摘 要：随着大数据时代的到来，实时数据处理需求日益增长，流数据库技术作为支持高效实时数据分析的核心手段，已成为研究热点。本研究旨在探索面向实时数据处理的流数据库关键技术，通过设计一种新型的分布式流数据处理框架，优化了数据摄入、查询处理及存储管理等核心环节。研究采用理论分析与实验验证相结合的方法，提出了一种基于动态负载均衡的查询执行策略，并结合机器学习算法实现了智能资源调度。实验结果表明，该框架在高吞吐量和低延迟方面表现出显著优势，能够有效应对复杂场景下的实时数据处理需求。此外，本研究创新性地引入了自适应索引机制，显著提升了查询响应效率。总体而言，本研究为流数据库技术的实际应用提供了重要参考，其成果可广泛应用于物联网、金融风控等领域，推动实时数据处理能力的进一步提升。
关键词：流数据库；实时数据处理；分布式框架
Abstract：With the advent of the big data era, the demand for real-time data processing is growing rapidly, and stream database technology, as a core means to support efficient real-time data analysis, has become a research hotspot. This study focuses on exploring key technologies of stream databases for real-time data processing by designing a novel distributed stream data processing fr amework that optimizes critical aspects such as data ingestion, query processing, and storage management. A combination of theoretical analysis and experimental validation is employed in this research, proposing a query execution strategy based on dynamic load balancing and integrating machine learning algorithms for intelligent resource scheduling. Experimental results demonstrate that the fr amework exhibits significant advantages in high throughput and low latency, effectively addressing real-time data processing requirements in complex scenarios. Additionally, this study innovatively introduces an adaptive indexing mechanism, which substantially enhances query response efficiency. Overall, this research provides crucial references for the practical application of stream database technology, with its outcomes being widely applicable in fields such as the Internet of Things and financial risk control, further promoting the advancement of real-time data processing capabilities.
引言 1
一、实时数据处理需求分析 1
(一)实时数据处理背景与意义 1
(二)实时数据处理的关键挑战 2
(三)需求驱动的技术发展方向 2
二、流数据库技术基础研究 3
(一)流数据库的基本概念 3
(二)流数据库的核心架构设计 3
(三)流数据库的性能优化策略 4
三、实时数据流管理机制 4
(一)数据流的采集与预处理 4
(二)数据流的存储与索引方法 5
(三)数据流的查询与分析技术 5
四、流数据库在实时场景的应用实践 6
(一)实时金融交易中的应用案例 6
(二)实时物联网监控的应用探索 6
(三)实时社交网络分析的技术实现 7
结论 7
参考文献 9
致谢 9
关键词：流数据库；实时数据处理；分布式框架
Abstract：With the advent of the big data era, the demand for real-time data processing is growing rapidly, and stream database technology, as a core means to support efficient real-time data analysis, has become a research hotspot. This study focuses on exploring key technologies of stream databases for real-time data processing by designing a novel distributed stream data processing fr amework that optimizes critical aspects such as data ingestion, query processing, and storage management. A combination of theoretical analysis and experimental validation is employed in this research, proposing a query execution strategy based on dynamic load balancing and integrating machine learning algorithms for intelligent resource scheduling. Experimental results demonstrate that the fr amework exhibits significant advantages in high throughput and low latency, effectively addressing real-time data processing requirements in complex scenarios. Additionally, this study innovatively introduces an adaptive indexing mechanism, which substantially enhances query response efficiency. Overall, this research provides crucial references for the practical application of stream database technology, with its outcomes being widely applicable in fields such as the Internet of Things and financial risk control, further promoting the advancement of real-time data processing capabilities.
Keywords: Stream Database；Real-Time Data Processing；Distributed fr amework
引言 1
一、实时数据处理需求分析 1
(一)实时数据处理背景与意义 1
(二)实时数据处理的关键挑战 2
(三)需求驱动的技术发展方向 2
二、流数据库技术基础研究 3
(一)流数据库的基本概念 3
(二)流数据库的核心架构设计 3
(三)流数据库的性能优化策略 4
三、实时数据流管理机制 4
(一)数据流的采集与预处理 4
(二)数据流的存储与索引方法 5
(三)数据流的查询与分析技术 5
四、流数据库在实时场景的应用实践 6
(一)实时金融交易中的应用案例 6
(二)实时物联网监控的应用探索 6
(三)实时社交网络分析的技术实现 7
结论 7
参考文献 9
致谢 9
