Real-Time Big Data Analytics by Sumit Gupta & Saxena Shilpi

Author:Sumit Gupta & Saxena Shilpi
Language: eng
Format: mobi, epub
Publisher: Packt Publishing
Published: 2016-02-25T22:00:00+00:00

The following diagram shows the high-level components and the master-worker view of Spark:

The preceding diagram depicts the various components involved in setting up the Spark cluster, and the same components are also responsible for the execution of the Spark job.

Although all the components are important, let's briefly discuss the cluster/resource manager, as it defines the deployment model and allocation of resources to our submitted jobs.

Spark enables and provides flexibility to choose our resource manager. As of Spark 1.5.1, the following are the resource managers or deployment models that are supported by Spark:

Apache Mesos: Apache Mesos (http://mesos.apache.org/) is a cluster manager that provides efficient resource isolation and sharing across distributed applications or frameworks. It can run Hadoop, MPI, Hypertable, Spark, and other frameworks on a dynamically shared pool of nodes. Apache Mesos and Spark are closely related to each other (but they are not the same). The story started way back in 2009 when Mesos was ready and there were talks going on about the ideas/frameworks that can be developed on top of Mesos, and that's exactly how Spark was born.

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