大数据应用中MySQL文档存储与Elasticsearch性能对比分析
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更新于2024-06-20
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"这篇外文文章是对大数据应用数据管理中MySQL文档存储与Elasticsearch作为数据复制解决方案替代后端的性能和配置特性进行比较研究的翻译。作者是Győrödi Cornelia A., Dumše Burescu Diana V., Zmaranda Doina R., Győrödi Robert Ş.，发表在《Big Data and Cognitive Computing》杂志2022年第6卷第2期。文章介绍了目前由于社交媒体、云计算服务和物联网（IoT）产生的大量数据爆炸现象，并探讨了物联网中涉及的复杂应用程序和交叉技术，如人机交互、机器学习、模式识别和无处不在的计算。随着IoT的广泛应用，数据量和种类都呈现出高速增长，这促使了对更高效的数据管理和分析工具的需求。"
在大数据应用的背景下，MySQL文档存储和Elasticsearch都是重要的数据管理工具。MySQL作为一个传统的SQL数据库系统，通过引入文档存储功能，允许处理半结构化和非结构化数据，以适应现代大数据的需求。而Elasticsearch则是一个开源的全文搜索引擎，以其强大的实时分析和分布式能力而闻名，特别适合处理海量日志、搜索和数据分析场景。
文章可能会深入探讨以下几点：
1. **性能对比**：比较两者在数据读写速度、查询效率、扩展性和并发处理能力等方面的差异。MySQL可能在事务处理和数据一致性方面表现出色，而Elasticsearch则在大规模数据检索和实时分析上具有优势。
2. **配置特性分析**：分析两种系统的配置选项，如索引构建、集群设置、容错机制等，以揭示它们如何适应不同的工作负载和业务需求。
3. **数据复制解决方案**：讨论MySQL的主从复制和Elasticsearch的复制机制，如何保证数据的一致性和高可用性，以及在故障恢复时的表现。
4. **大数据应用场景**：通过实际案例展示这两种技术在处理 IoT 数据、社交媒体数据或云服务数据时的适用性和效果。
5. **成本和运维考虑**：比较两者的部署、维护成本，包括硬件需求、软件许可证费用和运维复杂性。
6. **未来趋势**：根据当前技术发展趋势，预测MySQL文档存储和Elasticsearch在大数据环境中的进一步发展和潜在改进方向。
通过对这些方面的详尽分析，文章将为读者提供一个全面的理解，以便于选择合适的工具来应对大数据环境下的数据管理和分析挑战。对于计算机科学与技术专业，尤其是毕业设计和项目选择，这样的研究内容提供了宝贵的参考资料，同时也可作为外文翻译练习的素材。
An analysis of the state of the security of the most popular open-source databases,
representing both the relational and NoSQL databases, is described, and includes
MongoDB and MySQL. From a security point of view, both these databases need to
be properly configured so as to significantly reduce the risks of data exposure and
intrusion.
Between MongoDB and MySQL, several comparisons exist in the literature, most
of them focusing on a comparison with relational MySQL, and not with
document-based MySQL; for example, a login system project developed using Python
programming language was used to analyze the performance of MongoDB and
relational MySQL, based on the data-fetching speed from databases. This paper
performed an analysis of the two databases to decide which type of database was
more suitable for a login-based system. The paper presented presents information on
the upsides of the NoSQL databases over the relational databases during the
investigation of Big Data, by making a performance comparison of various queries
and commands in the MongoDB and relational MySQL. Additionally, the concepts of
NoSQL and the relational databases, together with their limitations. Consequently,
despite the fact that MongoDB has been approached in many scientific papers, to our
knowledge, at the time of writing this paper, no paper has focused directly on
comparing it with the document-based MySQL.
4. Method and Testing Architecture
For each database considered, an application was created in Java using IntelliJ
IDEA Community Edition (4 February 2020), which aims to develop a server for the
processing and storage of data on the frontend. When creating the testing architecture
setup, it was considered that it is very important to test the types of databases that
exactly fit the criteria that are imposed in an application that is similar to the one to be
developed, and not just by using their tools; such as for MongoDB, the MongoDB
web interface, or the Mongo shell, because there are differences, both in how to use
them and with regard to the response times, which if tested directly may seem easy
and fast, but in practice itself are found to be slower or more difficult to achieve.
The two applications are identical in terms of structure, with both containing the
objects that we need and a service class for each object, annotated with @Service. In
addition to these classes, each application contains a class within which there is a cron
(a process by which a method can be called automatically and repeatedly at a range
set by us, taking as a parameter a string that is composed of six digits separated by a
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