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Translating between SQL Dialects for Cloud Migration

Ran Zmigrod, Salwa Alamir, Xiaomo Liu

TL;DR

This work considers the difficulties of this migration for SQL databases, a novel yet vital industrial research problem for any large corporation that is considering cloud migrations, and introduces potential avenues of research to tackle this challenge.

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.

Translating between SQL Dialects for Cloud Migration

TL;DR

This work considers the difficulties of this migration for SQL databases, a novel yet vital industrial research problem for any large corporation that is considering cloud migrations, and introduces potential avenues of research to tackle this challenge.

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 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.
Paper Structure (7 sections, 1 figure)

This paper contains 7 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Problem Definition
  3. Possible Approaches
  4. Manual Rule Creation
  5. Imitation Learning
  6. Large Language Models
  7. Conclusion

Figures (1)

  • Figure 1: Example of SQL transformation challenge. The aim is for the SQL converter to transform an SQL dialect 1 segment with a conversion error into an SQL dialect 2 segment.