State access in existing distributed stream processing systems is restricted locally within each operator. However, in advanced stream analytics such as online learning and dynamic graph analytics, enabling state sharing across different operators makes application development easier and stream processing more efficient. In addition, when stream records are timestamped, proper time semantics should be defined for both state updates and fetches. We propose a new state abstraction to address the limitations of existing systems and develop a distributed stream processing system, Nova, with native support for timestamped state sharing. We validate the expressiveness and efficiency of Nova with extensive experiments.

中文翻译：

---

流分析的时间戳状态共享


现有分布式流处理系统中的状态访问受到每个操作员本地的限制。然而，在在线学习和动态图分析等高级流分析中，实现不同算子之间的状态共享使应用程序开发更容易，流处理更高效。此外，当流记录带有时间戳时，应该为状态更新和获取定义适当的时间语义。我们提出了一种新的状态抽象来解决现有系统的局限性，并开发了一个分布式流处理系统 Nova，它具有对时间戳状态共享的本机支持。我们通过大量实验验证了 Nova 的表现力和效率。