Keep Your Analytics Lint Free With SQLFluff
June 8th, 2021
1 hr 13 mins 13 secs
About this Episode
The growth of analytics has accelerated the use of SQL as a first class language. It has also grown the amount of collaboration involved in writing and maintaining SQL queries. With collaboration comes the inevitable variation in how queries are written, both structurally and stylistically which can lead to a significant amount of wasted time and energy during code review and employee onboarding. Alan Cruickshank was feeling the pain of this wasted effort first-hand which led him down the path of creating SQLFluff as a linter and formatter to enforce consistency and find bugs in the SQL code that he and his team were working with. In this episode he shares the story of how SQLFluff evolved from a simple hackathon project to an open source linter that is used across a range of companies and fosters a growing community of users and contributors. He explains how it has grown to support multiple dialects of SQL, as well as integrating with projects like DBT to handle templated queries. This is a great conversation about the long detours that are sometimes necessary to reach your original destination and the powerful impact that good tooling can have on team productivity.
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- Your host as usual is Tobias Macey and today I’m interviewing Alan Cruickshank about SQLFluff, a dialect-flexible and configurable SQL linter
- How did you get introduced to Python?
- Can you describe what SQLFluff is and the story behind it?
- SQL is one of the oldest programming languages that is still in regular use. Why do you think that there are so few linters for it?
- Who are the target users of SQLFluff and how do those personas influence the design and user experience of the project?
- What are some of the characteristics of SQL and how it is used that contribute to readability/comprehension challenges?
- What are some of the additional difficulties that are introduced by templating in the queries?
- How is SQLFluff implemented?
- How have the goals and design of the project changed since you first began working on it?
- How do you handle support of varying SQL dialects without undue maintenance burdens?
- What are some of the stylistic elements and strategies for making SQL code more maintainable?
- What are some strategies for making queries self-documenting?
- What are some signs that you should document it anyway?
- What are some of the kinds of bugs that you are able to identify with SQLFluff?
- What are some of the resources/references that you relied on for identifying useful linting rules?
- What are some methods for measuring code quality in SQL?
- What are the most interesting, innovative, or unexpected ways that you have seen SQLFluff used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on SQLFluff?
- When is SQLFluff the wrong choice?
- What do you have planned for the future of SQLFluff?
Keep In Touch
- alanmcruickshank on GitHub
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- The Wim Hof Method by Wim Hof
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- Project Euler
- SQL Window Functions
- ANSI SQL
- MS SQL Server
- Oracle DB
- SQL Subquery
- Common Table Expression (CTE)
- The Rise Of The Data Engineer blog post
- The Downfall Of The Data Engineer blog post
- Object-Relational Mapper (ORM)
- Fishtown Analytics SQL Styleguide
- Mozilla SQL Styleguide
- The Zen of Python
- dbt Packages
- Set Theory
- Flake8 SQL Plugin
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA