Are you planning to move your career into Apache spark? Well, it’s a lucrative career option in today’s IT world. Major companies like Amazon, eBay, JPMorgan, and more are also adopting Apache Spark for their big data deployments.
However, due to heavy competition in the market, it’s essential for us to know each and every concept of Apache Spark to clear the interview. To help you out, we have collected the top Apache Spark Interview Questions and Answers for both freshers and experienced. All these questions are compiled after consulting with Apache Spark training experts.
So utilize our Apache spark Interview Questions to maximize your chances in getting hired.
If you want to enrich your career as an Apache Spark Developer, then go through our Spark Training
Ans: Spark is an open-source and distributed data processing framework. It provides an advanced implementation engine which includes in-memory computation and periodic data flow. Apache Spark runs automatically both in Hadoop or cloud and able to access various data sources, including Hbase, HDFS, and Cassandra.
Ans: Following are the main features of Apache Spark:
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Resilient Distribution Datasets (RDD) represents a fault-tolerant set of elements that operate in parallel. The data in the RDD section is distributed and immutable. There are mainly two types of RDDs.
Ans: The objective of the Spark engine is to plan, distribute, and monitor data applications in a cluster.
Ans: Partition is a process of obtaining logical units of data for speeding up data processing. In simple words, partitions are smaller and logical data separation is similar to a ‘split’ in MapReduce.
Ans: Transformations and actions are the two types of operations supported by RDD.
Ans: In simple words, transformations are functions implemented in RDD. It will not work until action is performed. map() and filter() are some examples of transformations.
While map() function is repeated on each RDD line and splits into a new RDD, the filter function () creates a new RDD by selecting the elements that pass the function argument from the current RDD.
Ans: Actions in Spark makes it possible to bring data from RDD to the local machine. Reduce () and take () are the functions of Actions. Reduce() function is performed only when action repeats one by one until one value lefts. The take () accepts all RDD values to the local key.
Ans: Various functions are supported by Spark Core like job scheduling, fault-tolerance, memory management, monitoring jobs and much more.
To gain more knowledge of Apache Spark, check out Apache Spark Tutorial
Ans: Spark does not support data replication, so if you have lost information, it is reconstructed using RDD Lineage. RDD generation is a way to reconstruct lost data. The best thing to do is always remember how to create RDDs from other datasets.
Ans: The Spark driver is a program that runs on the main node of the device and announces transformations and actions on the data RDD. In a nutshell, the driver in Spark makes SparkContext in conjunction with the given Spark Master. It also provides RDD graphs to the master, where the cluster manager operates.
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By default, Hive supports Spark on YARN mode.
HIVE execution is configured in Spark through:
hive> set spark.home=/location/to/sparkHome;
hive> set hive.execution.engine=spark;
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Ans: Stream processing is an extension to the Spark API, which allows live data streaming. Data from various sources such as Kafka, Flume and Kinesis are processed and sent to the file system, live dashboards and databases. In terms of input data, its similar to batch processing for dividing data into streams like batches.
Ans: Spark uses GraphX for graphics processing and building. The GraphX lets programmers think about big data.
Ans: Spark supports MLlib, which is a scalable Machine Learning library. Its objective is to make Machine Learning easy and scalable with common learning algorithms and use cases like regression filtering, clustering, dimensional reduction, and the like.
Ans: Spark SQL is also known as Shark, is used for processing structured data. Using this module, relational queries on data are executed by Spark. It supports SchemaRDD, which consists of schema objects and row objects representing the data type of each column in a row. This is just like a table in relational databases.
Ans: The columnar format file supported for other data processing systems is called a Parquet file. Both read and write operations on Parquet files are executed by Spark SQL, making it one of the best big data analytics formats.
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Ans: Like Hadoop, YARN is one of the main features of Spark, which provides resource management platform to produce scalable operations across the entire cluster.
Ans: Following are the functions of Spark SQL:
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Ans: Yes, MapReduce is a standard used by many big data tools, including Apache Spark. As data grows, it becomes extremely important to use MapReduce. Many tools, such as Pig and Hive, convert queries to the MapReduce phases for optimizing them.
Ans: When SparkContext joins the Cluster Manager, it receives the executor on nodes in the cluster. The executors are the Spark processes that perform predictions and save data on workplaces. The final tasks from SparkContext are sent to the executor for executions.
Ans. Yes, we can use Spark for accessing and analyzing the data stored using the Spark Cassandra Connector. For connecting Spark to a Cassandra cluster, we need to add the Cassandra Connector to the Spark project.
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Ans: Broadcast variables enable the developers to have a read-only variable cached on each machine instead of copying it with tasks. In an efficient way, it allows every node to copy a large input dataset. Using efficient broadcast algorithms, Spark tries to share broadcast variables.
Ans: Akka is used for scheduling in Spark. All workers are allowed to task to master after registration. The master assigns only tasks. Here, Spark uses Akka to send messages between workers and teachers.
Ans: Spark SQL supports SQL and the Hive query language in the Spark Core engine without changing any syntax. In Spark SQL, you can combine an SQL table and an HQL table.
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