The year of Big Data was 2018 — a year in which big data and analytics made significant progress because of breakthrough technologies, data-driven decision-making, and outcome-centric analytics. Big data and business analytics (BDA) sales will increase from $130.1 billion in 2016 to more than $203 billion in 2021. (source IDC). Prepare with these top Apache Spark Interview Questions to gain a competitive advantage in the booming Big Data industry, where global and local companies, large and small, are seeking qualified Big Data and Hadoop professionals.
As a big data expert or beginner, you must be familiar with the appropriate keywords, master the relevant technologies, and be prepared to answer frequently requested Spark interview questions. This article is your doorway to your next Spark task, with questions and answers about Spark Core, Spark Streaming, Spark SQL, GraphX, and MLlib, among other topics.
Note: Before we get into the meat of Spark interview questions, we'd like to point out that these Spark interview questions have been hand-picked by experienced hiring managers with years of experience in the area. Top Spark recruitment managers carefully analyzed and organized each response in this article.
Spark Interview Questions And Answers:
1- Question: Define what is Apache Spark?
Answer: Apache Spark is a real-time cluster open-source computing framework. It has a prosperous open-source community and is now the most active Apache project. Spark provides an interface with implicit parallel data and fault tolerance for the programming of whole clusters. Spark is one of Apache Software Foundation's most successful projects. As the industry leader for large data processing, Spark has matured visibly. Many businesses run Spark on clusters with thousands of nodes. Today, important actors like Amazon, eBay, and Yahoo embrace Spark!
2- Question: What languages Apache Spark supports and which is the most popular one?
Answer: Apache Spark supports the following four languages: Scala, Java, Python, and R. Scala and Python have interactive Spark shells among these languages. The shell is accessible via ./bin/spark-shell and the shell of Python via ./bin/pyspark. The most frequently used of them is Scala because Spark is in Scala and is the most popular one for Spark.
3- Question: What are the benefits of Spark over MapReduce?
Answer: Spark offers MapReduce the following advantages:
- Due to the availability of memory processing, Spark uses persistent storage for any data processing work around 10 or 100 times quicker than Hadoop MapReduce.
- In contrast to Hadoop, Spark provides integrated libraries for several operations from the same core as batch processing, streaming, machine learning, and interactive SQL queries. Hadoop, on the other hand, only supports batch processing.
- Hadoop depends heavily on your drive whereas Spark encourages in-memory data storage and caching.
- In the same dataset, Spark is able to execute many calculations. Iterative computation is the term for this, although Hadoop does not support it.
4- Question: What is YARN?
Answer: Like Hadoop, YARN is one of Spark's major characteristics, offering a centralized platform for resource management to enable scalable business throughout the cluster. YARN is a distributed container management tool such as Mesos, whereas Spark is a tool for data processing. The same way Hadoop Map Reduce can operate on YARN is possible for Spark to run on YARN. Running Spark on YARN requires a binary Spark distribution built on YARN support.
5- Question: Do you need to install Spark on all nodes of the YARN cluster?
Answer: No, Spark is on YARN's top. Spark operates independently of its system. Spark provides various alternatives in place of its own built-in manager, Mesos, to use YARN for sending jobs to the cluster. Also, various YARN setups are available. These include master, deploy mode, drivers' memory, executors, and queues.
6- Question: Is there any benefit of learning MapReduce if Spark is better than MapReduce?
Answer: (this is a very important Spark interview question)Yes, MapReduce is also a paradigm for many Big Data technologies such as Spark. When the data becomes larger and larger, it is highly important to employ MapReduce. In order to optimize the best, most of the technologies like Pig and Hive transform their queries into MapReduce.
7- Question: Explain the concept of Resilient Distributed Dataset (RDD)?
Answer: RDD refers to Resilient Distributed Datasets. An RDD is operating elements that operate concurrently to a fault-tolerant collection. The divided data is unchanging in nature in RDD. Two forms of RDD exist mainly:
- Collections parallelized: The current RDDs execute in parallel.
- Hadoop Datasets: they conduct HDFS or other storage systems functions on each file record.
RDDs, in theory, are chunks of data that held across many nodes in memory. Spark analyzes RDDs randomly. This lazy assessment helps Spark's speed.
8- Question: What is Executor Memory in a Spark application?
Answer: The heap size and number of cores for an executor are the same in each spark application. The heap is the executor memory that the executor flag controls by the spark.executor.memory property. On each worker node, each spark application has one executor. Mainly the runner memory measures the memory utilized by the working node.
9- Question: Define Partitions in Apache Spark?
Answer: As the name implies, the division is smaller and more logical than 'split' data. It's a logical part of a huge data collection. Partitioning is the technique through which logical data units are derived to speed up the operation. In tandem with the distributor data processing, Spark handles data with little network traffic to transfer data across executors. Spark uses the default to read data from nearby nodes into the RDD. As Spark generally accesses dispersed divided data, it generates partitions to hold the data chunks to improve transformation processes. All is an RDD partitioner in Spark.
10- Question: What operations does RDD support?
Answer: The basic logical data unit of Spark is RDD (Resilient Distributed Dataset). RDD has given a variety of things. Each RDD is split into several divisions via distributed methods. These can keep on the disk of several machines in a cluster, or in the memory. The data structure of RDDs (Read-only) is immutable. You cannot modify the original RDD, but with all the modifications you desire, you can always turn it into a new RDD.
RDDs enable two sorts of activities: transformations and actions.
Transformations: Create transformations of the RDD, like map, reduByKey, and filter that we just saw. Transformations will take place upon request. So they are lazily calculated.
Actions: Actions return final RDD calculation results. Actions initiate execution utilizing a line graph in which the data is full into their original RDD, transformed intermediately, and returned final results to the Driver application or written into their system.
11- Question: What do you understand about Transformations in Spark?
Answer: Transformations are RDD functions, which lead to another RDD. It will only run till an action takes place. Map() and Filter() are instances in which the former transforms each RDD element with the function provided to it, and results in another RDD. The filter() generates a new RDD by picking the components that pass the function parameter from the current RDD.
12- Question: Define Actions in Spark?
Answer: An action helps to return RDD data to the local computer. Action is the consequence of all transformations already produced. Actions initiate execution utilizing a line graph in which the data is full into their original RDD, transformed intermediately, and returned final results to the Driver application or written into their system.
13- Question: Define functions of SparkCore?
Answer: For large-scale parallel and distributed data processing, Spark Core is the fundamental engine. The core is the distributed execution engine with the Java, Scala, and Python APIs for the creation of ETL distributed apps. SparkCore carries out many key activities such as memory management, surveillance jobs, fault tolerance, job planning, and storage system interface. Additional libraries on the top of the core also let a wide variety of streaming, SQL, and machine learning applications. He is in charge of:
- Memory control and recovery of faults
- Plan, distribute and monitor work on a cluster
- Storage systems interacting
14- Question: How is Streaming implemented in Spark? Explain with examples?
Answer: Spark streaming used for the processing of real-time stream data. So, the core Spark API is a useful addition. It allows the processing of live data streams with high throughputs and fault-tolerant streams. The mainstream unit is DStream, which is simply a collection of RDDs for processing the data in real-time. The data of many sources like Flume, HDFS are streamed into file systems, dashboards, and databases. The batch processing is analogous in that data usually split into streams similar to lots.
15- Question: Is there an API for implementing graphs in Spark?
Answer: GraphX is the graphical parallel graphics and graphics API. It thereby enhances the Spark RDD with a resilient graph. The characteristic graph is a multigraph directed with many edges. Each edge and vertex has related user-defined characteristics. There are several relations between the identical vertices and the parallel edges here. GraphX extends the abstraction from Spark RDD on a high level with the introduction of the resistant distributed property graph: a guided multigraph with properties connected to each vertex and edge.
GraphX offers a range of key operators (e.g. subgraph, joinVertices, and mapReduceTriplets) and an optimized version of the Pregel API with the purpose of supporting graph computing. GraphX also has an increasing number of graph algorithms and constructors to ease the job of graph analysis.
16- Question: What is PageRank within GraphX?
Answer: PageRank analyzes whether each vertex in a network supports the edge from u to v. For instance, if many others follow a Twitter account, the individual is highly qualified. GraphX provides PageRank as a method of PageRank Object with static and dynamic deployments. Static PageRank works for a set number of repetitions, whereas dynamic PageRank works to converge the rankings (i.e., stop changing by more than a specified tolerance).
17- Question: How is machine learning implemented in Spark?
Answer: MLlib is Spark's scalable study machine library. It intends to facilitate machine learning with common learning methods and to use cases like clustering, filters, reduction in size, and so on.
18- Question: Is there a module to implement SQL in Spark? How does it work?
Answer: (important Spark interview question) Spark SQL is a new Spark module that combines relation processing into the functional programming API of Spark. The SQL or Hive Query Language enables querying of data. Spark SQL will be an effortless transfer from prior technologies to enhance the borders of standard relational data processing for those of you who are familiar with RDBMS. With functional programming, Spark SQL combines relational processing. It also supports a number of data sources and enables code modifications to use for weaving SQL queries, which makes it a strong tool.
The next are the four Spark SQL libraries.
- Source Data API
- Interpreter and Optimizer of DataFrame API
- SQL service.
19- Questions: How can Hadoop work alongside Apache Spark?
Answer: Compatibility with Hadoop is the finest feature of Apache Spark. As a result, the combination of technologies is highly powerful. Here we'll see how Spark can profit from Hadoop's finest. Together with Spark and Hadoop, we can use Spark to make optimal use of Hadoop's HDFS and YARN.
20- What do you understand about worker node?
Answer: Any working node which can run the application code in a cluster is a worker node. The driver software has to hear and accept incoming links from its executors and must be reachable by the network from the working nodes. The worker's node is the slave node, essentially. The master node allocates work, and the node does the duties assigned. Worker nodes process and report to the master data stored in the node. The master schedule tasks depend on the available resources.
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