Building Reliable Data Pipelines for Managing Community Data Using Scientific Workflows

Yogesh L. Simmhan, C. Ingen, A. Szalay, R. Barga, J. Heasley
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引用次数: 13

Abstract

The growing amount of scientific data from sensors and field observations is posing a challenge to “data valets” responsible for managing them in data repositories. These repositories built on commodity clusters need to reliably ingest data continuously and ensure its availability to a wide user community. Workflows provide several benefits to modeling data-intensive science applications and many of these benefits can help manage the data ingest pipelines too. But using workflows is not panacea in itself and data valets need to consider several issues when designing workflows that behave reliably on fault prone hardware while retaining the consistency of the scientific data. In this paper, we propose workflow designs for reliable data ingest in a distributed environment and identify workflow framework features to support resilience. We illustrate these using the data pipeline for the Pan-STARRS repository, one of the largest digital surveys that accumulates 100TB of data annually to support 300 astronomers.
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构建可靠的数据管道,使用科学的工作流管理社区数据
来自传感器和实地观测的科学数据越来越多,这对负责在数据存储库中管理这些数据的“数据管家”构成了挑战。这些构建在商品集群上的存储库需要可靠地连续摄取数据,并确保其对广泛的用户社区可用。工作流为建模数据密集型科学应用程序提供了一些好处,其中许多好处也可以帮助管理数据摄取管道。但是使用工作流本身并不是万灵药,数据代工在设计工作流时需要考虑几个问题,这些工作流在容易发生故障的硬件上运行可靠,同时保持科学数据的一致性。在本文中,我们提出了在分布式环境中可靠数据摄取的工作流设计,并确定了支持弹性的工作流框架特征。我们使用Pan-STARRS存储库的数据管道来说明这些问题,Pan-STARRS存储库是最大的数字调查之一,每年积累100TB的数据,以支持300名天文学家。
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