TimeStream:可靠的云端流计算

Zhengping Qian, Yong He, Chunzhi Su, Zhuojie Wu, Hongyu Zhu, Taizhi Zhang, Lidong Zhou, Yuan Yu, Zheng Zhang
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引用次数: 252

摘要

TimeStream是一个分布式系统,专为大型商用机器集群上的大数据流的低延迟连续处理而设计。这个新兴应用程序领域的独特特征导致了与流行的mapreduce风格的批处理数据处理明显不同的设计。特别是,我们提倡一种强大的新抽象,称为弹性替代,以满足新计算模型中处理故障恢复和响应负载变化的动态重新配置的特定需求。在我们的原型上运行的几个实际应用程序已经被证明可以在低延迟的情况下健壮地扩展,同时保持简单而简洁的声明性编程模型。TimeStream以每秒700,000个url的速率处理在线广告聚合管道,延迟2秒,同时以接近每秒10,000条tweet的峰值速率执行Twitter数据的情感分析,延迟约2秒。
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TimeStream: reliable stream computation in the cloud
TimeStream is a distributed system designed specifically for low-latency continuous processing of big streaming data on a large cluster of commodity machines. The unique characteristics of this emerging application domain have led to a significantly different design from the popular MapReduce-style batch data processing. In particular, we advocate a powerful new abstraction called resilient substitution that caters to the specific needs in this new computation model to handle failure recovery and dynamic reconfiguration in response to load changes. Several real-world applications running on our prototype have been shown to scale robustly with low latency while at the same time maintaining the simple and concise declarative programming model. TimeStream handles an on-line advertising aggregation pipeline at a rate of 700,000 URLs per second with a 2-second delay, while performing sentiment analysis of Twitter data at a peak rate close to 10,000 tweets per second, with approximately 2-second delay.
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