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Scalable Analytics on Fast Data
ACM Transactions on Database Systems ( IF 1.8 ) Pub Date : 2019-01-23 , DOI: 10.1145/3283811
Andreas Kipf 1 , Varun Pandey 1 , Jan Böttcher 1 , Lucas Braun 2 , Thomas Neumann 1 , Alfons Kemper 1
Affiliation  

Today’s streaming applications demand increasingly high event throughput rates and are often subject to strict latency constraints. To allow for more complex workloads, such as window-based aggregations, streaming systems need to support stateful event processing. This introduces new challenges for streaming engines as the state needs to be maintained in a consistent and durable manner and simultaneously accessed by complex queries for real-time analytics. Modern streaming systems, such as Apache Flink, do not allow for efficiently exposing the state to analytical queries. Thus, data engineers are forced to keep the state in external data stores, which significantly increases the latencies until events become visible to analytical queries. Proprietary solutions have been created to meet data freshness constraints. These solutions are expensive, error-prone, and difficult to maintain. Main-memory database systems, such as HyPer, achieve extremely low query response times while maintaining high update rates, which makes them well-suited for analytical streaming workloads. In this article, we explore extensions to database systems to match the performance and usability of streaming systems.

中文翻译:

快速数据的可扩展分析

当今的流应用程序需要越来越高的事件吞吐率,并且经常受到严格的延迟限制。为了允许更复杂的工作负载,例如基于窗口的聚合,流系统需要支持有状态的事件处理。这给流引擎带来了新的挑战,因为需要以一致且持久的方式维护状态,并同时通过复杂查询访问以进行实时分析。现代流系统,例如 Apache Flink,不允许有效地将状态暴露给分析查询。因此,数据工程师被迫将状态保存在外部数据存储中,这会显着增加延迟,直到事件对分析查询可见。已经创建了专有的解决方案来满足数据新鲜度的限制。这些解决方案昂贵、容易出错且难以维护。主内存数据库系统(例如 HyPer)在保持高更新率的同时实现了极短的查询响应时间,这使得它们非常适合分析流工作负载。在本文中,
更新日期:2019-01-23
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