· 2013
Apache Hadoop YARN
- 1,843citations
- 2013year
Short summary
Apache Hadoop YARN addresses key shortcomings in the original Hadoop design by decoupling resource management from the MapReduce programming model and centralizing job control flow for improved scalability.
AI-generated from the title and abstract; the full text is not read.
Key points
- Original Hadoop design tightly coupled resource management with the MapReduce programming model.
- This coupling led to developers abusing MapReduce and scalability issues due to centralized job control.
- Apache Hadoop YARN decouples resource management from programming models.
- YARN centralizes job control flow, enhancing scheduler scalability.
AI-generated from the title and abstract; the full text is not read.
Abstract
The initial design of Apache Hadoop [1] was tightly focused on running massive, MapReduce jobs to process a web crawl. For increasingly diverse companies, Hadoop has become the data and computational agorá---the de facto place where data and computational resources are shared and accessed. This broad adoption and ubiquitous usage has stretched the initial design well beyond its intended target, exposing two key shortcomings: 1) tight coupling of a specific programming model with the resource management infrastructure, forcing developers to abuse the MapReduce programming model, and 2) centralized handling of jobs' control flow, which resulted in endless scalability concerns for the scheduler.
The authors' abstract, as published at the source., 2013 · DOI ↗
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Field: Information Systems
Information SystemsComputer Science