Advancements in YARN Resource Manager
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YARN is currently one of the most popular frameworks for scheduling jobs and managing resources in shared clusters. In this entry, we focus on the new features introduced in YARN since its initial version.
Apache Hadoop (2017), one of the most widely adopted implementations of MapReduce (Dean and Ghemawat 2004), revolutionized the way that companies perform analytics over vast amounts of data. It enables parallel data processing over clusters comprised of thousands of machines while alleviating the user from implementing complex communication patterns and fault tolerance mechanisms.
With its rise in popularity, came the realization that Hadoop’s resource model for MapReduce, albeit flexible, is not suitable for every application, especially those relying on low-latency or iterative computations. This motivated decoupling the cluster resource management infrastructure from specific programming models...
The authors would like to thank Subru Krishnan and Carlo Curino for their feedback while preparing this entry. We would also like to thank the diverse community of developers, operators, and users that have contributed to Apache Hadoop YARN since its inception.
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