A Fine-Grained Task Monitoring Mechanism in Spark Platform

DOI

10.2991/ammee-17.2017.75How to use a DOI?

Keywords

Spark, In-memory Computing, Fine-grained, Task monitoring.

Abstract

The existing coarse-grained monitoring mechanism in Spark has much difficulty in analyzing and locating the bottleneck during the execution of tasks. Aiming to solve this problem, we divide the execution process of Spark tasks into several subphases, and propose a fine-grained subphases-based monitoring mechanism. The fine-grained monitoring mechanism can help users to understand the detail execution status of Spark tasks and be beneficial to analyze and locate the performance bottleneck in Spark.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Cheng Chen
AU  - Fei Liu
AU  - Guangrui Li
AU  - Xiang Chen
AU  - Ying Hou
PY  - 2017/06
DA  - 2017/06
TI  - A Fine-Grained Task Monitoring Mechanism in Spark Platform
BT  - Proceedings of the Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)
PB  - Atlantis Press
SP  - 395
EP  - 399
SN  - 2352-5401
UR  - https://doi.org/10.2991/ammee-17.2017.75
DO  - 10.2991/ammee-17.2017.75
ID  - Chen2017/06
ER  -