S-Store：流处理满足事务处理
S-Store: Streaming Meets Transaction Processing
摘要：
流处理解决了实时应用的需求。事务处理解决短原子计算的协调和安全问题。因此，这两种运作模式存在于单独的、烟囱式的系统中。在这项工作中，我们试图融合两个计算范式在一个单一的系统称为S-商店。通过这种方式，S-Store可以同时适应OLTP和流应用程序。我们提出了一个简单的事务模型的流，无缝集成与传统的OLTP系统。我们选择构建S-Store作为H-Store的扩展，H-Store是一个开源的、内存中的分布式OLTP数据库系统。通过以这种方式实现S-Store，我们可以利用H-Store已经支持的事务处理功能，并且我们可以专注于支持流所需的其他实现特性。类似的实现可以使用其他主存OLTP平台来完成。我们表明，我们实际上可以实现更高的吞吐量流工作负载在S-Store比同等的部署在H-Store单独。我们还展示了如何在H-Store中添加适量的新功能来实现这一目标。此外，我们将S-Store与两个最先进的流媒体系统Spark Streaming和Storm进行了比较，并展示了S-Store如何匹配，有时甚至超过它们的性能，同时提供更强的事务保证。
英文摘要：
Stream processing addresses the needs of real-time applications. Transaction processing addresses the coordination and safety of short atomic computations. Heretofore, these two modes of operation existed in separate, stove-piped systems. In this work, we attempt to fuse the two computational paradigms in a single system called S-Store. In this way, S-Store can simultaneously accommodate OLTP and streaming applications. We present a simple transaction model for streams that integrates seamlessly with a traditional OLTP system. We chose to build S-Store as an extension of H-Store, an open-source, in-memory, distributed OLTP database system. By implementing S-Store in this way, we can make use of the transaction processing facilities that H-Store already supports, and we can concentrate on the additional implementation features that are needed to support streaming. Similar implementations could be done using other main-memory OLTP platforms. We show that we can actually achieve higher throughput for streaming workloads in S-Store than an equivalent deployment in H-Store alone. We also show how this can be achieved within H-Store with the addition of a modest amount of new functionality. Furthermore, we compare S-Store to two state-of-the-art streaming systems, Spark Streaming and Storm, and show how S-Store matches and sometimes exceeds their performance while providing stronger transactional guarantees.
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