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Mitigating YARN Container Overhead with Input Splits

机译:通过输入拆分减少YARN容器开销

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We analyze YARN container overhead and present early results of reducing its overhead by dynamically adjusting the input split size. YARN is designed as a generic resource manager that decouples programming models from resource management infrastructures. We demonstrate that YARN's generic design incurs significant overhead because each con- tainer must perform various initialization steps, including authentication. To reduce container overhead without changing the existing YARN framework significantly, we propose leverag- ing the input split, which is the logical representation of physical HDFS blocks. With input splits, we can combine multiple HDFS blocks and increase the input size of each container, thereby enabling a single map wave and reducing the number of containers and their initialization overhead. Experimental results shows that we can avoid recurring container overhead by selecting the right size for input splits and reducing the number of containers.
机译:我们分析了YARN容器的开销,并提出了通过动态调整输入拆分大小来减少其开销的早期结果。 YARN被设计为通用资源管理器,可将编程模型与资源管理基础结构分离。我们证明了YARN的通用设计会产生大量开销,因为每个容器都必须执行各种初始化步骤,包括身份验证。为了在不显着更改现有YARN框架的情况下减少容器开销,我们建议利用输入拆分,这是物理HDFS块的逻辑表示。通过输入拆分,我们可以组合多个HDFS块并增加每个容器的输入大小,从而启用单个映射波形,并减少容器的数量及其初始化开销。实验结果表明,通过为输入拆分选择合适的大小并减少容器数量,我们可以避免重复发生的容器开销。

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