Mogwaï:在大型模型上处理复杂查询的框架

Gwendal Daniel, G. Sunyé, Jordi Cabot
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引用次数: 33

摘要

当模型驱动工程获得更多的工业兴趣时,管理大型模型时的可伸缩性问题已经成为当前建模框架中的主要问题。通过使用NoSQL后端进行模型存储,可以实现可扩展的模型持久性,但是现有的建模框架api没有相应的发展,限制了NoSQL查询的性能优势。在本文中,我们提出了Mogwaï,一个可扩展和高效的模型查询框架,它基于将OCL查询直接翻译为Gremlin, Gremlin是几种NoSQL数据库支持的查询语言。生成的Gremlin表达式是在数据库内部计算的,绕过了现有框架api的限制,提高了整体性能,正如我们的实验结果所证实的那样,对于大型模型,执行时间提高了20倍,内存开销减少了75倍。
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Mogwaï: A framework to handle complex queries on large models
While Model Driven Engineering is gaining more industrial interest, scalability issues when managing large models have become a major problem in current modeling frameworks. Scalable model persistence has been achieved by using NoSQL backends for model storage, but existing modeling framework APIs have not evolved accordingly, limiting NoSQL query performance benefits. In this paper we present the Mogwaï, a scalable and efficient model query framework based on a direct translation of OCL queries to Gremlin, a query language supported by several NoSQL databases. Generated Gremlin expressions are computed inside the database itself, bypassing limitations of existing framework APIs and improving overall performance, as confirmed by our experimental results showing an improvement of execution time up to a factor of 20 and a reduction of the memory overhead up to a factor of 75 for large models.
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