Insights on Apache Spark Usage by Mining Stack Overflow Questions

L. J. Rodríguez, Xiaoran Wang, Jilong Kuang
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引用次数: 7

Abstract

Apache Spark is one of the most popular big data tools. Despite its popularity, there are no studies regarding its overall usage among software developers. As such, essential questions remain unanswered. For instance, it is not known what the common issues faced by Spark users are, what the most popular Spark libraries are, or what technologies are most commonly used together with Spark. In this paper, we mine Stack Overflow questions and try to shed some light into the above issues. Specifically, we first apply Latent Dirichlet Allocation (LDA) to Stack Overflow questions and obtain the main topics of discussion. By computing previously proposed metrics and a novel modification, we provide insights into Spark usage while taking question view count into account. Further insights are then given by applying newly proposed metrics to the question tags. Temporal trends are finally discussed after analyzing the proposed metrics over time.
通过挖掘堆栈溢出问题了解Apache Spark的使用情况
Apache Spark是最流行的大数据工具之一。尽管它很流行,但没有关于软件开发人员对它的总体使用情况的研究。因此,一些关键问题仍未得到解答。例如,我们不知道Spark用户面临的常见问题是什么,最流行的Spark库是什么,或者什么技术最常与Spark一起使用。在本文中,我们挖掘了堆栈溢出问题,并试图阐明上述问题。具体而言,我们首先将潜在狄利克雷分配(LDA)应用于堆栈溢出问题,并获得了讨论的主要主题。通过计算之前提出的指标和一个新的修改,我们可以深入了解Spark的使用情况,同时考虑到问题视图计数。然后,通过将新提出的指标应用于问题标签,可以获得进一步的见解。在分析建议的度量标准随时间的变化之后,最后讨论时间趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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