Real-time data processing and distributed messaging are problems that have been worked on for a long time. As the amount of spatial data being produced has increased, coupled with increasingly complex software solutions being developed, there is a need for platforms that address these needs. In this paper, we present a distributed and light streaming system for combating pandemics and give a case study on spatial analysis of the COVID-19 geo-tagged Twitter dataset. In this system, three of the major components are the translation of tweets matching with user-defined bounding boxes, name entity recognition in tweets, and skyline queries. Apache Pulsar addresses all these components in this paper. With the proposed system, end-users have the capability of getting COVID-19 related information within foreign regions, filtering/searching location, organization, person, and miscellaneous based tweets, and performing skyline based queries. The evaluation of the proposed system is done based on certain characteristics and performance metrics. The study differs greatly from other studies in terms of using distributed computing and big data technologies on spatial data to combat COVID-19. It is concluded that Pulsar is designed to handle large amounts of long-term on disk persistence.

实时数据处理和分布式消息传递是长期致力于解决的问题。随着生成的空间数据量的增加，加上正在开发的越来越复杂的软件解决方案，需要平台来满足这些需求。在本文中，我们提出了一个用于对抗流行病的分布式轻型流媒体系统，并提供了一个关于 COVID-19 地理标记 Twitter 数据集空间分析的案例研究。在这个系统中，三个主要组件是与用户定义的边界框匹配的推文翻译、推文中的名称实体识别和天际线查询。Apache Pulsar 在本文中解决了所有这些组件。使用建议的系统，最终用户能够在国外地区获取 COVID-19 相关信息，过滤/搜索位置，基于组织、人员和杂项的推文，并执行基于天际线的查询。所提出的系统的评估是根据某些特征和性能指标进行的。该研究在空间数据上使用分布式计算和大数据技术来对抗 COVID-19 方面与其他研究有很大不同。得出的结论是，Pulsar 旨在处理大量的长期磁盘持久化。