Now a day’s students learning Hadoop to analyze their Datasets. Hadoop a framework based on a
simple programming Model and it allows to computing the solution that is flexible, Scalable, cost
effective and fault-tolerant, this is the reason Learning Hadoop to analyze their data sets, Here, the main Trouble is to maintain speed in processing large datasets Waiting time and queries to run a
program, Spark introduced By Apache Software Foundation for speeding up the Hadoop
Computational computing software process, Apache Spark is not Modified version of Hadoop and it does not depend on Hadoop, Because it has own cluster management, Hadoop is one Of the ways to develop Spark.
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Apache Spark:
Spark using Hadoop in two ways one is processing and second are storage. Since Spark has own cluster management computation, it uses Hadoop For storage purpose, It based on Hadoop Map Reduce and it extends the Map Reduce model, which includes stream processing and interactive queries, The main feature Of Spark is in memory cluster compute that increases the processing
The speed of an application. Spark designed to cover a wide range of workloads such as interactive Queries iterative algorithms, and streaming batch applications Apart from Supporting all these workloads in a respective system, it reduces the management Burden of maintaining separate tools.
Features of Apache Spark:
Apache Spark has following features
Speed:
Spark used to run application in Hadoop cluster, it is faster In memory, this is possible by reducing the number of reading write operations to disk.
Supports many languages:
Spark provides built in APIs in Scale, Java, and Python. So, you can Write applicationshigh level in various languages. Spark come high-levelOperators for interactive querying
Advanced Analytics:
Spark not only supports Map and reduces; it also supports Streaming data, SQL queries, algorithms and Machine learning Graph.
Spark Built on Hadoop:
There are three ways for spark distribution given below,
Standalone:
Spark standalone means spark occupies top place of HDFS and space Is allocated for HDFS, Here, Map Reduce and Spark will run side By side to cover all spark jobs on the cluster.
Hadoop Yarn:
Hadoop Yarn deployment means sparks runs on Yarn without Any pre installation required. It helps to integrate Spark into Hadoop ecosystem. It allows other components to run On top of stack.
Spark in Map Reduce (SIMR):
Spark in Map Reduce used to launch spark job also Standalone deployment, With SIMR, user can start Spark and uses It shells without any administrative
access.
Components of Spark:
Four components given below
Apache Spark Core:
Spark Core is the fundamental execution engine for spark platform. It provides In-Memory computing and referencing datasets in external Storage systems.
Spark SQL:
Spark SQL is a component on top of Spark Core that introduces new Data called Schema RDD, which provides support for structure and semi-structured data.
Spark Streaming:
Spark Streaming leverages Spark Cores fast scheduling capability To perform streaming analytics, it ingests data in mini batches and performs Resilient Distributed Datasets(RDD) transformations On those mini batches of data
Graph:
Graph is processing framework on top of Spark. It provides API for expressing graph computation that can model The user defined graphs.