Spark is a fast, easy-to-use, and flexible data processing framework. It has an advanced execution engine supporting a cyclic data flow and in-memory computing. Apache Spark can run standalone, on Hadoop, or in the cloud and is capable of accessing diverse data sources including HDFS, HBase, and Cassandra, among others.
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RDD is the acronym for Resilient Distribution Datasets—a fault-tolerant collection of operational elements that run in parallel. The partitioned data in an RDD is immutable and distributed. There are primarily two types of RDDs:
A Spark engine is responsible for scheduling, distributing, and monitoring the data application across the cluster.
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As the name suggests, a partition is a smaller and logical division of data similar to a ‘split’ in MapReduce. Partitioning is the process of deriving logical units of data to speed up data processing. Everything in Spark is a partitioned RDD.
Transformations are functions applied to RDDs, resulting in another RDD. It does not execute until an action occurs. Functions such as map() and filer() are examples of transformations, where the map() function iterates over every line in the RDD and splits into a new RDD. The filter() function creates a new RDD by selecting elements from the current RDD that passes the function argument.
In Spark, an action helps in bringing back data from an RDD to the local machine. They are RDD operations giving non-RDD values. The reduce() function is an action that is implemented again and again until only one value if left. The take() action takes all the values from an RDD to the local node.
Serving as the base engine, Spark Core performs various important functions like memory management, monitoring jobs, providing fault-tolerance, job scheduling, and interaction with storage systems.
Spark does not support data replication in memory and thus, if any data is lost, it is rebuild using RDD lineage. RDD lineage is a process that reconstructs lost data partitions. The best thing about this is that RDDs always remember how to build from other datasets.
Spark driver is the program that runs on the master node of a machine and declares transformations and actions on data RDDs. In simple terms, a driver in Spark creates SparkContext, connected to a given Spark Master. It also delivers RDD graphs to Master, where the standalone Cluster Manager runs.
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Hive contains significant support for Apache Spark, wherein Hive execution is configured to Spark:
hive> set spark.home=/location/to/sparkHome;
hive> set hive.execution.engine=spark;
Hive supports Spark on YARN mode by default.
Spark supports stream processing—an extension to the Spark API allowing stream processing of live data streams. Data from different sources like Kafka, Flume, Kinesis is processed and then pushed to file systems, live dashboards, and databases. It is similar to batch processing in terms of the input data which is here divided into streams like batches in batch processing.
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Spark uses GraphX for graph processing to build and transform interactive graphs. The GraphX component enables programmers to reason about structured data at scale.
MLlib is a scalable Machine Learning library provided by Spark. It aims at making Machine Learning easy and scalable with common learning algorithms and use cases like clustering, regression filtering, dimensional reduction, and the like.
Spark SQL, better known as Shark, is a novel module introduced in Spark to perform structured data processing. Through this module, Spark executes relational SQL queries on data. The core of this component supports an altogether different RDD called SchemaRDD, composed of row objects and schema objects defining the data type of each column in a row. It is similar to a table in relational databases.
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Parquet is a columnar format file supported by many other data processing systems. Spark SQL performs both read and write operations with the Parquet file and considers it be one of the best Big Data Analytics formats so far.
Similar to Hadoop, YARN is one of the key features in Spark, providing a central and resource management platform to deliver scalable operations across the cluster. Running Spark on YARN needs a binary distribution of Spark that is built on YARN support.
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Spark SQL is capable of:
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Yes, MapReduce is a paradigm used by many Big Data tools, including Apache Spark. It becomes extremely relevant to use MapReduce when data grows bigger and bigger. Most tools like Pig and Hive convert their queries into MapReduce phases to optimize them better.
When SparkContext connects to Cluster Manager, it acquires an executor on the nodes in the cluster. Executors are Spark processes that run computations and store data on worker nodes. The final tasks by SparkContext are transferred to executors for their execution.
The Spark framework supports three major types of Cluster Managers.
A worker node refers to any node that can run the application code in a cluster.
A unique feature and algorithm in GraphX, PageRank is the measure of each vertex in a graph. For instance, an edge from u to v represents an endorsement of v‘s importance w.r.t. u. In simple terms, if a user at Instagram is followed massively, he/she will be ranked high on that platform.
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No, because Spark runs on top of YARN.
Since Spark utilizes more storage space when compared to Hadoop and MapReduce, there might arise certain problems. Developers need to be careful while running their applications on Spark. To resolve the issue, they can think of distributing the workload over multiple clusters, instead of running everything on a single node.
Spark provides two methods to create an RDD:
IntellipaatData = Array(2,4,6,8,10)
val distIntellipaatData = sc.parallelize(IntellipaatData)
When a dataset is organized into SQL-like columns, it is known as a DataFrame. This is, in concept, equivalent to a data table in a relational database or a literal ‘DataFrame’ in R or Python. The only difference is the fact that Spark DataFrames are optimized for Big Data.
Datasets are data structures in Spark (added since Spark 1.6) that provide the JVM object benefits of RDDs (the ability to manipulate data with lambda functions), alongside a Spark SQL-optimized execution engine.
Spark can be integrated with the following languages:
In-memory processing refers to the instant access of data from physical memory whenever the operation is called for. This methodology significantly reduces the delay caused by the transfer of data. Spark uses this method to access large chunks of data for querying or processing.
Spark implements a functionality, wherein if you create an RDD out of an existing RDD or a data source, the materialization of the RDD will not occur until the RDD needs to be interacted with. This is to ensure the avoidance of unnecessary memory and CPU usage that occurs due to certain mistakes, especially in the case of Big Data Analytics.
Good compilation of questions, thank you
Simple, accurate, useful; brilliant definitively
Thanks. A question about shuffling would be quite relevant, I find.
Thanks for sharing very useful Interview Q and A.
Excellent Tutorial. It’s very helpful for beginner’s as well as experienced.
So nice tutorial, very well explained…Thanks to Intellipaat team.
Awesome Apache Spark Interview Questions and Answers. It’s easy to understand and very informative.
That’s great,veary helpful:)
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