Swim extends the actor model to support applications composed of linked distributed actors that continuously analyze boundless streams of events from millions of sources, to respond in-sync with the real-world. Swim builds a running application from streaming events, creating a distributed dataflow graph of linked, stateful, concurrent streaming actors that is overlaid on a mesh of runtime instances. Streaming actors are vertices in the dataflow graph that concurrently analyze new events and modify their states. A link is an edge in the graph and is a URI binding to an actor's streaming API. The Swim runtime streams every actor state change over its links to other (possibly remote) actors using op-based CRDTs that asynchronously update remotely cached actor state replicas. This frees local actors to compute at any time, using the latest replicas of remote state. Actors evaluate parametric functions, including geospatial, analytical, and predictive, to discover new relationships and forge or break links, dynamically adapting the dataflow graph to model the changing real-world. Swim applications are tiny, robust and resource efficient, and remain effortlessly in-sync with the real-world, analyzing, learning, and predicting on-the-fly.
翻译:Swim 构建了一个运行中的应用程序。 Swim 构建了一个来自流流事件的运行应用程序, 创建了一个分布式数据流图, 覆盖在运行时间的网格上。 流动的行为体是数据流图中的悬崖, 同时分析新事件并修改其状态。 链接是图中的边缘, 是一个URI 连接到一个行为体流动的 API 。 每一个行为体的流动时间流都通过流动事件运行, 创建了一个运行中的应用程序, 创建了一个运行中的流动程序, 创建了一个分布式数据流图, 并创建了一个运行式的流动程序, 并与其他( 可能是远程的) 运行的行为体连接。 这让本地行为体可以随时进行编译, 使用最新的远程状态复制版 。 行为方评估参数功能, 包括地理空间、 分析和预测, 以发现新的关系和断开链接, 动态地将数据流图转换到不断变化的现实世界中。 Swamilling and folorlest- train- train to the real- listal- lishal- lishal- sligal- sal- ligal- sal andst