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Yarn by default (Spark on YARN)

With the rise of the cloud, data intensive systems and the Internet of Things the use of distributed systems have become widespread. The first big player was Hadoop, which provided an integral solution to Big Data storage and computation problems. Its popularity empowered many organizations to adopt this technology. However new challenges appeared, like the need to be able to execute iterative, interactive or in-memory algorithms without the disk-intensive burden of MapReduce. For that very reason Hadoop evolved, decoupling its resources manager from the main computation engine: YARN was born. As a result of its vast adoption, YARN has become the de-facto distributed operating system for Big Data. Since early releases, Apache Spark provided a way to be executed on YARN-powered clusters. In this talk we will take a look into that technology, and we will learn what it means having Spark running on this kind of infrastructure.

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YARN by default
(Spark on YARN)
Ferran Galí i Reniu
@ferrangali
About me
@ferrangali
100 MB/s
2 TB = 3.5 hours
The Big Data problem
100 MB/s
2 TB = 30 min
The Big Data problem
Source: Google data centers
Yarn by default (Spark on YARN)

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Yarn by default (Spark on YARN)