Translating between SQL Dialects for Cloud Migration (2403.08375v1)
Abstract: Migrations of systems from on-site premises to the cloud has been a fundamental endeavor by many industrial institutions. A crucial component of such cloud migrations is the transition of databases to be hosted online. In this work, we consider the difficulties of this migration for SQL databases. While SQL is one of the prominent methods for storing database procedures, there are a plethora of different SQL dialects (e.g., MySQL, Postgres, etc.) which can complicate migrations when the on-premise SQL dialect differs to the dialect hosted on the cloud. Tools exist by common cloud provides such as AWS and Azure to aid in translating between dialects in order to mitigate the majority of the difficulties. However, these tools do not successfully translate $100\%$ of the code. Consequently, software engineers must manually convert the remainder of the untranslated database. For large organizations, this task quickly becomes intractable and so more innovative solutions are required. We consider this challenge a novel yet vital industrial research problem for any large corporation that is considering cloud migrations. Furthermore, we introduce potential avenues of research to tackle this challenge that have yielded promising preliminary results.
- AI for Automated Code Updates. In 44th IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice, ICSE (SEIP) 2022, Pittsburgh, PA, USA, May 22-24, 2022. IEEE, 25–26. https://doi.org/10.1109/ICSE-SEIP55303.2022.9794071
- Amazon. [n. d.]. Amazon Web Services Schema Conversion Tool. https://aws.amazon.com/dms/schema-conversion-tool/
- Jonathan Berant and Percy Liang. 2015. Imitation Learning of Agenda-based Semantic Parsers. Trans. Assoc. Comput. Linguistics 3 (2015), 545–558. https://doi.org/10.1162/tacl_a_00157
- End-to-End Driving Via Conditional Imitation Learning. In 2018 IEEE International Conference on Robotics and Automation, ICRA 2018, Brisbane, Australia, May 21-25, 2018. IEEE, 1–9. https://doi.org/10.1109/ICRA.2018.8460487
- Code Generation Using Machine Learning: A Systematic Review. IEEE Access 10 (2022), 82434–82455. https://doi.org/10.1109/ACCESS.2022.3196347
- Taras Gleb. 2021. Cloud Migration Fundamentals. Apress, Berkeley, CA, 19–35. https://doi.org/10.1007/978-1-4842-7252-7_2
- A Practical Model for Measuring Maintainability. In Quality of Information and Communications Technology, 6th International Conference on the Quality of Information and Communications Technology, QUATIC 2007, Lisbon, Portugal, September 12-14, 2007, Proceedings, Ricardo Jorge Machado, Fernando Brito e Abreu, and Paulo Rupino da Cunha (Eds.). IEEE Computer Society, 30–39. https://doi.org/10.1109/QUATIC.2007.8
- Imitation Learning: A Survey of Learning Methods. ACM Comput. Surv. 50, 2 (2017), 21:1–21:35. https://doi.org/10.1145/3054912
- Arif Iqbal and Ricardo Colomo Palacios. 2019. Key Opportunities and Challenges of Data Migration in Cloud: Results from a Multivocal Literature Review. In CENTERIS 2019 - International Conference on ENTERprise Information Systems / ProjMAN 2019 - International Conference on Project MANagement / HCist 2019 - International Conference on Health and Social Care Information Systems and Technologies 2019, Sousse, Tunisia (Procedia Computer Science, Vol. 164), Maria Manuela Cruz-Cunha, Ricardo Martinho, Rui Rijo, Emanuel Peres, and Dulce Domingos (Eds.). Elsevier, 48–55. https://doi.org/10.1016/j.procs.2019.12.153
- Cloud Migration Research: A Systematic Review. IEEE Trans. Cloud Comput. 1, 2 (2013), 142–157. https://doi.org/10.1109/TCC.2013.10
- CURE: Code-Aware Neural Machine Translation for Automatic Program Repair. In 43rd IEEE/ACM International Conference on Software Engineering, ICSE 2021, Madrid, Spain, 22-30 May 2021. IEEE, 1161–1173. https://doi.org/10.1109/ICSE43902.2021.00107
- Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation. CoRR abs/2305.01210 (2023). https://doi.org/10.48550/arXiv.2305.01210 arXiv:2305.01210
- James Manyika. 2023. An overview of Bard: an early experiment with generative AI. Google (2023). https://ai.google/static/documents/google-about-bard.pdf
- Microsoft. [n. d.]. Microsoft Azure Database Schema Conversion Toolkit. https://learn.microsoft.com/en-us/sql/azure-data-studio/extensions/dsct/database-schema-conversion-toolkit
- Sourav Mukherjee. 2019. Benefits of AWS in Modern Cloud. CoRR abs/1903.03219 (2019). arXiv:1903.03219 http://arxiv.org/abs/1903.03219
- An Automated Code Update Tool For Python Packages. In 2023 IEEE International Conference on Software Maintenance and Evolution (ICSME). 536–540. https://doi.org/10.1109/ICSME58846.2023.00068
- OpenAI. 2023. GPT-4 Technical Report. CoRR abs/2303.08774 (2023). https://doi.org/10.48550/arXiv.2303.08774 arXiv:2303.08774
- LLM is Like a Box of Chocolates: The Non-determinism of ChatGPT in Code Generation. CoRR abs/2308.02828 (2023). https://doi.org/10.48550/arXiv.2308.02828 arXiv:2308.02828
- Unsupervised Translation of Programming Languages. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, Hugo Larochelle, Marc’Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.). https://proceedings.neurips.cc/paper/2020/hash/ed23fbf18c2cd35f8c7f8de44f85c08d-Abstract.html
- Why Adopting Cloud Is Still a Challenge?—A Review on Issues and Challenges for Cloud Migration in Organizations. In Ambient Communications and Computer Systems, Yu-Chen Hu, Shailesh Tiwari, Krishn K. Mishra, and Munesh C. Trivedi (Eds.). Springer Singapore, Singapore, 387–399.
- Perfection Not Required? Human-AI Partnerships in Code Translation. In IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, Tracy Hammond, Katrien Verbert, Dennis Parra, Bart P. Knijnenburg, John O’Donovan, and Paul Teale (Eds.). ACM, 402–412. https://doi.org/10.1145/3397481.3450656