Ans: Apache Spark is one of the popular tools used in big data and it’s an open-source cluster computing framework mainly used in real-time processing. This Apache Spark consists of thriving open-source features and actively used technology in most of the big data projects. This popular big data tool provides an interface for entire programming clusters with implicit fault-tolerance.
Ans: The following are the important differences between Apache Spark and MapReduce:
|Apache spark processes the data in the form of batches which can be used for real-time purpose||MapReduce also processes the data in the form of batches only|
|Apache spark runs almost 100 times faster than MapReduce||Whereas MapReduce is slower than Apache Spark|
|Spark stores all the data in the RAM||MapReduce stores all the data in HDFS memory|
|Spark supports caching and in-memory data storage capabilities||MapReduce is a disk-dependent tool|
Ans: There are 3 major components of the Apache Spark ecosystem;
The languages are JAVA, Python, R, and Scala.
1. Spark Core
2. Spark SQL
3. Spark streaming
4. Spark MLlib
The following are the important key features of Apache Spark:
1. Apache Spark allows users to integrate with Hadoop data.
2. This tool consists of many interactive languages like Shell, Scala.
3. Apache Spark offers multiple analytic tools and they are used for Interactive query analysis, graph processing, and graph processing.
4. The Apache spark consists of RDDs; this can be cached across the computing nodes in multiple data clusters.
Ans: The following are the key differences between Apache Spark and Hadoop:
|Easy to program and does not require any kind of abstraction||Difficult to program and requires different abstraction|
|Has in-built interactive mode||No in-built interactive mode except tools like pig and hive|
|Programmers can modify the data in real-time environments||Allows you to just process a batch of stored data|
Ans: Apache spark mainly supports four programming languages;
1. Scala language
Among these languages, the SCALA and python are interactive shells for Apache spark. Scala is the most used language because Apache spark codes will be written in Scala.
Ans: RDD is nothing but Resilient Distribution Datasets which is a fault-tolerated collection of operational datasets that run in parallel environments. There are 2 partitioned datasets available in an RDD such as Immutable and distributed.
There are 2 types of RDDs available such as,
1. Parallelized collections
2. Hadoop datasets.
Ans: The Apache Spark engine is responsible for distributing, scheduling, and monitoring the data applications across the data cluster.
Ans: YARN is a main key feature of Apache spark and this provides a central and resource management platform. This delivers a scalable operation across the data cluster. YARN is a distributed container manager, like Mesos. Spark runs on YARN and offers binary distribution built on YARN tools support.
Ans: Apache Spark offers two methods to create RDD:
1. By parallelizing a collection in the driver program.
2. This also makes use of Spark Context parallelize the data sets.
Ans: Partitions are a smaller and logical division of similar data sets which will be split in MapReduce. Partitions are the process of dividing the logical units of data which will speed up the data process. All the data sets in Apache spark are partitioned in RDD.
Ans: There are two types of operations that can be performed by RDD;
Ans: An Action in Apache spark helps users to bring back the data sets from an RDD to the local virtual machine. These data values will give Non-RDD actions. There are two kinds of actions that will be performed such as the Reduce () function used to implemented again and again until only one data value left. The take () action will take all the data values from an RDD to the available local nodes.
Ans: These functions in Apache spark serve as a base engine, helps to perform important functions such as memory management, monitoring the spark jobs, provides the fault-tolerance, schedules the job, and enable us to interact with the data storage system.
Ans: The following factors explain the important functionalities of the Apache Spark ecosystem component;
Ans: Apache spark streaming is just an extension used in Spark API which allows the live data streams. Users can retrieve the data from different multiple sources like Kafka, data Flume, Kinesis. Then these data can be processed and pushed to the file systems, databases, and live streams. This process is similar to batch processing where data can be divided into batches.
Ans: Apache Spark uses the GraphX for graph processing methods to build and transform the interactive graphs. These GraphX components enable the programmer to scale the structured data.
Ans: The Spark SQL also is known as Shark is nothing but a novel module used to perform with a structured data process. Using this novel module, apache-spark executes the relational database queries. The core components of Spark SQL support the different RDD, schema RDD, composed row objects, and schema objects. It is similar to the table in relational databases.
Ans: YARN is one of the important key features in Spark, this provides a central and resource management platforms to deliver the scalable operations across the data cluster. Running the Apache spark on YARN which needs binary distribution data sets.
Ans: The Apache spark framework supports the major three types of Cluster manager, they are
Ans: The following are the few demerits of using Apache spark;
1. As we know that Apache spark uses the more storage memory space when compared to Hadoop and MapReduce. This may cause certain issues while running extra data sets.
2. The developers need to be very careful while running their applications in the Spark platform.
3. A large amount of workload must be distributed over multiple data clusters, instead of running everything on a single node.
4. The spark’s in-memory management can be used as a bottleneck when it comes to expensive usage of data sets.
5. Spark application tool consumes a large amount of data when compared to Hadoop.
Ans: Apache spark can be integrated with the following languages such as;
Ans: The following are the important MLlib tools available in Spark;
Ans: Spark Driver is the type of program that runs on the master node of the machine and declares the transformations and perform actions on data RDDs. Simply, we can call it a spark context, which is connected to the given spark master. This also delivers the RDD data graphs to master, where the standalone cluster runs.
Ans: The RDD lineage is a process used to reconstruct the lost data partitions. The important thing about this lineage is that it enables users to build the cluster using other datasets.
Ans: The three types of file system the spark supports,
1. Hadoop distributed file system
2. Local file system
3. Amazon S3.
Ans: Hive is a type of configuration which executes the Spark data sets. The syntax is as follows;
hive > set spark. Home = /location /to /sparkhome;
hive > set hive. Execution. Engine = spark ;
Ans: Parquet file, JSON datasets, and Hive tables are the important data source available in Spark.
Ans: Datasets are data structures used in Apache spark available since the spark 1.6 version. This supports the java virtual machine to obtain the benefits of RDD (which has the ability to manipulate the data with lambda function). And also offers the Spark SQL to optimize the executable engines.
Ans: The answer would be No because the Spark runs on the top of YARN.
This Apache spark interview question and answers blog help you in understanding how to crack the questions in a spark interview. This blog also gives you a clear idea of the questions that will be asked in any Spark interview. I hope this blog may help for those who want to pursue their dream in Spark technology. And also helps you to interact with Apache experts across the world.