GoAutoBash: Golang-based multi-thread automatic pull-execute framework with GitHub webhooks and queuing strategy

Hao Bai

doi:10.1117/12.2639536

27 June 2022 GoAutoBash: Golang-based multi-thread automatic pull-execute framework with GitHub webhooks and queuing strategy

Hao Bai

Author Affiliations +

Proceedings Volume 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022); 1225315 (2022) https://doi.org/10.1117/12.2639536
Event: Second International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 2022, Qingdao, China

Abstract

Recently, more and more server tasks are done using full automation, including grading tasks for students in the college courses, integrating tasks for programmers in big projects and server-based transactions, and visualization tasks for researchers in a data-dense topic. Using automation on servers provides a great possibility for reducing the burden on manual tasks. Although server tools like CI/CD for continuous integration and Hexo for automated blog deployment have been developed, they’re highly dedicated to certain functionalities and thus lack general usage. In this paper, we introduce a Golang-based automation framework that reacts to the events happening on GitHub in a multi-thread approach. This framework utilizes a queue to arrange the tasks submitted and execute each task with a thread in a preemptive manner. We then use the project GoAutoGrader to illustrate a specific implementation of this framework and its value in implementing high-freedom server applications. As Golang is developing in a rapid way because of its incredible parallel programming efficiency and a super-easy way to learn on the basis of C-like programming languages, we decide to develop this system in Golang.

Citation Download Citation

Hao Bai "GoAutoBash: Golang-based multi-thread automatic pull-execute framework with GitHub webhooks and queuing strategy", Proc. SPIE 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 1225315 (27 June 2022); https://doi.org/10.1117/12.2639536

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