for in the future.
Top 11 Apache Spark Interview Questions are
1. What is Apache Spark?
Answer: Apache Spark is a big-data cluster computing framework used for real-time processing. It is open-source and has a huge open source community to back it up. With Apache Spark, anyone can program entire cluster with the help of API interface. The framework also supports fault-tolerance and implicit data parallelism.
Apache Spark went open source in 2010 and now more than 1000 companies are actively contributing to its core. Its success can easily be gauged by the fact that it is used by big players such as eBay, Amazon, and Microsoft!
2. What are the key features of Spark?
Answer: There are many key features of Spark. Some of the features are as below:
- It works seamlessly with Hadoop and HDFS.
- It comes with independent Scala interpreter.
- Spark’s speed is one of its key features. It beats Hadoop in large-scale data processing.
- Supports Resilient Distributed Datasets which can easily be used in a cluster.
- It also supports Real-Time Computation, Machine Learning, and Multiple Format.
3. What are the main components of Spark ecosystem?
Answer: The main components of Spark ecosystem are as follows:
- Spark core: The base engine that offers large-scale distributed and parallel data processing.
- Spark SQL: Relational functionality that works in tandem with Spark’s API.
- GraphX: offers all graph related features.
- MLib: Machine learning library.
- Spark Streaming: Offers real-time streaming data processing.
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4. What is the function of SparkCore?
Answer: SparkCore is the heart of Apache Spark. It handles important functions such as fault-tolerance, memory management, job scheduling, storage system interaction, and so on. The engine offers large-scale distributed and parallel processing. The SparkCore functionality can easily be accessed with the help of available Scala, Java, And Python APIs.
5. Which language is supported by Apache Spark?
Answer: Apache Spark is written using Scala. It means that Spark supports Scala and Java by default. Other than Scala and Java, R and Python can also be used efficiently. The choice for the programming language depends on the project requirement.
6. Can you explain RDD?
Answer: The fundamental data structure for Spark is Resilient Distributed Datasets (RDD). Data in the network is stored in this format. It has many features including immutability, resilience, and Parallel/Partitioned. As data is stored in the cluster, the above features help the data to be run parallelly and on multiple nodes.
RDD offers two operations: actions and transformations, and can be used to store any data. If there is a key associated with a value, it is known as Pair RDD.
7. Compare Hadoop MapReduce and Spark.
Answer: There is a lot of difference between these two frameworks even if they are used for same purpose, i.e., big data processing. However, there is some key difference between them.
- Speed: Spark is created with speed in mind. Hence, Apache Spark can go up to 100x than MapReduce on memory.
- Implementation: MapReduce is comparatively harder to work and maintain than Spark.
- Real-time analysis: Spark can do real-time analysis whereas MapReduce cannot.
- Security: Spark in comparison is less secure than MapReduce as it only supports secret password authentication. Hadoop, in addition to secret password authentication, supports ACLs.
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8. Define Actions.
Answer: Actions is one of the operations offered by RDD. When “Action” operation takes place, the local machine receives the data from RDD. Action’s execution is delayed to ensure that operations run smoothly. Two good example of Actions are – reduce( ) and take( ).
- reduce( ): it is executed until only one value is left. The function takes two arguments.
- take( ): take( ) method act as a data carrier from RDD to the local node.
9. Define Partitions.
Answer: Partitions are used to split the data into logical units. It helps the Spark to run computations parallelly on different nodes. Partitions are very similar to “split” in MapReduce. The partition created is located on different machine. Spark is designed to run data from different machine efficiently, and that’s why it is so popular among the big data community.
10. How is machine learning implemented in Spark?
Answer: With Spark, anyone can use the MLib to do machine learning. With MLib, common algorithms can easily be implemented. MLib library also supports common machine learning cases such as dimensional reduction, regression filtering, clustering, etc.
11. What is GraphX?
Answer: GraphX in Spark allows its user to create interactive graphs from scratch. With GraphX, building and transforming graphs is easy. It is a crucial part of the Spark ecosystem as it gives the necessary tools to visualize data.
These top 11 Apache Spark interview questions will surely help you in the Apache Spark interview. You can also choose to do a Spark Developer Certification (HDPCD) to make your CV strong.
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