解决分布式数据密集型系统的一致性相关设计挑战-行动研究
Tackling Consistency-related Design Challenges of Distributed Data-Intensive Systems - An Action Research Study
摘要：
背景：分布式数据密集型系统越来越多地被设计为最终一致。持久性数据不再使用序列化和事务性访问进行处理，从而使应用程序面临一系列需要由应用程序本身处理的潜在并发异常。在单片系统中控制并发数据访问已经是一个挑战，但在分布式系统中，这个问题更加严重。更糟糕的是，软件架构社区在这个问题上只提供了很少的系统工程指导。目的：在本文中，我们报告我们的研究的有效性和适用性的新的设计准则，我们在这方面提出的。方法：我们使用行动研究，并进行了多站点平台开发项目的软件架构设计过程的背景下。结果：我们关于有效性和适用性的假设已被接受的背景下的研究。最初的设计指南在整个研究过程中得到了完善。因此，我们也贡献了具体的指导方针，分布式数据密集型系统的架构，最终一致的数据。这些指南是领域驱动设计的一个进步，并为战术设计部分提供了额外的模式。结论：根据我们的研究结果，我们建议使用该指南来构建安全的最终一致的系统。由于分布式数据密集型系统的相关性，我们将推动这项研究，并在进一步的领域进行评估。
英文摘要：
Background: Distributed data-intensive systems are increasingly designed to be only eventually consistent. Persistent data is no longer processed with serialized and transactional access, exposing applications to a range of potential concurrency anomalies that need to be handled by the application itself. Controlling concurrent data access in monolithic systems is already challenging, but the problem is exacerbated in distributed systems. To make it worse, only little systematic engineering guidance is provided by the software architecture community regarding this issue. Aims: In this paper, we report on our study of the effectiveness and applicability of the novel design guidelines we are proposing in this regard. Method: We used action research and conducted it in the context of the software architecture design process of a multi-site platform development project. Results: Our hypotheses regarding effectiveness and applicability have been accepted in the context of the study. The initial design guidelines were refined throughout the study. Thus, we also contribute concrete guidelines for architecting distributed data-intensive systems with eventually consistent data. The guidelines are an advancement of Domain-Driven Design and provide additional patterns for the tactical design part. Conclusions: Based on our results, we recommend using the guidelines to architect safe eventually consistent systems. Because of the relevance of distributed data-intensive systems, we will drive this research forward and evaluate it in further domains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
