Visualizing Large-Scale Streaming Applications
Visualizing Large-Scale Streaming Applications
复制标题
可视化大规模流应用程序
DOI：
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复制
发表时间：
2009
影响因子：
2.3
通讯作者：
H. Andrade
中科院分区：
文献类型：
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作者：
Wim De Pauw;H. Andrade
流处理是一种新的重要计算范式。从科学应用（例如，环境监测）到商业智能（例如，欺诈检测和趋势分析）到金融市场（例如，算法交易系统），正在开发创新的流媒体应用程序。在本文中，我们描述了StreamSight，这是一种构建的新可视化工具，旨在检查，监视和帮助了解流应用程序的动态行为。 StreamSight可以通过使用层次图，多镜头可视化和去除策略来处理流处理应用程序的复杂，分布式和大规模的性质。为了解决这些应用程序的动态和自适应性质，StreamSight还提供了实时可视化以及记录和重播的能力。所有这些功能均用于调试，性能优化以及包括能力计划在内的资源管理。 IBM内部和外部的100多名开发人员一直在使用StreamSight来帮助设计和实施大规模的流处理应用程序。
Stream processing is a new and important computing paradigm. Innovative streaming applications are being developed in areas ranging from scientific applications (for example, environment monitoring), to business intelligence (for example, fraud detection and trend analysis), to financial markets (for example, algorithmic trading systems). In this paper we describe Streamsight, a new visualization tool built to examine, monitor and help understand the dynamic behavior of streaming applications. Streamsight can handle the complex, distributed and large-scale nature of stream processing applications by using hierarchical graphs, multi-perspective visualizations, and de-cluttering strategies. To address the dynamic and adaptive nature of these applications, Streamsight also provides real-time visualization as well as the capability to record and replay. All these features are used for debugging, for performance optimization, and for management of resources, including capacity planning. More than 100 developers, both inside and outside IBM, have been using Streamsight to help design and implement large-scale stream processing applications.